Presenting a three-objective model in location-allocation problems using combinational interval full-ranking and maximal covering with backup model

Document Type : Research Paper

Authors

Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran

Abstract

Covering models have found many applications in a wide variety of real-world problems; nevertheless, some assumptions of covering models are not realistic enough. Accordingly, a general approach would not be able to answer the needs of encountering varied aspects of real-world considerations. Assumptions like the unavailability of servers, uncertainty, and evaluating more factors at the same time, are a sort of assumptions, with which covering models are always faced; however, these models are not able to find any answers for them. Therefore, how to deal with these sorts of assumptions has been always a big question. In this research, for facing unavailability and uncertainty in input data, backup covering and interval full-ranking model were addressed, respectively. Furthermore, by combining backup covering and interval full-ranking models (also conceptions), not only time was saved and more factors like efficiency and cost were simultaneously evaluated, but also covering considerations were reachable in real aspects.

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Baron, B., Berman, O., Kim, S. and Krass, D. (2009). Ensuring feasibility in location problems with stochastic demands and congestions. IIE transactions, 41, 467-481.
Berman, O., Drenzer, Z., Krass, D. and Wesolowsky, G.O. (2009). The variable radius covering problem. European Journal of Operational Research, 196, 516-525.
Berman, O. and Wang, J. (2011). The mini-max regret gradual covering location problem on a network with incomplete information of demand weights. European Journal of Operational Research, 208, 233-238.
Chou, S.Y., Chang, Y.H. and Shen, C.Y. (2008). A fuzzy simple additive weighting system under group decision making for facility location selection with objective/subjective attributes. European Journal of Operational Research, 189, 132-145.
Church, R.L. and Revelle, C. (1974). The maximal covering location problem. Papers of the Regional Science Association, 32, 101-118.
Daskin, M.S. (1995). Networks and discrete location: Models, algorithms and applications. New York, US: John Wiley and Sons.
Daskin, M.S. and Stern, E.H. (1981). A hierarchical objective set covering model for emergency medical service vehicle deployment. Transportation Science, 15, 137-152.
Erdemir, E.T., Batta, R., Spielman S., Rogerson, P.A., Blatt, A. and Flanigan, M. (2010). Joint ground and air emergency medical services coverage models: A greedy heuristic solution approach. European Journal of Operational Research, 207, 736-749.
Ghodratnama, A., Tavakkoli-Mogaddam, R. and Azaron, A. (2015). Robust and fuzzy goal programming optimization approaches for a novel multi-objective hub location-allocation problem: A supply chain overview. Applied Soft Computing, 37, 255-276.
Hakimi, S.L. (1965). Optimum distribution of switching centers in a communication network and some related graph theoretic problems. Operational Research, 13, 462-475.
Hogan, K. and Revelle, C. (1986). Concepts and applications of backup coverage. Management Science, 32, 1434-1444.
Hosseininezhad, J., Jabalameli, M.S. and Jalali Naini, GH. (2014). Fuzzy algorithm for continuous capacitated location allocation model with risk consideration. Applied Mathematical Modeling, 38, 983-1000.
Kolen, A. and Tamir, A. (1990). Covering problems, In: P.B. Mirchandani, R.L. Francies (Eds.), Conf. Discrete Location Theory, Wily, New York, 263-304.
Lannoni, A.P. and Morabito, R. (2007). A multiple dispatch and partial backup hypercube queuing model to analyze emergency medical systems on highways. Transportation Research, 43, 755-771.
Lee, J.M. and Lee, Y.H. (2010). Tabu based heuristics for the generalized hierarchical covering location problem. Computers and Industrial Engineering, 58, 638-645.
Martinez-Salazar, I., Molina, J., Ángel-Bello, F., Gómez, T. and Caballero, R. (2014). Solving a bi-objective Transportation Location Routing Problem by meta-heuristic algorithms. European Journal of Operational Research, 234, 25-36.
Moheb-alizade, H., Rasouli, S.M. and Tavakkoli-mogaddam, R. (2011). The use of multi-criteria data envelopment analysis for location-allocation problems in a fuzzy environment. Expert Systems with Applications, 38, 5687-5695.
Ni, Y., (2012). Minimum weight covering problems in stochastic environments. Information Sciences, 214, 91-104.
Owen, S.H. and Daskin, M.S. (1998). Strategic facility location: A review. European Journal of Operational Research, 111, 423-447.
Peijun, G. (2009). Fuzzy data envelopment analysis and its application to location problems. Information Sciences, 179, 820-829.
Pereira, M. and Coelho, L. (2015). A hybrid method for the probabilistic maximal covering location-allocation problem. Computers and Operations Research, 57, 51-59.
Pirkul, H. and Schilling, D. (1989). The capacitated maximal covering location problem with backup service. Annals of Operational Research, 18, 141-154.
Revelle, C. and Hogan, K. (1989). The maximum reliability location problem and a-reliable p-center problems. Annals of Operational Research, 18, 155-174.
Shieh, B.S. (2013). Solution to the covering problem. Information Sciences, 222, 626-633.
Sohrabi Haghighat, M. and Khorram, E. (2005). The maximum and minimum number of efficient units in DEA with interval data. Applied Mathematics and Computation, 163, 919-930.
Thomas, P., Chan, Y., Lehmkuhl, L. and Nixon, W. (2002). Obnoxious-facility location and data envelopment analysis: A combined distance-based formulation. European Journal of Operational Research, 141, 495-514.
Toregas, C., Swain, R., Revelle C. and Berman L. (1971). The location of emergency service facilities. Operational Research, 19, 1363-1373.
Tsou, C.S. (2009). Evolutionary Pareto optimizers for continuous review stochastic inventory systems. European Journal of Operation Research, 195, 364-371.
Vidyarthi, N. and Jayaswal, S. (2014). Efficient solution of a class of location–allocation problems with stochastic demand and congestion. Computers and Operations Research, 48, 20-30.
Wen, M. and Kang, R. (2011). Some optimal models for facility location-allocation problem with random Fuzzy demands. Applied Soft Computing, 11, 1202-1207.
Zanjirani, R., Asgari, N., Heidari, N., Hosseininia, M. and Goh, M. (2012). Covering problems in facility location: A review. Computers and Industrial Engineering, 62, 368-407.
Zarandi, M.H., Davari, S. and Haddad Sisakht, A. (2013). The large-scale dynamic maximal covering location problem. Mathematical and Computer Modeling, 57, 710-719.