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A multi-objective decision-making framework using preference selection index for assembly job shop scheduling problem

Author

Listed:
  • Midhun Paul
  • R. Sridharan
  • T. Radha Ramanan

Abstract

Assembly job shop scheduling problem (AJSSP) is an important NP-hard scheduling problem very difficult to solve because of its complex precedence relationship between jobs and product structures. In the present work, a systematic multi-attribute decision-making methodology namely, preference selection index method is adopted for ranking priority dispatching rules for scheduling an assembly job shop. A simulation model of an assembly job shop with seven work stations and one assembly station is developed for the purpose of experimentation. Eight priority dispatching rules from literature are identified and incorporated in the simulation model. Performance measures considered for analysis include mean flow time, maximum flow time, mean tardiness, maximum tardiness and machine utilisation. Products with single level assembly structure, two level assembly structure and three level assembly structure are examined to demonstrate and to check the applicability of the proposed methodology. The best performing priority dispatching rules are determined in a multi-objective environment.

Suggested Citation

  • Midhun Paul & R. Sridharan & T. Radha Ramanan, 2016. "A multi-objective decision-making framework using preference selection index for assembly job shop scheduling problem," International Journal of Management Concepts and Philosophy, Inderscience Enterprises Ltd, vol. 9(4), pages 362-387.
    • Handle: RePEc:ids:ijmcph:v:9:y:2016:i:4:p:362-387
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    Cited by:

    1. Zamiela, Christian & Hossain, Niamat Ullah Ibne & Jaradat, Raed, 2022. "Enablers of resilience in the healthcare supply chain: A case study of U.S healthcare industry during COVID-19 pandemic," Research in Transportation Economics, Elsevier, vol. 93(C).
    2. Jian Zhang & Guofu Ding & Yisheng Zou & Shengfeng Qin & Jianlin Fu, 2019. "Review of job shop scheduling research and its new perspectives under Industry 4.0," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1809-1830, April.

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