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Integrated production planning and scheduling for a mixed batch job-shop based on alternant iterative genetic algorithm

Author

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  • Hong-Sen Yan

    (Southeast University, Nanjing, P. R. China)

  • Xiao-Qin Wan

    (Southeast University, Nanjing, P. R. China)

  • Fu-Li Xiong

    (1] Southeast University, Nanjing, P. R. China[2] Xi’an Jiaotong University, Xi’an, P. R. China)

Abstract

An integrated optimization production planning and scheduling based on alternant iterative genetic algorithm is proposed here. The operation constraints to ensure batch production successively are determined in the first place. Then an integrated production planning and scheduling model is formulated based on non-linear mixed integer programming. An alternant iterative method by hybrid genetic algorithm (AIHGA) is employed to solve it, which operates by the following steps: a plan is given to find a schedule by hybrid genetic algorithm; in turn, a schedule is given to find a new plan using another hybrid genetic algorithm. Two hybrid genetic algorithms are alternately run to optimize the plan and schedule simultaneously. Finally a comparison is made between AIHGA and a monolithic optimization method based on hybrid genetic algorithm (MOHGA). Computational results show that AIHGA is of higher convergence speed and better performance than MOHGA. And the objective values of the former are an average of 12.2% less than those of the latter in the same running time.

Suggested Citation

  • Hong-Sen Yan & Xiao-Qin Wan & Fu-Li Xiong, 2015. "Integrated production planning and scheduling for a mixed batch job-shop based on alternant iterative genetic algorithm," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(8), pages 1250-1258, August.
    • Handle: RePEc:pal:jorsoc:v:66:y:2015:i:8:p:1250-1258
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    Cited by:

    1. Lingye Tan & Tiong Lee Kong & Ziyang Zhang & Ahmed Sayed M. Metwally & Shubham Sharma & Kanta Prasad Sharma & Sayed M. Eldin & Dominik Zimon, 2023. "Scheduling and Controlling Production in an Internet of Things Environment for Industry 4.0: An Analysis and Systematic Review of Scientific Metrological Data," Sustainability, MDPI, vol. 15(9), pages 1-37, May.
    2. Feng, Yanling & Li, Guo & Sethi, Suresh P., 2018. "A three-layer chromosome genetic algorithm for multi-cell scheduling with flexible routes and machine sharing," International Journal of Production Economics, Elsevier, vol. 196(C), pages 269-283.
    3. 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.
    4. Kangzhou Wang & Shulin Lan & Yingxue Zhao, 2017. "A genetic-algorithm-based approach to the two-echelon capacitated vehicle routing problem with stochastic demands in logistics service," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(11), pages 1409-1421, November.

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