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Multi-objective process planning and scheduling using controlled elitist non-dominated sorting genetic algorithm

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  • P. Mohapatra
  • A. Nayak
  • S.K. Kumar
  • M.K. Tiwari

Abstract

The integration of process planning and scheduling is considered as a critical component in manufacturing systems. In this paper, a multi-objective approach is used to solve the planning and scheduling problem. Three different objectives considered in this work are minimisation of makespan, machining cost and idle time of machines. To solve this integration problem, we propose an improved controlled elitist non-dominated sorting genetic algorithm (NSGA) to take into account the computational intractability of the problem. An illustrative example and five test cases have been taken to demonstrate the capability of the proposed model. The results confirm that the proposed multi-objective optimisation model gives optimal and robust solutions. A comparative study between proposed algorithm, controlled elitist NSGA and NSGA-II show that proposed algorithm significantly reduces scheduling objectives like makespan, cost and idle time, and is computationally more efficient.

Suggested Citation

  • P. Mohapatra & A. Nayak & S.K. Kumar & M.K. Tiwari, 2015. "Multi-objective process planning and scheduling using controlled elitist non-dominated sorting genetic algorithm," International Journal of Production Research, Taylor & Francis Journals, vol. 53(6), pages 1712-1735, March.
    • Handle: RePEc:taf:tprsxx:v:53:y:2015:i:6:p:1712-1735
    DOI: 10.1080/00207543.2014.957872
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    Cited by:

    1. Xianhua Wu & Yaru Cao & Yang Xiao & Ji Guo, 2020. "Finding of urban rainstorm and waterlogging disasters based on microblogging data and the location-routing problem model of urban emergency logistics," Annals of Operations Research, Springer, vol. 290(1), pages 865-896, July.
    2. Boxuan Zhao & Jianmin Gao & Kun Chen & Ke Guo, 2018. "Two-generation Pareto ant colony algorithm for multi-objective job shop scheduling problem with alternative process plans and unrelated parallel machines," Journal of Intelligent Manufacturing, Springer, vol. 29(1), pages 93-108, January.
    3. Mohammad Reza Hosseinzadeh & Mehdi Heydari & Mohammad Mahdavi Mazdeh, 2022. "Mathematical modeling and two metaheuristic algorithms for integrated process planning and group scheduling with sequence-dependent setup time," Operational Research, Springer, vol. 22(5), pages 5055-5105, November.

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