Supplier selection and order allocation using two-stage hybrid supply chain model and game-based order price

Achieving an efficient supply chain is impossible without integrating supply chain processes and extending long-term relationships between its members. Evaluating the process, selecting a set of suppliers, and allocating orders are effective parameters in the coordination among supply chain members. In this study, to achieve an organized process, a two-stage hybrid model is presented to choose efficient suppliers, allocate order, and determine price in a supply chain with regard to coordination among members. First, an integrated Multi-Objective Mixed-Integer Nonlinear Programming (MOMINLP) model is provided to minimize costs and evaluate suppliers simultaneously. The proposed model includes a single-buyer multi-vendor coordination model and Data Envelopment Analysis (DEA). Then, the model is simplified and converted into a quadratic programming model. In the second stage, a model is presented to determine the price agreed upon by the buyer and the selected efficient suppliers using the bargaining game and the Nash equilibrium concept. The purpose of this model is to maximize the parties’ utilities considering the order quantity specified in the first stage. At the end of this paper, the data taken and adapted from the previous researches are applied to show the abilities of the proposed models.

[1]  Taebok Kim,et al.  Integrated product and container inventory model for a single-vendor single-buyer supply chain with owned and rented returnable transport items , 2016 .

[2]  Abraham Mendoza,et al.  Modeling actual transportation costs in supplier selection and order quantity allocation decisions , 2013, Oper. Res..

[3]  Wei Xiang,et al.  Order allocation for multiple supply-demand networks within a cluster , 2014, J. Intell. Manuf..

[4]  Yuh-Jen Chen,et al.  Structured methodology for supplier selection and evaluation in a supply chain , 2011, Inf. Sci..

[5]  Narges Banaeian,et al.  Green supplier selection using fuzzy group decision making methods: A case study from the agri-food industry , 2018, Comput. Oper. Res..

[6]  Gary W. Dickson,et al.  AN ANALYSIS OF VENDOR SELECTION SYSTEMS AND DECISIONS , 1966 .

[7]  Hadi Mokhtari,et al.  A single-supplier, multi-buyer, multi-product VMI production-inventory system under partial backordering , 2020, Oper. Res..

[8]  José A. Ventura,et al.  A two-stage supply chain coordination mechanism considering price sensitive demand and quantity discounts , 2018, Eur. J. Oper. Res..

[9]  Ravi Shankar,et al.  A goal programming model for joint decision making of inventory lot-size, supplier selection and carrier selection , 2014, Comput. Ind. Eng..

[10]  Reza Farzipoor Saen,et al.  A new fuzzy DEA model for evaluation of efficiency and effectiveness of suppliers in sustainable supply chain management context , 2015, Comput. Oper. Res..

[11]  Suresh Kumar Goyal,et al.  An integrated inventory model for a single supplier-single customer problem , 1977 .

[12]  Dengfeng Li,et al.  Fuzzy LINMAP approach to heterogeneous MADM considering comparisons of alternatives with hesitation degrees , 2013 .

[13]  Huseyin Selcuk Kilic,et al.  An integrated approach for supplier selection in multi-item/multi-supplier environment , 2013 .

[14]  Mahesh Nagarajan,et al.  Game-Theoretic Analysis of Cooperation Among Supply Chain Agents: Review and Extensions , 2008, Eur. J. Oper. Res..

[15]  Sharon M. Ordoobadi Development of a supplier selection model using fuzzy logic , 2009 .

[16]  Jinn-Tsair Teng,et al.  Nash equilibrium solution in a vendor-buyer supply chain model with permissible delay in payments , 2014, Comput. Ind. Eng..

[17]  Kaisa Miettinen,et al.  Nonlinear multiobjective optimization , 1998, International series in operations research and management science.

[18]  Naser Mollaverdi,et al.  A fuzzy multi-objective programming model for supplier selection with volume discount and risk criteria , 2014 .

[19]  Eric Sucky,et al.  Inventory management in supply chains: A bargaining problem , 2005 .

[20]  Vipul Jain,et al.  Supplier selection using fuzzy AHP and TOPSIS: a case study in the Indian automotive industry , 2018, Neural Computing and Applications.

[21]  Sergio Cavalieri,et al.  An improved multi-choice goal programming approach for supplier selection problems , 2015 .

[22]  Ezgi Aktar Demirtaş,et al.  An integrated multiobjective decision making process for supplier selection and order allocation , 2008 .

[23]  Felix T. S. Chan,et al.  A fuzzy AHP and fuzzy multi-objective linear programming model for order allocation in a sustainable supply chain: A case study , 2017, Int. J. Comput. Integr. Manuf..

[24]  S. Yousefi,et al.  Using supply chain visibility and cost for supplier selection: a mathematical model , 2017 .

[25]  Xuan Zhao,et al.  Coordination in multi-echelon supply chain under supply and demand uncertainty , 2012 .

[26]  R. C. Baker,et al.  A multi-phase mathematical programming approach for effective supply chain design , 2002, Eur. J. Oper. Res..

[27]  Eric Sucky,et al.  Production , Manufacturing and Logistics A bargaining model with asymmetric information for a single supplier – single buyer problem , 2005 .

[28]  Chi Kin Chan,et al.  A delayed payment method in co-ordinating a single-vendor multi-buyer supply chain , 2010 .

[29]  José A. Ventura,et al.  Determining the retailer's replenishment policy considering multiple capacitated suppliers and price-sensitive demand , 2015, Eur. J. Oper. Res..

[30]  Arpan Kumar Kar,et al.  Revisiting the supplier selection problem: An integrated approach for group decision support , 2014, Expert Syst. Appl..

[31]  Game-Theoretic Analysis of Cooperation Among Supply Chain Agents: Review and Extensions , 2006 .

[32]  B. C. Giri,et al.  A vendor–buyer JELS model with stock-dependent demand and consigned inventory under buyer’s space constraint , 2015, Oper. Res..

[33]  J. Nash THE BARGAINING PROBLEM , 1950, Classics in Game Theory.

[34]  Madjid Tavana,et al.  A hybrid goal programming and dynamic data envelopment analysis framework for sustainable supplier evaluation , 2016, Neural Computing and Applications.

[35]  Samuel Yousefi,et al.  A multi-objective model for closed-loop supply chain optimization and efficient supplier selection in a competitive environment considering quantity discount policy , 2017 .

[36]  K. Wong,et al.  Sustainable supplier selection and order lot-sizing: an integrated multi-objective decision-making process , 2015 .

[37]  Madjid Tavana,et al.  An integrated green supplier selection approach with analytic network process and improved Grey relational analysis , 2015 .

[38]  S. PrasannaVenkatesan,et al.  Multi-objective supplier selection and order allocation under disruption risk , 2016 .

[39]  Yong-Wu Zhou,et al.  Coordination of cooperative advertising models in a one-manufacturer two-retailer supply chain system , 2011, Comput. Ind. Eng..

[40]  J. Rezaei,et al.  A multi-objective model for lot-sizing with supplier selection for an assembly system , 2016 .

[41]  George B. Dantzig,et al.  Linear programming and extensions , 1965 .

[42]  Stefan Minner,et al.  Supplier selection under failure risk, quantity and business volume discounts , 2017, Comput. Ind. Eng..

[43]  Cecilia Temponi,et al.  A scenario-based stochastic model for supplier selection in global context with multiple buyers, currency fluctuation uncertainties, and price discounts , 2014, Eur. J. Oper. Res..

[44]  Desheng Dash Wu,et al.  Fuzzy multi-objective programming for supplier selection and risk modeling: A possibility approach , 2010, Eur. J. Oper. Res..

[45]  Sankaran Mahadevan,et al.  A fuzzy extended analytic network process-based approach for global supplier selection , 2015, Applied Intelligence.

[46]  Fariborz Jolai,et al.  A multi-objective quantity discount and joint optimization model for coordination of a single-buyer multi-vendor supply chain , 2011, Comput. Math. Appl..

[47]  Ferhan Çebi,et al.  A two-stage fuzzy approach for supplier evaluation and order allocation problem with quantity discounts and lead time , 2016, Inf. Sci..

[48]  Xinyang Deng,et al.  Supplier selection using AHP methodology extended by D numbers , 2014, Expert Syst. Appl..

[49]  Abbas Ahmadi,et al.  Selecting a supplier portfolio with value, development, and risk consideration , 2015, Eur. J. Oper. Res..

[50]  Robert B. Handfield,et al.  An integrated chance-constrained stochastic model for efficient and sustainable supplier selection and order allocation , 2018, Int. J. Prod. Res..

[51]  Zilla Sinuany-Stern,et al.  Combining ranking scales and selecting variables in the DEA context: the case of industrial branches , 1998, Comput. Oper. Res..

[52]  Ronald K. Klimberg,et al.  Modeling data envelopment analysis (DEA) efficient location/allocation decisions , 2008, Comput. Oper. Res..

[53]  Fei Ye,et al.  Cost allocation model for optimizing supply chain inventory with controllable lead time , 2010, Comput. Ind. Eng..

[54]  S. Goyal,et al.  Integrated inventory models: The buyer-vendor coordination , 1989 .

[55]  Udo Buscher,et al.  Vertical cooperative advertising and pricing decisions in a manufacturer-retailer supply chain: A game-theoretic approach , 2012, Eur. J. Oper. Res..

[56]  R. Kohli,et al.  A cooperative game theory model of quantity discounts , 1989 .