Ebrahimnezhad, S., Javanshir, H., Najafi, A. (2010). Solving a mathematical model with multi warehouses and retailers in distribution network by a simulated annealing algorithm. Journal of Industrial Engineering, International, 6(10), 42-54.
S Ebrahimnezhad; H Javanshir; A Najafi. "Solving a mathematical model with multi warehouses and retailers in distribution network by a simulated annealing algorithm". Journal of Industrial Engineering, International, 6, 10, 2010, 42-54.
Ebrahimnezhad, S., Javanshir, H., Najafi, A. (2010). 'Solving a mathematical model with multi warehouses and retailers in distribution network by a simulated annealing algorithm', Journal of Industrial Engineering, International, 6(10), pp. 42-54.
Ebrahimnezhad, S., Javanshir, H., Najafi, A. Solving a mathematical model with multi warehouses and retailers in distribution network by a simulated annealing algorithm. Journal of Industrial Engineering, International, 2010; 6(10): 42-54.
Solving a mathematical model with multi warehouses and retailers in distribution network by a simulated annealing algorithm
1Assistant Professor, Dep. of Industrial Engineering, Islamic Azad University, Karaj Branch, Iran
2Assistant Professor, Dep. of Industrial Engineering, Islamic Azad University, Tehran South Branch, Tehran, Iran
3M.Sc., Dep. of Industrial Engineering, , Islamic Azad University, Najaf Abad Branch, Iran
Abstract
Determination of shipment quantity and distribution problem is an important subject in today’s business. This paper describes the inventory/distribution network design. The system addresses a class of distribution network design problem, which is characterized by multiple products family, multiple warehouses and retail-ers. The maximum capacity of vehicles and warehouses are also known. The resulting system focuses on two key goals: minimizing the lost sales cost as a costumer’s satisfaction factor and balancing sum of service dis-tances for different warehouses. In this paper we consider the distribution network problem formulated by 0-1 mixed integer linear programming model. Due to difficulty of obtaining the optimum solution in medium and large scale problems, a simulated annealing algorithm (SA) is also applied. The efficiency of this algorithm is demonstrated by comparing its numerical experiment results with those of SA algorithm and LINGO 6. package.
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