Mehdizadeh, E., Afrabandpei, F. (2012). Design of a Mathematical Model for Logistic Network in a Multi-Stage Multi-Product Supply Chain Network and Developing a Metaheuristic Algorithm. Journal of Optimization in Industrial Engineering, 5(10), 35-43.
Esmaeil Mehdizadeh; Fariborz Afrabandpei. "Design of a Mathematical Model for Logistic Network in a Multi-Stage Multi-Product Supply Chain Network and Developing a Metaheuristic Algorithm". Journal of Optimization in Industrial Engineering, 5, 10, 2012, 35-43.
Mehdizadeh, E., Afrabandpei, F. (2012). 'Design of a Mathematical Model for Logistic Network in a Multi-Stage Multi-Product Supply Chain Network and Developing a Metaheuristic Algorithm', Journal of Optimization in Industrial Engineering, 5(10), pp. 35-43.
Mehdizadeh, E., Afrabandpei, F. Design of a Mathematical Model for Logistic Network in a Multi-Stage Multi-Product Supply Chain Network and Developing a Metaheuristic Algorithm. Journal of Optimization in Industrial Engineering, 2012; 5(10): 35-43.
Design of a Mathematical Model for Logistic Network in a Multi-Stage Multi-Product Supply Chain Network and Developing a Metaheuristic Algorithm
1Assistant Professor, Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
2MSc, Ilam Gas Treating Company, National Iranian Gas Company, Ilam, Iran
Abstract
Logistic network design is one of the most important strategic decisions in supply chain management that has recently attracted the attention of many researchers. Transportation network design is then one of the most important fields of logistic network. This study is concerned with designing a multi-stage and multi-product logistic network. At first, a mixed integer nonlinear programming model (MINLP) is formulated that minimizes transportation and holding costs. Then, a hybrid priority-based Genetic Algorithm (pb-GA) and simulated annealing algorithm (SA) is developed in two phases to find the optimal solution. The solution is represented by a matrix and a vector. Response Surface Methodology (RSM) is also used to adjust the significant parameters of the algorithm. Finally, several test problems are generated which show that the proposed metaheuristic algorithm can find good solutions in reasonable time spans.
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