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An Improved Intelligent Water Drops Optimization Algorithm for Achieving Single and Multiple Objective Job Shop Scheduling Solutions

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Handbook of Manufacturing Engineering and Technology

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Abstract

Job shop scheduling problem (JSSP) is a typical scheduling problem that aims to generate an optimal schedule to assign all the operations to the production equipments. JSSPs can be categorized into single objective JSSP (SOJSSP) and multiple objective JSSP (MOJSSP) based on the optimization objectives considered. SOJSSP involves generating schedules to allocate operations to different machines considering only one objective, while MOJSSP considers more than one objective in the scheduling process. SOJSSP and MOJSSP are typical NP-hard optimization problems which have significant values in real production. Intelligent Water Drops (IWD) is a new type of meta-heuristics which shows excellent ability of solving optimization problems. In this research, IWD is improved and customized to solve SOJSSP and MOJSSP problems. Experiments have been conducted, and the results show that the enhanced algorithms can solve these two types of problems better compared with current literature. To the best of the authors’ knowledge, this is among the first research employing IWD for solving SOJSSP and MOJSSP.

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Authors and Affiliations

  1. Department of Mechanical Engineering, National University of Singapore, 9 Engineering Drive 1, 117576, Singapore, Singapore

    Dr. S. H. Niu, Dr. S. K. Ong & Prof. A. Y. C. Nee

Authors
  1. Dr. S. H. Niu
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  2. Dr. S. K. Ong
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  3. Prof. A. Y. C. Nee
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Correspondence to S. H. Niu .

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Editors and Affiliations

  1. National University of Singapore, Singapore, Singapore

    Andrew Nee

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© 2014 Springer-Verlag London

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Niu, S.H., Ong, S.K., Nee, A.Y.C. (2014). An Improved Intelligent Water Drops Optimization Algorithm for Achieving Single and Multiple Objective Job Shop Scheduling Solutions. In: Nee, A. (eds) Handbook of Manufacturing Engineering and Technology. Springer, London. https://doi.org/10.1007/978-1-4471-4976-7_25-1

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  • DOI: https://doi.org/10.1007/978-1-4471-4976-7_25-1

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  • Online ISBN: 978-1-4471-4976-7

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