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A Harmony Search-Based Hybrid Intelligent Optimization Model for Order Planning with Learning Effects

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Intelligent Decision-making Models for Production and Retail Operations

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Abstract

This chapter addresses a multi-objective multi-site order planning (MMOP) problem in make-to-order manufacturing with the consideration of various real-world features such as production uncertainties and learning effects. The mathematical model for this order planning problem is presented with the objectives of minimizing the total tardiness of all orders, the total throughput time of all orders, and the total idle time of all production departments, respectively. A novel harmony search-based hybrid intelligent optimization (HSHIO) approach, integrating a harmony search-based Pareto optimization (HSPO) process and a Monte Carlo simulation (MCS) process, is presented to handle this problem. Extensive experiments are conducted to evaluate the effectiveness of the proposed approach based on industrial data. Results demonstrate that (1) the proposed approach can handle the problem investigated effectively; and (2) the HSPO process can generate the optimization performance superior to those generated by a multi-objective genetic algorithm (NSGA-II)-based process and an industrial method.

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Authors and Affiliations

  1. Business School, Sichuan University, Chengdu, China

    Zhaoxia Guo

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  1. Zhaoxia Guo
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Correspondence to Zhaoxia Guo .

Appendix

Appendix

See Tables 8.10, 8.11 and 8.12.

Table 8.10 Workload (standard man days) of each production process of each order (experiment 1)
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Table 8.11 Workload (standard man days) of each production process of each order (experiment 2)
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Table 8.12 Workload (standard man days) of each production process of each order (experiment 3)
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Guo, Z. (2016). A Harmony Search-Based Hybrid Intelligent Optimization Model for Order Planning with Learning Effects. In: Intelligent Decision-making Models for Production and Retail Operations. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-52681-1_8

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  • DOI: https://doi.org/10.1007/978-3-662-52681-1_8

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  • Print ISBN: 978-3-662-52679-8

  • Online ISBN: 978-3-662-52681-1

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