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A Multiple Ant Colony Optimisation Approach for a Multi-objective Manufacturing Rescheduling Problem

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Book cover Multi-objective Evolutionary Optimisation for Product Design and Manufacturing

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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.

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Author information

Authors and Affiliations

  1. Department of Management, Dublin City University Business School Dublin, Dublin 9, Republic of Ireland

    Vikas Kumar

  2. School of Management and Business, Aberystwyth University, Aberystwyth, UK

    Nishikant Mishra

  3. Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China

    Felix T. S. Chan

  4. Department of Management, School of Management, University of Bath, Bath, BA2 7AY, UK

    Niraj Kumar

  5. Computer Aided Manufacturing Laboratory, Department of Mechanical Engineering, University of Cincinnati, Cincinnati, USA

    Anoop Verma

Authors
  1. Vikas Kumar
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  2. Nishikant Mishra
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  3. Felix T. S. Chan
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  4. Niraj Kumar
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  5. Anoop Verma
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Corresponding author

Correspondence to Vikas Kumar .

Editor information

Editors and Affiliations

  1. , Virtual Systems Research Centre, University of Skövde, Skövde, 541 28, Sweden

    Lihui Wang

  2. , Virtual Systems Research Centre, University of Skövde, Skövde, 541 28, Sweden

    Amos H. C. Ng

  3. , Department of Mechanical Engineering, Indian Institute of Technology, Kanpur, 208016, Uttar Pradesh, India

    Kalyanmoy Deb

Appendices

Appendix 12.A

   
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Appendix 12.B

   
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Appendix 12.C: Steps of MACO algorithm

   
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Kumar, V., Mishra, N., Chan, F.T.S., Kumar, N., Verma, A. (2011). A Multiple Ant Colony Optimisation Approach for a Multi-objective Manufacturing Rescheduling Problem. In: Wang, L., Ng, A., Deb, K. (eds) Multi-objective Evolutionary Optimisation for Product Design and Manufacturing. Springer, London. https://doi.org/10.1007/978-0-85729-652-8_12

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  • DOI: https://doi.org/10.1007/978-0-85729-652-8_12

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