Genetic Algorithm and Particle Swarm Optimization for Solving Balanced Allocation Problem of Third Party Logistics Providers

Genetic Algorithm and Particle Swarm Optimization for Solving Balanced Allocation Problem of Third Party Logistics Providers

R. Rajesh (Noorul Islam University, India), S. Pugazhendhi (Annamalai University, India) and K. Ganesh (IBM India Private Limited, India)
Copyright: © 2013 |Pages: 20
DOI: 10.4018/978-1-4666-2461-0.ch010
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Abstract

Third party logistics (3PL) service providers play a growing responsibility in the management of supply chain. The global and competitive business environment of 3PLs has recognized the significance of a speedy and proficient service towards the customers in the past few decades. Particularly in warehousing, distribution, and transportation services, a number of customers anticipate 3PLs to improve lead times, fill rates, inventory levels, etc. Therefore, the 3PLs are under demands to convene a range of service necessities of customers in an active and uncertain business environment. As a consequence of the dynamic environment in which supply chain must operate, 3PLs should sustain an effective distribution system of high performance and must make a sequence of inter-related decisions over time for their distribution networks. Warehouses play an important role in sustaining the continual flow of goods and materials between the manufacturer and customers. The performance of the 3PL supply chain network can be effortlessly enhanced by a balanced allocation of customers to warehouses. In this paper, the authors develop a genetic algorithm and a particle-swarm-optimisation algorithm for solving the balanced allocation problem and the results are encouraging.
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Literature Review

Supply chain networks, consisting of manufacturers, distributors, retailers, and consumers, provide the critical infrastructure for the production of goods, their distribution, and their ultimate consumption in today’s globalized economies. The reality of supply chain networks today includes not only competition but also cooperation since decision-makers in the supply chain must interact not only in terms of the product flows but also in terms of pricing in order to satisfy the consumers. At the same time, decision-makers in supply chain are characterized by their individualized objectives, which may include profit maximization, but also risk minimization, as well as the incorporation of environmentally conscious objectives, to various degrees. The concept of supply chain networks is as applicable to services as it is to goods.

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