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Sustainable Integrated Process Planning and Scheduling Optimization Using a Genetic Algorithm with an Integrated Chromosome Representation

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  • Hyun Cheol Lee

    (School of Business, Korea Aerospace University, 76 Hanggongdaehak-ro, Goyang-si 10540, Korea)

  • Chunghun Ha

Abstract

This paper proposes a genetic algorithm (GA) to find the pseudo-optimum of integrated process planning and scheduling (IPPS) problems. IPPS is a combinatorial optimization problem of the NP-complete class that aims to solve both process planning and scheduling simultaneously. The complexity of IPPS is very high because it reflects various flexibilities and constraints under flexible manufacturing environments. To cope with it, existing metaheuristics for IPPS have excluded some flexibilities and constraints from consideration or have built a complex structured algorithm. Particularly, GAs have been forced to construct multiple chromosomes to account for various flexibilities, which complicates algorithm procedures and degrades performance. The proposed new integrated chromosome representation makes it possible to incorporate various flexibilities into a single string. This enables the adaptation of a simple and typical GA procedure and previously developed genetic operators. Experiments on a set of benchmark problems showed that the proposed GA improved makespan by an average of 17% against the recently developed metaheuristics for IPPS in much shorter computation times.

Suggested Citation

  • Hyun Cheol Lee & Chunghun Ha, 2019. "Sustainable Integrated Process Planning and Scheduling Optimization Using a Genetic Algorithm with an Integrated Chromosome Representation," Sustainability, MDPI, vol. 11(2), pages 1-23, January.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:2:p:502-:d:199012
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    References listed on IDEAS

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    1. Zhang, Luping & Wong, T.N., 2015. "An object-coding genetic algorithm for integrated process planning and scheduling," European Journal of Operational Research, Elsevier, vol. 244(2), pages 434-444.
    2. Thomalla, Christoph S., 2001. "Job shop scheduling with alternative process plans," International Journal of Production Economics, Elsevier, vol. 74(1-3), pages 125-134, December.
    3. Modi, Bimal K. & Shanker, Kripa, 1994. "A formulation and solution methodology for part movement minimization and workload balancing at loading decisions in FMS," International Journal of Production Economics, Elsevier, vol. 34(1), pages 73-82, February.
    4. Wenzhu Liao & Tong Wang, 2018. "Promoting Green and Sustainability: A Multi-Objective Optimization Method for the Job-Shop Scheduling Problem," Sustainability, MDPI, vol. 10(11), pages 1-19, November.
    5. S. Zhang & T. N. Wong, 2018. "Integrated process planning and scheduling: an enhanced ant colony optimization heuristic with parameter tuning," Journal of Intelligent Manufacturing, Springer, vol. 29(3), pages 585-601, March.
    6. Li, Xinyu & Shao, Xinyu & Gao, Liang & Qian, Weirong, 2010. "An effective hybrid algorithm for integrated process planning and scheduling," International Journal of Production Economics, Elsevier, vol. 126(2), pages 289-298, August.
    7. Li, Xinyu & Gao, Liang, 2016. "An effective hybrid genetic algorithm and tabu search for flexible job shop scheduling problem," International Journal of Production Economics, Elsevier, vol. 174(C), pages 93-110.
    8. Jianping Dou & Jun Li & Chun Su, 2018. "A discrete particle swarm optimisation for operation sequencing in CAPP," International Journal of Production Research, Taylor & Francis Journals, vol. 56(11), pages 3795-3814, June.
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    Cited by:

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    4. Qingmiao Liao & Jianjun Yang & Yong Zhou, 2019. "Sustainable Scheduling of an Automatic Pallet Changer System by Multi-Objective Evolutionary Algorithm with First Piece Inspection," Sustainability, MDPI, vol. 11(5), pages 1-24, March.

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