Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/64140
Title: A multiple ant colony optimisation approach for a multi-objective manufacturing rescheduling problem
Authors: Kumar, V
Mishra, N
Chan, FTS 
Kumar, N
Verma, A
Issue Date: 2011
Publisher: Springer
Source: In L Wang, AHC Ng & K Deb (Eds.), Multi-objective evolutionary optimisation for product design and manufacturing, p. 343-361. London ; New York: Springer, 2011 How to cite?
Abstract: Manufacturing scheduling is a well-known complex optimisation problem. A flexible manufacturing system on one side eases the manufacturing processes but on the other hand it increases the complexity in the decision making processes. This complexity further enhances when disruption in the manufacturing processes occurs or when arrival of new orders is considered. This requires rescheduling of the whole operation, which is a complex decision making process. Realising this complexity and taking into account the contradictory objective of making a trade-off between costs and time, this research aims to generate an effective manufacturing schedule. The existing approach of rescheduling sometimes generates entirely a new plan that requires a lot of changes in the decisions, which is not preferable by manufacturing firms. Therefore, in this research whenever a disruption occurs or a new order arrives, the proposed approach reschedules the remaining manufacturing operations in such a way that minimum changes occur in the original manufacturing plan. Evolutionary optimisation methods have been quite successful and widely addressed by researchers to handle such complex multi-objective optimisation problems because of their ability to find multiple optimal solutions in one single simulation run. Inspired by this, the present research proposes a multiple ant colony optimisation (MACO) algorithm to resolve the computational complexity of a manufacturing rescheduling problem. The performance of the proposed MACO algorithm will be compared with the simple ant colony optimisation (ACO) to judge its robustness and efficacy.
URI: http://hdl.handle.net/10397/64140
ISBN: 9780857296528 (electronic bk.)
0857296523 (electronic bk.)
9780857296177
DOI: 10.1007/978-0-85729-652-8_12
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