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Stochastic make-to-stock inventory deployment problem: an endosymbiotic psychoclonal algorithm based approach

Kumar, Vikas orcid logoORCID: 0000-0002-8062-7123, Kumar, Prakash, Tiwari, Manoj Kumar and Chan, Felix T.S. (2006) Stochastic make-to-stock inventory deployment problem: an endosymbiotic psychoclonal algorithm based approach. International Journal of Production Research, 44 (11). pp. 2245-2263. ISSN 1366-588X

Integrated steel manufacturers (ISMs) have no specific product, they just produce finished product from the ore. This enhances the uncertainty prevailing in the ISM regarding the nature of the finished product and significant demand by customers. At present low cost mini-mills are giving firm competition to ISMs in terms of cost, and this has compelled the ISM industry to target customers who want exotic products and faster reliable deliveries. To meet this objective, ISMs are exploring the option of satisfying part of their demand by converting strategically placed products, this helps in increasing the variability of product produced by the ISM in a short lead time. In this paper the authors have proposed a new hybrid evolutionary algorithm named endosymbiotic-psychoclonal (ESPC) to decide what and how much to stock as a semi-product in inventory. In the proposed theory, the ability of previously proposed psychoclonal algorithms to exploit the search space has been increased by making antibodies and antigen more co-operative interacting species. The efficacy of the proposed algorithm has been tested on randomly generated datasets and the results compared with other evolutionary algorithms such as genetic algorithms (GA) and simulated annealing (SA). The comparison of ESPC with GA and SA proves the superiority of the proposed algorithm both in terms of quality of the solution obtained and convergence time required to reach the optimal/near optimal value of the solution.
Item Type:Article (Published)
Subjects:Engineering > Production engineering
Computer Science > Artificial intelligence
Computer Science > Algorithms
DCU Faculties and Centres:DCU Faculties and Schools > DCU Business School
Publisher:Taylor and Francis
Official URL:http://dx.doi.org/10.1080/00207540500459678
Copyright Information:Copyright 2006 Taylor & Francis
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
ID Code:15776
Deposited On:02 Nov 2010 11:58 by Dr Vikas Kumar . Last Modified 25 Nov 2020 14:16

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