Title: A leagile inventory-location model: formulation and its optimisation

Authors: Sanjay Kumar Shukla, Hung-Da Wan

Addresses: Department of Mechanical Engineering, Center for Advanced Manufacturing and Lean Systems, University of Texas at San Antonio, San Antonio, TX, USA. ' Department of Mechanical Engineering, Center for Advanced Manufacturing and Lean Systems, University of Texas at San Antonio, San Antonio, TX, USA

Abstract: Leagility is defined as the capability of deploying lean and agile paradigms simultaneously. This paper uses transshipments (i.e. monitored movements of stocks among locations at the same echelon) as a strategic tool to achieve leagility in an inventory-location model. Authors have coined a new term |leagile inventory-location model (LILM)| that addresses leagility by managing inventory at numerous locations. In this paper, LILM is first formulated as a non-linear integer programme and then solved in real time with the aid of genetic algorithm (GA), genetic algorithm with chromosome differentiation (GACD) and virus-evolutionary genetic algorithm (VEGA). These algorithms are tested on a simulated 88-retailer problem with rigorous analyses of the results. It is found that, in three out of 13 instances, total costs obtained by VEGA is minimum; while in the remaining, GACD outperforms both the VEGA and the GA. Conversely, performance of GA dominates in terms of CPU time. Impact of various parameters on the results is also scrutinised and reported accordingly.

Keywords: genetic algorithms; chromosome differentiation; leagile inventory-location model; transshipments; virus-evolutionary GAs; agile paradigms; lean paradigms.

DOI: 10.1504/IJOR.2010.033135

International Journal of Operational Research, 2010 Vol.8 No.2, pp.150 - 173

Published online: 09 May 2010 *

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