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Dispatching strategies for managing uncertainties in automated manufacturing systems

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  • Jain, S.
  • Foley, W.J.

Abstract

Manufacturers in the western world need to exploit and perfect all their strengths to reduce the flight of manufacturing to global outsourcing destinations. Use of automated manufacturing systems (AMSs) is one such strength that needs to be improved to perfection. One area for improvement is the management of uncertainties on the production floor. This paper explores strategies for modifying detailed event list schedules following the occurrence of an interruption. Advanced planning and scheduling (APS) software packages provide a detailed advance plan of production events. However, the execution of this advance plan is disrupted by a myriad of unanticipated interruptions, such as machine breakdowns, yield variations, and hot jobs. The alternatives available to respond to such interruptions can be classified in four groups: regenerating the complete schedule using APS, switching to dispatching mode, modifying the existing schedule, and continuing to follow the schedule and letting the production system gradually absorb the impact of the interruption. Regeneration of the complete schedule using APS requires a large computation effort, may result in large changes in the schedule, and hence is not recommended. This paper reports on an experimental study for evaluating 10 strategies for responding to machine failures in AMSs that broadly fall in the latter three groups. The strategies are evaluated using simulation under an experimental design with manufacturing scenario, load level, severity and duration of interruptions as factors. The results are analyzed to understand the strengths and weaknesses of the considered strategies and develop recommendations.

Suggested Citation

  • Jain, S. & Foley, W.J., 2016. "Dispatching strategies for managing uncertainties in automated manufacturing systems," European Journal of Operational Research, Elsevier, vol. 248(1), pages 328-341.
    • Handle: RePEc:eee:ejores:v:248:y:2016:i:1:p:328-341
    DOI: 10.1016/j.ejor.2015.06.060
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    References listed on IDEAS

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    1. James C. Bean & John R. Birge & John Mittenthal & Charles E. Noon, 1991. "Matchup Scheduling with Multiple Resources, Release Dates and Disruptions," Operations Research, INFORMS, vol. 39(3), pages 470-483, June.
    2. Sabuncuoglu, I. & Bayiz, M., 2000. "Analysis of reactive scheduling problems in a job shop environment," European Journal of Operational Research, Elsevier, vol. 126(3), pages 567-586, November.
    3. Georgiadis, Patroklos & Michaloudis, Charalampos, 2012. "Real-time production planning and control system for job-shop manufacturing: A system dynamics analysis," European Journal of Operational Research, Elsevier, vol. 216(1), pages 94-104.
    4. Xiong, Jian & Xing, Li-ning & Chen, Ying-wu, 2013. "Robust scheduling for multi-objective flexible job-shop problems with random machine breakdowns," International Journal of Production Economics, Elsevier, vol. 141(1), pages 112-126.
    5. Piplani, Rajesh & Wetjens, Dennis, 2007. "Evaluation of entropy-based dispatching in flexible manufacturing systems," European Journal of Operational Research, Elsevier, vol. 176(1), pages 317-331, January.
    6. Aytug, Haldun & Lawley, Mark A. & McKay, Kenneth & Mohan, Shantha & Uzsoy, Reha, 2005. "Executing production schedules in the face of uncertainties: A review and some future directions," European Journal of Operational Research, Elsevier, vol. 161(1), pages 86-110, February.
    7. Jahangirian, Mohsen & Eldabi, Tillal & Naseer, Aisha & Stergioulas, Lampros K. & Young, Terry, 2010. "Simulation in manufacturing and business: A review," European Journal of Operational Research, Elsevier, vol. 203(1), pages 1-13, May.
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    Cited by:

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    3. Yin, Yunqiang & Cheng, T.C.E. & Wang, Du-Juan, 2016. "Rescheduling on identical parallel machines with machine disruptions to minimize total completion time," European Journal of Operational Research, Elsevier, vol. 252(3), pages 737-749.

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