Hybrid Estimation of Distribution Algorithm for Solving the Stochastic Job Shop Scheduling Problem

Xiao, Shichang

doi:10.3901/jme.2015.20.027

Cited by 4 publications

(4 citation statements)

References 6 publications

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“…To optimize the SJSSP, the HEDA proposed by Xiao et al [34] is adopted. To enhance the population diversity of EDA [35], the recombination process and (µ + λ) selection method of (µ + λ) − ES [36] are combined with EDA.…”

Section: Framework Of Hedamentioning

confidence: 99%

“…To enhance population diversity and avoid premature convergence in the evolutionary process of the EDA, a recombination method based on the inherit ratio of parent operation is used [34].…”

Section: Recombination and Selectionmentioning

confidence: 99%

“…The number of parents individuals (µ) is the same as the population size, by which λ = 100 new offspring will be generated through the recombination process. In addition, the number of positioning jobs is set to n/2 by referring to the previous research [34]. Therefore, the parameters setting of HEDA is shown in Table 2.…”

Section: Experiments Settingsmentioning

confidence: 99%

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Surrogate Measures for the Robust Scheduling of Stochastic Job Shop Scheduling Problems

2017

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This study focuses on surrogate measures (SMs) of robustness for the stochastic job shop scheduling problems (SJSSP) with uncertain processing times. The objective is to provide the robust predictive schedule to the decision makers. The mathematical model of SJSSP is formulated by considering the railway execution strategy, which defined that the starting time of each operation cannot be earlier than its predictive starting time. Robustness is defined as the expected relative deviation between the realized makespan and the predictive makespan. In view of the time-consuming characteristic of simulation-based robustness measure (RMsim), this paper puts forward new SMs and investigates their performance through simulations. By utilizing the structure of schedule and the available information of stochastic processing times, two SMs on the basis of minimizing the robustness degradation on the critical path and the non-critical path are suggested. For this purpose, a hybrid estimation of distribution algorithm (HEDA) is adopted to conduct the simulations. To analyze the performance of the presented SMs, two computational experiments are carried out. Specifically, the correlation analysis is firstly conducted by comparing the coefficient of determination between the presented SMs and the corresponding simulation-based robustness values with those of the existing SMs. Secondly, the effectiveness and the performance of the presented SMs are further validated by comparing with the simulation-based robustness measure under different uncertainty levels. The experimental results demonstrate that the presented SMs are not only effective for assessing the robustness of SJSSP no matter the uncertainty levels, but also require a tremendously lower computational burden than the simulation-based robustness measure.

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Section: Framework Of Hedamentioning

confidence: 99%

“…To enhance population diversity and avoid premature convergence in the evolutionary process of the EDA, a recombination method based on the inherit ratio of parent operation is used [34].…”

Section: Recombination and Selectionmentioning

confidence: 99%

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Surrogate Measures for the Robust Scheduling of Stochastic Job Shop Scheduling Problems

2017

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“…The production task sorting of an integrated job-shop with multiple production lines (MPLIJS) can be equivalent to a complex combinatory optimization problem. During the production planning, the job-shop should give fully consideration to the production technique and organization for the products [15][16][17][18]. Abdelmaguid [19] summarized the production task sorting and adjustment MPLIJS as the operation sequence update of the integrated job-shop with multi-channel buffer zones under the just-in-time production model, and took production load balancing and material consumption levelling as the goals of job-shop production control.…”

Section: Introductionmentioning

confidence: 99%

Production Management of Multi-Objective Flexible Job-Shop Based on Improved PSO

Mao¹

2021

Int. j. simul. model.

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It is of great practical significance to improve the traditional particle swarm optimization (PSO) for the production management of multi-objective flexible job-shop. However, the current studies have not solved the problem that the traditional PSO cannot apply to the production and processing environment with numerous uncertain changes. Therefore, this paper improves the PSO for the production management of multi-objective flexible job-shop under different conditions. Firstly, the author modelled the production management of high-dimensional dynamic multi-objective flexible job-shop, and explained the pre-reaction dynamic rescheduling method for production management. Then, the PSO was improved in terms of inertial weight, learning factors, global search ability, and local search ability, and the dynamic response mechanism was presented for the improved algorithm. The feasibility of the improved PSO was demonstrated through experiments. The research provides a reference for applying the improved PSO to the optimization in other fields.

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A new double flexible job-shop scheduling problem integrating processing time, green production, and human factor indicators

Gong

Deng

Gong

et al. 2018

Journal of Cleaner Production

129

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Hybrid Estimation of Distribution Algorithm for Solving the Stochastic Job Shop Scheduling Problem

Cited by 4 publications

References 6 publications

Surrogate Measures for the Robust Scheduling of Stochastic Job Shop Scheduling Problems

Surrogate Measures for the Robust Scheduling of Stochastic Job Shop Scheduling Problems

Production Management of Multi-Objective Flexible Job-Shop Based on Improved PSO

A new double flexible job-shop scheduling problem integrating processing time, green production, and human factor indicators

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