ORIGINAL_ARTICLE
Complete Closed-loop Supply Chain Network Design under Uncertainty of Demand and Return Products
In this research, we focus on complete closed loop supply chain, which includes forward and backward flows of materials. So a network has been considered including suppliers, manufacturers, distributers, customers, and collecting and disposal centers. In addition, to conform to real word conditions, and examine uncertainty of demands returns, scenario technic was used. In this research, we used a mixed integer linear programming model to minimize total cost of supply chain. The location of the facility, the production quantity of different products in each sites, and the flow of products between different nodes of network are the decision variables of the model. The computational complexity of the model, leads us to develop a particle swarm optimization algorithm to solve the problem in large-scale cases. Results show the efficiency of proposed algorithm in uncertain situations.
https://aie.ut.ac.ir/article_63154_86af337804dbb48064a4a6203f189a2f.pdf
2016-11-21
355
369
10.22059/jieng.2016.63154
Closed-Loop Supply Chain
Mixed integer linear programming
Particle Swarm Optimization Algorithm
uncertainty
Mohammad Reza
Akbari Jokar
reza.akbari@sharif.edu
1
Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran
LEAD_AUTHOR
Mosalreza
Abouchenari
musareza1369@gmail.com
2
Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran
AUTHOR
Hosein
Akefi
akefi2009@gmail.com
3
Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran
AUTHOR
1. Fleischmann, M., Bloemhof-Ruwaard, J. M., Dekker, R., Van der Laan, E., Van Nunen, J. A. and Van Wassenhove, L. N. (1997). "Quantitative models for reverse logistics: A review", European Journal of Operational Research, Vol. 103, No. 1, PP. 1-17.
1
2. Baumgarten, H., Butz, C., Fritsch, A. and Sommer-Dittrich, T. (2003). “Supply chain management and reverse logistics-integration of reverse logistics processes into supply chain management approaches”, In Electronics and the Environment, IEEE International Symposium on. IEEE, PP. 79-83.
2
3. Daskin, M. S. (1995). Network and discrete location: Models, algorithms, and applications, Wiley, New York.
3
4. Melo, M. T., Nickel, S. and Gama, F. S. (2009). “Facility location and supply chain management”, European Journal of Operation Research, Vol. 196, No. 2, PP. 401-412.
4
5. Melkote, S. and Daskin, M. S. (2001). “Capacitated facility location/network design problem”, European Journal of Operation Research, Vol. 129, No. 3, 481-495.
5
6. Yaghoubi, et al. (2016). "Location and Allocation of a Distribution System Considering Disruption in Mobile Warehouses and Backup Facilities", Journal of Industrial Engineering, Vol.50, No.1, PP. 147-164.
6
7. Ambrosino, D. and Scutella, M. G. (2005). “Distribution network design: New problems and related models”, European Journal of Operational Research, Vol. 165, No. 3, PP. 610-624.
7
8. Nga Thanh, P., Bostel, N. and Peton, O. (2008). “A dynamic model for facility location in the design of Complex supply chains”, International Journal of Production Economics, Vol. 113, No. 2, PP. 678-693.
8
9. Louwers, Dirk, et al. (1999). "A facility location allocation model for reusing carpet materials", Computers & Industrial Engineering, Vol. 36, No. 4,: PP. 855-869.
9
10. Lu, Z. and Bostel, N. (2007). “A facility location model for logistics systems including reverse flows: The case of remanufacturing activities”, Computers & Operations Research, Vol. 34, No. 2, 299–323.
10
11. Pishvaee, M. S. and Shakouri, H. (2009, November). A System Dynamics Approach for Capacity Planning and Price Adjustment in a Closed-Loop Supply Chain. In Computer Modeling and Simulation, 2009. EMS'09. Third UKSim European Symposium on (pp. 435-439). IEEE.
11
12. Chopra, S. and Meindl, P. (2007). Supply chain management. Strategy, planning & operation. Das summa summarum des management, 265-275.
12
13. Fleischmann, Moritz, et al. (2001). "The impact of product recovery on logistics network design", Production and operations management, Vol. 10, No. 2, PP. 156-173.
13
14. Jayaraman, V., Guide Jr, V. D. R. and Srivastava, R. (1999). "A closed-loop logistics model for remanufacturing", Journal of the operational research society, Vol.50, No.5, PP. 497-508.
14
15. Fleischmann, M. et al. (2001). "The impact of product recovery on logistics network design", Production and operations management, Vol. 10, No. 2, PP. 156-173.
15
16. Seuring, S. (2013). “A review of modeling approaches for sustainable supply chain management”, Decision Support Systems, Vol. 54, No. 4, PP. 1513-1520.
16
17. Minner, S. (2003). “Multiple-supplier inventory models in supply chain management: A review”, International Journal of Production Economics, Vol. 81-82, PP. 265-279.
17
18. Salema, M. I. G., Barbosa-Povoa, A. P., and Novais, A. Q. (2007). An optimization model for the design of a capacitated multi-product reverse logistics network with uncertainty. European Journal of Operational Research, Vol.179, No.3, 1063-1077.
18
19. Baghalian, A., Rezapour, S. and Farahani, R. Z. (2013). “Robust supply chain network design with service level against disruptions and demand uncertainties: A real-life case”, European Journal of Operational Research, Vol. 227, No. 1, PP. 199- 215.
19
20. Pishvaee, M. S., Rabbani, M. and Torabi, S. A. (2011). A robust optimization approach to closed-loop supply chain network design under uncertainty. Applied Mathematical Modelling, Vol. 35, No. 2, 637-649.
20
21. Amin, S. H. and Zhang, G. (2012). An integrated model for closed-loop supply chain configuration and supplier selection: Multi-objective approach. Expert Systems with Applications, Vol. 39, No. 8, 6782-6791.
21
22. Amin, S. H. and Zhang, G. (2013). A multi-objective facility location model for closed-loop supply chain network under uncertain demand and return. Applied Mathematical Modelling, Vol. 37, No. 6, 4165-4176.
22
23. Mirghafoori, H. (2001). Mathematical planning of supply chain of Yazd tire industry, Ph.D. Thesis in production management, Tabiat Modarress University.
23
24. El-Sayed, M., Afia, N. and El-Kharbotly, A. (2010). A stochastic model for forward–reverse logistics network design under risk. Computers & Industrial Engineering, Vol. 58, No. 3, 423-431.
24
25. Zhang, W. and Xu, D. (2014). Integrating the logistics network design with order quantity determination under uncertain customer demands. Expert Systems with Applications, Vol. 41, No. 1, 168-175.
25
26. Yousefi, B. A. and Shishebori, D. (2015). "Robust optimization of integrated reverse logistic network design at uncertain conditions", Journal of Industrial Engineering, Vol.49, No.2, PP. 299-313.
26
27. Saffar, M., Ganjavi, M., Shakouri, H. and Razmi, J. (2015). "A green closed loop supply chain network design considering operational risks under uncertainty and solving the model with NSGA II algorithm", Journal of Industrial Engineering, Vol.49, No.1, PP. 55-68.
27
28. Eberhart, R. and Kennedy, J. (1995, October). A new optimizer using particle swarm theory. In Micro Machine and Human Science, 1995. MHS'95., Proceedings of the Sixth International Symposium on (pp. 39-43). IEEE.
28
29 Eberhart, R. C. (1997). “A discrete binary version of the particle swarm algorithm”, In: Proceedings of 1997 conference systems man cyber-netics, NJ: Piscataway, PP. 4104–4108.
29
30. Roy, R. K., A primer on the Taguchi method. 2nd ed. 2010, Dearborn, MI: Society of Manufacturing Engineers.
30
ORIGINAL_ARTICLE
Monotonic Change Point Estimation in the Parameters of Polynomial Profile Model
In this paper, a maximum likelihood estimator is developed to estimate isotonic change point in the parameters of a polynomial profile in phase II. In addition, performance of the proposed estimator is compared to the performance of the step change point estimator, under increasing change types using simulation study. Accuracy and the precision of the estimators are considered as the performance measures in this paper. Simulation results show that the proposed estimator has an acceptable performance in terms of the accuracy and precision of the estimations. The proposed estimator also does not require any awareness about the change type, and its only assumption is that changes occur in an increasing manner. This is the advantage of the proposed estimator over the step change point estimator.
https://aie.ut.ac.ir/article_63155_69b1a670b9ab5d5858df3d62f05674c4.pdf
2016-11-21
371
379
10.22059/jieng.2016.63155
Change point estimation
Isotonic change
Maximum likelihood estimator (MLE)
Polynomial profile
Statistical Process Control
Mona
Ayoubi
m128ayoubi@yahoo.com
1
Faculty of Industrial Engineering, Tarbiat Modares University, Tehran, Iran
AUTHOR
Reza
Baradaran Kazemzadeh
rkazem@gmail.com
2
Faculty of Industrial Engineering, Tarbiat Modares University, Tehran, Iran
LEAD_AUTHOR
1- Kang, L. and Albin, S. (2000). “On-line Monitoring When the Process Yields a Linear Profile”, Journal of Quality Technology, Vol. 32, No. 4, PP. 418– 426.
1
2- Mahmoud, M. A. and Woodall, W. H. (2004). “Phase I analysis of linear profiles with calibration applications”, Technometrics, Vol. 46, No. 4, PP. 380– 391.
2
3- Kim, K., Mahmoud, M. A., and Woodall, W. H. (2003). “On the monitoring of linear profiles”, Journal of Quality Technology, Vol. 35, No. 3, PP. 317- 328.
3
4- Mahmoud, M. A., Parker, P. A., Hawkins, M. D. and Woodall, W. H. (2007). “A change point method for linear profile data”, Quality and Reliability Engineering International, Vol. 23, No. 2, PP. 247– 268.
4
5- Yeh, A. and Zerehsaz, Y. (2013). “Phase I control of simple linear profiles with individual observations”, Quality and Reliability Engineering International, Vol. 29, No. 6, PP. 829- 840.
5
6- Zou, C., Zhang, Y. and Wang, Z. (2006). “Control chart based on change-point model for monitoring linear profiles”, IIE Transactions, Vol. 38, No. 12, PP. 1093–1110.
6
7- Zou, C., Zhou, C., Wang, Z. and Tsung, F. (2007). “A self-starting control charts for linear profiles”, Journal of Quality Technology, Vol. 39, No. 4, PP. 364– 375.
7
8- Saghaei, A., Mehrjoo, M. and Amiri, A. (2009). “A CUSUM-based method for monitoring simple linear profiles”, The International Journal of Advanced Manufacturing Technology, Vol. 45, No. 11-12, PP. 1252- 1260.
8
9- Zarandi, M. F. and Alaeddini, A. (2010). “Using adaptive nero-fuzzy systems to monitor linear quality profiles”, Journal of Uncertain Systems, Vol. 4, No. 2, PP. 147-160.
9
10- Mahmoud, M. A., Morgan, J. P. and Woodall, W. H. (2010). “The monitoring of simple linear regression profiles with two observations per sample”, Journal of Applied Statistics, Vol. 37, No. 8, PP. 1249– 1263.
10
11- Hosseinifard, S. Z., Abdollahian, M. and Zeephongsekul, P. (2011). “Application of artificial neural networks in linear profile monitoring”, Expert Systems with Applications, Vol. 38, No. 5, PP. 4920–4928.
11
12- Zhang, J., Li, Z. and Wang, Z. (2009). “Control chart based on likelihood ratio for monitoring linear profiles”, Computational Statistics and Data Analysis, Vol. 53, No. 4, PP. 1440- 1448.
12
13- Farahani, E., Noorossana, R. and Koosha, M. (2014). “Profile monitoring in the presence of outliers”, The International Journal of Advanced Manufacturing Technology, Vol. 74, No. 1- 4, PP. 251- 256.
13
14- Kazemzadeh, R. B., Noorossana, R. and Amiri, A. (2008). “Phase I monitoring of polynomial profiles”, Communications in Statistics—Theory and Methods, Vol. 37, No. 10, PP. 1671– 1686.
14
15- Kazemzadeh, R. B., Noorossana, R. and Amiri, A. (2009). “Monitoring polynomial profiles in quality control applications”, The International Journal of Advanced Manufacturing Technology, Vol. 42, No. 7-8, PP. 703- 712.
15
16- Kazemzadeh, R. B. and Amiri, A. (2010). “Phase II monitoring of autocorrelated polynomial profiles in AR (1) processes”, Scientia Iranica, Vol. 17, No. 1, PP. 12- 24.
16
17- Amiri, A., Jensen, W. A. and Kazemzadeh, R. B. (2010). “A case study on monitoring polynomial profiles in the automotive industry”, Quality and Reliability Engineering International, Vol. 26, No. 5, PP. 509- 520.
17
18- Samuel, T. R. and Pignatiello, J. J. (1998). “Identifying the time of a change in a poisson rate parameter”, Quality Engineering, Vol. 10, No. 4, PP. 673- 681.
18
19- Pignatiello Jr, J. J., and Samuel, T. R. (2001). “Estimation of the change point of a normal process mean in SPC applications”, Journal of Quality technology, Vol. 33, No. 1, PP. 82– 95.
19
20- Pignatiello Jr, J. J. and Samuel, T. R. (2001). “Identifying the time of a step change in the process fraction nonconforming”, Quality Engineering, Vol. 13, No. 3, PP. 357- 365.
20
21- Perry, M. B., and Pignatiello Jr, J. J. (2010). “Identifying the time of step change in the mean of autocorrelated processes”, Journal of Applied Statistics, Vol. 37, No. 1, PP. 119- 136.
21
22- Noorosana, R., Saghaei, A., Paynabar, K. and Abdi, S. (2009). “Identifying the period of a step change in high-yield processes”, Quality and Reliability Engineering International, Vol. 25, No. 7, PP. 875-883.
22
23- Nedumaran, G., Pignatiello Jr, J. J. and Calvin, J. A. (2000). “Identifying the time of a step-change with x2 control charts”, Quality Engineering, Vol. 13, No. 2, PP. 153- 159.
23
24- Perry, M. B. and Pignatiello Jr, J. J. (2011). “Estimating the time of step change with Poisson CUSUM and EWMA control charts”, International Journal of Production Research, Vol. 49, No. 10, 2857- 2871.
24
25- Niaki, S. T. A. and Khedmati, M. (2014). “Step change-point estimation of multivariate binomial processes”, International Journal of Quality & Reliability Management, Vol. 31, No. 5, PP. 566- 587.
25
26- Perry, M. B. and Pignatiello Jr, J. J. (2006). “Estimation of the change point of a normal process mean with a linear trend disturbance in SPC”, Quality Technology and Quantitative Management, Vol. 3, No. 3, PP. 325- 334.
26
27- Perry, M. B., Pignatiello Jr, J. J. and Simpson J. R. (2006). “Estimating the change point of a poisson rate parameter with a linear trend disturbance”, Quality and Reliability Engineering International, Vol. 22, No. 4, PP. 371- 384.
27
28- Amiri, A. and Khosravi, R. (2012). “Estimating the change point of the cumulative count of a conforming control chart under a drift”, Scientia Iranica, Vol. 19, No. 3, PP. 856- 861.
28
29- Perry, M. B., Pignatiello Jr, J. J. and Simpson, J. R. (2007). “Estimating the change point of the process fraction non-conforming with a monotonic change disturbance in SPC”, Quality and Reliability Engineering International, Vol. 23, No. 3, PP. 327- 339.
29
30- Noorossana, R. and Shadman, A. (2009). “Estimating the change point of a normal process mean with a monotonic change”, Quality and Reliability Engineering International, Vol. 25, No. 1, PP.79- 90.
30
31- Amiri, A. and Khosravi, R. (2013). “Identifying time of a monotonic change in the fraction nonconforming of a high-quality process”, The International Journal of Advanced Manufacturing Technology, Vol. 68, No. 1- 4, PP. 547- 555.
31
32- Niaki, S. T. A. and Khedmati, M. (2014). “Monotonic change-point estimation of multivariate Poisson processes using a multi-attribute control chart and MLE”, International Journal of Production Research, Vol. 52, No. 10, PP. 2954- 2982.
32
33- Best, M. J. and Chakravarti, N. (1990). “Active set algorithms for isotonic regression; a unifying framework”, Mathematical Programming, Vol. 47, No. 1- 3, PP. 425– 439.
33
34- Kazemzadeh, R. B., Noorossana, R. and Ayoubi, M. (2015). “Change point estimation of multivariate linear profiles under linear drift”, Communication in Statistics-Simulation & Computation, Vol. 44, No. 6, PP. 1570- 1599.
34
ORIGINAL_ARTICLE
The Relationship between Productivity Factors and Organizational Performance with Regards Financial and Economic Benefits Using ISO 10014 Guidelines
Productivity and the related indicators have great importance for organizations due to the direct effects on the organization’s performance and efficiency. Otherwise, firms and organizations devote a considerable attention to the issue of financial and economic benefits, and relevant standards, in order to acquire competitive advantages. In the field of financial and economic benefits, the ISO 10014 standard is important. In this study, first the concepts of productivity and financial and economic benefits are considered separately. Then, the aspects and the criteria of each of them are extracted. Then, for the productivity issue, 20 indices and for the financial and economic benefits, 16 indices were extracted from papers and researches. This study has been conducted through two research methods: exploratory and descriptive. In the descriptive portion, the indices and the concepts effecting on the issues of productivity, and financial and economic benefits were extracted separately. In the exploratory method, the relationships among the defined concepts and indices in the two fields of productivity, and financial and economic benefits, which have been acquired from the literature, have been specified. Regarding the introduced issues for finding a meaningful relationship between productivity, and financial and economic benefits, a questionnaire was developed which was filled in by the experts in the industry and university sectors. In the first set, the questionnaire was designed for finding a significant relationship between financial and economic benefits, and productivity. The related indices were extracted by means of t-test with significant level of 5 percent. Among the related indices, the ones with strongest relationships were determined and reported. In the second set of questionnaires, the relationships between indicators and variables were recorded, and the work was done by calculating the Pearson correlation coefficient. The data was analyzed by Minitab software. Finally, we compared the analysis output of the two series of questionnaires, and decided that how much and in which parts, the performance issues are connected to productivity.
https://aie.ut.ac.ir/article_63156_d18cd0adceea528dafc0130851f43f60.pdf
2016-11-21
381
391
10.22059/jieng.2016.63156
Financial and economic benefits
indicator
organizational performance
Performance Evaluation
productivity
Amir
Bahrami
amir.bahrami@modares.ac.ir
1
Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran
AUTHOR
Bakhtiar
Ostadi
bostadi@modares.ac.ir
2
Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran
LEAD_AUTHOR
Mohammad
Aghdasi
aghdasim@modares.ac.ir
3
Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran
AUTHOR
1. Jakub, S., Viera, B. and Eva K. (2015). "Economic Value Added as a Measurement Tool of Financial Performance", Procedia Economics and Finance, Vol. 26, No. 1, PP. 484–489.
1
2. Nowravesh, I. and Mashayekhi, B. (2004). "Incremental information content of Economic Value Added and Cash Value Added versus earnings and operating cash flow", Journal of Financial Research, Vol. 6, No. 1, PP. 131–150.
2
3. Shishehbori, D. and Hejazi, S.R. (2009). "Application of fuzzy AHP technique to selection the most efficient method of improving of productivity", Journal of Industrial Engineering, Vol. 43. No. 1. PP. 59–66.
3
4. Prokopenko, J. (1987). "Productivity management: a practical handbook", International Labour Organization.
4
5. Kazemi, S.A. (2011). "Productuvity and its analysis in Organization", The Organization for Researching and Composing University Textbooks in the Humanities (SAMT).
5
6. Syverson, C. (2010). "What determines productivity?" National Bureau of Economic Research.
6
7. Rahimi, G. (2005). "Performance evaluation and continuous improvement of the organization", Journl of Tadbir, Vol. 17. No. 173, PP. 41-44.
7
8. Fatahi, S., Khoshnood, E. and Gholipour, I. (2016). "The effect of performance auditing on improving productivity of the public sector (Case study: the Supreme Audit Court of Islamic Republic of Iran (SAC))", Journal of Audit Science, Vol. 15, No. 61, 107–134.
8
9. Mohammadi, M., Javanmard, H., Khagkani, S. and Gharechahi, F. (2014). "The effect of education on productivity, financial performance of the organization and organizational performance (Case study: Isfahan branch of the Agricultural Bank)", 3th National Conference of Accounting and Management, 2014, Tehran, PP. 1-14.
9
10. Taheri, S. (2007), "Productuvity analysis in Organization", Hastan Press, Tehran.
10
11. Chiok Foong Loke, J. (2001). "Leadership behaviours: effects on job satisfaction, productivity and organizational commitment", Journal of Nursing Management, Vol. 9, No. 4, PP. 191–204.
11
12. Cummings, T. G. and Molloy, E. S. (1977). "Improving productivity and the quality of work life", Praeger.
12
13. Chevalier, A., Harmon, C. and Walker, I. (2004). "Does Education Raise Productivity, or Just Reflect it?" The Economic Journal, Vol. 114, No. 499, PP. F499–F517.
13
14. Augier, P., Dovis, M. and Gasiorek, M. (2012). "The business environment and Moroccan firm productivity", Economics of Transition, Vol. 20, No. 2, PP. 369–399.
14
15. Kahn, L. B. and Lange, F. (2010). "Employer learning, productivity and the earnings distribution: Evidence from performance measures", Discussion paper series//Forschungsinstitut zur Zukunft der Arbeit.
15
16.Nayeri, N. D., Salehi, T. and Noghabi, A.A.A. (2011). "Quality of work life and productivity among Iranian nurses", Contemporary Nurse, Vol. 39, No. 1, PP. 106–108.
16
17. ISO, TS "10014 (2006). "Guidelines for managing the economics of quality", Geneve.
17
18. Mežinska, I. and Apine, A. (2010). "Application of Standard ISO 10014: 2006 “Quality Management–Guidelines for Realizing Financial and Economic Benefits” Principles for Process Improvements", publication. edition Name, PP. 1–19.
18
19. Lechner, M. and Smith, J. (2007). "What is the value added by caseworkers?" Labour Economics, Vol. 14, No. 2, PP. 135–151.
19
20. Sumanth, D.J. (1981). "Productivity indicators used by major US manufacturing companies: The results of a survey", Industrial Engineering, Vol. 5, No. PP. 70–73.
20
21. Bloom, N., Kretschmer, T. and Van Reenan, J. (2009). "Work-life balance, management practices and productivity, in International differences in the business practices and productivity of firms", University of Chicago Press, PP. 15–54.
21
22. Bhat, S. and Siddharthan, N. (2013). "Human Capital, Labour Productivity and Employment, in Human Capital and Development", Springer. PP. 11–22.
22
23. Ramírez, Y.W. and Nembhard, D.A. (2004). "Measuring knowledge worker productivity: A taxonomy", Journal of Intellectual Capital, Vol. 5, No. 4, PP. 602–628.
23
24. Dedrick, J., Kraemer, K.L. and Shih, E. (2013). "Information Technology and Productivity in Developed and Developing Countries", Journal of Management Information Systems, Vol. 30, No. 1, PP. 97–122.
24
25. Baily, M.N. (1986). "Productivity growth and materials use in US manufacturing", The Quarterly Journal of Economics, Vol. 101, No. 1, PP. 185–196.
25
26. Lee, J.W. (1996). "Government interventions and productivity growth", Journal of Economic Growth, Vol. 1, No. 3, PP. 391-414.
26
27. Ali, I., Rehman, K.U., Ali, S.I., Yousaf, J. Zia, M. (2010). "Corporate social responsibility influences, employee commitment and organizational performance", African Journal of Business Management, Vol. 4, No. 12, PP. 2796–2801.
27
28. McMillan, M.S. and Rodrik, D. (2011). "Globalization, structural change and productivity growth", National Bureau of Economic Research.
28
29. Valentine, S. and Fleischman, G. (2008). "Ethics programs, perceived corporate social responsibility and job satisfaction", Journal of Business Ethics, Vol. 77, No. 2, PP. 159–172.
29
30. Choo, F. and Tan, K.B. (1997). "A study of the relations among disagreement in budgetary performance evaluation style, job-related tension, job satisfaction and performance", Behavioral Research in Accounting, Vol. 9, No. PP. 199–218.
30
31. Ross, S.A., Westerfield, R. and Jordan, B.D. (2008). "Fundamentals of corporate finance", Tata McGraw-Hill Education.
31
32. Hickman, B.G. (1992). "International productivity and competitiveness". Oxford University Press.
32
33. Dong, S. and Zhu. K. (2006). "The business value of CRM systems: Productivity, profitability, and time lag. in Proc", Workshop Inform. Systems Econom. (WISE 2006), Northwestern University, Evanston, IL.
33
34. Taticchi, P., Tonelli, F. and Cagnazzo, L. (2010). "Performance measurement and management: a literature review and a research agenda", Measuring Business Excellence, Vol. 14, No. 1, PP. 4–18.
34
35. Esty, D.C. (2008). "Environmental performance index", New Haven: Yale Center for Environmental Law and Policy, PP. 382.
35
36. Kanji, G.K. (2002). "Performance measurement system", Total Quality Management, Vol. 13, No. 5, PP. 715–728.
36
37. Li, S., Ragu-Nathan, B., Ragu-Nathan, T.S. and Subba Rao, S. (2006). "The impact of supply chain management practices on competitive advantage and organizational performance", Omega, Vol. 34, No. 2, PP. 107–124.
37
38. Weiss, D.J., Brennan, K., Thomas, R., Kirlik, A., and Miller, S.M. (2009). "Criteria for performance evaluation", Judgment and Decision Making, Vol. 4, No. 2, PP. 164–174.
38
ORIGINAL_ARTICLE
A Multi-objective Supply Chain Network Design Regarding Customer Relationship Management
In recent years, for governments and industries waste management has become more important than ever due to legal requirements, economic profitability, environmental sensitivities and customer awareness development. The main objective of this article is to develop a mathematical programming model, in order to simultaneously improve the existing forward supply chain (SC) as well as to design reverse SC for the aim of collecting and recycling the used products, and also to coordinate the whole SC. The presented multi-objective model is solved by using revised multi-choice goal programming approach. The other objective of this work is to develop a more customer centric SC, which is successfully achieved by modeling the customer relationship management concept with strategic and tactical SC decisions. More importantly, the realization rate of the objectives, considering their importance to SC is shown to help senior managers in the decision-making process. The proposed model is designed for the new and emerging industry of recycling used tires in Iran, and is implemented by software with the cited industry data.
https://aie.ut.ac.ir/article_63157_57123b218378c31fc9b996d7076a0407.pdf
2016-11-21
393
405
10.22059/jieng.2016.63157
Bi-objective mathematical model
Customer relationship management
Supply Chain Management
Ebrahim
Teymouri
teimoury@iust.ac.ir
1
Faculty of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
LEAD_AUTHOR
Mohammad Mahdi
Paydar
paydar@nit.ac.ir
2
Faculty of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
AUTHOR
Maedeh
Yadollahiniya
m_yadollahinia@yahoo.com
3
Faculty of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
AUTHOR
Krikke, H., Hofenk, D. and Wang, Y. (2013). “Revealing an invisible giant: A comprehensive survey into return practices within original (closed-loop) supply chains”, Resources, Conservation and Recycling, Vol. 73, No. 1, PP. 239-250.
1
Lund, R. T. and Hauser, W. M. (2010). “Remanufacturing-an American perspective”, 5th International Conference on Responsive Manufacturing-Green Manufacturing (ICRM 2010), China.
2
Jin, Y., Muriel, A. and Lu, Y. (2007). “On the profitability of remanufactured products”, Working paper in research gate.
3
Barros, A. I., Dekker, R. and Scholten, V. (1998). “A two-level network for recycling sand: A case study” European Journal of Operational Research, Vol. 110, No2, PP. 199-214.
4
Listeş, O. and Dekker, R. (2005). “A stochastic approach to a case study for product recovery network design”, European Journal of Operational Research, Vol. 160, No. 1, PP. 268-287.
5
Shih, L. H. (2001). “Reverse logistics system planning for recycling electrical appliances and computers in Taiwan”, Resources, Conservation and Recycling, Vol. 32, No. 1, PP. 55-72.
6
Realff, M. J., Ammons, J. C. and Newton, D. J. (2004). “Robust reverse production system design for carpet recycling”, IIE Transactions, Vol. 36, No. 8, PP. 767-776.
7
Dehghanian, F., Mansour, S. and Farahani, R. Z. (2010). “Applying interactive multi objective decision making method in design of a sustainable recovery network to include differeent stakeholders preferences”, World Applied Sciences Journal, Vol. 8, No. 3, PP. 596-607.
8
Dehghanian, F. and Mansour, S. (2009). “Designing sustainable recovery network of end-of-life products using genetic algorithm”, Resources, Conservation and Recycling, Vol. 53, No. 10, PP. 559-570.
9
Kara, S. S. and Onut, S. (2010). “A two-stage stochastic and robust programming approach to strategic planning of a reverse supply network: The case of paper recycling”, Expert Systems with Applications, Vol. 37, No. 9, PP. 6129-6137.
10
Vahdani, B., Tavakkoli-Moghaddam, R. and Jolai, F. (2013). “Reliable design of a logistics network under uncertainty: A fuzzy possibilistic-queuing model”, Applied Mathematical Modelling, Vol. 37, No. 5, PP. 3254-3268.
11
Atamer, B., Bakal, İ. S. and Bayındır, Z. P. (2013). “Optimal pricing and production decisions in utilizing reusable containers”, International Journal of Production Economics, Vol. 143, No. 2, PP. 222-232.
12
Ferri, G. L., Diniz Chaves, G. d. L. and Ribeiro, G. M. (2015). “Reverse logistics network for municipal solid waste management: The inclusion of waste pickers as a Brazilian legal requirement”, Waste Management, Vol. 40, No. 1, PP. 173-191.
13
Zhou, X. and Zhou, Y. (2015). “Designing a multi-echelon reverse logistics operation and network: A case study of office paper in Beijing”, Resources, Conservation and Recycling, Vol. 100, No. 1, PP. 58-69.
14
Galvez, D., Rakotondranaivo, A., Morel, L., Camargo, M. and Fick, M. (2015). “Reverse logistics network design for a biogas plant: An approach based on MILP optimization and Analytical Hierarchical Process (AHP)”, Journal of Manufacturing Systems, Vol. 37, No. 3, PP. 616-623.
15
Cardoso, S. R., Barbosa-Póvoa, A. P. F. D. and Relvas, S. (2013). “Design and planning of supply chains with integration of reverse logistics activities under demand uncertainty”, European Journal of Operational Research, Vol. 226, No. 3, PP. 436-451.
16
Entezaminia, A., Heydari, M. and Rahmani, D. (2016). “A multi-objective model for multi-product multi-site aggregate production planning in a green supply chain: Considering collection and recycling centers”, Journal of Manufacturing Systems, Vol. 40, No. 1, PP. 63-75.
17
Yousefi Babadi, A. and Shishebori, D. (2015). “Robust optimization of integrated reverse logistic network design at uncertain conditions”, Journal of Industrial Engineering, Vol. 49, No. 2, PP. 299-313.
18
Saffar, M. M., Shakouri, H., Ganjavi, and Razmi, J. (2015). “A green closed loop supply chain network design considering operational risks under uncertainty and solving the model with NSGA II algorithm”, Journal of Industrial Engineering, Vol. 49, No. 1, PP. 55-68.
19
Ghesmati, R., Ghazanfari, M. and Pishvaee, M. S. (2016). “A robust fuzzy-probabilistic programming model for a reliable supply chain network design problem”, Journal of Industrial Engineering, Vol. 50, No. 1, PP. 53-68.
20
Paydar, M. M. and Saidi-Mehrabad, M. (2015). “Revised multi-choice goal programming for integrated supply chain design and dynamic virtual cell formation with fuzzy parameters”, International Journal of Computer Integrated Manufacturing, Vol. 28, No. 3, PP. 251-265.
21
Chang, C. T. (2008). “Revised multi-choice goal programming”, Applied Mathematical Modelling, Vol. 32, No. 12, PP. 2587-2595.
22
Samadian, F. (2011). Tire recycling report, Iran ministry of mining and industry, Central library (in persian).
23
ORIGINAL_ARTICLE
Developing an Integrated Approach for Inventory Control, Pricing and Advertisement of Deteriorating Items
In this paper, an integrated approach for inventory control, pricing and advertisement of deteriorating items is proposed. The demand rate is a function of the selling price and advertisement, which is modeled as the frequency of advertisement in each replenishment cycle. To reflect a more practical situation, not only the prices of substitute products affect demand, but also the inventory holding cost is defined as a time-dependent function. In order to characterize the optimal solution, several theoretical results are derived which demonstrate existence and uniqueness of the optimal solution. Then an iterative solution algorithm is developed. Finally, to show validity of the proposed model and applicability of the developed algorithm procedure, numerical results are provided.
https://aie.ut.ac.ir/article_63160_9bd5938f6bc922e852bab586cfebd6ca.pdf
2016-11-21
407
4016
10.22059/jieng.2016.63160
Advertisement
Deterioration
Inventory Control
pricing
Masoud
Rabbani
mrabani@ut.ac.ir
1
Faculty of Industrial Engineering and Systems, Amirkabir University of Technology, Tehran, Iran
LEAD_AUTHOR
Nadia
Pourmohammad-Zia
nadia.pmz@gmail.com
2
Faculty of Industrial Engineering and Systems, Amirkabir University of Technology, Tehran, Iran
AUTHOR
Hamed
Rafiei
hrafiei@ut.ac.ir
3
Faculty of Industrial Engineering and Systems, Amirkabir University of Technology, Tehran, Iran
AUTHOR
1- Nasiri, M. M. and Pourmohammad Zia, N. (2015). “A hybrid model for supplier selection and order allocation in supply chain”, Journal of international engineering , Vol. 49, No. 1, PP. 117–128.
1
2- Panda, S., Saha, S. and Basu, M. (2013). “Optimal pricing and lot-sizing for perishable inventory with price and time dependent ramp-type demand”, International Journal of Systems sciences, Vol. 44, No. 1, PP. 127–138.
2
3- Whitin, T. M. (1955). “Inventory control and price theory”, Management sciences, Vol. 2, No. 1, PP. 61–68.
3
4- Eilon, S. and Mallaya, R. V. (1966). “Issuing and pricing policy of semi-perishables”, Proceedings of 4th International conference on operational research, New York, USA, PP. 205–215.
4
5- Cohen, M. A. (1977). “Joint pricing and ordering policy for exponentially decaying inventory with known demand”, Naval research logistic quarterly, Vol. 24, No. 2, PP. 257–268.
5
6- Abad, P. L. (2003). “Optimal pricing and lot-sizing under conditions of perishability, finite production and partial backordering and lost sale”, European journal of operations research, Vol. 144, No. 3, PP. 677–685.
6
7- Tsao, Y. C. and Sheen, G. J. (2008). “Dynamic pricing, promotion and replenishment policies for a deteriorating item under permissible delay in payments”, Computers & operations research , Vol. 35, No. 11, PP. 3562–3580.
7
8- Shah, N. H., Soni, H. N. and Patel, K. A. (2013). “Optimizing inventory and marketing policy for non-instantaneous deteriorating items with generalized type deterioration and holding cost rates”, Omega, Vol. 41, No. 2, PP. 421–430.
8
9- Shavandi, H., Mahlooji, H. and Nosratian, N.E. (2012). “A constrained multi-product pricing and inventory control problem”, Applied soft computing , Vol. 12, No. 8, PP. 2454–2461.
9
10- Giri, B. C. and Bardhan, S. (2012). “Supply chain coordination for a deteriorating item with stock and price dependent demand under revenue sharing contract”, International transaction on operations research , Vol. 19, No. 5, PP. 753–768.
10
11- Soni, H. N. (2013). “Optimal replenishment policies for non-instantaneous deteriorating items with price and stock sensitive demand under permissible delay in payment”, International journal of production economics, Vol. 146, No. 1, PP. 259–268.
11
12- Soni, H. N. and Joshi, M. (2013). “A fuzzy framework for coordinating pricing and inventory policies for deteriorating items under retailer partial trade credit financing”, Computers & industrial engineering, Vol. 66, No. 4, PP. 865–878.
12
13- Xiao, T., and Xu, T. (2013). “Coordinating price and service level decisions for a supply chain with deteriorating item under vendor managed inventory”, International journal of production economics, Vol. 145, No. 2, PP. 743–752.
13
14- Soni, H. and Patel, K. (2012). “Optimal pricing and inventory policies for non-instantaneous deteriorating items with permissible delay in payment: Fuzzy expected value model”, International journal of industrial engineering computation, Vol. 3, No. 3, PP. 281–300.
14
15- Soni, H. and Patel, K. (2013). “Joint pricing and replenishment policies for non-instantaneous deteriorating items with imprecise deterioration free time and credibility constraint”, Computers & industrial engineering, Vol. 66, No. 4, PP. 944–951.
15
16- Ghoreishi, M., Mirzazadeh, A. and Weber, G. W. (2013). “Optimal pricing and ordering policy for non-instantaneous deteriorating items under inflation and customer returns”, Optimization, (ahead-of-print), PP. 1–20.
16
17- Maihami, R. and Nakhai Kamalabadi, I. (2012). “Joint pricing and inventory control for non-instantaneous deteriorating items with partial backlogging and time and price dependent demand”, International journal of production economics, Vol. 136, No. 1, PP. 116–122.
17
18- Maihami, R. and Nakhai Kamalabadi, I. (2012). “Joint control of inventory and its pricing for non-instantaneously deteriorating items under permissible delay in payments and partial backlogging”, Mathematical computation modelling, Vol. 55, No. 5, PP. 1722–1733.
18
19- Teimouri, E. and Kazemi, M. M. (2015). “Development of pricing model for deteriorating items with constant deterioration rate considering replacement”, Journal of international engineering ,Vol. 49, No. 1, PP. 1–9.
19
20- Taleizadeh, A. A. and Babaei, M. (2015). “Pricing and inventory decisions of deteriorating complementary products”, Journal of international engineering ,Vol. 48, No. 1, PP. 83–94.
20
21- Wu, K. S., Ouyang, L. Y. and Yang, C. T. (2009). “Coordinating replenishment and pricing policies for non-instantaneous deteriorating items with price-sensitive demand”, International Journal of Systems sciences, Vol. 40, No. 12, PP. 1273–1281.
21
22- Qin, Y., Wang, J. and Wei, C. (2014). “Joint pricing and inventory control for fresh produce and foods with quality and physical quantity deteriorating simultaneously”, International journal of production economics, Vol. 152, PP. 42–48.
22
ORIGINAL_ARTICLE
Integration of Inventory Decisions and Carrier Mode Selection in a Two-echelon Supply Chain with Deteriorating Items
This study investigates a two-echelon supply chain consisting of a vendor and a buyer with deteriorating items. The deterioration rate of product is constant. To transport products from vendor to buyer, two carrier’s modes with different traveling time and cost are considered. The difference between the present study and the previous ones, is that the deterioration of product continues during the traveling time, which depends on the carrier’s mode to ship batches from vendor to buyer. So the structure of existing models changes. The purpose of this study, is to minimize the total cost of buyer and vendor, and to obtain the number of shipments, the vendor’s production cycle time, and the carrier’s type. We propose an algorithm to solve the independent model of buyer and vendor with the affirmation of cost functions’ convexity, and a heuristic algorithm to solve the integrated model. Sensitivity analysis is also carried out to examine the sensitivity of decision variables and the performance measure of supply chain. The proposed integrated model can produce lower cost rather than an independent decision by vendor and buyer.
https://aie.ut.ac.ir/article_63161_a8778ff87b1e2761a841a034ca5de10a.pdf
2016-11-21
417
428
10.22059/jieng.2016.63161
Deteriorating inventory
Integrated supply chain
Lead time
Optimization
Noshin
Shomalzadeh
nooshin_shomalzadeh@yahoo.com
1
Faculty of Industrial Engineering, Sharif University of Technology, Tehran, Iran
AUTHOR
Mohammad
Modarres
modarres@sharif.edu
2
Faculty of Industrial Engineering, Sharif University of Technology, Tehran, Iran
LEAD_AUTHOR
1. Glock, C. H. (2012). “The joint economic lot size problem: A review”, International Journal of Production Economics, Vol. 135, No. 2, PP. 671- 686.
1
2. Goyal, S. (1977). “An integrated inventory model for a single supplier-single customer problem”, International Journal Of Production Research, Vol. 15, No. 1, PP. 107- 111.
2
3. Banerjee, A. (1986). “A joint economic‐lot‐size model for purchaser and vendor”, Decision Sciences, Vol. 17, No. 3, PP. 292-311.
3
4. Goyal, S. K. (1988). “A joint economic‐lot‐size model for purchaser and vendor: A comment”, Decision Sciences, Vol. 19, No 1, PP. 236- 241..
4
5. Kim, S. L. and Ha, D. (2003). “A JIT lot-splitting model for supply chain management: Enhancing buyer–supplier linkage”, International Journal of Production Economics, Vol. 85, No. 1 PP. 1- 10.
5
6. Viswanathan, S. (1998). “Optimal strategy for the integrated vendor-buyer inventory model”, European Journal of Operational Research, Vol. 105 No. 1, PP. 38- 42.
6
7. Hill, R. M. (1997). “The single-vendor single-buyer integrated production-inventory model with a generalized policy”, European Journal of Operational Research, Vol. 97, No3, PP. 493- 499.
7
8. Rau, H., Wu, M. Y. and Wee, H. M. (2003). “Integrated inventory model for deteriorating items under a multi-echelon supply chain environment”, International Journal of Production Economics, Vol. 86, No. 2, PP. 155- 168.
8
9. Zhou, Y. W. and Wang, S. D. (2007). “Optimal production and shipment models for a single-vendor–single-buyer integrated system”, European Journal of Operational Research, Vol. 180, No. 1, PP. 309- 328.
9
10. Lo, S. T., Wee, H. M. and Huang, W. C. (2007). “An integrated production-inventory model with imperfect production processes and Weibull distribution deterioration under inflation”, International Journal of Production Economics, Vol. 106, No. 1, PP. 248- 260.
10
11. Tsai, D. M. (2011). “An optimal production and shipment policy for a single-vendor single-buyer integrated system with both learning effect and deteriorating items”, International Journal of Production Research, Vol. 49, No. 3, PP. 903- 922.
11
12. Yan, C., Banerjee, A. and Yang, L. (2011). “An integrated production–distribution model for a deteriorating inventory item”, International Journal of Production Economics, Vol.133, No. 1, PP. 228- 232.
12
13. Sarkar, B. (2012). “A production-inventory model with probabilistic deterioration in two-echelon supply chain management”, Applied Mathematical Modelling, Vol. 37, No.5, PP. 3138- 3151.
13
14. Wang, S. P., Lee, W. and Chang, C. Y. (2012). “Modeling the consignment inventory for a deteriorating item while the buyer has warehouse capacity constraint”, International Journal of Production Economics, Vol. 138, No. 2, PP. 284- 292.
14
15. Hadley, G. and Whitin, T. M. (1963). Analysis of inventory systems, Prentice Hall,.
15
16. Sajadieh, M. S., Akbari Jokar, M. R. and Modarres, M. (2009). “Developing a coordinated vendor–buyer model in two-stage supply chains with stochastic lead-times”, Computers & Operations Research, Vol. 36, No. 8, PP. 2484- 2489.
16
ORIGINAL_ARTICLE
Inventory Control for Deteriorating Items in Closed-loop Supply Chain with Stochastic Demand
Products such as ICs, computers, and cell phones can become out of date due to technology development; but they can be remanufactured and returned to market for sale. Determining the optimal inventory control policy for remanufactured products, that is considered in the closed loop supply chain, is one of the important problems in the supply chain management of deteriorating items (in closed loop supply chain, the customer is able to return the used products to the reverse flow for remanufacturing or reusing). In this paper, we analyze an inventory system for closed-loop supply chain with multi-manufacturing and multi-remanufacturing cycles under stochastic demands. The manufacturing cycle is used for direct flow of supply chain; while the remanufacturing cycle is applied for reverse flow, in which the used products return to the production system. The supply chain is for echelons including retailer, manufacturer, collector, and material supplier in which shortage is allowed and completely backlogged. The decision is made initially by the down-stream player (from retailer to supplier). We generalize three different cases: 1. Single manufacturing cycle and single remanufacturing cycle, 2. Single manufacturing cycle and multi-remanufacturing cycles, and 3. Multi-manufacturing cycles and single remanufacturing cycle. Moreover, a heuristic algorithm is presented to obtain the optimal solution. Finally, a numerical example is described to prove the applicability of the model and its solution algorithm.
https://aie.ut.ac.ir/article_63162_4c4a5d5605526eee321de3852d8be85c.pdf
2016-11-21
429
439
10.22059/jieng.2016.63162
Closed-Loop Supply Chain
Deteriorating items
Multi-echelon inventory
Stochastic demand
Sepideh
Zohoori
spdzohoori@gmail.com
1
Faculty of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran, Iran
AUTHOR
Behrooz
Karimi
b.karimi@aut.ac.ir
2
Faculty of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran, Iran
LEAD_AUTHOR
Reza
Maihami
r.maihami@aut.ac.ir
3
Faculty of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran, Iran
AUTHOR
Guo, C. and Li, X. (2014). “A multi-echelon inventory system with supplier selection and order allocation under stochastic demand”, Int. J. Production Economics, Vol. 151, PP. 37- 47.
1
Yang, P. C., Wee, H. M., Chung, S. L., Ho, P. C. (2010). “Sequential and global optimization for a closed-loop deteriorating inventory supply chain”, Mathematical and Computer Modelling, Vol. 52, No. 1-2, PP. 161- 176.
2
Schrady, D. A. (1967). “Adeterministic inventory model for repairable items”, Naval Research Logistics Quarterly, Vol. 14, No. 3, PP. 391- 398.
3
Chung, S. L., Wee, H. M. and Yang, P. C. (2008). “Optimal policy for a closed-loop supply chain inventory system with remanufacturing”, Mathematical and Computer Modelling, Vol. 48, No. 5- 6, PP. 867- 881.
4
Jaber, M. Y. and Saadany, A. M. A. E. (2009). “The production, remanufacture and waste disposal model with lost sales”, International Journal of Production Economics, Vol. 120, No. 1, PP. 115- 124.
5
Widyadana, G. A. and Wee, H. M. (2012). “An economic production quantity model for deteriorating items with multiple production setups and rework”, International Journal of Production Economics, Vol. 138, No. 1, PP. 62– 67.
6
Aliyu, M. D. S. and Boukas, E. K. (1998). “Discrete-time inventory models with deteriorating items”, International Journal of Systems science, Vol. 29, No. 9, PP. 1007- 1014.
7
Pakkala, T. P. M. and Achary, K. K. (1991). “A two warehouse probabilistic order level inventory model for deteriorating items”, Journal of Operational Research Society, Vol. 42, No. 12, PP. 1117- 1122.
8
Shah, N. H. (1998). “A discrete-time probabilistic inventory model for deteriorating items under a known price increase”, International Journal of Systems Science, Vol. 29, No. 8, PP. 823- 827.
9
Aggoun, L., Benkherouf, L. and Tadj, L. (2000). “Stochastic Jump inventory model with deteriorating items”, Stochastic Analysis and Applications, Vol. 18, No. 1, PP. 1- 10.
10
Benkherouf, L., Aggoun, L. and Tadj, L. (1999). “On the optimal EOQ for a stochastic jump inventory model with deteriorating items”, Journal of Statistical Research, Vol. 33, No. 1, PP. 1- 8.
11
Chen X., and Simchi Levi, D. (2004). “Coordinating inventory control and pricing strategies with random demand and fixed ordering cost the finite horizon case”, Operations Research, Vol. 52, No. 6, PP. 887–896.
12
Zhang J. L., Chen J. and Lee C. Y. (2008). “Joint optimization on pricing, promotion and inventory control with stochastic demand”, Int J Prod Econ. Vol. 116, No. 2, PP. 190– 198.
13
Maihami, R. and Karimi, B. (2014). “Optimizing the pricing and replenishment policy for non-instantaneous deteriorating items with stochastic demand and promotional efforts”, Computers & Operations Research, Vol. 51, PP. 302– 312.
14
Whitin, T. M. (1957). Theory of inventory management, Princeton University Press, Princeton, NJ.
15
Ghare, P. N. and Schrader, G. F. (1963). “A model for exponentially decaying inventories”, Journal of Industrial Engineering, Vol. 15, PP. 238- 243.
16
Ouyang, L. Y., Wu, K. S. and Yang, C. T. (2006). “A study on an inventory model for non- instantaneous deteriorating items with permissible delay inpayments”, Comput Ind Eng, Vol. 51, No. 4, PP. 637– 651.
17
Yang, C., Te, Quyang, L. Y. and Wu, H. H. (2009). “Retailers optimal pricing and ordering policies for Non-instantaneous deteriorating items with price-dependent demand and partial backlogging”, Math Prob Eng,Vol. 2009.
18
Maihami, R. and Nakhai, I. (2012). “Joint pricing and inventory control for non- instantaneous deteriorating items with partial backlogging and time and price dependent demand”, Int J Prod Econ, Vol. 136, No. 1, PP. 116– 122.
19
Wee, H. M., Lee, M. C., Yu, J. C. P. and Edward Wang, C. (2011). "Optimal replenishment policy for a deteriorating green product: Life cycle costing analysis", International Journal of Production Economics, Vol. 133, No. 2, PP. 603- 611.
20
Sazvar, Z., Mirzapour Al-e-hashem, S. M. J., Baboli, A. and Akbari Jokar, M. R. (2014). "A bi-objective stochastic programming model for a centralized green supply chain with deteriorating products", International Journal of Production Economics, Vol. 150, No. 0, PP. 140- 154.
21
Moussawi-Haidar, L., Dbouk, W., Jaber, M. Y. and Osman, I. H. (2014). "Coordinating a three-level supply chain with delay in payments and a discounted interest rate", Computers & Industrial Engineering, Vol. 69, PP. 29- 42.
22
Kapoor, S. (2014). "An inventory model for non-instantaneous deteriorating products with price and time dependent demand", Mathematical Theory and Modeling, Vol. 4, No. 10, PP. 160– 168.
23
Tat, R., Taleizadeh, A. A. and Esmaeili, M. (2015). "Developing economic order quantity model for non-instantaneous deteriorating items in vendor-managed inventory (VMI) system", International Journal of Systems Science, Vol. 46, No. 7, PP. 1257- 1268.
24
Chakraborty, D., Jana, D. K. and Roy, T. K. (2015). "Multi-item integrated supply chain model for deteriorating items with stock dependent demand under fuzzy random and bifuzzy environments", Computers & Industrial Engineering, Vol. 88, PP. 166- 180.
25
Ghiami, Y. and Williams, T. (2015). "A two-echelon production-inventory model for deteriorating items with multiple buyers", International Journal of Production Economics, Vol. 159, PP. 233- 240.
26
Geetha, K. and Udayakumar, R. (2016). "Optimal lot sizing policy for non-instantaneous deteriorating items with price and advertisement dependent demand under partial backlogging", International Journal of Applied and Computational Mathematics, Vol. 2, No. 2, PP. 171- 193.
27
Maihami, R., Karimi, B. and Fatemi Ghomi, S. M. T. (2016). "Effect of two-echelon trade credit on pricing-inventory policy of non-instantaneous deteriorating products with probabilistic demand and deterioration functions", Annals of Operations Research, Vol. 239, No. 2, PP. 1- 37.
28
Bai, Q., Xu, X., Xu, J. and Wang, D. (2016). "Coordinating a supply chain for deteriorating items with multi-factor-dependent demand over a finite planning horizon", Applied Mathematical Modelling, Vol. 40, No. 21-22, PP. 9342–9361.
29
Priyan, S. and Uthayakumar, R. (2016). "Economic design of an inventory system involving probabilistic deterioration and variable setup cost through mathematical approach", International Journal of Mathematics in Operational Research, Vol. 8, No. 3, PP. 312- 341.
30
Sarker, B. R. and Wu, B. (2016). "Optimal models for a single-producer multi-buyer integrated system of deteriorating items with raw materials storage costs", The International Journal of Advanced Manufacturing Technology, Vol. 82, No. 1, PP. 49- 63.
31
Aljazzar, S. M., Jaber, M. Y. and Moussawi-Haidar, L. (2016) "Coordination of a three-level supply chain (supplier–manufacturer–retailer) with permissible delay in payments", Applied Mathematical Modelling, Vol. 3, No. 3, PP. 176-188.
32
Chen, Z. and Sarker, B. R. (2017). "Integrated production-inventory and pricing decisions for a single-manufacturer multi-retailer system of deteriorating items under JIT delivery policy", The International Journal of Advanced Manufacturing Technology, Vol. 89, No. 5-8, PP. 2099–2117 .
33
Spiegel, M. R. (1960). “Applied Differential Equations, Prentice-Hall”, Englewood Cliffs, NJ.
34
Misra, R. B. (1975). “Optimal production lot size model for a system with deteriorating inventory”, International Journal of Production Research, Vol. 13, No. 5, PP. 495- 505.
35
ORIGINAL_ARTICLE
Scheduling in a Cross-Dock Based on the Departure Time of the Outboard Trucks
According to the development of cross-dock network concept in theory and establishing cross-dock centers around the world, these centers have been attended by many companies. Cross-dock centers reduce transportation costs significantly by consolidating the loads and transferring them together. There are some problems in planning and scheduling cross-dock systems that can affect the efficiency and productivity of these systems. In this paper, a model, based on time and capacity constraints is developed. The model schedules inbound truck unloading time according to the importance, volume and existing costs of the products, and delay loads are stored in temporary storages to transfer at the next period. Furthermore, the problem is formulated in a mathematical model. It can be solved to find optimal solution, and in large size problems a heuristic algorithm is developed.
https://aie.ut.ac.ir/article_63175_dbb64a5b3da255376d3aa265af914a45.pdf
2016-11-21
441
450
10.22059/jieng.2016.63175
Benders decomposition technique
Cross-dock network
Heuristic algorithm
Scheduling
Seyed Mohammad Taghi
Fatemi Ghomi
fatemi@aut.ac.ir
1
Faculty of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran
LEAD_AUTHOR
Sajjad
Rahmanzadeh Tootkaleh
sajjad.rahmanzadeh@gmail.com
2
Faculty of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran
AUTHOR
Mohsen
Sheikh Sajadiyeh
sajadieh@aut.ac.ir
3
Faculty of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran
AUTHOR
Stalk, G., Evans, P. and Shulman, L. (1992). “Competing on capabilities: The new rules of corporate strategy”, Harvard Business Review, Vol. 70, No. 2, PP. 57–69.
1
Witt, C. E. (1998). “Cross docking: Concepts demand choice”, Material Handling Engineering, Vol. 53, No. 7, PP. 44–49.
2
Cook, R. L., Gibson, B. and MacCurdy, D. (2005). “A lean approach to cross docking”, Supply Chain Management Review, Vol. 9, No. 2, PP. 54–59.
3
Boloory Arabani, A. R., Fatemi Ghomi, S. M. T. and Zandieh. M. (2011). “Meta-heuristics implementation for scheduling of trucks in a cross-docking system with temporary storage”, Expert Systems with Applications, Vol. 38, No. 3, PP. 1964-1979.
4
Agustina, D., Lee, C. K. M. and Piplani, R. (2010). “Cross docking scheduling with delivery time window and temporary storage”, Industrial Engineering and Engineering Management (IEEM), Singapore, Dec 2011.
5
Boysen, N. (2010). “Truck scheduling at zero-inventory cross docking terminals”, Computers & Operations Research, Vol. 37, No. 1, PP. 32-41.
6
Alpan, G., Larbi, R., and Penz, B. (2011). “A bounded dynamic programming approach to schedule operations in a cross docking platform”, Computers & Industrial Engineering, Vol. 60, No. 3, PP. 385-396.
7
Forouharfard, S. and Zandieh, M. (2010). “An imperialist competitive algorithm to schedule of receiving and shipping trucks in cross-docking systems”, The International Journal of Advanced Manufacturing Technology, Vol. 51, No. 9, PP. 1179-1193.
8
Boysen, N. and Fliedner, M. (2010). “Cross dock scheduling: Classification, literature review and research agenda”, Omega, Vol. 38, No. 6, PP. 413–422.
9
Van Belle, J., Valckenaers, P. and Cattrysse, D. (2012). “Cross-docking: state of the art”, Omega , Vol. 40, No. 6, PP. 827-846.
10
Boysen, N., Briskorn, D. and Tschöke, M. (2013). “Truck scheduling in cross-docking terminals with fixed outbound departures”, OR Spectrum, Vol. 35, No. 2, PP. 479-504.
11
Ladier, A. L. and Alpan, G. (2013). “Scheduling truck arrivals and departures in a crossdock: Earliness, tardiness and storage policies”, Industrial Engineering and Systems Management (IESM), morocco.
12
Geoffrion, A. M. (1972). “Generalized Benders Decomposition”, Journal of Optimization Theory and Applications, Vol. 10, No. 4, PP. 237-260.
13
ORIGINAL_ARTICLE
An Integrated Approach for Product-Mix Determination, Two-Sided Assembly Line Balancing and Worker assignment, Based on the Bottlenecks of System
In this paper, a heuristic algorithm for product-mix determination, two-sided assembly line balancing and worker assignment are presented. In this algorithm, in addition to assigning the tasks and workers to the stations for cycle time minimization, the quantity of each model is determined to have a suitable line to assemble the products. The efficiency of the heuristic algorithm is verified with several test problems and two different rules for worker assignment, which the obtained results showed the algorithm’s efficiency.
https://aie.ut.ac.ir/article_63176_e2cca3a68a9a253670d62f533ceef160.pdf
2016-11-21
451
460
10.22059/jieng.2016.63176
Heuristic algorithm
Mixed-model
Product-mix
Theory of constraints
Two-sided assembly line balancing problem
Worker assignment
Parviz
Fattahi
p.fattahi@alzahra.ac.ir
1
Faculty of Engineering, Alzahra University, Tehran, Iran
LEAD_AUTHOR
Parvaneh
Samouei
samouei_parvaneh@yahoo.com
2
Faculty of Engineering, Alzahra University, Tehran, Iran
AUTHOR
Mostafa
Zandiyeh
m_zandieh@sbu.ac.ir
3
Faculty of Engineering, Alzahra University, Tehran, Iran
AUTHOR
1. Salveson, M. E. (1955). "The assembly line balancing problem", Journal of Industrial Engineering, Vol. 6, No. 3, PP. 18– 25.
1
2. Amen, M. (2000). "Heuristic methods for cost-oriented assembly line balancing: A survey", International Journal of Production Economics, Vol. 68, No. 1, PP. 1- 14.
2
3. Becker, C. and Scholl, A. (2006). "A survey on problems and methods in generalized assembly line balancing", European Journal of Operational Research, Vol. 168, No. 3, PP. 694– 715.
3
4. Battaïa, O. and Dolgui, A. (2013). "A taxonomy of line balancing problems and their solution approaches", International Journal of Production Economics, Vol. 142, No. 2, PP. 259– 277.
4
5. Miralles, C., Garía-Sabater, J. P., Andrés, C. and Cardós, M. (2008). "Branch and bound procedures for solving the assembly line worker assignment and balancing problem: Application to sheltered work centres for disabled", Discrete Applied Mathematics, Vol. 156, No. 3, PP. 352- 367.
5
6. Blum, C. and Miralles, C., (2011). "On solving the assembly line worker assignment and balancing problem via beam search", Computers & Operations Research, Vol. 38, No. 1, PP. 328– 339.
6
7. Zaman, T., Paul, S. K. and Azeem, A. (2012). "Sustainable operator assignment in an assembly line using genetic algorithm", International Journal of Production Research, Vol. 50, No. 18, PP. 5077– 5084.
7
8. Zhang, W., Gen, M. and Lin, L. (2008). "A Multi-objective genetic algorithm for assembly line balancing problem with worker allocation", IEEE International Conference on Systems, Man and Cybernetics.
8
9. Mutlu, Ö., Polat, O. and Supciller, A. A. (2013). "An iterative genetic algorithm for the assembly line worker assignment and balancing problem of type-II", Computers & Operations Research, Vol. 40, No. 1, PP. 418– 426.
9
10. Vilà, M. and Pereira, J. (2014). "A branch-and-bound algorithm for assembly line worker assignment and balancing problems", Computers & Operations Research, Vol. 44, PP. 105– 114.
10
11. Pastor, R. (2011). "LB-ALBP: The lexicographic bottleneck assembly line balancing problem", International Journal of Production Research, Vol. 49, No. 8, PP. 2425- 2442.
11
12. Özcan, U. and Toklu, B., (2009). "Balancing of mixed-model two-sided assembly lines", Computers and Industrial Engineering, Vol. 57, No. 1, PP. 217– 227.
12
13. Purnomo, H. D., Wee, H. M. and Rau, H., (2013). "Two-sided assembly lines balancing with assignment restrictions", Mathematical and Computer Modelling, Vol. 57, No. 1-2, PP. 189– 199.
13
ORIGINAL_ARTICLE
Prediction of Rotating Machineries Failure by Intelligent Systems
Failure of machines, due to stopping the production line, results in financial losses. Preventive maintenance, significantly extends the machineries life, and reduces the costs. On the other hand, predicting the remaining useful life (URL) of the equipment and machineries, provides adequate time for maintenance engineers to repair or replace the parts before failure occurs, and avoid the overhaul costs (conditional-based maintenance). These actions are more important for rotary machines such as turbines, pumps and compressors, than the others. Hence, in this paper, we predict the URL of the Olefin unit of Pars Petrochemical Company turbine pumps based on the bearings health by artificial neural networks (ANN) and support vector machine. First, we provided the prediction model by the RMS, mean, peak and crest factor of one bearing, which was used to estimate the RUL of the four bearings using the above methods. Results showed that the accuracy of prediction by SVM method was more than single-layer ANN.
https://aie.ut.ac.ir/article_63177_9fe1589dfc1cbec69ee875bfb94d45fe.pdf
2016-11-21
461
470
10.22059/jieng.2016.63177
Artificial Neural Network
Bearing
prediction
Remaining useful life (RUL)
Support vector Machine
Turbine pump
Fatemeh
Farhadi
fa.farhady@yahoo.com
1
Department of Industrial Engineering, Tarbiat Modares University, Tehran, Iran
AUTHOR
Mohammad Reza
Amin-Nasseri
amin_nas@modares.ac.ir
2
Department of Industrial Engineering, Tarbiat Modares University, Tehran, Iran
LEAD_AUTHOR
Kim, H. E., Tan, A. C. C., Mathew, J., Kim, E. Y. H. and Choi, B. K. (2009). “Prognosis of bearing failure based on health state estimation”, Presented at the Proceedings of the 4th World Congress on Engineering Asset Management, Marriott Athens Ledra Hotel, Athens.
1
Jardine, A. K. S., Lin, D. and Banjevic, D. (2006). “A review on machinery diagnostics and prognostics implementing condition-based maintenance”, Mechanical Systems and Signal Processing, Vol. 20, No.7, PP. 1483- 1510.
2
Zhanj, S. and Ganesan, R. (1997). “Multivariable trend analysis using neural networks for intelligent diagnostics of rotating machinery”, Journal of Engineering for gas turbines and power, Vol. 119, No. 2, PP. 378- 384.
3
Wang, P. and Vachtsevanos, G. (2001). “Fault prognostics using dynamic wavelet neural networks”, AI EDAM, Vol. 15, No. 4, PP. 349- 365.
4
Yam, R. C. M., Tse, P. W., Li, L. and Tu, P. V. (2001). “Intelligent predictive decision support system for condition-based maintenance”, The International Journal of Advanced Manufacturing Technology, Vol. 17, No. 5, PP. 383- 391.
5
Dong, Y. L., GU, Y. J., Yang, K. and Zhang, W. K. (2004). “A combining condition prediction model and its application in power plant”, Proceedings of International Conference on Machine Learning and Cybernetics IEEE. Vol. 6 , PP. 3474- 3478.
6
Sugumaran, V., Muralidharan, V. and Ramachandran, K. I. (2007). “Feature selection using decision tree and classification through proximal support vector machine for fault diagnostics of roller bearing”, Mechanical Systems and Signal Processing, Vol. 21 No. 2, PP. 930- 942.
7
Sakthivel, N. R., Sugumaran, V. and Nair, B. B. (2010). “Application of support vector machine (SVM) and proximal support vector machine (PSVM) for fault classification of monoblock centrifugal pump”, International Journal of Data Analysis Techniques and Strategies, Vol. 2, No. 1, PP. 38- 61.
8
Kim, H. E., Tan, A. C. C., Mathew, J. and Choi, B. K. (2012). “Bearing fault prognosis based on health state probability estimation”, Expert Systems with Applications, Vol. 39, No. 5, PP. 5200- 5213.
9
Barakat, M., Elbadaoui, M. and Guillet, F. (2013). “Hard competitive growing neural network for the diagnosis of small bearing faults”, Mechanical Systems and Signal Processing, Vol. 37, No. 1-2, PP. 276-292.
10
Amar, M., Gondal, I. and Wilson, C. (2013). “Multi-size-window spectral augmentation: Neural network bearing fault classifier”, In Industrial Electronics and Applications (ICIEA), 8th IEEE Conference on. PP. 261- 266.
11
Guo, L., Li, N., Jia, F. and Lei, Y. (2017). “A recurrent neural network based health indicator for remaining useful life prediction of bearings”, Journal of Neurocomputing, Vol. 240, No. 31, PP. 98- 109.
12
Wang, W. Q., Golnaraghi, M. F. and Ismail, F. (2004). “Prognosis of machine health condition using neuro-fuzzy systems”, Mechanical Systems and Signal Processing, Vol. 18, No. 4, PP. 813- 831.
13
Chinnam, R. B. and Baruah, P. (2003). “Autonomous diagnostics and prognostics through competitive learning driven HMM-based clustering In Neural Networks”, Proceedings of the International Joint Conference on, Vol. 4 ,PP. 2466- 2471.
14
Chen, C., Vachtsevanos, G. and Orchard, M. E. (2012). “Machine remaining useful life prediction: An integrated adaptive neuro-fuzzy and high-order particle filtering approach”, Mechanical Systems and Signal Processing, Vol. 28, PP. 597- 607.
15
Sakthivel, N. R., Sugumaran, V. and Babudevasenapati, S. (2010). “Vibration based fault diagnosis of monoblock centrifugal pump using decision tree”, Expert Systems with Applications Vol. 37 , No. 6, PP. 4040- 4049.
16
Tian, Z. (2012). “An artificial neural network method for remaining useful life prediction of equipment subject to condition monitoring”, Journal of Intelligent Manufacturing, Vol. 23, No. 2, PP. 227- 237.
17
Chao, H., & Byeng D. Y., Taejin, K.and Pingfeng, W. (2015). “A co-training-based approachfor prediction of remaining useful life utilizing both failure and suspension data”, Journal of Mechanical Systems and Signal Processing, Vol. 62- 63, PP. 75- 90.
18
Engin, A. (2009). “Selecting of the optimal feature subset and kernel parameters in digital modulation classification by using hybrid genetic algorithm- support vector machines”, Expert Systems with Applications, Vol. 36, No. 2 , PP. 1391- 1402.
19
Cristianini, N. and Shawe-Taylor, J. (2000). An introduction to support vector machines and other kernel-based learning methods, Cambridge University Press, Cambridge.
20
Burges, C. (1998). “Tutorial on support vector machines for patternrecognition”, Data Mining and Knowledge Discovery, Vol. 2, No. 2, PP. 121- 167.
21
Bellotti, T. and Crook, J. (2008). “Support vector machines for credit scoring and discovery of significant features”, Expert Systems with Applications, Vol. 36, No. 2, PP. 102- 109.
22
Ben-Hur, A. and Weston, J. (2010). A user’s guide to support vector machines, In: Data Mining Techniques for the Life Sciences, In Carugo, O. and Eisenhaber, F. (Eds), Totowa, NJ: Humana Press, PP. 223- 239.
23
ORIGINAL_ARTICLE
Three Developed Meta-heuristic Algorithms to Solve RACP Minimizing Makespan and Total Resource Costs Simultaneously
In this paper, a bi-objective resource availability cost problem (RACP) is studied, in which the first objective function tries to minimize the completion time of the project, and the second one tries to minimize the total resource costs. Due to the problem complexity, three developed meta-heuristic algorithms, namely NSGA-II and NRGA and MOPSO, are applied to solve the model. To evaluate the algorithms, a set of tests’ problem are considered. In addition, a MADM approach called TOPSIS is employed to compare the algorithms' results. Finally, the sensitivity analysis in terms of problem’s performance is fulfilled.
https://aie.ut.ac.ir/article_63178_ffe698baa7942ed5ca551d159e7a4bb9.pdf
2016-11-21
471
482
10.22059/jieng.2016.63178
MOPSO
Multi-objective optimization
NSGA-II
RACP
RCPSP
Amir Abbas
Najafi
aanajafi@kntu.ac.ir
1
Faculty of Industrial Engineering, Khajeh Nasir Toosi University of Technology, Tehran, Iran
LEAD_AUTHOR
Masoud
Arjmand
aa_najafi@yahoo.com
2
Faculty of Industrial Engineering, Khajeh Nasir Toosi University of Technology, Tehran, Iran
AUTHOR
Blazewicz, J., Lenstra, J. K. and Rinnooy Kan A. H. G. (1983). “Scheduling subject to resource constraints”, Discrete Appl Math, Vol. 1, No. 5, PP. 11–24.
1
Paraskevopoulos, D. C., Tarantilis, C. D. and Ioannou, G. (2012). "Solving project scheduling problems with resource constraints via an event list-based evolutionary algorithm", Expert. Syst. Appl, Vol. 39, No. 4, PP. 3983-3994.
2
Hartmann, S. and Kolisch, R. (2000). “Experimental evaluation of state-of-the-art heuristics for the resource constrained project scheduling problem”, Eur. J. Oper. Res.,Vol. 127, No. 2, PP. 394-407.
3
Afshar-Nadjafi, B., Karimi, H., Rahimi, A. and Khalili, S. (2013). “Project scheduling with limited resources using an efficient differential evolution algorithm”, Journal of KingSaud University, In Press, Corrected Proof.
4
Zamani, R. (2013). "A competitive magnet-based genetic algorithm for solving the resource-constrained project scheduling problem", Eur. J. Oper. Res., Vol. 229, No. 2, PP. 552-559.
5
Jairo, R., Torres, M. and Edgar, G. F. (2010). "Carolina Pirachicán-Mayorga, project scheduling with limited resources using a genetic algorithm", Int. J. project.Manage., Vol, 28, No. 6, PP. 619-628.
6
Hartmann, S. and Briskorn, D. A. (2010). "Survey of variants and extensions of the resource constrained project scheduling problem", Eur. J. Oper. Res., Vol. 207, No. 1, PP. 1-14.
7
Agarwal, R., Colak, S. and Erenguc, S. (2011). “A Neurogenetic approach for the resource constrained project scheduling problem”, Comput. Oper. Res., Vol. 38, No. 1, PP. 44- 50.
8
Fang, C. and Wang, L. (2012). “An effective shuffled frog-leaping algorithm for resource constrained project scheduling problem”, Comput. Oper. Res., Vol. 39, No. 5, PP. 890-901.
9
Koné, O. (2012). "New approaches for solving the resource constrained project scheduling problem", 4OR, Vol. 10, No. 1, PP. 105-106.
10
Mohring, R. H. (1984). “Minimizing Costs of Resource Requirements in project Networks subject to a Fix Completion Time”, Op Research, Vol. 32, No. 1, PP. 89-120.
11
Nubel, H. (1999). “A Branch and Bound procedure for Resource Investment problem subject to Temporal Constraints’’, Technical Report 574, University Karlsruhe, Germany.
12
Neumann, K. and Zimmermann, J. (1997). “Resource Leveling for project with schedule dependent time windows”, Technical Report 508, Institut fur wirtschafttheorie and Operations Research, Universitat Karsruhe.
13
Akpan, E. O. P. (1997). “Optimal Resource Determination for project scheduling”, Project Planning and Control, Vol. 8, No. 5, PP .462-468.
14
Kimms, A. (2001). “Minimizing the Net Present Value of a project under Resource Constraints Using a lagrangian Relaxation Based Heuristic with tight upper bound”, Annual of operation Research, Vol. 102, No. 1, PP. 221-236.
15
Najafi, A. A. and Niaki, S. T. A. (2006). “A genetic algorithm for resource investment problem with discount cash flow”, Applied Mathematical and Computation, Vol. 183, No. 2, PP. 1057-1070.
16
Deb, K. (2001). Multi-objective optimization using evolutionary algorithms, Wiley, Chichester.
17
Shadrokh, S. and Kianfar, F. (2007). “A genetic algorithm for resource investment project scheduling problem, tardiness permitted with penalty”, Eur J Oper Res, Vol. 181, No. 1, PP. 86–101.
18
Coello-Coello, C. A., Van Veldhuizen, D. A. and Lamont, G. B. (2002). Evolutionary algorithms for solving multi-objective problems, Kluwer Academic Publishers.
19
Kennedy, J. and Eberhart, R. C. (1995). “Particle swarm optimization”, Proceeding of IEEE International Conference on Neural Networks, Piscataway, NJ, Seoul, Korea, Vol. IV, PP. 1942-1948
20
Hwang, C. L. and Yoon, K. (1981). Multiple attribute decision making, Springer.
21
ORIGINAL_ARTICLE
Air Cargo Revenue Management in Variable Operating Conditions of Capacity, Considering the Possibility of Double Booking
Revenue Management (RM) is a subfield of operations research that aims at maximizing revenues acquired by selling perishable products/services in a specified period. Due to the substantial growth in air cargo industry over the past few years, some techniques are needed to maximize revenue. In this paper, space allocation problem in two cases including overbooking possibility and impossibility are studied. Since the proposed dynamic programming needs much memory for obtaining exact solution, three heuristics including deterministic integer linear programming (DILP), bid price (BP) and dynamic programming decomposition (DPD) are proposed. Results show that BP and DILP performance is better than other approaches. In addition, results show that when overbooking is possible, it leads to revenue increment by more than 10 percent.
https://aie.ut.ac.ir/article_63179_1c8a1fdec217b7089deb876ca07f272b.pdf
2016-11-21
483
495
10.22059/jieng.2016.63179
Air cargo
Canceling request
Overbooking
Revenue management
Mohammad
Vardi Chari
m.vardi@modares.ac.ir
1
Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran
AUTHOR
Ali
Husseinzadeh Kashan
a.kashan@modares.ac.ir
2
Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran
LEAD_AUTHOR
1. Talluri, K.T. and Van Ryzin, G.J. (2006). The theory and practice of revenue management (Vol. 68), Springer Science & Business Media.
1
2. Boeing Company (2005b). World Air Cargo Forecast 2002–2003. http://www.boeing.com/ commercial/ cargo/exec_summary.htm
2
3. Hendricks, G. and Kasilingam, R. (1993). Cargo revenue management at American airlines cargo, Presentation at the AGIFORS Cargo Study Group Meeting, Rome, Italy.
3
4. Slager, B. and Kapteijns, L. (2004). "Implementation of cargo revenue management at KLM", Journal of Revenue & Pricing Management, Vol. 3, No. 1, PP. 80–90.
4
5. Nielsen, K. (2004). Revenue management at virgin Atlantic cargo, Presentation at the AGIFORS Cargo Study Group Meeting, Washington DC, USA.
5
6. Karaesmen, I. Z. (2001). Three essays on revenue management, PhD Thesis, Columbia University.
6
7. Pak, K. and Dekker, R. (2004). Cargo revenue management: Bid-prices for a 0-1 multi knapsack problem (Technical report). Erasmus University, Erasmus Research Institute of Management, Rotterdam.
7
8. Huang, K. and Hsu, W. (2005). Revenue management for air cargo space with supply uncertainty, Proceedings of the Eastern Asia Society for Transportation Studies, Vol. 5, PP. 570–580.
8
9. Luo, L. and Shi, X. (2006). The stochastic model of multi-leg capacity allocation for air cargo revenue management, In Proceedings of the international conference on service systems and service management, Vol. 2, pp. 917-921, 25-27 Oct, Troyes, France.
9
10. Popescu, A., Keskinocak, P., Johnson, E., LaDue, M. and Kasilingam, R. (2006). "Estimating air-cargo overbooking based on a discrete show-up-rate distribution", Interfaces, Vol. 36, No. 3, PP. 248–258.
10
11. Amaruchkul, K., Cooper, W. L. and Gupta, D. (2007). "Single-leg air-cargo revenue management", Transportation Science, Vol. 41, No. 4, PP. 457–469.
11
12. Huang, K. and Chang, K.C. (2010). "An approximate algorithm for the two-dimensional air cargo revenue management problem", Transportation Research: Logistics and Transportation Review, Vol. 46, No. 3, PP. 426–435.
12
13. Zhuang, W., Gumus, M. and Zhang, D. (2011). “A single-resource revenue management problem with random resource consumptions”, Journal of the Operational Research Society, (Advance online publication 14 December 2011, doi: 10.1057/jors.2011.129).
13
14. Han, D. L., Tang, L. C. and Huang, H. C. (2010). "A Markov model for single-leg air cargo revenue management under a bid-price policy", European Journal of Operational Research, Vol. 200, No. 3, PP. 800–811.
14
15. Levin, Y., Nediak, M. and Topaloglu, H. (2012). "Cargo capacity management with allotments and spot market demand", Operations Research, Vol. 60, No. 2, PP. 351–365.
15
16. Levina, T., Levin, Y., McGill, J. and Nediak, M. (2011). "Network cargo capacity management", Operations Research, Vol. 59, No. 4, PP. 1008–1023.
16
17. Hoffmann, R., (2013). Dynamic Capacity Control in Air Cargo Revenue Management, KIT Scientific Publishing.
17
18. Huang, K. and Lu, H. (2015). "A linear programming-based method for the network revenue management problem of air cargo", Transportation Research Part C: Emerging Technologies, No. 59, PP.248–259.
18
19. Wang, X., (2016). "Stochastic resource allocation for containerized cargo transportation networks when capacities are uncertain", Transportation Research Part E: Logistics and Transportation Review, No. 93, PP. 334–357.
19
20. Wannakrairot, A. and Phumchusri, N. (2016). "Two-dimensional air cargo overbooking models under stochastic booking request level, show-up rate and booking request density", Computers & Industrial Engineering, No. 100, PP. 1–12.
20
21. Kashan, A.H., Akbari, A.A. and Ostadi, B. (2015). "Grouping evolution strategies: An effective approach for grouping problems", Applied Mathematical Modelling, Vol. 39, No. 9, PP. 2703–2720.
21
ORIGINAL_ARTICLE
Applying Queuing Theory to Optimize Perishable Products Supply Chain with (S-1, S) Ordering Policy and Increasing Customers Satisfaction
Applying queuing theory to optimize inventory control systems, is an important field in the literature of perishable inventory systems. However, a few studies have considered it with (S-1, S) ordering policy and customer satisfaction. In this paper, queuing theory was used to optimize inventory control system and to increase customer satisfaction in a two-stage supply chain of perishable products with exponential life time. The supply chain consists of a manufacturer and a supplier. Customers arrive at the manufacturer according to a Poisson process. Manufacturer uses (S-1, S) ordering policy for stock replenishment. Lead time and processing time are exponentially distributed. The aim is to determine the optimal values of manufacturer’s storage capacity and waiting room capacity. Therefore, the supply chain is modeled as a queuing system. After deriving steady state equations, system performance measures were calculated and a mathematical model was developed to minimize total cost. Optimal solutions were obtained by enumeration and direct search techniques. The sensitivity analysis of the model is performed by a numerical example.
https://aie.ut.ac.ir/article_63180_4dc2d8df148ed03f2c23ddb187611982.pdf
2016-11-21
497
505
10.22059/jieng.2016.63180
Perishable product
Queuing theory
(S-1
S) ordering policy
Supply Chain
Tahereh
Hashemi
hashemi_961@yahoo.com
1
Faculty of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
AUTHOR
Ebrahim
Teymouri
teimoury@iust.ac.ir
2
دانشیار دانشکدة مهندسی صنایع، دانشگاه علم و صنعت ایران
AUTHOR
Fariborz
Jolai
fjolai@ut.ac.ir
3
Faculty of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
LEAD_AUTHOR
1. Kalpakam, S. and Sapna, K. (1994). “Continuous review (s, S) inventory system with random lifetimes and positive leadtimes”, Operations Research letters, Vol.16, No.2, PP. 115– 119.
1
2. Sivakumar, B. and Arivarignan, G. (2005). "A perishable inventory system with service facilities and negative customers", Advanced Modeling and Optimization, Vol.7, No. 2, PP. 193- 210.
2
3. Sivakumar, B., Elango, C. and Arivarignan, G. (2006). "A perishable inventory system with service facilities and batch markovian Demands", International Journal of pure and Applied Mathematics, Vol. 32, No.1, PP. 33- 40.
3
4. Yadavalli, V. S. S., Sivakumar, B. and Arivarignan, G. (2007). "Stochastic inventory management at a service facility with a set of reorder levels", ORION, Vol. 23, No. 2, PP. 137- 149.
4
5. Satheesh Kumar, R. and Elango, C. (2010). "Markov decision processes for service facility systems with perishable inventory", International Journal of Computer Applications, Vol. 9, Issue 4, PP. 14- 17.
5
6. Shophia Lawrence, A., Sivakumar, B. and Arivarignan, G. (2013). “A perishable inventory system with service facility and finite source”, Applied MathematicalModelling, Vol. 37, Issue 7, PP. 4771– 4786.
6
7. Jeganathan, K. (2014). “A Perishable Inventory Model with Bonus Service for Certain Customers, Balking and N + 1 Policy”, Mathematical Economics Letters, Vol. 2, No. 3– 4, PP. 83– 104.
7
8. Jeganathan, K. and Periyasamy, C. (2014). “A perishable inventory system with repeated customers and server interruptions”, Applied Mathematics & Information Sciences Letters, Vol. 2, No. 2, PP. 1- 11.
8
9. Al Hamadi, H. M., Sangeetha, N. and Sivakumar, B. (2015). “Optimal control of service parameter for a perishable inventory system maintained at service facility with impatient customers”, Annals of Operations Research, Vol. 233, Issue 1, PP. 3– 23.
9
10. Laxmi, V. P. and Soujanya, M. L. (2015). “Perishable inventory system with service interruptions, retrial demands and negative customers”, Applied Mathematics and Computation, Vol. 262, No. 1, PP. 102–110.
10
11. Jeganathan, K. (2015). “A single server perishable inventory system with N additional options for service”, Journal of Mathematical Modeling, Vol. 2, No. 2, PP. 187- 216.
11
12. Amirthakodi, M., Radhamani, V. and Sivakumar, B. (2015). “A perishable inventory system with service facility and feedback customers”, Annals of Operations Research, Vol. 233, Issue. 1, PP. 25-55.
12
13. Jeganathan, K., Sumathi, J. and Makalakshmi, G. (2016). “Markovian inventory model with two parallel queues, jockeying and impatient customers”, Yugoslav Journal of Operations Research, Vol. 26, No. 4, PP. 467– 506.
13
14. Mahmoodi, A., Haji, A., and Haji, R. (2014). “One for one period policy for perishable inventory”, Computers & Industrial Engineering, Vol. 79, No. 1, PP. 10-17.
14
15. Kouki, C. and Jouini, O. (2015). “On the effect of lifetime variability on the performance of inventory systems”, Int. J. Production Economics, Vol. 167, No. ???, PP. 23– 34.
15
16. Kalpakam, S. and Sapna, K. (1995).“(S-1,S) perishable systems with stochastic leadtimes”, Mathematical and Computer Modeling, Vol. 21, No. 6, PP. 95– 104.
16
17. Kalpakam, S. and Shanthi, S. (2001). “A perishable inventory system with modified (S-1, S) policy and arbitrary processing times”, Computers and Operations Research, Vol. 28, Issue. 5, PP. 453– 471.
17
18. Ioannidis, S., Jouini, O. and Economopoulos, A. A. (2012). "Control policies for single stage production systems with perishable inventory and customer impatience", Operations Research, Vol. 209, Issue.1, PP. 115- 138.
18
19. Mahmoodi, A., Haji, A. and Haji, R. (2016). “A two-echelon inventory model with perishable items and lost sales”, Scientia Iranica E, Vol. 23, No. 5, PP. 2277- 2286.
19
20. Jewkes, E. M. and Alfa, A. S. (2009). “A queueing model of delayed product differentiation”, European Journal of Operational Research, Vol. 199, No. 3, PP. 734- 743.
20