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Job scheduling in virtual manufacturing cells with lot-streaming strategy: a new mathematical model formulation and a genetic algorithm approach

Author

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  • S E Kesen

    (Selcuk University, Konya, Turkey)

  • Z Güngör

    (Gazi University, Ankara, Turkey)

Abstract

This paper discusses the job scheduling problem in virtual manufacturing cells (VMCs) with the objective of makespan minimization. In the VMC scheduling problem, each job undergoes different processing routes and there is a set of machines to process any operation. Jobs are produced in lot and lot-streaming is permitted. In addition, machines are distributed through the facility, which raises the travelling time issue. For this reason, the decisions are machine assignments, starting times and sub-lot sizes of the operations. We develop a new Mixed Integer Linear Programming (MILP) formulation that considers all aspects of the problem. Owing to the intractability matter, it is unlikely that the MILP could provide solutions for big-sized instances within a reasonable amount of time. We therefore present a Genetic Algorithm (GA) with a new chromosome structure for the VMC environment. Based on a wide range of examinations, comparative results show that GA is quite favourable and that it obtains the optimum solution for any of the instances in the case where sub-lot number equals 1.

Suggested Citation

  • S E Kesen & Z Güngör, 2012. "Job scheduling in virtual manufacturing cells with lot-streaming strategy: a new mathematical model formulation and a genetic algorithm approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 63(5), pages 683-695, May.
  • Handle: RePEc:pal:jorsoc:v:63:y:2012:i:5:p:683-695
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    Cited by:

    1. Byung Duk Song & Young Dae Ko, 2017. "Effect of Inspection Policies and Residual Value of Collected Used Products: A Mathematical Model and Genetic Algorithm for a Closed-Loop Green Manufacturing System," Sustainability, MDPI, vol. 9(9), pages 1-14, September.

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