Genetic algorithm optimisation of a class of inventory control systems

Disney, Stephen Michael ORCID: https://orcid.org/0000-0003-2505-9271, Naim, Mohamed Mohamed ORCID: https://orcid.org/0000-0003-3361-9400 and Towill, Denis Royston 2000. Genetic algorithm optimisation of a class of inventory control systems. International Journal of Production Economics 68 (3) , pp. 259-278. 10.1016/S0925-5273(99)00101-2

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Abstract

The paper describes a procedure for optimising the performance of an industrially designed inventory control system. This has the three classic control policies utilising sales, inventory and pipeline information to set the order rate so as to achieve a desired balance between capacity, demand and minimum associated stock level. A first step in optimisation is the selection of appropriate “benchmark” performance characteristics. Five are considered herein and include inventory recovery to “shock” demands; in-built filtering capability; robustness to production lead-time variations; robustness to pipeline level information fidelity; and systems selectivity. A genetic algorithm for optimising system performance, via these five vectors is described. The optimum design parameters are presented for various vector weightings. This leads to a decision support system for the correct setting of the system controls under various operating scenarios. The paper focuses on a single supply chain interface, however the methodology is also applicable to complete supply chains.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Cardiff Centre for Crime, Law and Justice (CCLJ) Centre for Advanced Manufacturing Systems At Cardiff (CAMSAC)
Subjects:	H Social Sciences > H Social Sciences (General) H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management H Social Sciences > HF Commerce
Uncontrolled Keywords:	Inventory control; Optimisation; Simulation; Ordering algorithms
Additional Information:	PDF uploaded in accordance with publisher's policies at http://www.sherpa.ac.uk/romeo/issn/0925-5273/ (accessed 18.3.16).
Publisher:	Elsevier
ISSN:	0925-5273
Last Modified:	09 Nov 2023 23:27
URI:	https://orca.cardiff.ac.uk/id/eprint/38150

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