Stochastic customer order scheduling using simulation-based genetic algorithm

Xu, Xiaoyun; Zhao, Yaping; Li, Haidong; Zhou, Zihuan; Liu, Yanni

doi:10.1109/wsc.2015.7408343

Cited by 3 publications

(6 citation statements)

References 27 publications

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“…The problem is shown to be computationally difficult and several important optimality properties are provided. Simulation optimization methods are adopted in (Xu et al. , 2015b; Zhao et al.…”

Section: Literature Reviewmentioning

confidence: 99%

“…, 2004). In this computational study, the primary parameters of GA, SPSA and SA are set similarly to those in (Xu et al. , 2015b; Wang and Spall, 2003; Kim et al.…”

Section: Computational Experimentsmentioning

confidence: 99%

“…The problem is shown to be computationally difficult and several important optimality properties are provided. Simulation optimization methods are adopted in (Xu et al, 2015b;Zhao et al, 2018a) to further improve the computational performance. Zhao et al (2018b) further consider the same objective with product-dependent setup time considerations.…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, they assume that all customer orders are available at time 0, and only very limited scenarios are used to model uncertainties. Based on the above literature review, it can be seen that the problem studied in this research shares the same objective and machine environment setting as those in (Xu et al, 2015b(Xu et al, , 2016Zhao et al, 2018a), but in addition considers resource allocation constraints. In contrast, none of the above literature studies the effect of resource allocation while scheduling customer orders.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Luu et al, 2002;Min and Cheng, 1999;Van Laarhoven et al, 1992;Kim et al, 2002;Melouk et al, 2004). In this computational study, the primary parameters of GA, SPSA and SA are set similarly to those in (Xu et al, 2015b;Wang and Spall, 2003;Kim et al, 2002), respectively.…”

Section: Experiments Designmentioning

confidence: 99%

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Resource-controlled stochastic customer order scheduling via particle swarm optimization with bound information

Zhao

Kong

et al. 2022

IMDS

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PurposeCycle time reduction is important for order fulling process but often subject to resource constraints. This study considers an unrelated parallel machine environment where orders with random demands arrive dynamically. Processing speeds are controlled by resource allocation and subject to diminishing marginal returns. The objective is to minimize long-run expected order cycle time via order schedule and resource allocation decisions.Design/methodology/approachA stochastic optimization algorithm named CAP is proposed based on particle swarm optimization framework. It takes advantage of derived bound information to improve local search efficiency. Parameter impacts including demand variance, product type number, machine speed and resource coefficient are also analyzed through theoretic studies. The algorithm is evaluated and benchmarked with four well-known algorithms via extensive numerical experiments.FindingsFirst, cycle time can be significantly improved when demand randomness is reduced via better forecasting. Second, achieving processing balance should be of top priority when considering resource allocation. Third, given marginal returns on resource consumption, it is advisable to allocate more resources to resource-sensitive machines.Originality/valueA novel PSO-based optimization algorithm is proposed to jointly optimize order schedule and resource allocation decisions in a dynamic environment with random demands and stochastic arrivals. A general quadratic resource consumption function is adopted to better capture diminishing marginal returns.

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Section: Literature Reviewmentioning

confidence: 99%

“…, 2004). In this computational study, the primary parameters of GA, SPSA and SA are set similarly to those in (Xu et al. , 2015b; Wang and Spall, 2003; Kim et al.…”

Section: Computational Experimentsmentioning

confidence: 99%