Abstract
In this chapter, a new Hybrid Algorithm (HA)-based approach has been developed to facilitate the integration and optimization of these two systems. To improve the optimization performance of the approach, the efficient genetic representation, operator, and local search strategy have been developed. Experimental studies have been used to test the performance of the proposed approach and make the comparisons between this approach and some previous works. The results show that the research on Integrated Process Planning and Scheduling (IPPS) is necessary and the proposed approach is a promising and very effective method on the research of IPPS.
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References
Franz R (2006) Representations for genetic and evolutionary algorithms. Springer, Verlag, Berling, Heidelberg, Netherlands
Glover F, Laguna M (1997) Tabu search. Kluwer Academic Publishers
Nowicki E, Smutnicki C (1996) A fast taboo search algorithm for the job shop scheduling problem. Manage Sci 42(6):797–813
Langdon WB, Qureshi A (1995) Genetic programming—computers using “Natural Selection” to generate programs. Technical Report RN/95/76, Gower Street, London WCIE 6BT, UK
Chan FTS, Kumar V, Tiwari MK (2006) Optimizing the performance of an integrated process planning and scheduling problem: an AIS-FLC based approach. In: Proceedings of CIS, IEEE
Kim KH, Song JY, Wang KH (1997) A negotiation based scheduling for items with flexible process plans. Comput Ind Eng 33(3–4):785–788
Kim YK (2003) A set of data for the integration of process planning and job shop scheduling. Available at http://syslab.chonnam.ac.kr/links/data-pp&s.doc
Kim YK, Park K, Ko J (2003) A symbiotic evolutionary algorithm for the integration of process planning and job shop scheduling. Comput Oper Res 30:1151–1171
Leung CW, Wong TN, Mak KL, Fung RYK (2009) Integrated process planning and scheduling by an agent-based ant colony optimization. Comput Ind Eng. https://doi.org/10.1016/j.cie.2009.09.003
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Li, X., Gao, L. (2020). An Effective Hybrid Algorithm for IPPS. In: Effective Methods for Integrated Process Planning and Scheduling. Engineering Applications of Computational Methods, vol 2. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-55305-3_12
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DOI: https://doi.org/10.1007/978-3-662-55305-3_12
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