IJSOM Kharazmi University International Journal of Supply and Operations Management 23831359 Kharazmi University 2739 10.22034/2017.4.01 optimization in supply chain management Dynamic Planning of Reusable Containers in a Close-loop Supply Chain under Carbon Emission Constrain Dynamic Planning of Reusable Containers in a Close-loop Supply Chain under Carbon Emission Constrain Ech-Charrat Mohammed Rida National School of Applied Sciences of Tangier, Tangier, Morocco. Amechnoue Khalid National School of Applied Sciences of Tangier, Tangier, Morocco. Zouadi Tarik BEAR Lab, International University of Rabat, Technopolis Shore Rocade Sala Al Jadida, Morocco 01 11 2017 4 4 279 297 01 08 2016 11 04 2018 Copyright © 2017, Kharazmi University. 2017 http://www.ijsom.com/article_2739.html

Nowadays, Companies need to collect and to deliver goods from and to their depots and their customers. Reusable containers are considered as a greener choice and a cost saving strategy. This paper addresses a dynamic management of reusable containers (e.g gases bottles, wood pallets, maritime containers, etc.) in a Closed-loop supply chain. The aim of the study is to find an optimal lot sizing and assignment strategy that minimizes the cost of reusable containers management under environmental constraint. In this contribution, a new integer-linear-programming model and two hybrid approaches based on the genetic algorithm are proposed to solve the problem. The second hybrid method is enhanced with a local search based on the VNS (variable neighborhood search). The numerical results show the performance of the two hybrid approaches in terms of solution quality and response time.

Reverse logistics return flow Hybrid algorithm Reusable container Lot-sizing Assignment problem
Absi, N., Dauzère-Pérès, S., Kedad-Sidhoum S., Penz, B., Rapine, C. (2013). Lot sizing with carbon emission constraints. European Journal of Operational Research. Vol 227 (1), pp. 55–61. Accorsi, R., Cascini, A., Cholette, S., Manzini, R., Mora, C. (2014). Economic and environmental assessment of reusable plastic containers: A food catering supply chain case study. International Journal of Production Economics. Vol. 152, pp. 88–101 Aras, N., Aksen, D., Tekin, M.T. (2011). Selective multi-depot vehicle routing problem with pricing. Transportation Research (Part C. Emerging Technologies). Vol. 19(5), pp. 866–884. Atamer, B., Bakal, İ.S., Bayindir, Z.P. (2013). Optimal pricing and production decisions in utilizing reusable containers. International Journal of Production Economics. Vol. 143(2), pp. 222–232. Bazan, E., Jaber, M. Y., and Zanoni, S. (2017). Carbon emissions and energy effects on a two-level manufacturer-retailer closed-loop supply chain model with remanufacturing subject to different coordination mechanisms. International Journal of Production Economics, Vol. 183, pp. 394-408. Bhattacharjya, J. and Kleine-Moellhoff, P. (2013). Environmental Concerns in the Design and Management of Pallets. International Federation for Information Processing. pp. 569–576. Carrano, A.L.; Pazour, J.A.; Roy, D.; Thorn, B.K. (2015): Selection of pallet management strategies based on carbon emissions impact. International Journal of Production Economics, Vol. 164 (6), pp. 258-270. Castillo, E. and Cochran, J.K. (1996). Optimal Short Horizon Distribution Operations in Reusable Container Systems. The Journal of the Operational Research Society. Vol. 47(1), pp. 48-60. Chanintrakul, P., Coronado Mondragon, A. E., Lalwani, C., and Wong, C. Y. (2009). Reverse logistics network design: a state-of-the-art literature review. International Journal of Business Performance and Supply Chain Modelling, Vol. 1(1), pp. 61-81. Cobb, B. R. (2016a). Inventory control for returnable transport items in a closed-loop supply chain. Transportation Research (Part E: Logistics and Transportation Review), Vol. 86, pp. 53-68. Cobb, B. R. (2016b). Estimating cycle time and return rate distributions for returnable transport items. International Journal of Production Research, Vol. 54(14), pp. 4356-4367. Ech-Charrat, M. R., and Amechnoue, K. (2016). Dynamic hybrid approach for reusable containers management in a close-loop supply chain. In Multimedia Computing and Systems (ICMCS), 2016 5th International Conference on (pp. 548-553). IEEE. Ech-Charrat, M. R., Amechnoue, K., and Zouadi, T. (2017a). Genetic Algorithm for Reusable Containers Management Problem. In International Conference on Advanced Information Technology, Services and Systems (pp. 50-58). Springer, Cham. Ech-Charrat, M. R., Amechnoue, K., and Zouadi, T. (2017b). Hybrid resolution approaches for dynamic assignment problem of reusable containers. In MATEC Web of Conferences (Vol. 105, p. 00009). EDP Sciences. Frazzon, E. M., Albrecht, A., Pires, M., Israel, E., Kück, M., and Freitag, M. (2017). Hybrid approach for the integrated scheduling of production and transport processes along supply chains. International Journal of Production Research, pp. 1-17. Glock, C. H. (2017). Decision support models for managing returnable transport items in supply chains: A systematic literature review. International Journal of Production Economics, Vol. 183, pp. 561-569. Glock, C. H., and Kim, T. (2016). Safety measures in the joint economic lot size model with returnable transport items. International Journal of Production Economics, Vol. 181, pp. 24-33. Goh, T.N.; Varaprasad, N. (1986): A statistical methodology for the analysis of the life-cycle of reusable containers. IIE Transactions, Vol. 18 (1), pp. 42-47. Goren, G.H., Tunali, S., Jans, R. (2010). A review of applications of genetic algorithms in lot-sizing. J. Intelligent Manufacturing, Vol. 21, pp. 575–590. Goudenege, G., Chu C., Jemai, Z. (2013). Reusable Containers Management. From a Generic Model to an Industrial Case Study. Supply Chain Forum (An International Journal). Vol. 14(2). pp. 26-38 Govindan, K., and Soleimani, H. (2017). A review of reverse logistics and closed-loop supply chains: a Journal of Cleaner Production focus. Journal of Cleaner Production, Vol. 142, pp. 371-384. 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, Vol. 240(3), pp. 603-626. Gribkovskaia, I., Halskau, O., Laporte, G., Vlcek, M. (2007). General solutions to the single vehicle routing problem with pickups and deliveries. European Journal of Operational Research. Vol. 180(2), pp. 568-584. Hanafi, J., Kara, S., Kaebernick, H. (2008). Reverse logistics strategies for end-of-life products. International Journal of Logistics Management. Vol. 19(3), pp. 367-388. Hansen, J P. and Mladenovic, N. (1997). Variable Neighborhood Search for the p-Median. Les Cahiers du GERAD G-97-39, Montreal, Canada. Hansen, P. and Mladenovic, N. (1999). An introduction to variable neighborhood search, in: Metaheuristics, Advances and Trends in Local Search Paradigms for Optimization. S. Voss et al., eds, Kluwer, Dordrecht, pp. 433-458. Hansen, P. and Mladenovic, N. (2002). Variable neighborhood search, in: Handbook of Applied Optimization. P. Pardalos and M. Resende, Oxford University Press, New York, pp. 221-234. Hansen, P. and Mladenovic, N. (2003). Variable neighborhood search, in: Handbook of Metaheuristics. Glover, F. and Kochenberger, G. eds, Kluwer, Dordrecht, pp. 145-184. Hansen, P., Mladenovic, N., Perez-Brito, D. (2001). Variable neighborhood decomposition search. J. Heuristics 7.335-350. Hariga, M., Glock, C. H., and Kim, T. (2016). Integrated product and container inventory model for a single-vendor single-buyer supply chain with owned and rented returnable transport items. International Journal of Production Research, Vol. 54(7), pp. 1964-1979. Iassinovskaia, G., Limbourg, S., and Riane, F. (2017). The inventory-routing problem of returnable transport items with time windows and simultaneous pickup and delivery in closed-loop supply chains. International Journal of Production Economics, Vol. 183, pp. 570-582. Johansson, O. and Hellström D. (2007). The effect of asset visibility on managing returnable transport items. International Journal of Physical Distribution & Logistics Management. Vol. 37(10), pp.799 – 815. John ST, Sridharan R, Ram Kumar PN (2017) Multi-period reverse logistics network design with emission cost. The International Journal of Logistics Management, Vol. 28(1), pp. 127-149. Kelle, P. Silver, E.A. (1989). Purchasing policy of new containers considering the random returns of previously issued containers. IIE Transactions, Vol. 21 (4), pp. 349-354 Keyvanshokooh, E., Fattahi, M., Seyed-Hosseini, S. M., and Tavakkoli-Moghaddam, R. (2013). A dynamic pricing approach for returned products in integrated forward/reverse logistics network design. Applied Mathematical Modelling, Vol. 37(24), pp. 10182-10202. Kim, T., and Glock, C. H. (2014). On the use of RFID in the management of reusable containers in closed-loop supply chains under stochastic container return quantities. Transportation Research (Part E: Logistics and Transportation Review), Vol. 64, pp.12-27. Kim, T., Glock, C.H., Kwon, Y. (2014). A closed-loop supply chain for deteriorating products under stochastic container return times. Omega. Vol. 43, pp. 30–40. Kumar, V. N. S. A., Kumar, V., Brady, M., Garza-Reyes, J. A., and Simpson, M. (2017). Resolving forward-reverse logistics multi-period model using evolutionary algorithms. International Journal of Production Economics, Vol.183, pp. 458-469. Lambert S., Riopel D. (2003). Logistique inverse : revue de littérature. Les cahiers du GERAD G- 2003 – 61. Lambert, D. M. and Cooper, M.C. (2000). Issues in Supply Chain Management. Industrial Marketing Management, Vol. 29(1), pp. 65–83. Lin, C., Choy, K.L., Ho, G.T.S., Chung, S.H., Lam, H.Y. (2014). Survey of Green Vehicle Routing Problem: Past and future trends. Expert Systems with Applications. Vol. 41(4), Part 1, pp. 1118–1138. Martinez-Sala, A. S., Egea-Lopez, E., Garcia-Sanchez, F., Garcia-Haro, J. (2009). Tracking of Returnable Packaging and Transport Units with Active RFID in the Grocery Supply Chain. Computers in Industry. Vol. 60(3), pp. 161–171. Mensendiek, A. (2015): Scheduling with returnable containers. Journal of Scheduling, Vol. 18 (6), pp. 593-605. Moon, C., Kim, J., Hui S. (2002). Integrated process planning and scheduling with minimizing total tardiness in multi-plants supply chain. Computers and Industrial Engineering. Vol. 43, pp. 331–349. Palsson, H., Finnsgard, C., Wänström, C. (2013). Selection of packaging systems in supply chains from a sustainability perspective: The case of Volvo. Packaging Technology and Science, Vol. 26 (5), pp. 289-310. Park, Y. B. (2005). An integrated approach for production and distribution planning in supply chain management. International Journal of Production Research. Vol.43 (6). pp. 1205- 1224. Prive, J., Renaud, J., Boctor, F., Laporte, G. (2006). Solving a vehicle-routing problem arising in soft-drink distribution.  Journal of the Operational Research Society. Vol. 57, pp. 1045-1052. Puchinger, J. and Raidl, G.R. (2005). Combining metaheuristics and exact algorithms in combinatorial optimization: survey and classification. In Proceedings of the first international work-conference on the interplay between Natural and artificial computation, edited by J. Mira and J.R. Alvarez, Vol. 3562, pp. 41–53. RetelHelmrich, M. J., Jans, R., Heuvel, W. v. d., Wagelmans, A. P. M. (2014).The economic lot-sizing problem with an emission capacity Constraint. European Journal of Operational Research. Vol. 241(1), pp. 50-62. Rezaei, J. and Davoodi, M. (2011). Multi-objective models for lot-sizing with supplier selection. International Journal of Production Economics. Vol. 130(1), pp. 77–86. Rogers, D.S. and Tibben-Lembke, R.S. (1998). Going Backwards: Reverse Logistics Trends and Practices. University of Nevada.Reno Center for Logistics Management. Reverse Logistics Executive Council, pp. 101–15. Sarkar, B., Ullah, M., and Kim, N. (2017). Environmental and economic assessment of closed-loop supply chain with remanufacturing and returnable transport items. Computers & Industrial Engineering, Vol. 111, pp. 148-163. Sasikumar P. & Kannan G. (2009). Issues in reverse supply chain, part III: classification and simple analysis. International Journal of Sustainable Engineering, Vol. 2(1), pp. 2-27. Sasikumar, P. & Kannan, G. (2008a). Issues in reverse supply chains, part I: end-of-life product recovery and inventory management - an overview. International Journal of Sustainable Engineering, Vol. 1(3), pp.154-172. Sasikumar, P. & Kannan, G. (2008b). Issues in reverse supply chains, part II: reverse distribution issues – an overview. International Journal of Sustainable Engineering, Vol. 1(4), pp. 243-249. Sasikumar, P. & Kannan, G. (2008c). Issues in reverse supply chains, part III: Classification and simple analysis. International Journal of Sustainable Engineering, Vol. 2(1), pp. 2-27. Sheu, J.B., Chou, Y.H., Hu, C.C. (2005). An integrated logistics operational model for green-supply chain management. Transport. Res. (Part E: Logist. Transport. Rev). Vol. 41 (4), pp. 287–313. Silva, D. A. L., Renó, G. W. S., Sevegnani, G., Sevegnanic, T. B., Truzzib, O. M. S. (2013). Comparison of disposable and returnable packaging: a case study of reverse logistics in Brazil. Journal of Cleaner Production. Vol. 47, pp. 377–387 Singh, J., Shani, A. B., Femal, H., & Deif, A. (2016). Packaging’s Role in Sustainability: Reusable Plastic Containers in the Agricultural-Food Supply Chains. In Organizing Supply Chain Processes for Sustainable Innovation in the Agri-Food Industry (pp. 175-204). Emerald Group Publishing Limited. Soysal, M. (2016). Closed-loop Inventory Routing Problem for returnable transport items. Transportation Research (Part D: Transport and Environment), Vol. 48, pp. 31-45. Stock, J.R., (1992). Reverse Logistics, Council of Logistics Management, Oak Brook, IL. Supithak, W., Liman, S.D., Montes, E.J. (2010). Lot-sizing and scheduling problem with earliness tardiness and setup penalties. Computers & Industrial Engineering. Vol. 58(3), pp. 363-372. Tavana, M., Zareinejad, M., Di Caprio, D., and Kaviani, M. A. (2016a). An integrated intuitionistic fuzzy AHP and SWOT method for outsourcing reverse logistics. Applied Soft Computing, Vol. 40, pp. 544-557. Tavana, M., Zareinejad, M., Santos-Arteaga, F. J., and Kaviani, M. A. (2016b). A conceptual analytic network model for evaluating and selecting third-party reverse logistics providers. The International Journal of Advanced Manufacturing Technology, Vol. 86(5-8), pp. 1705-1721. Teunter, R.H., Bayindirand, Z.P., van den Heuvel, W. (2006). Dynamic lot sizing with product returns and remanufacturing. Int. J. Prod. Res. Vol. 44, pp. 4377–4400. Thoroe, L.; Melski, A.; Schumann, M. (2009). The impact of RFID on management of returnable containers. Electronic Markets, Vol. 19 (2-3), pp. 115-124. Toledo, C. F. M., Oliveira, R. R., França, P. M. (2013). A hybrid multi-population genetic algorithm applied to solve the multi-level capacitated lot sizing problem with backlogging. Computers & Operations Research, Vol. 40. pp. 910–919. Vidal, T., Crainic, T. G., Gendreau, M., Lahrichi, N., Rei, W. (2011). A Hybrid Genetic Algorithm for Multidepot and Periodic Vehicle Routing Problems. Operations Research, Vol. 60(3), pp. 611–624. Yang, T., Fu, C., Liu, X., Pei, J., Liu, L., and Pardalos, P. M. (2018). Closed-loop supply chain inventory management with recovery information of reusable containers. Journal of Combinatorial Optimization, Vol. 35(1), pp. 266-292. Zouadi, T., Yalaoui, A., Reghioui, M., and El Kadiri, K. E. (2016). Hybrid manufacturing/remanufacturing lot-sizing problem with returns supplier’s selection under, carbon emissions constraint. IFAC-Papers On Line, Vol. 49(12), pp. 1773-1778. Zouadi, T., Yalaoui, A., Reghioui, M., EL Kadiri, K. E. (2015). Lot-sizing for production planning in a recovery system with returns. RAIRO Operations Research. Vol 49(1), pp. 123-142.
IJSOM Kharazmi University International Journal of Supply and Operations Management 23831359 Kharazmi University 2744 10.22034/2017.4.02 optimization in supply chain management A Multi-period Multi-objective Location- routing Model for Relief Chain Management under Uncertainty A Multi-period Multi-objective Location- routing Model for Relief Chain Management under Uncertainty Saffarian Mohsen Faculty of Industrial Engineering, Birjand University of Technology, Birjand, Iran Barzinpour Farnaz Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran Kazemi Seyed Mahmood Faculty of Industrial Engineering, Birjand University of Technology, Birjand, Iran 01 11 2017 4 4 298 317 04 01 2018 28 04 2018 Copyright © 2017, Kharazmi University. 2017 http://www.ijsom.com/article_2744.html

Natural disasters, accidents, and crises, that cause widespread destruction and inflict heavy casualties, accentuate the importance of a careful planning to deal with the aftermath and mitigate their impacts responsively. Thus, the logistics of disaster relief is one of the main activities in disaster management. In this paper, the response phase of the disaster management cycle is considered and a multi-objective model for location and routing of vehicles is presented. Uncertainties in transfer time, demands of regional warehouses in the damaged areas and inventories at supply centers in different periods are taken into account. Three objectives are considered in this model. Two objectives consist of minimizing total time required to reach the damaged areas and maximizing satisfaction of the damaged areas. The third objective, which is of secondary importance, attempts to minimize total costs, including startup costs, transfer costs, and shortage costs. In order to convert the proposed multi-objective formulation to a single objective one, Global Criterion approach is applied. Afterwards, the obtained single objective model is solved using an efficient genetic algorithm and simulated annealing. Finally, a case study in Southern Khorasan is conducted and the applicability of the proposed model is examined.

Relief logistics Location-routing problem Cumulative vehicle routing Multi-objective optimization Uncertainty
Ahmadi, M., Seifi, A., Tootoni, B., (2015). A humanitarian logistics model for disaster relief operation considering network failure and standard relief time: A case study on San Francisco district. Transportation Research Part E: Logistics and Transportation Review, Vol. 45, pp. 145-163. Barbarosoglu, G., Arda, Y. (2004). A Two-Step Stochastic Programming Framework for Transportation Planning in Disaster Response. Journal of Operational Research Society, Vol. 55, pp. 43–53. Barbarosoglu, G., Ozdamar, L., Cevik, A. (2000). An Interactive Approach for Hierarchical Analysis of Helicopter Logistics in Disaster Relief Operations. European Journal of Operation Research, Vol. 140, pp. 118–133. Barzinpur, F., Saffarian, M., Makoui, A., Teimoury, E., (2014). Metaheuristic Algorithm for Solving Biobjective Possibility Planning Model of Location-Allocation in Disaster Relief Logistics. Journal of Applied Mathematics, ID: 239868. Beltrami, E.J., Bodin, L.D. (1974). Networks and Vehicle Routing for Municipal Waste collection. Networks, Vol. 4(1), pp. 65-94. Berkoune, D., Renaud, J., Rekik, M, Ruiz, A. (2012). Transportation in Disaster Response Operations. Socio-Economic Planning Sciences, Vol. 46, pp. 23-32. Chiappetta Jabbour, C. J., Sobreiro, V. A., Lopes de Sousa Jabbour, A. B., de Souza Campos, L. M., Mariano, E. B., & Renwick, D. W. S. (2017). An analysis of the literature on humanitarian logistics and supply chain management: paving the way for future studies. Annals of Operations Research. doi:10.1007/s10479-017-2536-x Christofides, N., Beasley, J.E. (1984). The Period Routing Problem. Networks, Vol. 14(2), pp. 237–256. Eshghi, K., Najafi, M. (2013). A Logistics Planning Model to Improve the Response Phase of Earthquake, International Journal of Industrial Engineering & Production Management, Vol. 23, pp. 401-416. Golabi, M., Shavarani, S. M., and Izbirak, G. (2017). An edge-based stochastic facility location problem in UAV-supported humanitarian relief logistics: a case study of Tehran earthquake. Natural Hazards, Vol. 87(3), pp. 1545-1565. Jabal-Ameli, M.S., Bozorgi-Amiri, A., Heydari, M., (2011). A Multi-Objective Possibilistic Programming Model for Relief Logistics Problem. International Journal of Industrial Engineering & Production Management, Vol. 22, pp. 65-75. Jha, A., Acharya, D., and Tiwari, M. K. (2017). Humanitarian relief supply chain: a multi-objective model and solution. Sādhanā, Vol. 42(7), pp. 1167-1174. John, L. (2018). Review of Empirical Studies in Humanitarian Supply Chain Management: Methodological Considerations, Recent Trends and Future Directions. In G. Kovács, K. Spens, and M. Moshtari (Eds.), The Palgrave Handbook of Humanitarian Logistics and Supply Chain Management (pp. 637-673). London: Palgrave Macmillan UK. Ke, L., Feng, Z. (2013). A two-phase metaheuristic for the cumulative capacitated vehicle routing problem. Computers & Operations Research, Vol. 40, pp. 633–638. Knott, R., (1988). Vehicle Routing for Emergency Relief Management: A Knowledge - Based Approach. Disaster, Vol. 12, pp. 285–293. Lin, Y.H, Batta, R., Rogerson, A.P. Blatt, A., Flanigan, M. (2011). A logistics model for emergency supply of critical items in the aftermath of a disaster, Socio-Economic Planning Sciences, Vol. 45, pp. 132-145 Ngueveu, S.U. , Prins, C., Calvo, R.W. (2010).  An effective memetic algorithm for the cumulative capacitated vehicle routing problem. Computers & Operations Research, Vol. 37, pp. 1877-1885. Nolz, P.C., Semet, F., Doerner, K.F. (2011). Risk approaches for delivering disaster relief supplies, OR Spectrum, Vol. 33, pp. 543–569. Oh, S., Haghani, A. (1996). Formulation and Solution of a Multi-Commodity, Multi-Modal Network Flow Model for Disaster Relief Operations. Transport. Res., Vol. 30, pp. 231–250. Ozdamar, L., Ekinci, E., Kucukyazici, B. (2004). Emergency Logistics Planning in Natural Disasters. Annals of Operations Research, Vol. 129, pp. 217–245. Pishvaee, M.S., Torabi, S.A. (2010). A Possibilistic Programming Approach for Closed-Loop Supply Chain Network Design under Uncertainty. Fuzzy Sets and Systems, Vol. 161(20), pp. 2668-2683. Rao, S.S. (1996). Engineering optimization: theory and practice, 3rd ed. John Wiley & Sons, New Jers. Rath, S., Gutjahr, W.J. (2014). A math-heuristic for the warehouse location–routing problem in disaster relief. Computers & Operations Research, Vol. 42, pp. 25-39. Saffarian, M., Barzinpour, F., Eghbali, M.A., (2015). A robust programming approach to bi-objective optimization model in the disaster relief logistics response phase, International Journal of Supply and Operations Management, Vol. 2(1), pp. 595-616. Tofighi, S., Torabi, S.A., ansouri, S.A., (2016). Humanitarian logistics network design under mixed uncertainty. European Journal of Operational Research, Vol. 250, pp. 239-250. Thomas, A.S., Kopczak, L.R. (2005). From logistics to supply chain management: the path forward in the humanitarian sector. http://www.fritzinstitute.org/PDFs /WhitePaper/ From Logisticsto.pdf. Uslu, A., Cetinkaya, C., & İŞLEYEN, S. K. (2017). Vehicle pouting problem in post-disaster humanitarian relief logistics: a case study in Ankara. Sigma Journal of Engineering and Natural Sciences-SIGMA MUHENDISLIK VE FEN BILIMLERI DERGISI, Vol. 35(3), pp. 481-499. Van Wassenhove, L.N. (2006). Humanitarian aid logistics: supply chain management in high gear. Journal of the Operational Research Society, Vol. 57, pp. 475–489. Van Wassenhove, L.N., Pedraza Martinez A.J. (2010). Using OR to adapt supply chain management best practices to humanitarian logistics. International Transactions in operational Research, Vol. 19, pp. 307-322.  Wang, H., Du, L., Ma, S., (2014). Multi-objective open location-routing model with split delivery for optimized relief distribution in post-earthquake. Transportation Research Part E: Logistics and Transportation Review, Vol. 69, pp. 160-179.
IJSOM Kharazmi University International Journal of Supply and Operations Management 23831359 Kharazmi University 2747 10.22034/2017.4.03 optimization in supply chain management Integrating DEA and Group AHP for Efficiency Evaluation and Identification of Most Efficient DMU Integrating DEA and Group AHP for Efficiency Evaluation and Identification of Most Efficient DMU Amini Amir Department of industrial engineering, Urmia university of technology, Urmia, Iran Alinezhad Alireza Faculty of Industrial and Mechanical Engineering, Qazvin branch, Islamic Azad University, Qazvin, Iran 01 11 2017 4 4 318 327 25 01 2018 07 05 2018 Copyright © 2017, Kharazmi University. 2017 http://www.ijsom.com/article_2747.html

Selection problems which contain many criteria are important and complex problems and different approaches have been proposed to fulfill this job. The Analytic Hierarchy Process (AHP) can be very useful in reaching a likely result which can satisfy the subjective opinion of a decision maker. On the other hand, the Data Envelopment Analysis (DEA) has been a popular method for measuring relative efficiency of decision making units (DMUs) and ranking them objectively with the quantitative data. In this paper, a Three-step procedure based on both DEA and AHP is formulated and applied to a case study. The procedure maintains the philosophy inherent in DEA by allowing each DMU to generate its own vector of weights. These vectors of weights are used to construct a group of pairwise comparison matrices which are perfectly consistent. Then, we utilize group AHP method to produce the best common weights which are compatible with the DMUs judgments. Using the proposed approach can give precise evaluation, combining the subjective opinion with the objective data of the relevant factors. The applicability of the proposed integrated model is illustrated using a real data set of a case study, which consists of 19 facility layout alternatives

DEA Group AHP Common weights Efficiency evaluation Most efficient DMU
Alonso, S., Pérez, I. J., Cabrerizo, F. J. and Herrera-Viedma, E. (2013). A linguistic consensus model for Web 2.0 communities. Applied Soft Computing, Vol. 13, pp. 149-157. Altuzarra, A., Moreno-Jimenez, J. M. and Salvador, M. (2010). Consensus Building in AHP-Group Decision Making: A Bayesian Approach, Operations Research, Vol. 58, pp. 1755-1773. Amini, A., Alinezhad, A. and Salmanian, S. (2016). Development of Data Envelopment Analysis for the Performance Evaluation of Green Supply Chain with Undesirable Outputs, International Journal of Supply and Operations Management, Vol. 3(2), pp. 1267-1283. Azadeh A., Ghaderi S. F. and Izadbakhsh, H. (2008). Integration of DEA and AHP with computer simulation for railway system improvement and optimization, Applied Mathematics and Computation, Vol. 195(2), pp. 755-785. Azadi, M., Jafarian, M., Farzipoor Saen, R. and Mirhedayatian, S.M. (2015). A new fuzzy DEA model for evaluation of efficiency and effectiveness of suppliers in sustainable supply chain management context, Computers & Operations Research, Vol. 54, pp. 274–285. Blagojevic, B., Srdjevic, B., Srdjevic, Z. and Zoranovic, T. (2015). Heuristic aggregation of individual judgments in AHP group decision making using simulated annealing algorithm, Information Sciences (Article in press). Bolloju, N. (2001). Aggregation of analytic hierarchy process models based on similarities in decision makers’ preferences, European Journal of Operational Research, Vol. 128, pp. 499-508. Brock, H. W. (1980). The Problem of" Utility Weights" in Group Preference Aggregation, Operations Research, Vol. 28, pp. 176-187. Cabrerizo, F. J., Moreno, J. M., Perez, I. J. and Herrera-Viedma, E. (2010). Analyzing consensus approaches in fuzzy group decision making: advantages and drawbacks, Soft Computing, Vol. 14, pp. 451-463. Charnes A., Cooper W.W. and Rhodes E. (1978). Measuring the efficiency of decision making units, European Journal of Operational Research, Vol. 2, pp. 429-444. Chiclana, F., Herrera, F. and Herrera-Viedma, E. (2001). Integrating multiplicative preference relations in a multipurpose decision-making model based on fuzzy preference relations, Fuzzy Sets and Systems, Vol. 122, pp. 277-291. Contreras I. (2011). A DEA-inspired procedure for the aggregation of preferences, Expert Systems with Applications, Vol. 38, pp. 564-570. Dobos, I. and Vörösmarty, G. (2018). Inventory-related costs in green supplier selection problems with Data Envelopment Analysis (DEA), International Journal of Production Economics (Article in press). Dong, Q. and Cooper, O. (2015). A peer-to-peer dynamic adaptive consensus reaching model for the group AHP decision making, European Journal of Operational Research (Article in press). Dyer, R. F. and Forman, E. H. (1992). Group decision support with the Analytic Hierarchy Process, Decision Support Systems, Vol. 8, pp. 99-124. Ertay T., Ruan D. and Tuzkaya U. R. (2006). Integrating data envelopment analysis and analytic hierarchy for the facility layout design in manufacturing systems, Information Sciences, Vol. 176, pp. 237–262. Forman, E. and Peniwati, K. (1998). Aggregating individual judgments and priorities with the analytic hierarchy process, European Journal of Operational Research, Vol. 108, pp. 165-169. Greco, S., Kadziński, M., Mousseau, V. and Słowiński, R. (2012). Robust ordinal regression for multiple criteria group decision: UTAGMS-GROUP and UTADISGMS-GROUP, Decision Support Systems, Vol. 52, pp. 549-561. HakimiAsl, M., Sadegh Amalnicka, M., Zorriassatineb, F. and HakimiAsl, A. (2016). Green Supplier Evaluation by Using an Integrated Fuzzy AHP- VIKOR Approach, International Journal of Supply and Operations Management, Vol. 3(2), pp. 1284-1300. Herrera, F., Herrera-Viedma, E. and Verdegay, J. L. (1995). A sequential selection process in group decision making with a linguistic assessment approach, Information Sciences, Vol. 85, pp. 223-239. Herrera, F., Herrera-Viedma, E. and verdegay, J. L. (1996). A model of consensus in group decision making under linguistic assessments, Fuzzy Sets and Systems, Vol. 78, pp. 73-87. Ho, W. and Ma, X. (2017). The state-of-the-art integrations and applications of the analytic hierarchy process, European Journal of Operational Research (Article in press). Ho, W. (2008). Integrated analytic hierarchy process and its applications – A literature review, European. Journal of. Operational. Research. Vol. 186, pp. 211–228. Huang, Y.-S., Chang, W.-C., Li, W.-H. and Lin, Z.-L. (2013). Aggregation of utility-based individual preferences for group decision-making, European Journal of Operational Research, Vol. 229, pp. 462-469. Keeney, R. L. and Kirkwood, C. W. (1975). Group Decision Making Using Cardinal Social Welfare Functions, Management Science, Vol. 22, pp. 430-437. Li, X., Liu, Y., Wang, Y. and Gao, Z. (2016). Evaluating transit operator efficiency: An enhanced DEA model with constrained fuzzy-AHP cones, Journal of traffic and transportation engineering, Vol. 3(3), pp. 215-225. Shang J. and Sueyoshi T. (1995). A Unified Framework for the Selection of a Flexible Manufacturing System, European Journal of Operational Research, Vol. 85(2), pp. 297-315. Sinuany-Stern Z., Mehrez A. and Hadad Y. (2000). An AHP/DEA methodology for ranking decision making units, International Transactions in Operational Research, Vol. 7, pp. 109-124. Ramanathan, R. & Ganesh, L.S. (1994). Group preference aggregation methods employed in AHP: An evaluation and an intrinsic process for deriving members’ weight ages, Eur. J. Oper. Res., Vol. 79, pp. 249–265. Saaty T.L. (1980). The Analytic Hierarchy Process, McGraw-Hill: New York. Saaty, T. L. (1994). Fundamentals of decision making and Priority Theory with The Analytic Hierarchy Process, RWS Publications, Pittsburgh. Saaty, T. L. (1977). A scaling method for priorities in hierarchical structures, Journal of Mathematical Psychology, Vol. 15, pp. 234-281. Tanino, T. (1984). Fuzzy preference orderings in group decision making, Fuzzy Sets and Systems, Vol. 12, pp. 117-131. Tseng F., Chiu Y. and Chen J. (2009). Measuring business performance in the high-tech manufacturing industry: A case study of Taiwan’s large-sized TFT-LCD panel companies, Omega, Vol. 37(3), pp. 686-697. Van Den Honert, R. C. and Lootsma, F. A. (1997). Group preference aggregation in the multiplicative AHP The model of the group decision process and Pareto optimality, European Journal of Operational Research, Vol. 96, pp. 363-370. Van den Honert, R. C. (2001). Decisional power in group decision making: A note on the allocation of group members' weights in the multiplicative AHP and SMART. Group Decision and Negotiation 10, 275-286. Information Sciences, Vol. 181, pp. 150-162. Wang , C. N., Nguyen, X. T. and Nguyen, X. H. (2015). Strategic Alliance Decision-making for the Auto Industry base on an Integrate DEA and GM (1,1) Approach, International Journal of Supply and Operations Management, Vol. 2, No. 3, pp. 856-870. Wang Y., Liu J. and Elhag T. (2008). An integrated AHP–DEA methodology for bridge risk assessment, Computers and Industrial Engineering, Vol. 54(3), pp. 513-525. Wu, Z. and Xu, J. (2012). Consensus reaching models of linguistic preference relations based on distance functions. Soft Computing, Vol. 16, pp. 577-589. Xu, Z. (2009). An automatic approach to reaching consensus in multiple attribute group decision making, Computers & Industrial Engineering, Vol. 56, pp. 1369-1374. Xu, Z. and Cai, X. (2011). Group consensus algorithms based on preference relations. Information Sciences, Vol. 181, pp. 150-162. Xu, Y., Li, K. W. and Wang, H. (2013). Distance-based consensus models for fuzzy and multiplicative preference relations, Information Sciences, Vol. 253, pp. 56-73. Yang T. and Kuo C.A. (2003). A hierarchical AHP/DEA methodology for the facilities layout design problem, European Journal of Operational Research, Vol. 147, pp. 128–136. Yousefi A. and Hadi-Vencheh A. (2010). An integrated group decision making model and its evaluation by DEA for DEA for automobile industry, Expert Systems with Applications, Vol. 37(12), pp. 8543-8556.
IJSOM Kharazmi University International Journal of Supply and Operations Management 23831359 Kharazmi University 2737 10.22034/2017.4.04 logistics, transportation, distribution, and materials Handling An Enhanced Evolutionary Local Search for the Split Delivery Vehicle Routing Problem An Enhanced Evolutionary Local Search for the Split Delivery Vehicle Routing Problem Larioui Sanae ENSATE, University of Abdelmalek Essaadi, Mhannech II, Tetouan, Morocco 01 11 2017 4 4 328 340 08 06 2016 09 04 2018 Copyright © 2017, Kharazmi University. 2017 http://www.ijsom.com/article_2737.html

We present a simple and effective metaheuristic algorithm for the Split Delivery Vehicle Routing Problem (SDVRP). The SDVRP is a relaxation of the classical Vehicle Routing Problem in which a customer demand may be serviced by more than one vehicle. The objective is to find a set of least cost trips for a fleet of identical vehicles to service geographically scattered customers with or without splitting. The proposed method is a hybridization between a Variable Neighborhood Search (VNS), an Evolutionary Local Search (ELS) and a Variable Neighborhood Descent (VND). It combines the multi-start approach of VNS and ELS and the VND intensification and diversification strategies. This new method is tested on three sets of instances from literature containing a total of 77 benchmark problems. The obtained results show that the algorithm outperforms all previously published metaheuristics. 62 instances out of 77 are improved.

Vehicle routing problem Split delivery Variable neighborhood search Evolutionary local search Variable neighborhood descent
B.E. Gillett and L.R. Miller (1974). A heuristic algorithm for the vehicle dispatch problem. Operations Research, Vol. 22, pp. 340–349. C. Archetti, M.W.P. Savelsbergh, and M.G. Speranza (2006). Worst-case analysis for split delivery vehicle routing problems. Transportation Science, Vol. 40(2), pp. 226–234. C. Archetti, M.G. Speranza, and A. Hertz (2006). A tabu search algorithm for the split delivery vehicle routing problem. Transportation Science, Vol. 40(1), pp. 64–73. C. Archetti, M.G. Speranza, and M.W.P. Savelsbergh. (2008) An optimization based heuristic for the split delivery vehicle routing problem. Transportation Science, Vol. 42(1), pp. 22–31. C. Gu´eguen (1999). Exact solution methods for vehicle routing problems. PhD thesis, Central School of Paris, France, (in French). C. Prins (2009). Bio-inspired Algorithms for the Vehicle Routing Problem, chapter A GRASP × evolutionary local search hybrid for the vehicle routing problem, pp. 35–53. Springer. E. Mota, V. Campos, and A. Corber´an (2007). A new metaheuristic for the vehicle routing problem with split demands. In C. Cotta and J. Van Hemert, editors, Evolutionary computation in combinatorial optimization, Lecture Notes in Computer Science, Vol. 4446, pp. 121–129, Berlin, Springer. G. Clarke and J.W Wright. (1964), Scheduling of vehicles from a central depot to a number of delivery points. Operations Research, Vol. 12, pp. 568–581. J.M. Belenguer, E. Benavent, N. Labadi, C. Prins, and M. Reghioui (2010). Split delivery capacitated arc-routing problem: Lower bound and metaheuristic. Transportation Science, Vol. 44(2), pp. 206–220. J.M. Belenguer, M.C. Martinez, and E. Mota (2000). A lower bound for the split delivery vehicle routing problem. Operations Research, Vol. 48(5), pp. 801–810. L. Moreno, M. Poggi de Arag˜ao, and E. Uchoa (2010). Improved lower bounds for the split delivery vehicle routing problem. Operations Research Letters, Vol. 38, pp. 302–306. M. Boudia, C. Prins, andM. Reghioui (2007). An effective memetic algorithm with population management for the split-delivery vehicle routing problem. In T. Bartz- Beielstein et al., editor, Hybrid Metaheuristics, Lecture Notes in Computer Science, Vol. 4771, pp. 16–30, Berlin. Springer. M. Dror and P. Trudeau (1989). Savings by split delivery routing. Transportation Science, Vol. 23(2), pp. 141–145. M. Dror and P. Trudeau (1990). Split delivery routing. Naval Research Logistics, Vol. 37, pp. 383–402. M. Dror, G. Laporte, and P. Trudeau (1994). Vehicle routing with split deliveries. Discrete Applied Mathematics, Vol. 50, pp. 239–254. M. Gendreau, P. Dejax, D. Feillet, and C. Gueguen (2006). Vehicle routing with time windows and split deliveries. Technical Report 2006–851, Laboratoire d’Informatique d’Avignon. M. Jin, K. Liu, and R. Bowden (2007). A two stage algorithm with valid inequalities for the split delivery vehicle routing problem. International Journal of Production Economics, Vol. 105, pp. 228–242. M. Jin, K. Liu, and B. Eksioglu (2008). A column generation approach for the split delivery vehicle routing problem. Operations Research Letters, Vol. 36, pp. 265–270. M. Reghioui. Vehicle routing problems with time windows or split deliveries. PhD thesis, University of Technology of Troyes, 2008 (in French). N. Mladenovi´c and P. Hansen (1997). Variable neighborhood search. Computers & Operations Research, Vol. 24(11), pp. 1097–1100. P.A. Mullaseril, M. Dror, and J. Leung (1997). Split-delivery routing heuristics in livestock feed distribution. Journal of the Operational Research Society, Vol. 48(2), pp. 107–116. P.W. Frizzell and J.W. Giffin( 1995). The split delivery vehicle scheduling problem with time windows and grid network distances. Computers and Operations Research, Vol. 22(6), pp. 655–667. S. Chen, B.L. Golden, and E. Wasil (2007). The split delivery vehicle routing problem: applications, algorithms, test problems and computational results. Networks, Vol. 49 (4), pp. 318–329. S.C. Ho and D. Haugland (2004). A tabu search heuristic for the vehicle routing problem with time windows and split deliveries. Computers and Operations Research, Vol. 31, pp.1947–1964. S. Wolf and P. Merz (2007). Evolutionary local search for the super-peer selection problem and the p-hub median problem. In T. Bartz-Beielstein et al., editor, Hybrid Metaheuristics, Lecture Notes in Computer Science, Vol. 4771, pp.  1–15, Berlin,. Springer. T.A. Feo and J. Bard (1989). Flight scheduling and maintenance base planning. Management Science, Vol. 35(12), pp. 1415–1432. R.E. Aleman, X. Zhang, and R.R. Hill (2010). An adaptive memory algorithm for the split delivery vehicle routing problem. Journal of Heuristics, Vol. 16 (3), pp. 441–473.
IJSOM Kharazmi University International Journal of Supply and Operations Management 23831359 Kharazmi University 2742 10.22034/2017.4.05 logistics, transportation, distribution, and materials Handling A Library Review Study: Conceptual Model for Maritime Inventory-Routing Problem A Library Review Study: Conceptual Model for Maritime Inventory-Routing Problem Nazemi Ali Faculty of Economics, Kharazmi University, Tehran, Iran Sheikhtajian Sanaz Faculty of Economics, Kharazmi University, Tehran, Iran Feshari Majid Faculty of Economics, Kharazmi University, Tehran, Iran 01 11 2017 4 4 341 358 02 01 2018 22 04 2018 Copyright © 2017, Kharazmi University. 2017 http://www.ijsom.com/article_2742.html

The main focus of this paper is presenting a conceptual structure for Maritime Inventory-Routing Problem. We have looked at the matter from the supply chain angel and summed up the main comprising elements and major hypotheses in the framework of a conceptual model. We have surveyed in details the related literature in a classified manner, separated various issues and eventually in accordance with the identified vacuums, presented the grounds for development in the same particular framework. What we deal with in this article is in fact the zero level of the for Maritime Inventory-Routing Problem while for focusing on higher levels it is possible to deal with greater details by providing arithmetic models on more comprehensive navigation of naval goods in a more compact and sold manner. According to this review most of researches are deterministic at the tactical level, on the basis of discreet time and in the arc-flow framework, generally solved by exact or heuristic methods.

Maritime inventory-routing problem Conceptual model Supply chain
Arijit Dea, Sri Krishna Kumara, Angappa Gunasekaranb and Manoj Kumar Tiwaria (2017), Sustainable maritime inventory routing problem with time window, Engineering Applications of Artificial Intelligence, Vol. 61, pp. 77–95 Agra, A., Christiansen, M., Hvattum, L. and Rodrigues, F. (2016). A MIP Based Local Search Heuristic for a Stochastic Maritime Inventory Routing Problem. Springer International Publishing Switzerland, pp. 18-34 Agra, A., Christiansen, M., Delgado, A. and Hvattum, L. M. (2015). A maritime inventory routing problem with stochastic sailing and port times. Computer and operations research. Vol. 61, pp. 18–30. Agra, A., Andersson, H., Christiansen, M., and Wolsey, L. (2013a). A maritime inventory routing problem: Discrete time formulations and valid inequalities. Networks, Vol. 62(4), pp. 297–314. Agra, A., Christiansen, M., and Delgado, A. (2013b). Mixed integer formulations for a short sea fuel oil distribution problem. Transportation Science, Vol. 47(1), pp. 108–124 Agra, A., Christiansen, M., Figueiredo, R., Hvattum, L.M., Poss, M. and Requejo, C. (2013c). The robust vehicle routing problem with time windows. Computer and operations research. Vol.  40, pp. 856–866. Al-Khayyal, F. and Hwang, S. (2007). Inventory constrained maritime routing and scheduling for multi-commodity liquid bulk, part i: applications and model. European Journal of Operational Research, Vol. 176, pp. 106–130. Al-Haidous, S., Msakni,M. and Haouari, M.(2016), Optimal planning of liquefied natural gas deliveries, Transportation Research Part C, Vol. 69, pp. 79 –90 Alvarez, J. F., Tsilingiris, P., Engebrethsen, E., and Kakalis, N. M. P. (2011). Robust flee sizing and deployment for industrial and independent bulk ocean shipping companies. INFOR, Vol. 49(2), pp. 93–107. Anderson, H., Christiansen, M., and Fagerholt, K. (2010). Transportation planning and inventory management in the LNG supply chain. In Bjørndal, E., Pardolos, P. M., and Ronnqvist, M., editors, Energy, Natural Resources and Environmental Economics. Springer, Berlin. Andersson, H., Christiansen, M., and Desaulniers, G. (2015a). A new decomposition algorithm for a liquefied natural gas inventory routing problem. International Journal of Production Research, Accepted 29 Jan 2015, Published online: 07 May 2015. Andersson, H., Christiansen, M., and Desaulniers, G. and Rakke, J., (2015b). Creating annual delivery programs of liquefied naturalgas, Springer Science+Business Media New York, Published online. Barnhart, C., Laporte, G. (Eds.), Handbook in OR & MS, Vol. 14, 2007 Elsevier. Chengliang Zhang, George Nemhauser, Joel Soko lexible (2017). Flexible Solutions to Maritime Inventory Routing Problems with Delivery Time Windows, Computers and Operations Research, Vol. 89, pp. 153-162. Cheng, L. and Duran, M.A. (2004). Logistics for world-wide crude oil transportation using discrete event simulation and optimal control. Comput. Chem. Eng. Vol. 28, pp. 897–911. Christiansen, M. (1999). Decomposition of a combined inventory and time constrained ship routing problem. Transportation Science, Vol. 33, pp. 3–26. Christiansen, M. and Fagerholt, K. (2002). Robust ship scheduling with multiple time windows. Naval Res. Logistics, Vol. 49, pp. 611–625 Christiansen, M. and Fagerholt, K. (2009). Maritime inventory routing problems. In Floudas, C. A. and Pardalos, P., editors, Encyclopedia of Optimization, pp. 1947–1955. Springer. Christiansen, M., Fagerholt, K., Flatberg, T., Haugen, Ø., Kloster, O., and Lund, E. H. (2011). Maritime inventory routing with multiple products: A case study from the cement industry. European Journal of Operational Research, Vol. 208(1), pp. 86–94. Christiansen, M., Fagerholt, K., Nygreen, B., and Ronen, D. (2013). Ship routing and scheduling in the new millennium. European Journal of Operational Research, Vol. 228(3), pp. 467–483. Christiansen, M., Fagerholt, K., and Ronen, D. (2004). Ship routing and scheduling: Status and perspectives. Transportation Science, Vol. 38, pp. 1–18. Coelho, L. C., Cordeau, J.-F., and Laporte, G. (2013). Thirty years of inventory routing. Transportation Science. (In press). Dung-Ying Lin and Yu-Ting Chang, (2018). Ship routing and freight assignment problem for liner shippiApplication to the Northern Sea Route planning problem, Transportation Research Part E, Vol. 110, pp. 47-70 Popović, D., Bjelić, N. and Radivojević, G., (2011), Simulation Approach to Analyse Deterministic IRP Solution of the Stochastic Fuel Delivery Problem, Procedia Social and Behavioral Sciences, Vol. 20, pp. 273–282 Engineer, F. G., Furman, K. C., Nemhauser, G. L., Savelsbergh, M. W. P., and Song, J. (2012). A branch-price-and-cut algorithm for single product maritime inventory routing. Operations Research, Vol. 60, pp. 106–122. Ethan Malinowskia, and Mark H. Karwana, José M. Pintob, Lei Sunc, (2018). A mixed-integer programming strategy for liquid helium global, Transportation Research Part E, Vol. 110, pp. 168–188. Fodstad, M., Uggen, K. T., Rّmo, F., Lium, A., and Stremersch, G. (2010). LNGScheduler: A rich model for coordinating vessel routing, inventories and trade in the liquefied natural gas supply chain. Journal of Energy Markets, Vol. 3(4), pp. 31–64. Furman, K. C., Song, J.-H., Kocis, G. R., McDonald, M. K., and Warrick, P. H. (2011). Feedstock routing in the exxonmobil downstream sector. Interfaces, Vol. 41(2), pp. 149–163. Ghiami, Y., Woensel, T., Christiansen, M., and Laporte, G.,(2015), A Combined Liquefied Natural Gas Routing and Deteriorating Inventory Management Problem, Springer International Publishing Switzerland 2015, F. Corman et al. (Eds.): ICCL 2015, LNCS. Goel, V., Furman, K. C., Song, J.-H., and El-Bakry, A. S. (2012). Large neighborhood search for lng inventory routing. Journal of Heuristics, Vol. 18(6), pp. 821– 848. Goel, V., Slusky, M., van Hoeve, W.-J., Furman, K., and Shao, Y. (2015). Constraint programming for lng ship scheduling and inventory management. European Journal of Operational Research, Vol. 241(3), pp. 662–673. Grønhaug, R., Christiansen, M., Desaulniers, G., and Desrosiers, J. (2010). A branch-and-price method for a liquefied natural gas inventory routing problem. Transportation Science, Vol. 44(3), pp. 400–415. Halvorsen-Weare, E. E. and Fagerholt, K. (2013). Routing and scheduling in a liquefied natural gas shipping problem with inventory and berth constraints. Annals of Operations Research, Vol. 203(1), pp. 167–186. Halvorsen-Weare, E. E., Fagerholt, K.: Robust supply vessel planning. In: Pahl, J., Reiners, T., Voک, S. (eds.) INOC 2011. LNCS, Vol. 6701, pp. 559–573. Springer, Heidelberg (2011). Hemmati, A., Hvattum, L., Christiansen, M. and Laporte, G., (2016), An iterative two-phase hybrid matheuristic for a multi-product short sea inventory-routing problem, European Journal of Operational Research, Vol. 252, pp. 775–788. Hemmati , A., Stålhane, M., Hvattum, L. and  Andersson, H., (2015), An effective heuristic for solving a combined cargo and inventory routing problem in tramp shipping, Computers & Operations Research, Vol. 64, pp. 274-282. Hewitt, M., Nemhauser, G. L., Savelsbergh, M., and Song, J. (2013). A branch and-price guided search approach to maritime inventory routing. Computers and Operations Research, Vol. 40, pp. 1410–1419. Hoff, A., Andersson, H., Christiansen, M., Hasle, G., and Lّkketangen, A. (2010).Industrial aspects and literature survey: Combined inventory management and routing. Computers & Operations Research, Vol. 37(9), pp. 1515–1536. Li, J., Karimi, I., & Srinivasan, R. (2010). Efficient bulk maritime logistics for the supply and delivery of multiple chemicals. Computers & Chemical Engineering, Vol. 34(12), pp. 2118–2128 Meredith J. (1993). Theory building through conceptual methods. International Journal of Operations & Production Management, Vol. 13(5), pp. 3–11. Mutlu, F., Msakni, M., Yildiz, H., Sonmez, E., Pokhare, S,. A Comprehensive Annual Delivery Program for Upstream Liquefied Natural Gas Supply Chain, Journal of Operational Research, Vol. 250, pp. 120- 130. Nikhalat-Jahromi, N., Bell, G., Fontes, D., Cochrane, R. and Angeloudis, P,. (2016), Spot sale of uncommitted LNG from Middle East: Japan or the UK?, Energy Policy, Vol. 96, pp. 717–725. Papageorgiou, D. J., Cheon, M.-S., Nemhauser, G., and Sokol, J. (2014a). Approximate dynamic programming for a class of long-horizon maritime inventory routing problems. Transportation Science. Vol. 49, pp. 850- 885. Papageorgiou, D. J., Keha, A. B., Nemhauser, G. L., and Sokol, J. (2014b). Two-stage decomposition algorithms for single product maritime inventory routing. INFORMS Journal on Computing, Vol. 26(4), pp. 825–847. Papageorgiou, D. J., Nemhauser, G. L., Sokol, J., Cheon, M.-S., and Keha, A. B. (2014c). Mirplib–a library of maritime inventory routing problem instances: Survey, core model, and benchmark results. European Journal of Operational Research, Vol. 235(2), pp. 350–366. Persson, J. A. and Gothe-Lundgren, M. (2005). Shipment planning at oil refineries using column generation and valid inequalities. European Journal of Operational Research, Vol. 163, pp. 631–652. Rakke, J. G., Andersson, H., Christiansen, M., and Desaulniers, G. (2014). A new formulation based on customer delivery patterns for a maritime inventory routing problem. Transportation Science, Vol. 49(2), pp. 384–401. Rakke, J. G., Stalhane, M., Moe, C. R., Christiansen, M., Andersson, H., Fagerholt, K., and Norstad, I. (2011). A rolling horizon heuristic for creating a liquefied natural gas annual delivery program. Transportation Research Part C, Vol. 19, pp. 896–911. Rocha, R., Grossmann, I. E., and de Aragمo, M. V. P. (2013). Cascading knapsack inequalities: reformulation of a crude oil distribution problem. Annals of Operations Research, Vol. 203(1), pp. 1–18. Ronen, D. et al. (2002). Marine inventory routing: Shipments planning. Journal of the Operational Research Society, Vol. 53(1), pp. 108–114. Seuring, S. and Muller, M., (2008). From a literature review to a conceptual framework for sustainable supply chain management, Journal of Cleaner Production, Vol. 16, pp. 1699–1710. Shao, Y., Furman, K. C., Goel, V., and Hoda, S. (2015). A hybrid heuristic strategy for liquefied natural gas inventory routing. Transportation Research Part C: Emerging Technologies, Vol. 53, pp. 151–171. Shen, Q., Chu, F., and Chen, H. (2011). A Lagrangian relaxation approach for a multimode inventory routing problem with transshipment in crude oil transportation. Computers & Chemical Engineering, Vol. 35(10), pp. 2113–2123. Shyshou, A., Gribkovskaia, I., & Barcelَ, J. (2010). A simulation study of the fleetsizing problem arising in offshore anchor handling operations. European Journal of Operational Research, Vol. 203(1), pp. 230–240. Siswanto, N., Essam, D., and Sarker, R. (2011). Multi-heuristics based genetic algorithm for solving maritime inventory routing problem. In Industrial Engineering and Engineering Management (IEEM), 2011 IEEE International Conference on, pp. 116–120. IEEE. St¨alhane, M., Rakke, J. G., Moe, C. R., Andersson, H., Christiansen, M., and Fagerholt, K. (2012). A construction and improvement heuristic for an lng inventory routing problem. Computers & Industrial Engineering, Vol. 62, pp. 245– 255. Song, J. and Furman, K. C. (2013). A maritime inventory routing problem: Practical approach. Computers and Operations Research, Vol. 40, pp. 657–665. SteadieSeifi, M.,Dellaert,N.P., Nuijten,W., Woensel, T. and Raoufi, R.,(2014), Multimodal freight transportation planning: A literature review, European Journal of Operational Research, Vol. 233, pp. 1–15. Uggen, K., Fodstad, M., and Nørstebø, V. (2013). Using and extending fix-and-relax to solve maritime inventory routing problems. TOP, Vol. 21(2), pp. 355–377. Yongheng, J. and Grossmann, I. E. (2015). Alternative mixed-integer linear programming models of a maritime inventory routing problem. Computers & Chemical Engineering, Vol. 77, pp. 147–161. European Commission. European transport policy for 2010: time to decide. White Paper. Luxembourg: Office for Official Publications of the European Communities; 2001. https://ec.europa.eu/transport/themes/strategies/2001_white_paper_en. UNCTAD (2012). Review of maritime transport. United Nations, New York and Geneva.
IJSOM Kharazmi University International Journal of Supply and Operations Management 23831359 Kharazmi University 2745 10.22034/2017.4.06 optimization in supply chain management Supplier and Carrier Selection and Order Allocation by considering disruptions with AHP and Multi-objective Mathematical Programming Supplier and Carrier Selection and Order Allocation by considering disruptions with AHP and Multi-objective Mathematical Programming Hasannia Kolaee Mansoureh School of Industrial and Systems Engineering, College of Engineering, University of Tehran, Tehran, Iran Torabi Seyed Ali School of Industrial and Systems Engineering, College of Engineering, University of Tehran, Tehran, Iran 01 11 2017 4 4 359 369 06 01 2017 28 04 2018 Copyright © 2017, Kharazmi University. 2017 http://www.ijsom.com/article_2745.html

In this study, supplier and carrier selection and order allocation are considered to go for joint decision-making. To this end, some criteria including cost, quality, delivery, resilience, social responsibility and supplier profile are determined to select the weight of each supplier by AHP. Then, multi objective mathematical model is developed to select the appropriate supplier and carrier and allocate orders of multi product with different discount levels in each period by suitable carriers to each supplier. The objective functions in this study are minimizing cost, delivery and the number of defective items, and maximizing the efficiency of suppliers. Moreover, some constraints such as inventory capacity, shortage capacity, number of vehicle and breakdowns of them and etc. are applied in this novel mathematical model. Finally, this model is solved by augmented ɛ-constraint method for determining pareto-optimal solution. This study can help decision makers to solve problem by integrating AHP and multi-objective model.

Supplier and carrier selection Order allocation Resilience Social responsibility Price discount Breakdown
Basnet, C., Leung, J.M.Y., (2005). Inventory lot-sizing with supplier selection, Computer and Operations Research, Vol. 32, pp. 1–14. Bevilacqua, M., Ciarapica, F.E., Giacchetta, G., (2006). A fuzzy-QFD approach to supplier selection, Journal of Purchasing & Supply Management, Vol. 12, pp. 14–27. Chen, C.T., Lin, C.T., Huang, S.F., (2006). A fuzzy approach for supplier evaluation and selection in supply chain management, International Journal of Production Economy, Vol. 102, pp. 289–301. Choudhary, D., Shankar, R., (2011). Modeling and analysis of single item multiperiod procurement lot-sizing problem considering rejections and late deliveries, Computers & Industrial Engineering, Vol. 61, pp. 1318–1326. Demirtas, E.A., Ustun, O., (2009). Analytic network process and multi-period goal programming integration in purchasing decisions, Computers & Industrial Engineering, Vol. 56(2), pp. 677–690. Dickson, G., (1966). An analysis of vendor selection systems and decisions, Journal of Purchasing, Vol. 2, pp. 28–41. Ha, B., Park, Y., Cho, S., (2011). Suppliers’ affective trust and trust in competency in buyer, International Journal of Operation Production Management, Vol. 31, pp. 56–77. Haimes, Y.Y., Wismer, D.A., Lasdon, D.S., (1971). On bi-criterion formulation of the integrated systems identification and system optimization, IEEE Transaction System Manufacturing Cybern SMC, Vol. 1, pp. 296–297. Hollnagel, E., (2006). Resilience: the challenge of the unstable. In: Hollnagel, E., Woods, D., Leveson, N. (Eds.), Resilience Engineering: Concepts and Precepts. Ashgate, London, pp. 8–17. Kannan, V.R., Tan, K.C., (2002). Supplier selection and assessment: Their impact on business performance, Journal of Supply Chain Management, Vol. 38, pp. 11–21. Lee, A.H.I., Kang, H.Y., Lai, C.M., Hong, W.Y., (2013). An integrated model for lot sizing with supplier selection and quantity discounts, Applied Mathematical Modelling, Vol. 37, pp. 4733–4746. Liao, Z., Rittscher, J., (2007). Integration of supplier selection, procurement lot sizing and carrier selection under dynamic demand conditions, International Journal of Production Economics, Vol. 107, pp. 502–510. Mafakheri, F., Breton, M., Ghoniem, A., (2011). Supplier selection-order allocation: a two stage multiple criteria dynamic programming approach, International Journal of Production Economics, Vol. 132, pp. 52-57. Mansini, R., Tocchella, B., Savelsbergh, M., (2012). The supplier selection problem with quantity discounts and truck load shipping, Omega, Vol. 40, pp. 445–455. Razmi, J., Rafiei, H., 2010. An integrated analytic network process with mixed integer non-linear programming to supplier selection and order allocation, International Journal of Advance Manufacturing Technology, Vol. 49, pp. 1195–1208. Rezaei, J., Davoodi, M., (2008). A deterministic, multi-item inventory model with supplier selection and imperfect quality, Applied Mathematical Modelling, Vol. 32, pp. 2106–2116. Rezaei, J., Davoodi, M., (2011). Multi objective models for lot-sizing with supplier selection, International Journal of Production Economics, Vol. 130(1), pp. 77–86. Simchi-Levi, D., Kaminsky, P., Simchi-Levi, E., (2003). Designing and Managing the Supply Chain: Concepts, Strategies, and Case Studies, 2nd ed., McGraw-Hill, Boston, MA.  Thrulogachantar, P., Zailani, S., (2011). The influence of purchasing strategies on manufacturing performance, Journal of Manufacture Technology Management, Vol. 22, pp. 641–663. Ustun, O., Demirtas, E.A., (2008a). An integrated multi-objective decision-making process for multi-period lot-sizing with supplier selection, Omega, Vol. 36, pp. 509–521. Ustun, O., Demirtas, E.A., (2008b). Multi-period lot-sizing with supplier selection using achievement scalarizing functions, Computers & Industrial Engineering, Vol. 54(4), pp. 918–931. Wilson, E.J., (1994). The relative importance of supplier selection criteria: A review and update, International Journal of Purchasing and Materials Management, Vol. 30, pp. 34–41.  Zhang, W.J., Lin, Y., (2010). On the principle of design of resilient systems—application to enterprise information systems, Enterprise Information Systems, Vol. 4, pp. 99–110.
IJSOM Kharazmi University International Journal of Supply and Operations Management 23831359 Kharazmi University 2741 10.22034/2017.4.07 logistics, transportation, distribution, and materials Handling Behavioural Aspects of Supply Chain Management: Strategy, Commitment, Integration and Firm Performance – A Conceptual Framework Behavioural Aspects of Supply Chain Management: Strategy, Commitment, Integration and Firm Performance – A Conceptual Framework Patel Hiren V M Patel Institute of Management Ganpat University, Gujarat, India 01 11 2017 4 4 370 375 17 01 2017 22 04 2018 Copyright © 2017, Kharazmi University. 2017 http://www.ijsom.com/article_2741.html

Supply Chain Management is one of the key areas in the field of operations management to improve the performance of the firm. This study proposes a model with four constructs named as (Supply Chain Management Strategy, Supply Chain Management Commitment, Supply Chain Management Integration and Firm Performance) into it. The proposed model is unique in the way of providing four constructs simultaneously with its possible inter-relationships. Total five Inter-relationships are offered in the form of propositions for the said model. Previous studies in the field of Supply Chain Management are failed in offering all the four constructs and its relationship simultaneously. This model lay down new foundation in the field of Supply Chain Management which helps practitioner and academician to do further research on it.

Supply chain management strategy Supply chain management commitment Supply chain management integration Firm performance
Allen, N. and Meyer, J. (1990). The measurement and antecedents of affective, continuance and normative commitment to the organization.  Journal of Occupational Psychology. Vol. 63. pp.1-8. Altekar, Rahul V. (2005). Supply Chain Management: concept and cases. PHI, New Delhi. p. 101. Andaleeb, S. (1996). An experimental investigation of satisfaction and commitment and marketing channels: the role of trust and dependence. Journal of Retailing. Vol.72, pp.71-93. Anderson, E. and Weitz, B. (1992), The use of pledge to build and sustain commitment Distribution channels. Journal of Marketing Research. Vol.29, pp.18-34. Claycomb,C., Dro ¨ge, D. and Germain, R. (1999). The effect of just-in-time with customers on organizational design and performance. International Journal of Logistics Management .Vol.10 (1), pp.37-58. Davenport, T. (1993).  Process Innovation, Reengineering Work through Information Technology, Harvard Business School Press, Boston, MA. D’Avanzo, R. L., Starr, C. E. and vonLewinski, H. (2004). Supply chain and the bottom line: a critical link”, Outlook: Accenture, Vol.1, pp. 39-45. Frohlich, M. T., & Westbrook, R. (2001). Arcs of integration: An international study of supply chain strategies. Journal of Operations Management. Vol. 19 (2), pp. 185-200. Gimenez, Cristina and Eva Ventura (2005). Logistics-Production., Logistics-Marketing and External Integration: Their Impact on Performance. International Journal of Operations & Production Management. Vol. 25(1), pp. 20-38.  Goodman, L. E. and Dion, P. A.  (2001). The determinants of commitment in the distributor-manufacturer relationship. Industrial Marketing Management, Vol.30, pp. 287-300. Gossman, John (1997). Presentation to supply chain management council meeting, Kellog center, Michigan state University. Green Jr Kenneth W.  McGaughey Ron and K. Casey Michael (2006). Does supply chain management strategy mediate the association between market orientation and organizational performance? Supply Chain Management:  An International Journal, Vol.11 (5), pp. 407–414. Il Ryu, SoonHu So, Chulmo Koo. (2009). The role of partnership in supply chain performance. Industrial Management and Data Systems. Vol. 109 (4), pp. 496-514. Kannan, V. R., Tan, K. C., (2010). Supply chain integration: cluster analysis of the impact of   Span of integration. Supply Chain Management: An International Journal, Vol. 15(3), pp. 207-15. Kim, S. W., (2006). Effects of Supply Chain Management Practices, Integration and    Competitive Capability on Performance. Supply Chain Management: An International Journal, Vol.11 (3), pp. 241-48. Knowles Graeme, whicker Linda, Javier Heraldez Fema and Francisco del campo Canales (2005). A conceptual model for the application of six sigma methodologies to supply chain improvement. International journal of logistics: research and applications, Vol. 8(1), pp. 51–65. Kohli, A. K. and Jaworski, B. J. (1990). Market orientation: the construct, research propositions, and managerial implications. Journal of Marketing. Vol. 54(2), pp.1-18. Lambert, D., Copper, M. and Pagh, J. (1998). Supply chain management: implementation issues and research opportunities. The International Journal of Logistics Management. Vol.9(2), pp.1-18. Lambert D M and Cooper M C (2000), “Issues in Supply Chain Management”, Industrial Marketing Management, Vol. 29, pp. 65-83. Lee,H.L.(2000), Creating value through supply chain integration. Supply Chain Management Review. Vol.4. pp.30-60. Lehtinen ,J. and Ahola, T.(2009). Is performance measurement suitable for an extended enterprise?”, International Journal of Operations & Production Management, Vol.30 (2), pp.181-204. Leuschner, Rudolf., Rogers Dale S., and Charvet, Francois F (2013). A meta-analysis of supply chain integration and firm performance. Journal of Supply Chain Management. Vol.49 (2), pp. 34-57. Lummus, R. R,, Duclos, L. K. and Vokurka, R. J. (2003). The Impact of Marketing Initiatives on the Supply Chain. Supply Chain Management: An International Journal, Vol.8 (3 and 4), pp. 317-323. Morgan, J. (1997). Integrated supply chains: how to make them work!. Purchasing. May 22, pp. 32. Morgan, R.M. and Hunt, S.D. (1994).The commitment-trust theory of relationship marketing. Journal of Marketing, Vol. 58(3), pp. 20-38. Ntayi, J. M. and S. Eyaa, (2010). Collaborative Relationships, Procurement Practices and Supply Chain Performance: The Case of Small and Medium Enterprises in Uganda. Otchere, A. F., Annan, J. & Anin, E. K., (2013) Achieving Competitive Advantage through Supply Chain Integration in the Cocoa Industry: A Case Study of Olam Ghana Limited and Produce Buying Company Limited.  International Journal of Business and Social Research (IJBSR). Vol. 3(2), 131-145 Rainbird M (2004). Demand and Supply Chains: The Value Catalyst. International Journal of Physical Distribution & Logistics Management, Vol. 34(3 and 4), pp. 230-250. Roh James Jungbae, Hong Paul and Park Youngsoo (2008). Organizational culture and supply chain strategy: a framework for effective information flows. Journal of Enterprise Information Management, Vol. 21(4), pp. 361-376. Sanders, Nada R. and Robert Premus (2005). Modeling the relationship between IT capability, Collaboration and Performance. Journal of Businesws Logistics. Vol 26(1), pp.1-24. Salam, Asif Mohammad (2011). Supply chain commitment and business process integration. European Journal of Marketing, Vol. 45(3), pp. 358-382. Shah, Janat (2009). Supply Chain Management: Text and Cases. Supply Chain Integration. Dorling Kindersley (India) Pvt. Ltd. pp. 213-238. Spekman, R., D.Salmond and J. Kamauff (1994). At Last Procurement Becomes Strategic. Long- Range Planning, Vol. 27(2), pp. 76-84. Stank, Theodore P., Scott B. Keller and David J. Closs (2001). Supply Chain Collaboration and Logistical Integration. Transportation Journal. Vol 41(2/3), pp. 32-46. Stevens, G., (1989). Integrating the supply chain.  International Journal of Physical Distribution and Materials Management, Vol. 19(8), 3-8. vanHoek,R.I.(2001).The contribution of performance measurement to the expansion of third party logistics alliances in the supply chain”, International Journal of Operations and Production Management. Vol. 21 (1/2), pp.15-29. Vickery, Shawnee, Calantone, Roger and Droge, Cornelia (1999). Supply Chain Flexibility: An empirical study. The Journal of Supply Chain Management, Vol. 35 (3), pp. 16-23. Wisner, J.D. (2003). A structural equation model of supply chain management strategies and firm performance. Journal of Business Logistics, Vol. 24 (1), pp. 1-26. Wu, Wann-Yih; Chiang, Chwan-Yi; Ya-Fung, Wu; Hui-Fu Tu (2004). The influencing factors of commitment and business integration on supply chain management. Industrial Management and Data Systems; Vol. 104, ¾. pp. 322-333. Wu, Mei-Ying., Yung-ChienWeng and I-Chiao Huang (2012). A study of supply chain Partnerships based on the commitment – trust theory. Asia Pacific Journal of Marketing and Logistics Vol.24 No.4. pp. 690-707. Zelbst Pamela J., Green Jr Kenneth W, Sower Victor E. and Baker Gary (2010). RFID utilization and information sharing: the impact on supply chain performance. Journal of Business & Industrial Marketing, Vol. 25 (8), pp. 582–589.