ORIGINAL_ARTICLE
A Hybrid Fuzzy Decision-Making Approach to Select the Best online-taxis business
In the recent decade, significant growth of internet-based platforms and changes in people’s moving preferences has led to an increase in the electronic taxis businesses. Hence, investigating the factors affected by such businesses can help increase their profits and, at the same, time their customers’ satisfaction level. In this study, a hybrid fuzzy decision-making approach is proposed to examine the best online-taxis business selection problem. The proposed framework firstly determines the interrelationships between criteria and sub-criteria, by applying the Fuzzy Decision making trial and evaluation laboratory (FDEMATEL) method. Then, the weights of the criteria and sub-criteria are calculated using an integrated Fuzzy Best-Worst Method (FBWM) and the Fuzzy Analytic network process (FANP). In this regard, at first, the local weights of indicators are calculated using the FBWM regardless of interrelationships between them. Then, the final (i.e. global) weights of indicators, considering their interrelationships, are measured employing the FANP method. Afterwards, the feasible alternatives are prioritized by employing the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (FTOPSIS) method. For each step of the proposed framework, a questionnaire is designed and distributed between experts. The results show that the most important criteria and sub-criteria for passengers are cost and reasonable price, respectively. Finally, some managerial insights are provided.
https://aie.ut.ac.ir/article_81727_848c2a7f898d2cf3eac0d52b0c99441f.pdf
2020-04-01
99
120
10.22059/jieng.2021.320051.1754
Startup-Based Business
Online-taxis
Multiple-attribute decision-making
Hybrid methods
S. Sina
Aria
s.sina.aria@ut.ac.ir
1
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Ira
AUTHOR
S. Ali
Torabi
satorabi@ut.ac.ir
2
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran.
LEAD_AUTHOR
Sina
Nayeri
sina.nayeri@ut.ac.ir
3
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran.
AUTHOR
[1] Liu, X.. (2019). “Evolution and simulation analysis of co-opetition behavior of E-business internet platform based on evolutionary game theory.” Cluster Computing, Vol.22, No.4, pp.10241-10250.
1
[2] Zhang, Y., Guo, H., Li, C., Wang, W., Jiang, X. and Liu, Y. (2016). “Which one is more attractive to traveler, taxi or tailored taxi? An empirical study in China.” Procedia engineering, Vol.137, pp.867-875.
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[3] Munandar, J.M. and Munthe, R.C.F. (2019). “How technology affects behavioral intention (case study of online transportation in Indonesia and Thailand).” The South East Asian Journal of Management.
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[4] Etminani-Ghasrodashti, R. and Hamidi, S. (2019). “Individuals’ Demand for Ride-hailing Services: Investigating the Combined Effects of Attitudinal Factors, Land Use, and Travel Attributes on Demand for App-based Taxis in Tehran, Iran”. Sustainability, Vol.11, No. 20, p.5755.
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[5] Eboli, L. and Mazzulla, G. (2012). “Structural equation modelling for analysing passengers’ perceptions about railway services”. Procedia-Social and Behavioral Sciences, Vol.54, pp.96-106.
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[6] Si, Y., Guan, H. and Cui, Y. (2019). “Research on the Choice Behavior of Taxis and Express Services Based on the SEM-Logit Model”. Sustainability, Vol.11, No.10, p.2974.
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[7] Cheewathanakornkul, C. and Jiratchot, C. (2018). “Customer satisfaction and word of mouth towards online taxi providers: a case study of Grab and Uber”. Journal of Supply Chain Management: Research and Practice, Vol.12 No.1, pp.66-76.
7
[8] Sir, G.D.B. and Çalışkan, E. (2019). “Assessment of development regions for financial support allocation with fuzzy decision making: A case of Turkey”. Socio-Economic Planning Sciences, Vol. 66, pp.161-169.
8
[9] Skalna, I., Rebiasz, B., Gawel, B., Basiura, B., Duda, J., Opila, J. and Pelech-Pilichowski, T. (2015). “Advances in fuzzy decision making”. Studies in Fuzziness and Soft Computing, Vol. 333.
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[10] Lin, C.J. and Wu, W.W. (2008). “A causal analytical method for group decision-making under fuzzy environment”. Expert Systems with Applications, Vol. 34, No.1, pp.205-213.
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[14] You, P., Guo, S., Zhao, H. and Zhao, H. (2017). “Operation performance evaluation of power grid enterprise using a hybrid BWM-TOPSIS method”. Sustainability, Vol. 9, No. 12, p.2329.
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[16] Büyüközkan, G. and Çifçi, G. (2012). “A novel hybrid MCDM approach based on fuzzy DEMATEL, fuzzy ANP and fuzzy TOPSIS to evaluate green suppliers”. Expert Systems with Applications, Vol. 39, No. 3, pp.3000-3011.
16
[17] Jain, V., Sangaiah, A.K., Sakhuja, S., Thoduka, N. and Aggarwal, R. (2018). “Supplier selection using fuzzy AHP and TOPSIS: a case study in the Indian automotive industry”. Neural Computing and Applications, Vol. 29, No. 7, pp.555-564.
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[18] Hsieh, C.H. and Chen, S.H. (1999). A model and algorithm of fuzzy product positioning. Information sciences, Vol. 121, No. 2, pp.61-82.
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[19] Chen, S.H., Wang, P.W., Chen, C.M. and Lee, H.T. (2010). “An analytic hierarchy process approach with linguistic variables for selection of an R&D strategic alliance partner”. Computers & Industrial Engineering, Vol. 58, No. 2, pp.278-287.
19
ORIGINAL_ARTICLE
Portfolio Selection Optimization Problem Under Systemic Risks
Abstract: Portfolio selection is of great importance among financiers, who seek to invest in a financial market by selecting a portfolio to minimize the risk of investment and maximize their profit. Since there is a covariant among portfolios, there are situations in which all portfolios go high or down simultaneously, known as systemic risks. In this study, we proposed three improved meta-heuristic algorithms namely, genetic, dragonfly, and imperialist competitive algorithms to study the portfolio selection problem in the presence of systemic risks. Results reveal that our Imperialist Competitive Algorithm are superior to Genetic algorithm method. After that, we implement our method on the Iran Stock Exchange market and show that considering systemic risks leads to more robust portfolio selection. . Results reveal that our Imperialist Competitive Algorithm are superior to Genetic algorithm method. After that, we implement our method on the Iran Stock Exchange market and show that considering systemic risks leads to more robust portfolio selection.
https://aie.ut.ac.ir/article_81728_cab0fdd2da76993b72c911d1aaaff114.pdf
2020-04-01
121
140
10.22059/jieng.2021.321882.1759
Portfolio Selection
Systemic Risks
Genetic Algorithm
Imperialist competitive algorithm
Mohammad Ali
Dehghan Dehnavi
dehghandehnavi@gmail.com
1
Department of Finance and Banking, Faculty of Accounting and Management Allameh Tabataba`i University, Tehran, Iran
AUTHOR
Mohammad Mahdi
Bahrololoum
m.bahrololoum@gmail.com
2
Department of Finance and Banking, Faculty of Accounting and Management Allameh Tabataba`i University, Tehran, Iran
LEAD_AUTHOR
Moslem
Peymany Foroushany
m.peymany@atu.ac.ir
3
Department of Finance and Banking, Faculty of Accounting and Management Allameh Tabataba`i University, Tehran, Iran
AUTHOR
Sayyed Ali
Raeiszadeh
reiszadeh1357@gmail.com
4
Department of Finance and Banking, Faculty of Accounting and Management Allameh Tabataba`i University, Tehran, Iran
AUTHOR
[1] Abdelaziz, F. B., Aouni, B., & El Fayedh, R. (2007). Multi-objective stochastic programming for portfolio selection. European Journal of Operational Research, 177(3), 1811-1823.
1
[2] Abdelaziz, F. B., El Fayedh, R., & Rao, A. (2009). A discrete stochastic goal program for portfolio selection: The case of United Arab Emirates equity market. INFOR: Information Systems and Operational Research, 47(1), 5-13.
2
[3] Abdelaziz, F. B., & Masmoudi, M. (2014). A multiple objective stochastic portfolio selection problem with random Beta. International Transactions in Operational Research, 21(6), 919-933.
3
[4] Atashpaz-Gargari, E. and Lucas, C. Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. in 2007 IEEE congress on evolutionary computation. 2007. Ieee.
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[5] Biglova, A., Ortobelli, S., & Fabozzi, F. J. (2014). Portfolio selection in the presence of systemic risk. Journal of Indicator Management, 15(5), 285-299.
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[6] Caçador, S., Dias, J. M., & Godinho, P. (2021). Portfolio selection under uncertainty: a new methodology for computing relative‐robust solutions. International Transactions in Operational Research, 28(3), 1296-1329.
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[7] Chen, S., & Ge, L. (2021). A learning-based strategy for portfolio selection. International Review of Economics & Finance, 71, 936-942.
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[8] Di Tollo, G., & Roli, A. (2008). Metaheuristics for the portfolio selection problem. International Journal of Operations Research, 5(1), 13-35.
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[9] Dubois D., H. Prade, (1988). Furzy Sets and Systems: Theory and Applications, Academic Press, New York.
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[10] Frej, E. A., Ekel, P., & de Almeida, A. T. (2021). A benefit-to-cost ratio based approach for portfolio selection under multiple criteria with incomplete preference information. Information Sciences, 545, 487-498.
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[11] Garcia, F., González-Bueno, J., Oliver, J., & Tamošiūnienė, R. (2019). A credibilistic mean-semivariance-PER portfolio selection model for Latin America. Journal of Business Economics and Management, 20(2), 225-243.
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[12] Gen, M. and R. Cheng, Genetic algorithms and engineering optimization. Vol. 7. 1999: John Wiley & Sons.
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[13] Ghahtarani, A., & Najafi, A. A. (2013). Robust goal programming for multi-objective portfolio selection problem. Economic Modelling, 33, 588-592.
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[15] Holland, J.H., Genetic algorithms. Scientific American, 1992. 267(1): p. 66-73.
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[16] Kocada ˘glı, R. Keskin, (2015). A novel portfolio selection model based on fuzzy goal programming with different im portance and priorities, Expert Syst. Appl. 42 (20), 6,898-6,912.
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[17] Landsman, Z., Makov, U., & Shushi, T. (2018). A generalized measure for the optimal portfolio selection problem and its explicit solution. Risks, 6(1), 19.
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[18] Li, X., Huang, Y. H., Fang, S. C., & Zhang, Y. (2020). An alternative efficient representation for the project portfolio selection problem. European Journal of Operational Research, 281(1), 100-113.
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[25] Mirjalili, S. (2016). Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Computing and Applications, 27(4), 1053-1073.
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42
ORIGINAL_ARTICLE
A Simulation-Optimization Model for Solar PV Panel Selection Under Solar Irradiance and Load Uncertainty
In this reserach, a multi-objective model is presented considering simulated behavior of high-efficiency rooftop solar PV panels in factory, which are among the largest producers of green-house gases. The paper proposes a simulation-optimization approach is used to maximize the net present value (NPV) of economic benefits along with minimizing the payback period (PBP) of the investment, and maximizing solar energy consumption rate (SECR). In addition, the solar PV panels degradation and maintenance cost, as well as the uncertainty in solar irra-diance and demand load, are also considered. The study consists of two scenarios, in the first of which both electricity tariffs and feed-in-tariffs (FiT) are fixed by a long-term contract. The second scenario investigates the situation in which subsidies on electricity tariff are removed. The best type of panels are found in each scenario considering trade-off between objective functions. The preferred trade-off solution in the first scenario, with 2% increase in PBP, achieves more than 10% growth in NPV which is about $15000 in a year. In the second sce-nario, with only about 0.2% decrease in NPV and 3% increase in PBP, the preferred solution attains 9% increase in SECR.
https://aie.ut.ac.ir/article_81729_19c2e064e43d9c88b524f60e93974242.pdf
2020-04-01
141
164
10.22059/jieng.2021.323127.1760
simulation
Rooftop solar PV panels
Electricity tariffs policy
maintenance
Uncertainty
Maedeh
Motalebi
maede.motalebi@ut.ac.ir
1
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
AUTHOR
Mohammad Mahdi
Nasiri
mmnasiri@ut.ac.ir
2
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
LEAD_AUTHOR
Hamed
Shakouri G.
hshakouri@ut.ac.ir
3
ctrical and Computer Engineering (ECE) Department, University of British Columbia, Canada
AUTHOR
Hosein
Taghaddos
htaghaddos@ut.ac.ir
4
School of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran
AUTHOR
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53
ORIGINAL_ARTICLE
Optimizing a Reverse Logistics System by Considering Quality of Returned Products
Coordination is one of the critical issues in remanufacturing systems that can persuade supply chain parties to make optimal centralized decisions leading to higher profits. Accordingly, this paper aims to examine a reverse logistics system, including one manufacturer along with a collector who collects used products based on the consumers' willingness to return such products. Consumers’ willingness is dependent on the take-back price, which is adjusted based on various quality levels affecting the processing cost of the collected items. This study developed mathematical models under both decentralized and centralized scenarios. Besides, to align the interests of both members and better profit-sharing, a cost-sharing contract is implemented. According to the results, in the coordination model, the take-back price of the high-quality level is increased compared to the decentralized model while the take-back price of the low-quality level is decreased. Hence, it suggests collecting and repairing higher-quality products to achieve higher profits for the whole system. Besides, the paper provides valuable suggestions for managers to resolve the conflicts of interest among participants of reverse logistics systems in an efficient manner.
https://aie.ut.ac.ir/article_81730_2b4b1a7b6af359bca23d77895b0acc2a.pdf
2020-04-01
165
184
10.22059/jieng.2021.323213.1762
Reverse logistics
Quality of Returned Product
coordination
Recycling Strategy
Cost-Sharing Contract
Fariborz
Jolai
fjolai@ut.ac.ir
1
School of Industrial Engineering, College of Engineering, University of Tehran, Iran
AUTHOR
Parisa
Hashemi
parisa.hashemi@ut.ac.ir
2
School of Industrial Engineering, College of Engineering, University of Tehran, Iran
AUTHOR
Jafar
Heydari
j.heydari@ut.ac.ir
3
School of Industrial Engineering, College of Engineering, University of Tehran, Iran
LEAD_AUTHOR
Alireza
Bakhshi
alirezabakhshi@ut.ac.ir
4
School of Industrial Engineering, College of Engineering, University of Tehran, Iran
AUTHOR
Abbas
Keramati
akeramati@ryerson.ca
5
Ted Rogers School of Information Technology Management, Ryerson University, Toronto, Canada and School of Industrial Engineering, College of Engineering, University of Tehran, Iran
AUTHOR
[1] Agrawal, S., and Singh, R. K. (2020). Outsourcing and reverse supply chain performance: a triple bottom line approach. Benchmarking: An International Journal.
1
[2] Mathiyazhagan, K., Rajak, S., Panigrahi, S. S., Agarwal, V., and Manani, D. (2020). Reverse supply chain management in manufacturing industry: a systematic review. International Journal of Productivity and Performance Management.
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[3] Dev, N. K., Shankar, R., and Qaiser, F. H. (2020). Industry 4.0 and circular economy: Operational excellence for sustainable reverse supply chain performance. Resources, Conservation and Recycling, 153, 104583.
3
[4] Lampón, J. F., Pérez-Elizundia, G., and Delgado‐Guzmán, J. A. (2021). Relevance of the cooperation in financing the automobile industry's supply chain: the case of reverse factoring. Journal of Manufacturing Technology Management.
4
[5] Govindan, K., Soleimani, H., and Kannan, D. (2015). Reverse logistics and closed-loop supply chain: A comprehensive review to explore the future. European Journal of Operational Research, 240(3), 603-626.
5
[6] Qiang, Q., Ke, K., Anderson, T., and Dong, J. (2013). The closed-loop supply chain network with competition, distribution channel investment, and uncertainties. Omega, 41(2), 186-194.
6
[7] Blumberg, D. F. (2004). Introduction to management of reverse logistics and closed loop supply chain processes: CRC Press.
7
[8] Guide Jr, V. D. R., and Van Wassenhove, L. N. (2001). Managing product returns for remanufacturing. Production and Operations Management, 10(2), 142-155.
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[10] Lizarraga-Mollinedo, E., Carreras-Badosa, G., Xargay-Torrent, S., Remesar, X., Mas-Pares, B., Prats-Puig, A., ... and Bassols, J. (2021). Catch-up growth in juvenile rats, fat expansion, and dysregulation of visceral adipose tissue. Pediatric Research, 1-9.
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13
[14] Singh, R. K., and Agrawal, S. (2018). Analyzing disposition strategies in reverse supply chains: fuzzy TOPSIS approach. Management of Environmental Quality: An International Journal.
14
[15] Wang, M., Wang, B., and Chan, R. (2020). Reverse logistics uncertainty in a courier industry: a triadic model. Modern Supply Chain Research and Applications.
15
[16] Liu, H., Lei, M., Deng, H., Leong, G. K., and Huang, T. (2016). A dual channel, quality-based price competition model for the WEEE recycling market with government subsidy. Omega, 59, 290-302.
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[17] Aćimović, S., Mijušković, V., and Rajić, V. (2020). The impact of reverse logistics onto green supply chain competitiveness evidence from Serbian consumers. International Journal of Retail and Distribution Management.
17
[18] Heydari, J., Govindan, K., and Sadeghi, R. (2018). Reverse supply chain coordination under stochastic remanufacturing capacity. International Journal of Production Economics, 202, 1-11.
18
[19] Heydari, J., and Ghasemi, M. (2018). A revenue sharing contract for reverse supply chain coordination under stochastic quality of returned products and uncertain remanufacturing capacity. Journal of Cleaner Production, 197, 607-615.
19
[20] Kaya, O. (2010). Incentive and production decisions for remanufacturing operations. European Journal of Operational Research, 201(2), 442-453.
20
[21] Govindan, K., and Popiuc, M. N. (2014). Reverse supply chain coordination by revenue sharing contract: A case for the personal computers industry. European Journal of Operational Research, 233(2), 326-336.
21
[22] Yu, H., and Solvang, W. D. (2017). A carbon-constrained stochastic optimization model with augmented multi-criteria scenario-based risk-averse solution for reverse logistics network design under uncertainty. Journal of cleaner production, 164, 1248-1267.
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[23] Tseng, S. H., Wee, H. M., Song, P. S., and Jeng, S. (2019). Optimal green supply-chain model design considering full truckload. Kybernetes.
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[24] Aldoukhi, M., and Gupta, S. M. (2020). Use of Maximal Covering Location Problem to Design a Closed Loop Supply Chain Network Under Product Substitution. In Applications of Management Science. Emerald Publishing Limited.
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[25] Kushwaha, S., Ghosh, A., and Rao, A. K. (2020). Collection activity channels selection in a reverse supply chain under a carbon cap-and-trade regulation. Journal of Cleaner Production, 260, 121034.
25
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[29] Giri, B., and Sharma, S. (2015). Optimizing a closed-loop supply chain with manufacturing defects and quality dependent return rate. Journal of manufacturing systems, 35, 92-111.
29
[30] Taleizadeh, A. A., Moshtagh, M. S., and Moon, I. (2018). Pricing, product quality, and collection optimization in a decentralized closed-loop supply chain with different channel structures: Game theoretical approach. Journal of Cleaner Production, 189, 406-431.
30
[31] Heydari, J., Chaharsooghi, S. K., and Alipour, L. (2009). Animation supply chain modelling and diagnosis: a case study in animation industry of Iran. International Journal of Business Performance and Supply Chain Modelling, 1(4), 319-332..
31
[32] He, L., Mao, J., Hu, C., and Xiao, Z. (2019). Carbon emission regulation and operations in the supply chain supernetwork under stringent carbon policy. Journal of cleaner production, 238, 117652.
32
[33] Xie, J., Zhang, W., Liang, L., Xia, Y., Yin, J., and Yang, G. (2018). The revenue and cost sharing contract of pricing and servicing policies in a dual-channel closed-loop supply chain. Journal of Cleaner Production, 191, 361-383.
33
[34] Zhang, Z., Liu, S., and Niu, B. (2020). Coordination mechanism of dual-channel closed-loop supply chains considering product quality and return. Journal of cleaner production, 248, 119273.
34
[35] Wang, N., Song, Y., He, Q., and Jia, T. (2020). Competitive Dual-Collecting Regarding Consumer Behavior and Coordination in Closed-Loop Supply Chain. Computers and Industrial Engineering, 106481.
35
[36] Bakhshi, A., and Heydari, J. (2021). An optimal put option contract for a reverse supply chain: case of remanufacturing capacity uncertainty. Annals of Operations Research, 1-24.
36
[37] Yi, P., Huang, M., Guo, L., and Shi, T. (2016). Dual recycling channel decision in retailer oriented closed-loop supply chain for construction machinery remanufacturing. Journal of Cleaner Production, 137, 1393-1405.
37
[38] Genc, T. S., and De Giovanni, P. (2017). Trade-in and save: A two-period closed-loop supply chain game with price and technology dependent returns. International Journal of Production Economics, 183, 514-527.
38
[39] Tang, S., Wang, W., and Zhou, G. (2020). Remanufacturing in a competitive market: A closed-loop supply chain in a Stackelberg game framework. Expert Systems with Applications, 161, 113655.
39
[40] Toktaş-Palut, P. (2021). An integrated contract for coordinating a three-stage green forward and reverse supply chain under fairness concerns. Journal of Cleaner Production, 279, 123735.
40
[41] Wu, D., Chen, J., Li, P., and Zhang, R. (2020). Contract coordination of dual channel reverse supply chain considering service level. Journal of Cleaner Production, 260, 121071.
41
[42] Jin, L., Zheng, B., and Huang, S. (2021). Pricing and coordination in a reverse supply chain with online and offline recycling channels: A power perspective. Journal of Cleaner Production, 298, 126786.
42
[43] Bai, H. (2008). Reverse Supply Chain Coordination and Design for Profitable Returns--an Example of Ink Cartridge. Worcester Polytechnic Institute.
43
[44] Heydari, J., Govindan, K., and Jafari, A. (2017). Reverse and closed loop supply chain coordination by considering government role. Transportation Research Part D: Transport and Environment, 52, 379-398.
44
[45] Jafarkhan, F., Yaghoubi, S., Gilani Larimi, N., and Farhang Moghadam, B. F. M. (2019). The Inventory–Routing Problem for Distribution of Red Blood Cells considering Compatibility of Blood Group and Transshipment between Hospitals. Advances in Industrial Engineering, 53(3), 31-44.
45
[46] Changizi, M., Rahmani, D., and Rmezanian, R. (2020). A Multi-Visit Heterogeneous Drone Routing Model Considering Recharging Decision in Disaster. Advances in Industrial Engineering, 54(1), 53-73.
46
[47] Zafari, F., and Shishebori, D. (2019). Designing a Multi-Objective Three-Stage Location-Routing Model for Humanitarian Logistic Planning under Uncertainty. Advances in Industrial Engineering, 53(4), 149-167.
47
[48] Bakhtiari, M., Ebrahimnejad, S., and Yavari-Moghaddam, M. (2019). A Mathematical Model for Solving Location-Routing Problem with Simultaneous Pickup and Delivery Using a Robust Optimization Approach. Advances in Industrial Engineering, 53(4), 185-208.
48
[49] Chen, C. (2001). Design for the environment: A quality-based model for green product development. Management Science, 47(2), 250-263.
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[50] Gao, J., Han, H., Hou, L., and Wang, H. (2016). Pricing and effort decisions in a closed-loop supply chain under different channel power structures. Journal of Cleaner Production, 112, 2043-2057.
50
[51] Modak, N. M., Kazemi, N., and Cárdenas-Barrón, L. E. (2019). Investigating structure of a two-echelon closed-loop supply chain using social work donation as a Corporate Social Responsibility practice. International Journal of Production Economics, 207, 19-33.
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[52] Mondal, C., and Giri, B. C. (2020). Pricing and used product collection strategies in a two-period closed-loop supply chain under greening level and effort dependent demand. Journal of cleaner production, 121335.
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[54] Santana, J. C. C., Guerhardt, F., Franzini, C. E., Ho, L. L., Júnior, S. E. R. R., Cânovas, G., ... and Berssaneti, F. T. (2021). Refurbishing and recycling of cell phones as a sustainable process of reverse logistics: A case study in Brazil. Journal of Cleaner Production, 283, 124585.
54
ORIGINAL_ARTICLE
Designing a Multi-Level Blood Supply Chain Network with the Likelihood of Shortage and Perishability in the Inventory
Blood is a vital substance for human life. A blood unit goes through various stages from its donation by the donor until its reception by the person in need of blood. This process can be explored the context of supply chain management. For this purpose, a mathematical model is developed in this study to design a blood supply chain network. The noticeable feature of this network is the inclusion of the shortage and perishability of blood products as two important indicators. The mathematical model proposed in this regard has the two objective functions of minimizing the blood supply chain costs and, at the same time, maximizing the average amount of blood sent from blood centers to hospitals. The model examines the problem in the case of a single product. The modified weighted Chebyshev, the improved version of ε-constraint (AUGEMCON2), and unscaled goal programming are used to solve the mathematical model. Then, to evaluate and compare the proposed solution methods and select the best one, the statistical hypothesis test and the VIKOR technique are used respectively. The results show that the model proposed for the blood supply chain is efficient and acceptable; hence, it can be of benefit in different types of blood supply chains where the shortage and perishability of blood products are taken into account.
https://aie.ut.ac.ir/article_81731_cc269f4b220dcaa9e96a7802b33660ec.pdf
2020-04-01
185
204
10.22059/jieng.2021.323832.1764
Blood supply chain management
Multi-objective decision-making
VIKOR Technique
Exact solution methods
Shortage and perishability of blood products
Majid
Alimohammadi Ardekani
m.alimohammadi@ardakan.ac.ir
1
Department of Industrial Engineering, Faculty of Engineering, Ardakan University, Ardakan, Iran
LEAD_AUTHOR
Mehdi
Kabiri Naeini
m_kabirinaeini@yahoo.com
2
Department of Industrial Engineering, Faculty of Engineering, Payame Noor University, Yazd Center, Yazd, Iran
AUTHOR
[1] Abdolazimi, O., & Abraham, A. (2020). Designing a multi-objective supply chain model for the oil indus-try in conditions of uncertainty and solving it by meta-heuristic algorithms.
1
[2] Abdolazimi, O., Esfandarani, M. S., & Shishebori, D. (2020a). Design of a supply chain network for determining the optimal number of items at the inventory groups based on ABC analysis: a comparison of exact and meta-heuristic methods. Neural Computing and Applications, 1-16.
2
[3] Abdolazimi, O., Esfandarani, M. S., Salehi, M., & Shishebori, D. (2020b). Robust design of a multi-objective closed-loop supply chain by integrating on-time delivery, cost, and environmental aspects, case study of a Tire Factory. Journal of Cleaner Production, 121566.
3
[4] Abdolazimi, O., Esfandarani, M. S., & Abraham, A. (2020d). Design of a Closed Supply Chain with regards to the Social and Environmental Impacts under Uncertainty.
4
[5] Abdolazimi, O., Salehi Esfandarani, M., Salehi, M., & Shishebori, D. (2020c). A Comparison of Solution Methods for the Multi-Objective Closed Loop Supply Chains. Advances in Industrial Engineering, 54(1), 75-98.
5
[6] Alfonso, E., Xie, X., Augusto, V., & Garraud, O. (2012). Modeling and simulation of blood collection systems. Health care management science, 15(1), 63-78.
6
[7] American Red Cross, (2021). Blood Components. https://www.redcrossblood.org/donate-blood/how-to-donate/types-of-blood-donations/blood-components.html. Accessed February 1, 2021.
7
[8] Aouni, B., Colapinto, C., & La Torre, D. (2014). Financial portfolio management through the goal programming model: Current state-of-the-art. European Journal of Operational Research, 234(2), 536-545.
8
[9] Bhattacharjee, S., & Ramesh, R. (2000). A multi-period profit maximizing model for retail supply chain management: An integration of demand and supply-side mechanisms. European journal of operational research, 122(3), 584-601.
9
[10] Charnes, A., Cooper, W. W., & Ferguson, R. O. (1955). Optimal estimation of executive compensation by linear programming. Management science, 1(2), 138-151.
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[11] Dutta, P., & Nagurney, A. (2019). Multitiered blood supply chain network competition: Linking blood service organizations, hospitals, and payers. Operations Research for Health Care, 23, 100230.
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[12] Fahimnia, B., Jabbarzadeh, A., Ghavamifar, A., & Bell, M. (2017). Supply chain design for efficient and effective blood supply in disasters. International Journal of Production Economics, 183, 700-709.
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[13] Farahani, R. Z., Rezapour, S., Drezner, T., & Fallah, S. (2014). Competitive supply chain network design: An overview of classifications, models, solution techniques and applications. Omega, 45, 92-118.
13
[14] Ghare, P. M. (1963). A model for an exponentially decaying inventory. J. ind. Engng, 14, 238-243.
14
[15] Haghjoo, N., Tavakkoli-Moghaddam, R., Shahmoradi-Moghadam, H., & Rahimi, Y. (2020). Reliable blood supply chain network design with facility disruption: A real-world application. Engineering Applications of Artificial Intelligence, 90, 103493.
15
[16] Haijema, R., Van Der Wal, J., & Van Dijk, N. M. (2007). Blood platelet production: Optimization by dynamic programming and simulation. Computers & Operations Research, 34(3), 760-779.
16
[17] Hamdan, B., & Diabat, A. (2020). Robust design of blood supply chains under risk of disruptions using Lagrangian relaxation. Transportation Research Part E: Logistics and Transportation Review, 134, 101764.
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[18] Hemmelmayr, V., Doerner, K. F., Hartl, R. F., & Savelsbergh, M. W. (2010). Vendor managed inventory for environments with stochastic product usage. European Journal of Operational Research, 202(3), 686-695.
18
[19] Hosseinifard, Z., & Abbasi, B. (2018). The inventory centralization impacts on sustainability of the blood supply chain. Computers & Operations Research, 89, 206-212.
19
[20] Hosseini-Motlagh, S. M., Samani, M. R. G., & Cheraghi, S. (2020). Robust and stable flexible blood supply chain network design under motivational initiatives. Socio-Economic Planning Sciences, 70, 100725.
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[21] Hwang, C. L., & Masud, A. S. M. (2012). Multiple objective decision making—methods and applications: a state-of-the-art survey (Vol. 164). Springer Science & Business Media.
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[22] Kaliszewski, I. (1987). A modified weighted Tchebycheff metric for multiple objective programming. Computers & operations research, 14(4), 315-323.
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[23] Karimi-Nasab, M., Shishebori, D., & Jalali-Naini, S. G. R. (2013). Multi-objective optimisation for pricing and distribution in a supply chain with stochastic demands. International Journal of Industrial and Systems Engineering, 13(1), 56-72.
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[24] Khakestari, M., & Abdolazimi, O. (2020). Determine the optimal number of item groups in the werehouse based on ABC analysis within the framework of a supply chain network. Industrial Management Studies, 18(57), 307-344.
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[25] Mavrotas, G. (2009). Effective implementation of the ε-constraint method in multi-objective mathematical programming problems. Applied mathematics and computation, 213(2), 455-465.
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[26] Mavrotas, G., & Florios, K. (2013). An improved version of the augmented ε-constraint method (AUGMECON2) for finding the exact pareto set in multi-objective integer programming problems. Applied Mathematics and Computation, 219(18), 9652-9669.
26
[27] Mehrjerdi, Y. Z., & Shafiee, M. (2021). A resilient and sustainable closed-loop supply chain using multiple sourcing and information sharing strategies. Journal of Cleaner Production, 289, 125141.
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[29] Nahmias, S. (1982). Perishable inventory theory: A review. Operations research, 30(4), 680-708.
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[30] Niakan, F., & Rahimi, M. (2015). A multi-objective healthcare inventory routing problem; a fuzzy possibilistic approach. Transportation Research Part E: Logistics and Transportation Review, 80, 74-94.
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[31] Nurjanni, K. P., & Carvalho, M. S. (2016). Author’ s Accepted Manuscript. Intern. Journal of Production Economics.
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[32] Opricovic, S., & Tzeng, G. H. (2007). Extended VIKOR method in comparison with outranking methods. European journal of operational research, 178(2), 514-529.
32
[33] Pirabán, A., Guerrero, W. J., & Labadie, N. (2019). Survey on blood supply chain management: Models and methods. Computers & Operations Research, 112, 104756.
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[34] Puranam, K., Novak, D. C., Lucas, M. T., & Fung, M. (2017). Managing blood inventory with multiple independent sources of supply. European Journal of Operational Research, 259(2), 500-511.
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[37] Shishebori, D., & Ghaderi, A. (2015). An integrated approach for reliable facility location/network design problem with link disruption. International Journal of Supply and Operations Management, 2(1), 640-661.
37
[38] Shishebori, D., Yousefi Babadi, A., & Noormohammadzadeh, Z. (2018). A Lagrangian relaxation approach to fuzzy robust multi-objective facility location network design problem. Scientia Iranica, 25(3), 1750-1767.
38
[39] Subulan, K., Taşan, A. S., & Baykasoğlu, A. (2015). A fuzzy goal programming model to strategic planning problem of a lead/acid battery closed-loop supply chain. Journal of Manufacturing Systems, 37, 243-264.
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[40] Teimoury, E., Nedaei, H., Ansari, S., & Sabbaghi, M. (2013). A multi-objective analysis for import quota policy making in a perishable fruit and vegetable supply chain: A system dynamics approach. Computers and electronics in agriculture, 93, 37-45.
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[41] Thangam, A., & Uthayakumar, R. (2009). Two-echelon trade credit financing for perishable items in a supply chain when demand depends on both selling price and credit period. Computers & Industrial Engineering, 57(3), 773-786.
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[42] Van Zyl, G. J. J. (1964). Inventory Control for Perishable Commodities, Unpublished Ph. D. Dissertation, University of North Carolina, Chapel Hill, NC.
42
[43] Wang, W., Fung, R. Y., & Chai, Y. (2004). Approach of just-in-time distribution requirements planning for supply chain management. International journal of production economics, 91(2), 101-107.
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[44] Whitin, T. M. (1957). Theory of inventory management. Princeton University Press.
44
[45] Zografidou, E., Petridis, K., Petridis, N. E., & Arabatzis, G. (2017). A financial approach to renewable energy production in Greece using goal programming. Renewable energy, 108, 37-51.
45
ORIGINAL_ARTICLE
Multivariate Process Incapability Index Considering Measurement Error in Fuzzy Environment
Process Capability Indices (PCI) show that the process conforms to the specification limits; when the product quality depends on more than one characteristic, Multivariate Process Capability Indices (MCPI) are used. By modifying in the process capability indices, the process incapability indices are created; these indices then provide information about the accuracy and precision of the process separately. In the real world, in most cases, the parameters cannot be specified precisely; therefore, the use of fuzzy sets can solve this problem in statistical quality control. The purpose of this paper is to present, for the first time, a Multivariate Process Incapability Index by considering the measurement error in a fuzzy environment. The presented index is shown for practical examples solved by considering Triangular Fuzzy Numbers; then the capability of the model is compared to the time when fuzzy logic is not used. The obtained results emphasize that ignoring the measurement error also leads to the incorrect calculation of process capability, causing a lot of damage to manufacturing industries, especially high-tech ones.
https://aie.ut.ac.ir/article_81732_3b981eaa0a98608d3a938fea31a1eae3.pdf
2020-04-01
205
220
10.22059/jieng.2021.323883.1765
Fuzzy multivariate process incapability Index
Fuzzy Mmeasurement Error
Multivariate normal distribution
Fuzzy logic
Hossein
Shirani Bidabadi
hshirani43@yahoo.com
1
Department of Industrial Engineering, Yazd University, Yazd, Iran
AUTHOR
Davood
Shishebori
shishebori@yazd.ac.ir
2
Department of Industrial Engineering, Yazd University, Yazd, Iran
LEAD_AUTHOR
Ahmad
Ahmadi Yazdi
a.ahmadiyazdi@yazd.ac.ir
3
Department of Industrial Engineering, Yazd University, Yazd, Iran
AUTHOR
[1] Yongting, C., (1996), Fuzzy quality and analysis on fuzzy probability. Fuzzy sets systems. 83(2): p. 283-290.
1
[2] Lee, Y.-H., Wei, C.-C. and Chang, C.-L., (1999), Fuzzy design of process tolerances to maximise process capability. The International Journal of Advanced Manufacturing Technology. 15(9): p. 655-659.
2
[3] Lee, H.T., (2001), Cpk index estimation using fuzzy numbers. European Journal of Operational Research. 129(3): p. 683-688.
3
[4] Sadeghpour Gildeh, B. (Year) Comparison of Cp, Cpk and Cp- tilde process capability indices in the case of measurement error occurrence. in 10th IFSA World Congress. of Conference.
4
[5] Parchami, A., Mashinchi, M. and Maleki, H.R., (2006), Fuzzy confidence interval for fuzzy process capability index. Journal of Intelligent Fuzzy Systems. 17(3): p. 287-295.
5
[6] Parchami, A., Mashinchi, M., Yavari, A.R., and Maleki, H.R., (2005), Process capability indices as fuzzy numbers. Austrian Journal of Statistics. 34(4): p. 391–402-391–402.
6
[7] Parchami, A. and Mashinchi, M., (2007), Fuzzy estimation for process capability indices. Information Sciences. 177(6): p. 1452-1462.
7
[8] Parchami, A. and Mashinchi, M., (2010), A new generation of process capability indices. Journal of Applied Statistics. 37(1): p. 77-89.
8
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