Publication Type:

Conference Paper

Authors:

Rao, T.S.

Source:

Materials Today: Proceedings, Elsevier Ltd, Volume 4, Number 2, p.2263-2268 (2017)

URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018409654&doi=10.1016%2fj.matpr.2017.02.074&partnerID=40&md5=30b4b5bdbfaf6871eb742355abb933fb

Keywords:

Demand Clustering, Evolutionary Algorithm (GA), Simmulated Annealing, Supply chain management, Web based Agent

Abstract:

One of the supply chain management active research areas is the Ecommerce-based material demand clustering. Demand clustering technique aggregates and combines the requirements with the application of a ecommerce-agent based to give a clear data of supplier requirements from the manufacturers. The analysis of material demand clustering is to offer all the manufacturers vehicle routing cost in a cost effective manner. Once the Ecommerce based agent identifies the raw material suppliers the next step is to devise a shortest route thru which the material is dispatched to all the manufacturers. In this paper we design a genetic approach to solve the shortest route and compare the solution with nearest neighbor solution and simulated annealing technique. © 2017 Elsevier Ltd.

Notes:

cited By 0

Cite this Research Publication

T. S. Rao, “A Comparative Evaluation of GA and SA TSP in a Supply Chain Network”, in Materials Today: Proceedings, 2017, vol. 4, pp. 2263-2268.

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