A hybrid fuzzy bi-objective delivery planning model for leagile e-tailing under uncertainty

References

• Agatz, N. A. H., Fleischmann, M., & Van Nunen, J. A. E. E. (2008). E-fulfillment and multi-channel distribution – a review. European Journal of Operational Research, 187, 339–356.
   Web of Science ®Google Scholar
• Babazadeh, R., & Razmi, J. (2012). A robust stochastic programming approach for agile and responsive logistics under operational and disruption risks. Logistics Systems and Management, 13, 458–482.
   Google Scholar
• Becerril-Arreola, R., Leng, M., & Parlar, M. (2013). Online retailers' promotional pricing, free-shipping threshold, and inventory decisions: A simulation-based analysis. European Journal of Operational Research, 2, 272–283.
   Web of Science ®Google Scholar
• Bretthauer, K. M., Mahar, S., & Venakataramanan, M. A. (2010). Inventory and distribution strategies for retail/e-tail organizations. Computers & Industrial Engineering, 58, 119–132.
   Web of Science ®Google Scholar
• Chen, L. H., & Tsai, F. C. (2001). Fuzzy goal programming with different importance and priorities. European Journal of Operational Research, 133, 548–556.
   Web of Science ®Google Scholar
• Christiansen, P. E., Kotzab, H., & Mikkola, J. H. (2007). Coordination and sharing logistics information in leagile supply chains. Procurement Management, 1, 79–96.
   Google Scholar
• GhasemyYaghin, R., Torabi, S. A., & FatemiGhomi, S. M. T. (2012). Integrated markdown pricing and aggregate production planning in a two echelon supply chain: A hybrid fuzzy multiple objective approach. Applied Mathematical Modelling, 36, 6011–6030.
   Web of Science ®Google Scholar
• Goldsby, T. J., Griffis, S. E., & Roath, A. S. (2006). Modeling lean, agile, and leagile supply chain strategies. Business Logistics, 27, 57–80.
   Google Scholar
• Hannan, E. L. (1981). Linear programming with multiple fuzzy goals. Fuzzy Sets and Systems, 6, 235–248.
   Web of Science ®Google Scholar
• Hsu, C. I., & Li, H. C. (2006). Optimal delivery service strategy for Internet shopping with time-dependent consumer demand. Transportation Research, 42, 473–497.
   Google Scholar
• Inuiguchi, M., & Ramik, J. (2000). Possibilistic linear programming: A brief review of fuzzy mathematical programming and a comparison with stochastic programming in portfolio selection problem. Fuzzy Sets and Systems, 111, 3–28.
   Web of Science ®Google Scholar
• Jamalnia, A., & Soukhakian, M. A. (2009). A hybrid fuzzy goal programming approach with different goal priorities to aggregate production planning. Computers & Industrial Engineering, 56, 1474–1486.
   Web of Science ®Google Scholar
• Jimenez, M., Arenas, M., Bilbao, A., & Rodriguez, M. V. (2007). Linear programming with fuzzy parameters: An interactive method resolution. European Journal of Operational Research, 177, 1599–1609.
   Web of Science ®Google Scholar
• Johansson, H. J., McHugh, P., Pendlebury, A. J., & Wheeler, W. A. (1993). Business Process Reengineering: Breaking Strategies for Market Dominance. Chichester, UK: Wiley.
   Google Scholar
• Li, L., & Lai, K. K. (2001). Fuzzy dynamic programming approach to hybrid multi objective multi stage decision making problems. Fuzzy Sets and Systems, 117, 13–25.
   Web of Science ®Google Scholar
• Liu, F. O. (2013). Price or proficiency? A household production explanation on consumers' choice of online retailer. Journal of Business and Economics, 4, 1247–1264.
   Google Scholar
• Lo, S. K., Chou, Y. J., & Teng, C. I. (2013). Source effect of advertised reference price influences on transaction value in online shopping environments. Electronic Commerce Research, 4, 411–421.
   Web of Science ®Google Scholar
• Mason-Jones, R., Naylor, B., & Towill, D. R. (2010). Lean, agile or leagile? Matching your supply chain to the marketplace. International Journal of Production Research, 38, 4061–4070.
   Web of Science ®Google Scholar
• Min, H., Ko, C. S., & Ko, H. J. (2008). Managing product returns in customer-centric e-tail environments. Services Technology and Management, 9, 51–70.
   Google Scholar
• Mohebbi, S., & Shafaei, R. (2012). e-Supply network coordination: The design of intelligent agents for buyer–supplier dynamic negotiations. Journal of Intelligent Manufacturing, 23, 375–391.
   Web of Science ®Google Scholar
• Mula, J., Poler, R., & Garcia, J. P. (2006). MRP with flexible constraints: A fuzzy mathematical programming approach. Fuzzy Sets and Systems, 157, 74–97.
   Web of Science ®Google Scholar
• Narasimhan, R. (1980). Goal programming in a fuzzy environment. Decision Sciences, 11, 325–336.
   Google Scholar
• Okongwu, U., Laurasab, M., Dupont, L., & Humez, V. (2012). A decision support system for optimising the order fulfilment process. Production Planning & Control, 8, 581–598.
   Web of Science ®Google Scholar
• Parra, M. A., Terol, A. B., Gladish, B. P., & Rodriguez, M. V. (2005). Solving a multi objective possibilistic problem through compromise programming. European Journal of Operational Research, 164, 748–759.
   Web of Science ®Google Scholar
• Tagaras, G. (1989). Effects of pooling in two-location inventory systems. IEE Transactions, 21, 250–257.
   Web of Science ®Google Scholar
• Watada, J. (1997). Fuzzy portfolio selection and its applications to decision making. Tatra Mountains Mathematics Publication, 13, 219–248.
   Google Scholar
• Yan, T., Rabinovich, E., Dooley, K., & Evers, P. T. (2010). Managing backlog variation in order fulfillment: The case of Internet retailers. Production Economics, 128, 261–268.
   Web of Science ®Google Scholar
• Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338–353.
   Web of Science ®Google Scholar
• Zimmermen, H. J. (1976). Description and optimization of fuzzy sets. International Journal of General Systems, 2, 209–215.
   Web of Science ®Google Scholar
• Zimmermen, H. J. (1978). Fuzzy programming and linear programming with several objective functions. Fuzzy Sets and Systems, 1, 45–56.
   Google Scholar