Mehdi Seifbarghy; Shima Zangeneh
Abstract
In the classic models of facility location, it is assumed that the selected facilities always work based on the schedule while, in the real world, facilities are always exposed to disruption risk and sometimes these disruptions have long-term effects on the supply chain network and cause a lot of problems. ...
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In the classic models of facility location, it is assumed that the selected facilities always work based on the schedule while, in the real world, facilities are always exposed to disruption risk and sometimes these disruptions have long-term effects on the supply chain network and cause a lot of problems. In this paper, a mixed integer programing (MIP) model presented in order to determine how to serve the customers at the time of disruption in distribution centers in a two-echelon supply chain, including distribution centers and customers. This model selects potential places that minimize traditionally supply chain costs and also the transportation cost after distribution centers disruptions. In fact, the model tries to choose the distribution centers facilities with lowest cost and highest reliability and also allocate them to customers. The problem divided into two sub-problems using Lagrangian relaxation approach. By examining sub-problems optimal conditions, a heuristic solution is used for the first sub-problem and a genetic algorithm is used for the second sub-problem to solve large-scale problems. Finally, numerical examples are presented to examine the performance and efficiency of the proposed model and approach
Mehdi Seifbarghy; Chero Ziaei Naghshbandi
Volume 9, Issue 24 , March 2012, , Pages 67-84
Abstract
In this paper, a Decision Support System (DSS) is developed in order to evaluate customers. The addressed system can specially be applied in captive markets. This paper extends Chamodrakas et al. [8] in which the customer evaluation in order to customer selection is executed using a fuzzy TOPSIS method. ...
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In this paper, a Decision Support System (DSS) is developed in order to evaluate customers. The addressed system can specially be applied in captive markets. This paper extends Chamodrakas et al. [8] in which the customer evaluation in order to customer selection is executed using a fuzzy TOPSIS method. The proposed DSS includes 6 different models and the user can select among them. The system is coded utilizing C# programming language. To present the system performance, some parts of the system are presented along with a numerical problem.
Mehdi Seifbarghy; Razieh Forghani; Zarifeh Rathi
Volume 8, Issue 18 , September 2010, , Pages 1-13
Abstract
Maximal Covering Location Problem (MCLP) aims at maximizing a population of customers which are located within a specified range of time or distance from some new servers which should be located. A number of extensions have been proposed for this problem, one of which is considering queuing constraints ...
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Maximal Covering Location Problem (MCLP) aims at maximizing a population of customers which are located within a specified range of time or distance from some new servers which should be located. A number of extensions have been proposed for this problem, one of which is considering queuing constraints in the mode; for example, location of a limited number of servers in such a way as to maximize the covering considering the constraint regarding to the queue length. In this paper, we extend the proposed model by Correa and Lorena [3] which maximizes the covering. We consider a more objective function in such a way as to minimize the total distance between the servers and demand points. A genetic algorithm based heuristic is proposed to solve the model and results are compared with that of given by CPLEX as a standard solver to estimate the performance of the given algorithm.
