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1 - Development Optimization Model of a Zero-Defect Single Sampling Plan https://aie.ut.ac.ir/article_80158.html 10.22059/jieng.2020.305193.1730 1 One way to control the quality of products is to inspection the lot inputs. The focus of this paper is on a non-linear integer programming model for determining an optimal single sampling plan for inspecting different parts so that the total cost of the quality control is minimized and we try to improve the quality of inputs to the assembly line by applying a rectifying inspection policy. The optimization model includes the cost of inspection, the cost of non- conforming items entering the assembly line and the cost of rejecting the items. In this research, it is assumed that the inspection is perfect and zero acceptance number policy is employed for inspection. If a non- conforming item is found in the sample, the total lot is rejected. Each part is different in the risk of non- conforming items, the cost of non- conforming items, the size of the lot and the cost of inspection. In the practical example, it can be seen that the rate of defective items, followed by the cost of defective items and the cost of lot rejection, have been greatly reduced following the proposed methods and minimized the cost of quality control. 0 - 1 14 - - Mahdi Nakhaeinejad Department of Industrial Engineering, Yazd University, Yazd, Iran. Iran m.nakhaeinejad@yazd.ac.ir - - Mohammad Saber Fallahnezhad Department of Industrial Engineering, Yazd University, Yazd, Iran Iran fallahnezhad@yazd.ac.ir - - Soroush Yazdi Department of Industrial Engineering, Science and Art University, Yazd, Iran. Iran soroushy17@gmail.com - - Fatemeh Borabadi Department of Industrial Engineering, University of Bojnord, Bojnord, Iran Iran fatemehborabadi@gmail.com Acceptance sampling Non-linear integer programming Incoming inspection rectifying inspection [1]Montgomery, Douglas, C. “Statistical Quality Control”, Noorossana, R., Iran University of Science and Technology, (2013), (In Persian)##[2] Wetherill, G.B., and Chiu, W.K. “A review of acceptance sampling schemes with emphasis on the economic aspect”, International Statistical Review, 43, pp. 191-210, (1975).##[3] Hald, A. “Statistical theory of sampling inspection by attributes”, Academic Press, New York, U.S.A, (1981).##[4] Lieberman, G, I. and Resnikov, G.J. “Sampling plans for inspection by variables”, Journal of the American Statistical Associative, 50, pp. 457-516 (1955).##[5] Bennett, G.K.; Schmidt, J.W.; Case, K.E. “The choice of variables sampling plans using cost effective criteria”, AIIE Transactions, 6, pp. 178-184 (1974).##[6] Schmidt, J.W.; Bennet, G.K.; Case, K.E. “Three action cost model for acceptance sampling by variables”, Journal of Quality Technology, 16(3), pp. 10-18 (1980).##[7] Taguchi, G. “Quality evaluation for quality assurance”, American supplier institute, Romulus, Michigan, U.S.A, (1984).##[8] Zlatan Hamzic, Elizabet A. Cudney, and Ruwen Qin. “Development of an optimization model to determine sampling levels”, Missouri University of science and technology (2013).##[9] Ruwen Qin, Elizabet A. Cudney, Zlatan Hamzic. “An optimal plan of zero-defect single-sampling by attributes for incoming inspections in assembly lines”, European Journal of Operational Research 246 (2015) 907–915, (2015).##[10] Willemain, T. R. “Estimating the population median by nomination sampling”, Journal of the American Statistical Association, 75(372), 908-911, (1980).##[11] Ferrell W.G. and Choker jr.A. “Design of economically optimal acceptance sampling plane with inspection error”, Computer & Operations Research, Vol.29, pp. 1283-1300, (2002).##[12] Niaki, S. A., Nezhad, M. F. “Designing an optimum acceptance sampling plan using Bayesian inferences and a stochastic dynamic programming approach”, Scientia Iranica Transaction E-Industrial Engineering, 16(1), 19-25, (2009).##[13] Nezhad, M. S. F., Nasab, H. 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G. “The economics of sequential sampling procedures for defectives”, Applied Statistics, 118-130, (1953).##[19] Barnard, George A. “Sampling inspection and statistical decisions”, Journal of the Royal Statistical Society. Series B (Methodological), 151-174, (1954).##[20] Hamaker, H. C. “Some basic principles of sampling inspection by attributes”, Applied Statistics, 149-159, (1958).##[21] Calvin, T. “Quality Control Techniques for Components, Hybrids, and Manufacturing Technology”, IEEE Transactions on components, hybrids and manufacturing technology, 6(3), 323-328, (1983).##[22] Salameh, M. K., and Jaber, M. Y. “Economic production quantity model for items with imperfect quality”, International journal of production economics, 64(1), 59-64, (2000).##[23] Maddah, B., Jaber, M. Y. “Economic order quantity for items with imperfect quality”, Revisited. 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1 - Designing a Recommendation Model Based on Tobit Regression, GANN-DEA and PSOGA to Evaluate Efficiency and Benchmark Efficient and Inefficient Units https://aie.ut.ac.ir/article_80159.html 10.22059/jieng.2020.307008.1734 1 The main purpose of this study is to design a privatized proposal model for Tavanir regional electricity distribution and transmission companies. This proposed model is based on Tobit regression, GANN-DEA and PSOGA to evaluate the efficiency and modeling of efficient and inefficient units. This three-step process is benefited a hybrid data envelopment analysis model with a neural network optimized by a genetic algorithm to evaluate the relative efficiency of 16 Tavanir regional electricity companies. To measure the effect of environmental variables on the average efficiency of companies, two-stage data envelopment analysis and Tobit regression were used. Finally, with a hybrid model of particle mass algorithm and genetic algorithm, we have modeled for efficient and inefficient units. The average of efficiency of regional electricity companies during the years 2012 to 2017 has increased from 0.8934 to 0.9147. And companies in regions 1, 2, 4, 5, 8, 12, 13 and 16 have always had the highest efficiency average (one). And the power companies in regions 10 and 11 with the average efficiency values of 0.7047 and 0.6025 had the lowest efficiency values. 0 - 15 30 - - Mohammad Reza Mirzaei Department of Industrial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran Iran mohammad_reza_mirzaei@yahoo.com - - Mohammad Ali Afshar Kazemi Department of Industrial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran Iran dr.mafshar@gmail.com - - Abbas Toloie Eshlaghy Department of Industrial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran Iran toloie@gmail.com Hybrid Algorithm of particle swarm optimization with genetic algorithm modeling Tobit regression Efficiency combined model of data envelopment analysis with neural network and genetic algorithm 1] Mehregan, M .(2004). Quantitative models in evaluating organizations performance (DEA). Chapter 2 and 3, Tehran University Press Faculty Publishing.##[2] Ajalli, M., & Safari, H. (2011). Analysis of the Technical Efficiency of the Decision Making Units Making Use of the Synthetic Model of Performance Predictor Neural Networks, and Data Envelopment Analysis (Case Study: Gas National Co. Of Iran), Journal of Industrial Engineering Vol, 45, No.1.##[3] Rezaeian, J., & Asgarinezhad, A. (2014). Performance Evaluation of Mazandaran Water and Wastewater by Data Envelopment Analysis and Artificial Neural Network, Journal of Industrial Engineering Vol, 48, No.2.##[4] Toloie-Eshlaghy, A., Alborzi, M., & Ghafari, B. (2012). Assessment of the personnel’s efficiency with Neuro/DEA combined model. Elixir Mgmt. Arts Vol. 43, No.1. PP.6605-6617.##[5] Shokrollahpour, E., Lotfi, F. H., & Zandieh, M. (2016). An integrated data envelopment analysis–artificial neural network approach for benchmarking of bank branches. Journal of Industrial Engineering International, 12(2), 137-143.##[6] Çelen, A. (2013). Efficiency and productivity (TFP) of the Turkish electricity distribution companies: An application of two-stage (DEA&Tobit) analysis. Energy Policy, 63, 300-310.##[7] Wu, Y., Hu, Y., Xiao, X., & Mao, C. (2016). Efficiency assessment of wind farms in China using two-stage data envelopment analysis. Energy Conversion and Management, 123, 46-55.##[8] Hjalmarsson, L., & Veiderpass, A. (1992). Efficiency and ownership in Swedish electricity retail distribution. International Applications of Productivity and Efficiency Analysis (pp. 3-19): Springer.##[9] Bagdadioglu, N., Price, C. M. W., & Weyman-Jones, T. G. (1996). Efficiency and ownership in electricity distribution: a non-parametric model of the Turkish experience. Energy Economics, 18(1-2), 1-23.##[10] Pérez-Reyes, R., & Tovar, B. (2009). Measuring efficiency and productivity change (PTF) in the Peruvian electricity distribution companies after reforms. Energy Policy, 37(6), 2249-2261.##[11] Hattori, T., Jamasb, T., & Pollitt, M. G. (2003). A comparison of UK and Japanese electricity distribution performance 1985-1998: lessons for incentive regulation.##[12] Hess, B., & Cullmann, A. (2007). Efficiency analysis of East and West German electricity distribution companies–Do the “Ossis” really beat the “Wessis”? Utilities Policy, 15(3), 206-214.##[13] Goto, M., & Tsutsui, M. (2008). Technical efficiency and impacts of deregulation: An analysis of three functions in US electric power utilities during the period from 1992 through 2000. Energy Economics, 30(1), 15-38.##[14] Bongo, M. F., Ocampo, L. A., Magallano, Y. A. D., Manaban, G. A., & Ramos, E. K. F. (2018). Input–output performance efficiency measurement of an electricity distribution utility using super-efficiency data envelopment analysis. Soft Computing. doi:10.1007/s00500-018-3007-2##[15] Meibodi, A. E. (1998). Efficiency considerations in the electricity supply industry: The case of Iran .Chapter 1, university of Surrey.##[16] Sadjadi, S., & Omrani, H. (2008). Data envelopment analysis with uncertain data: An application for Iranian electricity distribution companies. Energy Policy, 36(11), 4247-4254.##[17] Fallahi, M., & Ahmadi, V. (2005). Cost efficiency analysis of electricity distribution companies in Iran. Journal of Economic Researches, 71, 297-320.##[18] Russell, R. R. (1985). Measures of technical efficiency. Journal of Economic Theory, 35(1), 109-126.##[19] Charnes, A., Cooper, W., Lewin, A., & Seiford, L. (1995). Data Envelopment Analysis: Theory, Methodology and Applications, Kluwer Publications.##[20] Tone, K. (2001). A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research, 130(3), 498-509.##[21] Tobin, J. (1958). Estimation of Relationships for Limited Dependent Variables. Econometrica, 26(1), 24-36.##[22] Olatubi, W. and Dismukes D. (2000), “A Data Envelopment Analysis of the Levels and Determinants of Coal-fired Electric Power Generation Performance”, Utilities Policy, 9, PP. 47–59.##[23] Dreyfus, G. (2005). Neural networks: methodology and applications: Springer Science & Business Media.##[24] Alborzi, Mahmood. (2014). Translated of neural computing: an introduction-R Beale & T Jackson. Chapter 4, Institute of Scientific Publications of Sharif University of Technology: Tehran.##[25] Athanassopoulos, A. D., & Curram, S. P. (1996). A Comparison of Data Envelopment Analysis and Artificial Neural Networks as Tools for Assessing the Efficiency of Decision Making Units. Journal of the Operational Research Society, 47(8), 1000-1016.##[26] Costa, Á., & Markellos, R. N. (1997). Evaluating public transport efficiency with neural network models. Transportation Research Part C: Emerging Technologies, 5(5), 301-312.##[27] Wu, D. D., Yang, Z., & Liang, L. (2006). Using DEA-neural network approach to evaluate branch efficiency of a large Canadian bank. Expert systems with applications, 31(1), 108-115.##[28] Samoilenko, S., & Osei-Bryson, K.-M. (2010). Determining sources of relative inefficiency in heterogeneous samples: Methodology using Cluster Analysis, DEA and Neural Networks. European Journal of Operational Research, 206(2), 479-487.##[29] Alborzi, Mahmood. (2014). Genetic algorithm. Chapter 3-5, Institute of Scientific Publications of Sharif University of Technology: Tehran.##[30] Angeline, P. J. (1998). Using selection to improve particle swarm optimization. Paper presented at the Evolutionary Computation Proceedings, 1998. IEEE World Congress on Computational Intelligence., The 1998 IEEE International Conference on.##[31] Garg, H. (2016). A hybrid PSO-GA algorithm for constrained optimization problems. Applied Mathematics and Computation, 274, 292-305.##[32] Toloie Ashlaghi, A, Afshar Kazemi, M.A. And Abbasi, F. (2013) Evaluation of the performance of insurance companies based on a balanced scorecard card and data envelopment analysis technique and providing a development path for inefficient companies. Journal of Business Management (17), 65-82.##[33] Azar, A. Daneshvar, M. Khodadad Hosseiny, S. H. Azizi, S.(2012). Designing a multilevel performance evaluation model: A robust data envelopment analysis approach. Enterprise Resource Management Research, Volume II (3), 1-22.##[34] Cook WD and Green RH.( 2005). Evaluating power company efficiency: a hierarchical model. Computers & Operations Research; 32: 813-823.##

1 - The Inventory–Routing Problem for Distribution of Red Blood Cells considering Compatibility of Blood Group and Transshipment between Hospitals https://aie.ut.ac.ir/article_80160.html 10.22059/jieng.2020.308132.1736 1 This paper presents an inventory-routing problem (IRP) for Red Blood Cells (RBCs) distribution, in which -to avoid shortage- supplying the demand with compatible blood groups (substitution) and the RBC transshipments between hospitals (transshipment) are considered. The mentioned problem is investigated in four conditions: 1- Allowing the transshipment and substitution, 2- Allowing the transshipment, but no substitution, 3- Allowing the substitution, but no transshipment, 4- No allowing the transshipment and substitution. Since the mentioned problem is NP-Hard, the adaptive large neighborhood search algorithm (ALNS) has been used to solve all conditions. The cost in the first condition is the least one, because the feasible solution space is the largest. Also, the results show that the transshipment has a more active role than the substitution in reducing the shortage. Moreover, in the first and third conditions, the O+ blood group is used more than the other blood groups to meet the other compatible blood groups' demands. 0 - 31 44 - - Saeed Yaghoubib Institute for Management and Planning Studies (IMPS), Tehran, Iran Iran yaghoubi@iust.ac.ir - - Fatemeh Jafarkhan Institute for Management and Planning Studies (IMPS), Tehran, Iran Iran f.jafarkhan@gmail.com - - Niloofar Gilani Larimi School of Industrial Engineering, Iran University of Science & Technology Iran niloofar.gilanii@gmail.com - - Babak Farhang Moghadama Farhang Moghadama Institute for Management and Planning Studies (IMPS), Tehran, Iran Iran farhang@iust.ac.ir Adaptive large neighborhood search algorithm Compatibility of blood group Red Blood Cells Transshipment Inventory routing problem [1] Hosseini-Motlagh, S. M., Larimi, N. G., & Nejad, M. O. (2020). A qualitative, patient-centered perspective toward plasma products supply chain network design with risk controlling. Operational Research, 1-46. DOI 10.1007/s12351-020-00568-4##[2] Osorio, A. F., Brailsford, S. C., and Smith, H. K. (2015). “A structured review of quantitative models in the blood supply chain: a taxonomic framework for decision-making”. International Journal of Production Research, Vol. 53, No. 24, PP. 7191-7212.##[3] Gunpinar, S., and Centeno, G. (2015). “Stochastic integer programming models for reducing wastages and shortages of blood products at hospitals”. Computers & Operations Research, Vol. 54, PP. 129-141.##[4] Federgruen, A., Prastacos, G., and Zipkin, P. H. (1986). “An allocation and distribution model for perishable products”. Operations Research, Vol. 34, No. 1, PP. 75-82.##[5] Hemmelmayr, V., Doerner, K. F., Hartl, R. F., and Savelsbergh, M. W. (2009). “Delivery strategies for blood products supplies”. OR spectrum, Vol. 31, No. 4, PP. 707-725.##[6] Hemmelmayr, V., Doerner, K. F., Hartl, R. F., and Savelsbergh, M. W. (2010). “Vendor managed inventory for environments with stochastic product usage”. European Journal of Operational Research, Vol. 202, No. 3, PP. 686-695.##[7] Jafarkhan, F., & Yaghoubi, S. (2018). An efficient solution method for the flexible and robust inventory-routing of red blood cells. Computers & Industrial Engineering, 117, 191-206.##[8] Jafarkhan, F., & Yaghoubi, S. (2017). A robust mathematical model and heuristic solution algorithm for integrated production-routing-inventory problem of perishable products with lateral transshipment. Journal of Industrial Engineering Research in Production Systems, Vol. 4, PP. 195-211.##[9] Yaghoubi, S., Hosseini-Motlagh, S. M., Cheraghi, S., & Gilani Larimi, N. (2020). Designing a robust demand-differentiated platelet supply chain network under disruption and uncertainty. 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C., and Seagle, J. P. (1977). “Cost effectiveness of use of frozen blood to alleviate blood shortages”. Transfusion, Vol. 17, No. 6, PP. 602-606.##[15] Sapountzis, C. (1984). “Allocating blood to hospitals from a central blood bank”. European Journal of Operational Research, Vol. 16, No. 2, PP. 157-162.##[16] Sivakumar, P., Ganesh, K., and Parthiban, P. (2008). “Multi-phase composite analytical model for integrated allocation-routing problem-application of blood bank logistics”. International Journal of Logistics Economics and Globalisation, Vol. 1, No. 3-4, PP. 251-281.##[17] Arvan, M., Tavakkoli-Moghaddam, R., and Abdollahi, M. (2015). “Designing a bi-objective and multi-product supply chain network for the supply of blood”. Uncertain Supply Chain Management, Vol. 3, No. 1, PP. 57-68.##[18] Gilani Larimi, N., Yaghoubi, S., & Hosseini-Motlagh, S. M. (2019). Itemized platelet supply chain with lateral transshipment under uncertainty evaluating inappropriate output in laboratories. Socio-Economic Planning Sciences, 68(February 2018), 100697.##[19] Coelho, L. C., Cordeau, J. F., and Laporte, G. (2012). “The inventory-routing problem with transshipment”. Computers & Operations Research, Vol. 39, No. 11, PP. 2537-2548.##[20] Desrochers, M., and Laporte, G. (1991). “Improvements and extensions to the Miller-Tucker-Zemlin subtour elimination constraints”. Operations Research Letters, Vol. 10, No. 1, PP. 27-36.##[21] Archetti, C., Bertazzi, L., Laporte, G., and Speranza, M. G. (2007). “A branch-and-cut algorithm for a vendor-managed inventory-routing problem”. Transportation Science, Vol. 41, No. 3, PP. 382-391.##

1 - Multi-Objective Modeling of Scheduling and Routing Trucks in a Cross-Dock for Perishable Items https://aie.ut.ac.ir/article_80161.html 10.22059/jieng.2021.309559.1739 1 Supply chain management plays an important role in creating competitive advantages for companies. One of the most important factors in supply chain management is the control of physical flow for materials and products. Cross dock strategy is an effective way to synchronic control of materials flow, logistic costs, distribution operations, and tuning customer service level. Today's use of this strategy, to reduce inventory holdings and reduce the time spent in the supply chain is increasing. Perishable items supply chain is more complicated than many others. In this supply chain,changing the quality of items because of the nature of perishability is very important for customers, so distributors face a lot of logistical challenges. Distribution management of these products through the cross-dock center is very efficient for delivering items to customers in appropriate quality, and at the right time, and right place. In this research, we provide a multi-objective mathematical model for truck scheduling and routing in a cross-dock for perishable items by considering the perishability rate based on distribution time and condition by two types of trucks that are effective on product quality in distribution. The objective functions are minimizing the cost of delivery, including transportation costs, the penalty costs of shortage, and perishable items in distribution time and the total spent time. The VRSP system is modeled as a mixed-integer non-linear program in GAMS and an NSGA-II algorithm is provided. 0 - 45 60 - - reyhaneh shafiee Department of industrial engineering, Amirkabir University of Technology,Tehran, Iran Iran reyahaneh_ashofteh@yahoo.com - - Mohsen Akbarpour Shirazi Department of industrial engineering, Amirkabir University of Technology,Tehran, Iran Iran akbarpour@aut.ac.ir Cross dock Truck scheduling Routing Perishable items Multi objective [1] Larbi, R. Alpan, G. Baptiste, P. Penz, B. (2011).“ Scheduling cross docking operations under full, partial and no information on inbound arrivals”, Computers & Operations Research, Vol. 38, No.6, PP.889-900.##[2] Boysen, N. Fliedner, M. (2010).“Cross dock scheduling: Classification, literature review and research agenda”. Omega, Vol .38,No.6, PP.413-422.##[3] Forouharfard, S. 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, PP. 1179-1193.##[4] Baniamerian, A. Bashiri,M. Tavakkoli-Moghaddam, R. (2019).” Modified variable neighborhood search and genetic algorithm for profitable heterogeneous vehicle routing problem with cross-docking “, Applied Soft Computing, Vol.75, PP. 441-460.##[5] Van Belle, J. Valckenaers, P. Cattrysse, D. (2012).“Cross-docking: State of the art”. Omega, Vol. 40, No. 6, PP. 827–846.##[6] Yiyo, K.(2013).” Optimizing truck sequencing and truck dock assignment in a cross docking system”, Expert Systems with Applications , Vol. 40, No. 1, PP. 5532–5541.##[7] Dinçer, K. Mihalis, M. Golias.(2013).” Cost-stable truck scheduling at a cross-dock facility with unknowntruck arrivals: A meta-heuristic approach “.Transportation Research Part E, Vol. 20, No. 49, PP. 71-91.##[8] Liao, TW. Chang, P. Kuo, R.J. Liao, C.(2014).” A comparison of five hybrid metaheuristic algorithms for unrelated parallel-machine scheduling and inbound trucks sequencing in multi-door cross docking systems”. Applied Soft Computing, Vol. 21, No. 1, PP. 180-193.##[9] Mohtashami, A. (2015).” Scheduling trucks in cross docking systems with temporary storage and repetitive pattern for shipping trucks “.Applied Soft Computing, Vol. 36, No. 2, PP. 468-586.##[10] Madani-Isfahani, M. Tavakoli-Moghaddam, R. Naderi, B. (2014).” Multiple cross-docks scheduling using two meta-heuristic algorithms “.Computers & Industrial Engineering., Vol. 74, PP. 129-138.##[11] Ladier, A. Alpan, G. (2016).” Robust cross-dock scheduling with time windows “.Applied Soft Computing. Computers & Industrial Engineering, Vol. 99, PP. 16-28.##[12] Golshahi-Roudbaneh, A. Hajiaghaei-Keshteli, M. Paydar, M. (2017).” Developing a lower bound and strong heuristics for a truck scheduling problem in a cross-docking center “. Knowledge-Based Systems, Vol. 129, PP. 17-38.##[13] Hosseini, S. D. Akbarpour Shirazi, M. Karimi, B.(2014). “Cross-docking and milk run logistics in a consolidation network: A hybrid of harmony search and simulated annealing approach,” J. Manuf. Syst., Vol. 33, No. 4, PP. 67-77.##[14] Yin, P. Chuang, Y. (2014). “Adaptive memory artificial bee colony algorithm for green vehicle routing with cross-docking,” Appl. Math. Model., Vol. 45, No. 21, PP. 310-352.##[15] Moghadam, S. FatemiGhomi, S.M.T. Karimi, B. (2014).” Vehicle routing scheduling problem with cross docking and split deliveries “. Computers and Chemical Engineering, Vol. 69, PP. 98-107.##[16] Vinicius, W.C. Morais, Geraldo R. Mateus, Thiago F. Noronha. (2014).” Iterated local search heuristics for the Vehicle Routing Problem with Cross-Docking”, Expert Systems with Applications ,Vol. 41, PP.7495–7506.##[17] Chen, M.C. Hsiao, Y. Himadeep Reddy. Tiwari, M.K. (2016). “The Self-Learning Particle Swarm Optimization approach for routing pickup and delivery of multiple products with material handling in multiple cross-docks,” Transp. Res. Part E Logist. Transp. Rev., 1,258–226.##[18] Grangier, P. Gendreau,M. Lehuede, F. Rousseau, L.M.. (2017).” A matheuristic based on large neighborhood search for the vehicle routing problem with cross-docking “. Computers and Operations Research, Vol. 84, PP. 116-126.##[19] Agustina, D. Lee, C.K.M. Rajesh, P. (2014).” Vehicle scheduling and routing at a cross docking center for food supply chains “.Int. J. Production Economics, Vol. 152, PP. 29-41.##[20] Mousavi, S. M. Tavakkoli-Moghaddam, R. (2013).“A hybrid simulated annealing algorithm for location and routing scheduling problems with cross-docking in the supply chain,” J. Manuf. Syst., Vol. 32, No. 2, PP. 330–347.##[21] Fatemi Ghomi, S.M,T,. Rahmanzadeh, S. Sheikh Sajadieh, M. .(2017).“ Scheduling at a cross dock based on the specific departure time of the outbound trucks “ . Journal of Industrial Engineering., Vol. 50, No. 3, PP. 441-450.##[22] Ercat, J.Ghods,P.Ahmadizar,F.(2017).“ Optimization of sequence and allocation of inbound and outbound trucks in cross dock “ . Journal of Industrial Engineering., Vol. 50, No. 2, PP. 177-189.##[23] Mokhtarinejad, M. Ahmadi, A. Karimi, B. Rahmati, S.H.A. (2015). “A novel learning based approach for a new integrated location-routing and scheduling problem within cross-docking considering direct shipment,” Appl. Soft Comput. J., Vol. 34, PP. 274–280.##[24] Babaee Tirkolaee, E. Goli, A. Faridnia ,A. Soltanie, M. WilhelmWeberf, G. (2020). “Multi-objective optimization for the reliable pollution-routing problem with cross-dock selection using Pareto-based algorithms “, Journal of Cleaner Production.##[25] Peng-YengYinn,Sin-RuLyu,Ya-LanChuang. 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1 - Identifying and Classifying Behavioral Barriers in Implementation of Strategic Transformation Plans: Qualitative Meta- Synthesis Approach https://aie.ut.ac.ir/article_80163.html 10.22059/jieng.2021.316956.1747 1 Existence of behavioral barriers in employees is not a subject to be easily ignored in implementation of strategic transformational plans, but it is often neglected or less addressed because in this process the preparation of technical and feasibility aspects of transformational plans is the focal point and implementation steps and evaluation of these plans are ignored. Therefore, this research seeks to identify and classify behavioral barriers in implementation of strategic transformational plans by reviewing previous researches and based on qualitative meta-Synthesis analysis method. Statistical population includes papers and researches related to implementation of strategic transformational plans and the sampling method is also meaningful. The data analyzed in the present study are extracted from (secondary data) researches published from 1985 to 2019 from two ISI and Scopus databases, the subject of which is “strategy implementation”. The method of data analysis is open source coding based on which selected papers have been reviewed and the initial codes have been extracted and the results are classified in 112 initial codes, 14 concepts and 5 categories. Finally, five categories including Inefficiency of managers, Inefficiency of leaders, Inefficiency of employees, Cultural inefficiencies and Systems Inefficiency, were identified and introduced as behavioral barriers in implementation of strategic transformational plans. 0 - 61 78 - - REZA AKBARIASL Organizational Behavior Trend, Aras Campus, University of Tehran, Jolfa, Iran Iran akbariaslreza@gmail.com - - hasan Zarei Matin Faculty of Management and Accounting, Farabi Campus, University of Tehran, Tehran, Iran Iran matin@ut.ac.ir - - Hamid Reza Yazdani Faculty of Management and Accounting, Farabi Campus, University of Tehran, Tehran, Iran Iran hryazdani@ut.ac.ir Behavioral barriers implementation strategy implementation strategic plan. transformational plans [1] Derya, Y. and Gökhan, K. (2013). Resistance To Change And Ways Of Reducing Resistance In Educational Organizations. European Journal Of Research On Education, 1(1), 14-21.##[2] Chijioke, N., Helena, C. and Olaunji, F. (2018). The Relationship Between Employee Commitment To Strategy Implementation And Employee Satisfaction. Trends Economics And Management. 31(1), 45–56.##[3] Mintzberg, Honari, Alstrand, Bruce and Lampell, Joseph (2005), Forest Strategy, Translation: Mahmoud Ahmadpour Dariani, Tehran: Pardis Company.##[4] [4] Samson, A., A., J. and Mary, B.F. (2012), The Impact Of Working Capital Management On The Profitability Of Small And Medium Scale Enterprises In Nigeria, Res. J. Bus. Manage, 6(15), 61-69.##[5] Butamani, M.A., Babashahi, J., Yazdani, H.R., Zarei Matin, H. and Akhavan Alavi, S.H. (2018). "Explaining the Dimensions of Human Resource Management:A Trans-Qualitative Approach", Organizational Resource Management Researches, Volume 8, No. 3.##[6] Muthoni, E. (2012). Effects Of Organizational Culture On Strategy Implementation In Commercial Banks In Kenya. Unpublished MBA Project, School Of Business, University Of Nairobi.##[7] Wheelen, T. and Huger, J. (2012). Concepts in Strategic management and Business policy,13th Edition, by Pearson / Prentice Hall, Boston, USA.##[8] Hourani, M. (2017). Conceptual Frameworks for Strategy Implementation: ALiterature Review. Journal of Management Research, 9(3).##[9] Abok, A., A. Waititu, R. Gakure and Ragui, M. (2013). Culture's Role In The Implementation Of Strategic Plans In Nongovernmental Organizations In Kenya. Prime Journal Of Social Science. Jomo Kenyatta University Of Agriculture And Technology (JKUAT), Nairobi, Kenya.##[10] Waihenya, C. M. (2019). Influence of Organizational Barriers to Strategy Implementation in USIU-A University (Doctoral dissertation, United States International University-Africa).##[11] Wolczek, P. (2018). Strategy Implementation Problems in Small and Large Companies–Similarities and Differences in Light of the Research Results, Lessons From the Polish Experience, Lessons From the Polish Experience. Argumenta Oeconomica, (2), 41.##[12] Mohammadi Janaki, D., Sobhanallahi, A. M. and Mohammadi Janaki, M. (2015). Identifying the implementation barriers of strategic management using factor analysis and TOPSIS Technique. International Journal of Academic Research in Business and Social Sciences, 5(11) 323-336.##[13] Njiri, P. (2016). Strategic Leadership And Strategy Implementation: The Case Of Kca University. Nairobi, KE: MBA Thesis Nairobi University.##[14] Maleki, M., Mohaghar, F and Karimi Dastjerdi, D. (2010). Compiling and evaluating the organizational strategies using SWOT and analytic network process ANP, Organizational culture management magazine. Eighth year, No. 21.##[15] Oke, A. and Oke, M. (2007). Implementing Flexible Labor Strategies: Challenges and Key Success factors, Journal of Change Management, Vol. 7, No. 1, pp. 69-87.##[16] Donselaar, R. (2012). The organisational drivers and barriers of strategy implementation within a non-profit organisation: a case study at the Netherlands Red Cross (Master's thesis, University of Twente).##[17] Omuse, G., Kihara, P. and Munga, J. (2018). Determinants Of Strategic Plan Implementation In Public Universities: A Case Of Selected Public Universities In Nairobi County, Kenya. International Academic Journal Of Human Resource And Business Administration, 3(2), 452-477.##[18] Heide, M., Grønhaug, K. and Johannessen, S. (2002). Exploring Barriers to The Successful Implementation of a Formulated strategy, Scandinavian Journal of Management, Vol. 18, pp. 217 – 231.##[19] Pearce, J. A. and Robinson, R. B. (2005). Strategic Management: Formulation, Implementation,and Control, McGraw – Hill Education – Europe. 10th Revised edition.##[20] Tasa, K., Sears, G. J. and Schat, A. C. H. (2011). Personality and teamwork behavior in context: The cross-level moderating role of collective efficacy, Journal of Organizational Behavior, 39, 12–,2.##[21] Pereira, L., Durão, T., and Santos, J. (2019). Strategic communication and barriers to strategy implementation. In 2019 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC) (pp. 1-5). IEEE.##[22] Pella, M. D. A., Sumarwan, U. and Daryanto, A. (2013). Factors affecting poor strategy implementation, Gadjah Mada International Journal of Business, 15(2), 183-204.##[23] Hrebiniak, L. G. (2006). Obstacles to Effective Strategy Implementation, Organizational Dynamics, Vol. 35, pp. 12 – 31.##[24] Arabi, S.M., (1931), Strategic Planning Department, Tehran: Cultural Research Office.##[25] Alexander, L. D. (1985), Successfully implementing strategic decisions, Long range planning, 18(3), 91-97.- Athlete, Ahmad (2012). Full Collection, Volume 2, at a Managerial Perspective, MANAGERIAL.IR.##[26] Hosseini, S. H. K., S. F. Hosseini, A. Kordaniej and Ahmadi, P. (2016). Survey and explain the role of sensemaking in successful strategy implementation in Iran’s automotive companies. Business: Theory and Practice, 17(3), 202-215.##[27] Nosratollahi, A., (2007), Using Fuzzy Logic in Formulating, Evaluating and Implementing Strategic Projects, Expert Thesis, Faculty of Industrial Engineering, Tafresh University.##[28] Beer, M. and Eisenstat, R.A. (2000).The Silent Killers of Strategy Implementation and Learning. Sloan Management Review. Summer, pp. 29 – 42.##[29] Makina, I. and Keng’ara, R. (2018), Managing Strategic Change Of An Organization's Performance: A Case Study Of Nzoia Sugar Company, Kenya, Universal Journal Of Management, 6(6), 198-212, 2018.##[30] Nabwire, M. (2014). Factors Affecting Implementation of Strategy A Case of Barclays Bank of Kenya (Doctoral dissertation, United States International University-Africa).##[31] Verweire K. (2014). Strategy Implementation. First edition, by Routledge, London and NewYork.##[32] Verhagen, N. (2017). The link between the Strategy Implementation Problem and the Construal Level Theory.##[33] Jensen, L, A and Allen. (1996). Meta-Synthesis of Qualitative Findings, Qualitative Health Research, 6 (4), pp. 553-560.##

1 - A Novel Hybrid MCDM Method for Optimal Location Selection of Free Trade Zones, Case Study: Mazandaran Province https://aie.ut.ac.ir/article_80164.html 10.22059/jieng.2021.203748.1752 1 Free trade zone has attracted significant attention, especially in developing countries. It facilitates attracting foreign capital and skilled workforce and experts to achieve economic development, which is its ultimate goal. An efficient free trade zone has different features, most of which are related to its location. Therefore, location selection has an important role in its success. Facility location planning is a strategic decision that is very expensive, but can decrease future costs. This paper aims to find the optimal location for establishing a free trade zone. The current paper applies multi-criteria decision-making (MCDM) to capture all the features and essentials of a thriving free trade zone. To this end, a novel hybrid MCDM method is developed to obtain the optimal solution with fewer paired comparisons and less reliance on estimations. Then, to assess the applicability of the developed method, a real case study was conducted in Mazandaran Province, Iran. Finally, the results of the proposed method were evaluated by comparison with the results of the AHP method. 0 - 79 92 - - Ali Bozorgi-Amiri School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran Iran alibozorgi@ut.ac.ir - - Amirhossein Ranjbar School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran Iran amirhosseinranjbar1994@gmail.com - - Amir Jamali School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran Iran amirjamali@ut.ac.ir Multi Criteria Decision Making Free trade zone Best-Worst Method Location problem hybrid MCDM method [1] Krupat, K. (1997). From war zone to free trade zone: a history of the National Labor Committee. No Sweat: Fashion Free Trade and the Rights of Garment Workers.##[2] Kou, G., Peng, Y., & Wang, G. (2014). Evaluation of clustering algorithms for financial risk analysis using MCDM methods. Information Sciences, 275, 1–12.##[3] Zavadskas, E. K., Turskis, Z., & Kildiene, S. (2014). State of art surveys of overviews on MCDM/MADM methods. Technological and Economic Development of Economy, 20(1).##[4] Rezaei, J. (2016). Best-worst multi-criteria decision-making method: Some properties and a linear model. Omega (United Kingdom), 64, 126–130.##[5] Liu, J., Long, G., & Xu, X. (2017). A Method of Multi-attribute Decision Making Based on Basic Point and Weighting Coefficients Range. Procedia Computer Science, 107(Icict), 202–205.##[6] Owen, S. H., & Daskin, M. S. (1998). Strategic facility location: A review. European Journal of Operational Research, 111(3), 423–447.##[7] Chao, H. K. (2018). Shaping space through diagrams: The case of the history of location theory. Research in the History of Economic Thought and Methodology, 36B, 59–72.##[8] Zabihi, A., & Gharakhani, M. (2018). A literature survey of HUB location problems and methods with emphasis on the marine transportations. Uncertain Supply Chain Management, 6(1), 91–116.##[9] Hakli, H., and Ortacay, Z. (2019). An improved scatter search algorithm for the uncapacitated facility location problem. Computers and Industrial Engineering, 135(December 2017),##[10] Mladenović, N., Alkandari, A., Pei, J., Todosijević, R., & Pardalos, P. M. (2020). Less is more approach: basic variable neighborhood search for the obnoxious p-median problem. International Transactions in Operational Research, 27(1), 480–493.##[11] Lin, G., Xu, H., Chen, X., and Guan, J. (2020). An effective binary artificial bee colony algorithm for maximum set k-covering problem. Expert Systems with Applications, 161, 113717.##[12] Drezner, T., Drezner, Z., & Kalczynski, P. (2020). Multiple obnoxious facilities location: A cooperative model. IISE Transactions, 52(12), 1403–1412.##[13] Gadegaard, S. L., Klose, A., & Nielsen, L. R. (2018). A bi-objective approach to discrete cost-bottleneck location problems. Annals of Operations Research, 267(1–2), 179–201.##[14] Shapira, A., & Goldenberg, M. (2005). AHP-Based Equipment Selection Model for Construction Projects. Journal of Construction Engineering and Management, 131(12), 1263–1273.##[15] Badri, M. A. (1999). Combining the analytic hierarchy process and goal programming for global facility location-allocation problem. International Journal of Production Economics, 62(3), 237–248.##[16] Shang, J., & Sueyoshi, T. (1995). A unified framework for the selection of a Flexible Manufacturing System. European Journal of Operational Research, 85(2), 297–315.##[17] Yang, T., & Kuo, C. (2003). A hierarchical AHP/DEA methodology for the facilities layout design problem. European Journal of Operational Research, 147(1), 128–136.##[18] Rezaei, J., Nispeling, T., Sarkis, J., & Tavasszy, L. (2016). A supplier selection life cycle approach integrating traditional and environmental criteria using the best worst method. Journal of Cleaner Production, 135, 577–588.##[19] Motevali Haghighi, S., & Torabi, S. A. (2018). A novel mixed sustainability-resilience framework for evaluating hospital information systems. International Journal of Medical Informatics, 118(July), 16–28.##[20] Kilkenny, M., & Thisse, J. F. (1999). Economics of location: A selective survey. Computers and Operations Research, 26(14), 1369–1394.##[21] Li, Y., Liu, X., & Chen, Y. (2011). Selection of logistics center location using Axiomatic Fuzzy Set and TOPSIS methodology in logistics management. Expert Systems with Applications, 38(6), 7901–7908.##[22] Pellenbarg, P. H. (2002). Sustainable business sites in the Netherlands: A survey of policies and experiences. Journal of Environmental Planning and Management, 45(1), 59–84.##[23] Fernández, I., & Ruiz, M. C. (2009). Descriptive model and evaluation system to locate sustainable industrial areas. Journal of Cleaner Production, 17(1), 87–100.##[24] Deng, X., Wang, Y., & Yeo, G. T. (2017). Enterprise Perspective-based Evaluation of Free Trade Port Areas in China oa. Maritime Economics and Logistics, 19(3), 451–473.##[25] Grubel, H. G. (1982). Towards a theory of free economic zones. Weltwirtschaftliches Archiv, 118(1), 39–61.##[26] Sánchez-Lozano, J. M., Teruel-Solano, J., Soto-Elvira, P. L., & Socorro García-Cascales, M. (2013). Geographical Information Systems (GIS) and Multi-Criteria Decision Making (MCDM) methods for the evaluation of solar farms locations: Case study in south-eastern Spain. Renewable and Sustainable Energy Reviews, 24, 544–556.##