Game of elimination in a shared market: a novel multi-period game approach to competition of supply chains based on simulation

References

• Allon, G., & Federgruen, A. (2007). Competition in service industries. Operations Research, 55, 37–55.
   Web of Science ®Google Scholar
• Atashpaz-Gargari, E., & Lucas, C. (2007). Imperialist Competitive Algorithm: An algorithm for optimization inspired by imperialistic competition. IEEE Congress on Evolutionary Computation (pp. 4661–4667). Singapore: IEEE.
   Google Scholar
• Bakal, I. S., Geunes, J., & Romeijn, H. E. (2008). Market selection decisions for inventory models with price-sensitive demand. Journal of Global Optimization, 41, 633–655.
   Web of Science ®Google Scholar
• Basuroy, S., & Nguyen, D. (1998). “Multinomial logit market share models: Equilibrium characteristics and strategic implications. Management Science, 44, 1396–1408.
   Web of Science ®Google Scholar
• Beckham, M., & Funke, U. (1978). Product attraction, advertising and sales: Towards a utility model of market behavior. Mathematical Methods of Operations Research, 22(1), 1–11.
   Google Scholar
• Bernstein, F., & Federgruen, A. (2004). A general equilibrium model for industries with price and service competition. Operations Research, 52, 868–886.
   Web of Science ®Google Scholar
• Bernstein, F., & Federgruen, A. (2007a). Competition in service industries. Operations Research, 55, 37–55.
   Web of Science ®Google Scholar
• Bernstein, F., & Federgruen, A. (2007b). Coordination mechanisms for supply chains under price and service competition. Manufacturing and Service Operations Management, 9, 242–262.
   Web of Science ®Google Scholar
• Bourlakis, C. A. (1997). Testing the competitive environment and the persistence of profits hypotheses. Review of Industrial Organization, 12, 203–218.
   Web of Science ®Google Scholar
• Boussaïda, I., Lepagnotb, J., & Siarry, P. (2013). A survey on optimization metaheuristics. Information Sciences, 237, 82–117.
   Web of Science ®Google Scholar
• Butnariu, B. (1978). Fuzzy games: A description of the concept. Fuzzy Sets and Systems, 1, 181–192.
   Google Scholar
• Cachon, G., & Netessine, S. (2004). Game theory in supply chain analysis. In D. Simchi-Lev, S. D. Wu, & Z.-J. Shen (Eds.), Handbook of quantitative supply chain analysis: modeling in the e-business era (pp. 13–66). Boston: Kluwer Academic.
   Google Scholar
• Cho, D. W., & Lee, Y. H. (2013). The value of information sharing in a supply chain with a seasonal demand process. Computers & Industrial Engineering, 65, 97–108.
   Web of Science ®Google Scholar
• Chopra, S., & Meindl, P. (2006). Supply chain management: Strategy, planning, and operation. Upper Saddle River, N.J: Prentice Hall.
   Google Scholar
• Duan, H., & Huang, L. (2014). Imperialist competitive algorithm optimized artificial neural networks for UCAV global path planning. Neurocomputing, 125, 166–171.
   Web of Science ®Google Scholar
• Duan, H., Xu, C., Liu, S., & Shao, S. (2010). Template matching using chaotic imperialist competitive algorithm. Pattern Recognition Letters, 31, 1868–1875.
   Web of Science ®Google Scholar
• Enayatifar, R., & Abdullah, A. H. (2013). MOICA: A novel multi-objective approach based on imperialist competitive algorithm. Applied Mathematics and Computation, 219, 8829–8841.
   Web of Science ®Google Scholar
• Enayatifar, R., Abdulla, A. H., & Lee, M. (2013). A weighted discrete imperialist competitive algorithm (WDICA) combined with chaotic map for image encryption. Optics and Lasers in Engineering, 51, 1066–1077.
   Web of Science ®Google Scholar
• Matsatsinis, F. N., & Samaras, A. P. (2000). Brand choice model selection based on consumers’ multicriteria preferences and experts’ knowledge. Computers & Operations Research, 27, 689–707.
   Web of Science ®Google Scholar
• Cooper, G. L. (1993). Market-share models. In J. Eliashberg, & G. Lilien (Eds.), Handbooks of operations research and management science (pp. 257–313). Amsterdam: Elsevier Science.
   Google Scholar
• Garagic, D., & Cruz Jr, J. B., (2003). An approach to fuzzy noncooperative Nash games. Journal of Optimization Theory and Applications, 118, 475–491.10.1023/B:JOTA.0000004867.66302.16
   Web of Science ®Google Scholar
• Gharavi, H., Ardehali, M. M., & Ghanbari-Tichi, S. (2015). Imperial competitive algorithm optimization of fuzzy multi-objective design of a hybrid green power system with considerations for economics, reliability, and environmental emissions. Renewable Energy, 78, 427–437.
   Web of Science ®Google Scholar
• Gilbert, S., & Cvsa, V. (2003). Strategic commitment to price to stimulate downstream innovation in a supply chain. European Journal of Operational Research, 150, 617–639.
   Web of Science ®Google Scholar
• Hadidi, A., Hadidi, M., & Nazari, A. (2013). A new design approach for shell-and-tube heat exchangers using imperialist competitive algorithm (ICA) from economic point of view. Energy Conversion and Management, 67( March 2013), 66–74.
   Web of Science ®Google Scholar
• Hafezalkotob, A., Makui, A., & Sadjadi, S. J. (2011). Strategic and tactical design of competing decentralized supply chain networks with risk-averse participants for markets with uncertain demand. Mathematical Problems in Engineering, 2011, 27p.
   Web of Science ®Google Scholar
• Khabbazi, A., Atashpaz-Gargari, E., & Lucas, C. (2009). Imperialist competitive algorithm for minimum bit error rate beamforming. International Journal of Bio-Inspired Computation, 1, 125–133.
   Web of Science ®Google Scholar
• Kogan, K., & Tapiero, C. S. (2007). Supply chain games: Operations management and risk valuation. New York, NY: Springer, US.
   Google Scholar
• Kunlei Lian, C. Z. (2012). A modified colonial competitive algorithm for the mixed-model U-line balancing and sequencing problem. International Journal of Production Research, 40(16), 5117–5131.
   Web of Science ®Google Scholar
• Kurts, R. D., & Rhoades, S. A. (1992). A note on the market share-profitability relationship. Review of Ondustrial Organization, 7, 39–50.
   Google Scholar
• Larbani, M. (2009). Non cooperative fuzzy games in normal form: a survey. Fuzzy Sets and Systems, 160, 3184–3210.
   Web of Science ®Google Scholar
• Leng, M., & Parlar, M. (2005). Game theoretic applications in supply chain management: A review. Information Systems and Operations Research, 43, 187–220.
   Web of Science ®Google Scholar
• Li, B., Zhu, M., Jiang, Y., & Li, Z. (2016). Pricing policies of a competitive dual-channel green supply chain. Journal of Cleaner Production., 112, 2029–2042.
   Web of Science ®Google Scholar
• Manski, C. F. (1977). The structure of random utility models. Theory and Decision, 8, 229–254.
   Web of Science ®Google Scholar
• McFadden, D. (1974). Conditional logit analysis of qualitative choice behavior. In Frontiers In (Ed.), Econometrics (pp. 105–142). New York, NY: Academic Press.
   Google Scholar
• Nazari-Shirkouhi, S., Eivazy, H., Ghodsi, R., Rezaie, K., & Atashpaz-Gargari, E. (2010). Solving the integrated product mix-outsourcing problem by a novel meta-heuristic algorithm: Imperialist competitive algorithm. Expert Systems with Applications, 37, 7615–7626.
   Web of Science ®Google Scholar
• Nagurney, A., Saberi, S., Shukla, S., & Floden, J. (2015). Supply chain network competition in price and quality with multiple manufacturers and freight service providers. Transportation Research Part E: Logistics and Transportation Review, 77, 248–267.
   Web of Science ®Google Scholar
• Niknam, T., & Amiri, B. (2010). An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis. Applied Soft Computing, 10(1), 183–197.
   Web of Science ®Google Scholar
• Onur, I., Ozcan, R., & Taş, B. K. (2012). Public procurement auctions and competition in Turkey. Review of Industrial Organization, 40, 207–223.
   Web of Science ®Google Scholar
• Qian, L. (2014). Market-based supplier selection with price, delivery time, and service level dependent demand. International Journal of Production Economics Part-C, 147, 697–706.
   Web of Science ®Google Scholar
• Pasandideh, S. H., Niakib, S. T., & Malekia, L. (2014). A bi-objective remanufacturing problem within queuing framework: An imperialist competitive algorithm. International Journal of Management Science and Engineering Management, 10, 199–209.
   Google Scholar
• Raab, R. L. (1984). A note on dominant firm market share and economic performance. Review of Industrial Organization, 1, 148–153.
   Google Scholar
• Sadigh, A. N., Mozafari, M., & Karimi, B. (2012). Manufacturer-retailer supply chain coordination: A bi-level programming approach. Advances in Engineering Software, 45, 144–152.
   Web of Science ®Google Scholar
• Sadhu, R. K., Rakshit, P., & Konar, A. (2016). A modified imperialist competitive algorithm for multi-robot stick-carrying application. Robotics and Autonomous Systems, 76, 15–35.
   Web of Science ®Google Scholar
• Sawik, T. (2015). On the fair optimization of cost and customer service level in a supply chain under disruption risks. Omega, 53, 58–66.
   Web of Science ®Google Scholar
• Seetharaman, P. B. (2003). Probabilistic versus random-utility models of state dependence: An empirical comparison. International Journal of Research Marketing, 20, 87–96.
   Web of Science ®Google Scholar
• Taher, S. A., & Bagherpour, R. (2013). A new approach for optimal capacitor placement and sizing in unbalanced distorted distribution systems using hybrid honey bee colony algorithm. International Journal of Electrical Power & Energy Systems, 49, 430–448.
   Web of Science ®Google Scholar
• Tsay, A. A., & Agrawal, N. (2000). Channel dynamics under price and service competition. Manufacturing and Service Operations Management, 2, 372–391.
   Google Scholar
• Xiao, T., & Yang, D. (2008). Price and service competition of supply chains with risk-averse retailers under demand uncertainty. International Journal of Production Economics, 114, 187–200.
   Web of Science ®Google Scholar
• Xiao, T., & Yang, D. (2009). Risk sharing and information revelation mechanism of a one-manufacturer and one-retailer supply chain facing an integrated competitor. European Journal of Operational Research, 196, 1076–1085.
   Web of Science ®Google Scholar
• Yousefikhoshbakht, M., & Sedighpour, M. (2013). New imperialist competitive algorithm to solve the travelling salesman problem. International Journal of Computer Mathematics Archive, 90, 1495–1505.
   Web of Science ®Google Scholar
• Zhang, J., & Chen, J. (2013). Coordination of information sharing in a supply chain. International Journal of Production Economics, 143, 178–187.
   Web of Science ®Google Scholar
• Zhang, Y., Wang, Y., & Peng, C. (2009). Improved imperialist competitive algorithm for constrained optimization. In Minrui Fei, Chen Peng, Zhou Su, Yang Song, Qinglong Han, (Eds.) Computer science-technology and applications, 2009 (pp. 204–207). Chongqing China: IFCSTA.
   Google Scholar