Solving a Reverse Supply Chain TSP by Genetic Algorithm

Abstract:

Stringent legislative, social concerns & clean carbon emissions are constraining companies to take a fresh look at the impact of supply chain operations on environment, society and individuals when designing reverse supply chain networks. A challenging task in today’s globalised environment where companies mandatorily have to collect back goods after its reliable life is making companies integrate supply chain decisions objective here is to minimize transportation cost and distance. In this paper problem of designing a TSP (Travelling Sales man problem) is addressed which is one of the NP-hard problem in combinatorial optimization. Computational experiments conducted with GA (Genetic algorithm) on large and small size TSP cases where compared with NNA (Nearest neighboring algorithm) and have proved that the GA provides optimal tour every time in reasonable time by outperforming the NNA solution when number of cities are increased.

• Periodical: Applied Mechanics and Materials (Volumes 813-814)
• Pages: 1203-1207
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.813-814.1203
• Online since: November 2015
• Authors: Srinivas Rao Tatavarthy*, Gopi Sampangi
• Keywords: Genetic Algorithm (GA), Nearest Neighboring Algorithm (NNA), Reverse Supply Chain Network, Supply Chain, Travelling Salesman Problem (TSP)

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