Multi-Objective Artificial Bee Colony Algorithm for Multi-Echelon Supply Chain Optimization Problem: An Indian Case Study

Multi-Objective Artificial Bee Colony Algorithm for Multi-Echelon Supply Chain Optimization Problem: An Indian Case Study

Mayank Gupta (Indian Institute of Technology, Department of Mechanical Engineering, Guwahati, India), Anirban Kundu (Indian Institute of Technology, Department of Mechanical Engineering, Delhi, India), and Vipul Gupta (Haub School of Business, Saint Joseph's University, Philadelphia, PA, USA)
DOI: 10.4018/IJORIS.2017100105
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Supply Chain Management has become an integrated part of today's industries. Advancement in technology in this field is the key to the successful operation of businesses. Many techniques and algorithms have a risen dealing with the challenging problems of present industry. In this paper, we have deployed an Artificial Bee Colony algorithm to solve a multi-echelon, multi-objective supply chain problem. Also, we have explained the working of the algorithm while applying it to our problem through various mathematical formulations to get a set of results.
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Literature Review

In the early 1990’s facility location problem was of critical concern in minimizing transportation costs and maximizing market supply. Additionally, these two parameters played a critical role in supply chain network design and optimization (Melo, Nickel, and Saldanha-da-Gama, 2009). Supply and demand uncertainty also made the facility location problem more challenging (Owen and Daskin, 1998). Review of literature on this topic reveals an expanding and complex body of research issues such as, (i) “stochastic behavior” of supply and demand, (ii) complexity of procurement, (iii) routing and transportation issues with integrated location decisions, and (iv) forward/reverse supply chain integration (Melo et al., 2009).

Initially researchers focused on solving facility location and product allocation problem with multiple competing objectives (Lee, Green, & Kim, 1981). Same authors proposed a model that describes a scenario for evaluating six potential production plant location sites in four states serving four distribution centers. This model considers multiple objectives including several qualitative factors of location analysis and associated transportation costs. The result of this model indicates integer goal programming as one of the promising applicable techniques in aiding real-world location/allocation problems.

In the last decade, researchers have started to focus on Multi-Objective Optimization of Multi-Echelon Supply Chain Networks. In 2000, Sabri & Beamon developed an integrated multi-objective supply chain model for use in simultaneous strategic and operational planning. Multi-objective decision analysis was adopted to allow the use of a performance measurement system that includes costs, customer service levels, and flexibility of volume or delivery. This measurement system provided a comprehensive analysis of supply chain system performance. Moreover, the model incorporated production, delivery, and demand uncertainty providing a multi-objective performance vector for the entire SC network. The model aided: (i) design of efficient, effective, and flexible SC systems and (ii) evaluation of competing SC networks.

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