Multi-Level Programming Approach to a Closed-loop Supply Chain Network Design

2013 ◽  
Vol 4 (4) ◽  
pp. 55-71 ◽  
Author(s):  
Sima Ghayebloo ◽  
Mohammad Jafar Tarok ◽  
Mostafa Abedzadeh ◽  
Claver Diallo

In this paper, the authors address the problem of network design for a closed-loop supply chain. The problem is formulated as a mixed zero-one bi-level optimization model, with the manufacturer as the leader who minimizes his costs at the upper level, and a forwarding agent dealt with as the follower. The leader decides on the locations of the facilities, and the forwarding agent builds the forward and reverse transportation plans so as to minimize the total transportation cost. A genetic algorithm solution method is used to obtain the Stackelberg solution. Furthermore, the algorithm uses penalty functions to handle the constraints. The solution algorithm is implemented in Matlab, utilizing LINGO 11.0 (2008) to solve each lower level problem instance. Finally, the accuracy of the model is tested on a set of numerical experiments.

Author(s):  
Nasrin Mohabbati-Kalejahi ◽  
Alexander Vinel

Hazardous materials (hazmat) storage and transportation pose threats to people’s safety and the environment, which creates a need for governments and local authorities to regulate such shipments. This paper proposes a novel mathematical model for what is termed the hazmat closed-loop supply chain network design problem. The model, which can be viewed as a way to combine several directions previously considered in the literature, includes two echelons in the forward direction (production and distribution centers), three echelons in the backward direction (collection, recovery, and disposal centers), and emergency response team positioning. The two objectives of minimizing the strategic, tactical, and operational costs as well as the risk exposure on road networks are considered in this model. Since the forward flow of hazmat is directly related to the reverse flow, and since hazmat accidents can occur at all stages of the lifecycle (storage, shipment, loading, and unloading, etc.), it is argued that such a unified framework is essential. A robust framework is also presented to hedge the optimization model in case of demand and return uncertainty. The performance of both models is evaluated based on a standard dataset from Albany, NY. Considering the trade-offs between cost and risk, the results demonstrate the design of efficient hazmat closed-loop supply chain networks where the risk exposure can be reduced significantly by employing the proposed models.


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