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Genetic Algorithm to Solve Multi-Period, Multi-Product, Bi-Echelon Supply Chain Network Design Problem

Genetic Algorithm to Solve Multi-Period, Multi-Product, Bi-Echelon Supply Chain Network Design Problem

R. Dhanalakshmi, P. Parthiban, K. Ganesh, T. Arunkumar
Copyright: © 2009 |Volume: 2 |Issue: 4 |Pages: 19
ISSN: 1935-5726|EISSN: 1935-5734|ISSN: 1935-5726|EISBN13: 9781616920708|EISSN: 1935-5734|DOI: 10.4018/jisscm.2009062902
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MLA

Dhanalakshmi, R., et al. "Genetic Algorithm to Solve Multi-Period, Multi-Product, Bi-Echelon Supply Chain Network Design Problem." IJISSCM vol.2, no.4 2009: pp.24-42. http://doi.org/10.4018/jisscm.2009062902

APA

Dhanalakshmi, R., Parthiban, P., Ganesh, K., & Arunkumar, T. (2009). Genetic Algorithm to Solve Multi-Period, Multi-Product, Bi-Echelon Supply Chain Network Design Problem. International Journal of Information Systems and Supply Chain Management (IJISSCM), 2(4), 24-42. http://doi.org/10.4018/jisscm.2009062902

Chicago

Dhanalakshmi, R., et al. "Genetic Algorithm to Solve Multi-Period, Multi-Product, Bi-Echelon Supply Chain Network Design Problem," International Journal of Information Systems and Supply Chain Management (IJISSCM) 2, no.4: 24-42. http://doi.org/10.4018/jisscm.2009062902

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Abstract

In many multi-stage manufacturing supply chains, transportation related costs are a significant portion of final product costs. It is often crucial for successful decision making approaches in multi-stage manufacturing supply chains to explicitly account for non-linear transportation costs. In this article, we have explored this problem by considering a Two-Stage Production-Transportation (TSPT). A two-stage supply chain that faces a deterministic stream of external demands for a single product is considered. A finite supply of raw materials, and finite production at stage one has been assumed. Items are manufactured at stage one and transported to stage two, where the storage capacity of the warehouses is limited. Packaging is completed at stage two (that is, value is added to each item, but no new items are created), and the finished goods inventories are stored which is used to meet the final demand of customers. During each period, the optimized production levels in stage one, as well as transportation levels between stage one and stage two and routing structure from the production plant to warehouses and then to customers, must be determined. The authors consider “different cost structures,” for both manufacturing and transportation. This TSPT model with capacity constraint at both stages is optimized using Genetic Algorithms (GA) and the results obtained are compared with the results of other optimization techniques of complete enumeration, LINDO, and CPLEX.

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