Document Type : Original Article

Authors

Industrial Engineering Department, Faculty of Engineering, Yazd University, Yazd, Iran.

10.22116/jiems.2022.143415

Abstract

Competitive advantage in features, number of branches, or location of any company enables it to provide better services to customers than competitors. In this article, the issue of location in a situation where competitors can decide based on competitor conditions to maximize their profits is examined. First, based on the conditions and characteristics of each competitor, including the number of branches and budget limit, the performance range of each competitor is determined as the radius of effect. Two mathematical formulas are presented for the player and using the concepts of game theory, each player's market share in the competitive environment is determined to earn maximum profit. To solve the problem, first, the initial answers were obtained through the ant colony algorithm, then these answers were entered as input to the Simulated Annealing algorithm, which has a high speed to obtain the answer. The models developed for the two supermarkets have been evaluated and the results have been approved by experts.

Keywords

Ahmadi-Javid, A., & Seddighi, A. H. (2013). A location-routing problem with disruption risk. Transportation Research Part E: Logistics and Transportation Review, 53, 63-82.
Ambrosino, D., Sciomachen, A., & Scutellà, M. G. (2009). A heuristic based on multi-exchange techniques for a regional fleet assignment location-routing problem. Computers & Operations Research, 36(2), 442-460.
Ayough, A., Khorshidvand, B., Massomnedjad, N., & Motameni, A. (2020). An integrated approach for three-dimensional capacitated vehicle routing problem considering time windows. Journal of Modelling in Management.
Babaee Tirkolaee, E., Hadian, S., & Golpira, H. (2019). A novel multi-objective model for two-echelon green routing problem of perishable products with intermediate depots. Journal of Industrial Engineering and Management Studies, 6(2), 196-213.
Behzadi, G., Sundarakani, B., & Mardaneh, E. (2013). Robust optimisation model for the cold food chain logistics problem under uncertainty. International Journal of Logistics Economics and Globalisation, 5(3), 167-179.
Bozkaya, B., Yanik, S., & Balcisoy, S. (2010). A GIS-based optimization framework for competitive multi-facility location-routing problem. Networks and Spatial Economics, 10(3), 297-320.
Burkart, C., Nolz, P. C., & Gutjahr, W. J. (2017). Modelling beneficiaries’ choice in disaster relief logistics. Annals of Operations Research, 256(1), 41-61.
Crainic, T. G., Mancini, S., Perboli, G., & Tadei, R. (2008). Clustering-based heuristics for the two-echelon vehicle routing problem (Vol. 46). Montréal: CIRRELT.
Drexl, M., & Schneider, M. (2015). A survey of variants and extensions of the location-routing problem. European Journal of Operational Research, 241(2), 283-308.
Drezner, T., Drezner, Z., & Kalczynski, P. (2012). Strategic competitive location: improving existing and establishing new facilities. Journal of the Operational Research Society, 63(12), 1720-1730.
Fakhrzad, M. B., & Alidoosti, Z. (2018). A realistic perish ability inventory management for location-inventory-routing problem based on Genetic Algorithm. Journal of Industrial Engineering and Management Studies, 5(1), 106-121.
Fazayeli, S., Eydi, A., & Kamalabadi, I. N. (2018). A model for distribution centers location-routing problem on a multimodal transportation network with a meta-heuristic solving approach. Journal of Industrial Engineering International, 14(2), 327-342.
Zarandi, M. H. F., Hemmati, A., & Davari, S. (2011). The multi-depot capacitated location-routing problem with fuzzy travel times. Expert systems with applications, 38(8), 10075-10084.
Zarandi, M. H. F., Hemmati, A., Davari, S., & Turksen, I. B. (2013). Capacitated location-routing problem with time windows under uncertainty. Knowledge-Based Systems, 37, 480-489.
Ferreira, K. M., & de Queiroz, T. A. (2018). Two effective simulated annealing algorithms for the location-routing problem. Applied Soft Computing, 70, 389-422.
Hadian, H., Golmohammadi, A., Hemmati, A., & Mashkani, O. (2019). A multi-depot location routing problem to reduce the differences between the vehicles’ traveled distances; a comparative study of heuristics. Uncertain Supply Chain Management, 7(1), 17-32.
Hadiguna, R. A. (2012). Decision support framework for risk assessment of sustainable supply chain. International Journal of Logistics Economics and Globalisation, 4(1-2), 35-54. 
Hamidi, M., Farahmand, K., & Sajjadi, S. (2012). Modeling a four-layer location-routing problem. International Journal of Industrial Engineering Computations, 3(1), 43-52.
Hamidi, M., Farahmand, K., Sajjadi, S., & Nygard, K. (2014). A heuristic algorithm for a multi-product four-layer capacitated location-routing problem. International Journal of Industrial Engineering Computations, 5(1), 87-100.
Hamidi, M., Farahmand, K., Reza Sajjadi, S., & Nygard, K. E. (2012). A hybrid GRASP-tabu search metaheuristic for a four-layer location-routing problem. International Journal of Logistics Systems and Management, 12(3), 267-287.
Khorshidvand, B., Soleimani, H., Sibdari, S., & Esfahani, M. M. S. (2021). A hybrid modeling approach for green and sustainable closed-loop supply chain considering price, advertisement and uncertain demands. Computers & Industrial Engineering, 157, 107326.
Khorshidvand, B., Soleimani, H., Sibdari, S., & Esfahani, M. M. S. (2021). Developing a two-stage model for a sustainable closed-loop supply chain with pricing and advertising decisions. Journal of Cleaner Production, 309, 127165.
Leksakul, K., Smutkupt, U., Jintawiwat, R., & Phongmoo, S. (2017). Heuristic approach for solving employee bus routes in a large-scale industrial factory. Advanced Engineering Informatics, 32, 176-187.
Lin, S. W., Yu, V. F., Lee, W., & Ting, C. J. (2009, May). Solving the location routing problem based on a simulated annealing heuristic. In POMS 20th Annual Conference.
Martínez-Salazar, I. A., Molina, J., Ángel-Bello, F., Gómez, T., & Caballero, R. (2014). Solving a bi-objective transportation location routing problem by metaheuristic algorithms. European Journal of Operational Research, 234(1), 25-36.
Manimaran, P., & Selladurai, V. (2014). Glowworm swarm optimisation algorithm for nonlinear fixed charge transportation problem in a single stage supply chain network. International Journal of Logistics Economics and Globalisation, 6(1), 42-55.
Mokhtari, H. (2015). A mixed integer linear programming formulation for a multi-stage, multi-Product, multi-vehicle aggregate production-distribution planning problem. Journal of Industrial Engineering and Management Studies, 2(2), 55-82.
Nadizadeh, A., & Hosseini Nasab, H., (2014). Solving the dynamic capacitated location-routing problem with fuzzy demands by a hybrid heuristic algorithm, European Journal of Operational Research, Vol. 238, No. 2, pp. 458–470.
Nagy, G., & Salhi, S. (2007). Location-routing: Issues, models and methods. European journal of operational research, 177(2), 649-672.
Perboli, G., Tadei, R., & Vigo, D. (2011). The two-echelon capacitated vehicle routing problem: Models and math-based heuristics. Transportation Science, 45(3), 364-380.
Prodhon, C., & Prins, C. (2014). A survey of recent research on location-routing problems. European Journal of Operational Research, 238(1), 1-17.
Rabbani, M., Sadati, S. A., & Farrokhi-Asl, H. (2020). Incorporating location routing model and decision making techniques in industrial waste management: Application in the automotive industry. Computers & Industrial Engineering, 148, 106692.
Rajagopal, S., Krishnamoorthy, B., & Khanapuri, V. B. (2018). Competitive logistics capability for sustainable organisational performance: a study of the textile industry in India. International Journal of Logistics Economics and Globalisation, 7(2), 105-124.
Rath, S., & Gutjahr, W. J. (2014). A math-heuristic for the warehouse location–routing problem in disaster relief. Computers & Operations Research, 42, 25-39.
Venkateswara Reddy, P., Kumar, A. C. S., Bhat, M. S., Dhanalakshmi, R., & Parthiban, P. (2010). Balanced centroids (BC) k-means clustering algorithm to transform MTSP to TSP. International Journal of Logistics Economics and Globalisation, 2(3), 187-197.
Sayyah, M., Larki, H., & Yousefikhoshbakht, M. (2016). Solving the vehicle routing problem with simultaneous pickup and delivery by an effective ant colony optimization. Journal of Industrial Engineering and Management Studies, 3(1), 15-38.
Schneider, M., & Drexl, M. (2017). A survey of the standard location-routing problem. Annals of Operations Research, 259(1), 389-414.
Schwengerer, M., Pirkwieser, S., & Raidl, G. R. (2012, April). A variable neighborhood search approach for the two-echelon location-routing problem. In European conference on evolutionary computation in combinatorial optimization (pp. 13-24). Springer, Berlin, Heidelberg.
Sivakumar, P., Ganesh, K., & Parthiban, P. (2008). Multi-phase composite analytical model for integrated allocation-routing problem–application of blood bank logistics. International Journal of Logistics Economics and Globalisation, 1(3-4), 251-281.
Trivedi, S., Negi, S., & Anand, N. (2019). Role of food safety and quality in Indian food supply chain. International Journal of Logistics Economics and Globalisation, 8(1), 25-45. 
Yu, X., Zhou, Y., & Liu, X. F. (2019). A novel hybrid genetic algorithm for the location routing problem with tight capacity constraints. Applied Soft Computing, 85, 105760.
Farahani, R. Z., Rezapour, S., Drezner, T., & Fallah, S. (2014). Competitive supply chain network design: An overview of classifications, models, solution techniques and applications. Omega, 45, 92-118.
Mehrjerdi, Y. Z., & Nadizadeh, A. (2013). Using greedy clustering method to solve capacitated location-routing problem with fuzzy demands. European Journal of Operational Research, 229(1), 75-84.