Abbasi, B., Mirzazadeh, A., Mohammadi, M. (2018). Multi Stage Investment Planning and Customer Selection in A Two Echelon Multi-Period Supply Chain. Scientia Iranica, (), -. doi: 10.24200/sci.2018.5608.1373
B. Abbasi; Aboulfazl Mirzazadeh; Mohammad Mohammadi. "Multi Stage Investment Planning and Customer Selection in A Two Echelon Multi-Period Supply Chain". Scientia Iranica, , , 2018, -. doi: 10.24200/sci.2018.5608.1373
Abbasi, B., Mirzazadeh, A., Mohammadi, M. (2018). 'Multi Stage Investment Planning and Customer Selection in A Two Echelon Multi-Period Supply Chain', Scientia Iranica, (), pp. -. doi: 10.24200/sci.2018.5608.1373
Abbasi, B., Mirzazadeh, A., Mohammadi, M. Multi Stage Investment Planning and Customer Selection in A Two Echelon Multi-Period Supply Chain. Scientia Iranica, 2018; (): -. doi: 10.24200/sci.2018.5608.1373
Multi Stage Investment Planning and Customer Selection in A Two Echelon Multi-Period Supply Chain
1Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran
2Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Karaj, Iran
Abstract
In the supply chain of Fast-moving consumer goods, logistics costs are a main part of the expenses. In low levels of these chains, we usually face with a Vehicle Routing Problem. In practice, due to the high cost of service in many cases, some customers are not chosen for serving. Restrictions associated with the investment in many cases makes impossible serve to some potential clients. In this problem, design a supply chain network, including a location-allocation problem in the warehouse, Multiple Depot Vehicle Routing Problem at the level of distribution and customer selection at the retail level in several periods of time is considered. In this issue, in addition to certain methods that can be used in small sizes, Meta-heuristic algorithms to solve large-scale model have been used. With the aim of improving performance, if not improve in a few diversifications, algorithms are temporarily enhanced. In GA this change leads to improve 1.2 percent and 1.6 percent about the mean of solutions and the best solution and in SA algorithm, this change is led to improving by 2.1% and 2.4% in average solutions and the best solutions. Finally, this approach about a real problem in Sepahan Oil Company is employed.
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