A hybrid estimation of distribution algorithm for simulation-based scheduling in a stochastic permutation flowshop

Wang, K.; Choi, Shiu Hong; Lu, Haifei

doi:10.1016/j.cie.2015.09.007

Cited by 26 publications

(14 citation statements)

References 48 publications

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“…In the literature on stochastic scheduling, various types of probability distributions for stochastic processing times have been studied, such as exponential distribution [3], normal distribution [30,31] and uniform distribution [25]. One method is to treat both the stochastic processing time and the scheduling objective as the stochastic variables, which aims at providing the processing ordering of the tasks on the machines with an expected optimization model.…”

Section: Problem Description and Analysismentioning

confidence: 99%

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: Problem Description and Analysismentioning

confidence: 99%

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

2017

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“…In this research, each factor has three levels: Psize (30,50,80),  % (10%, 20%, 30%), and a s (10%, 20%, 30%).…”

Section: Parameter Calibration Of Edamentioning

confidence: 99%

Estimation of Distribution Algorithm for Energy-Efficient Scheduling in Turning Processes

et al. 2016

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Abstract:With the increasing concern for the environment, energy-efficient scheduling of the manufacturing industry is becoming urgent and popular. In turning processes, both spindle speed and processing time affect the final energy consumption and thus the spindle speed and scheduling scheme need to be optimized simultaneously. Since the turning workshop can be regarded as the flexible flow shop, this paper formulates a mixed integer nonlinear programming model for the energy-efficient scheduling of the flexible flow shop. Accordingly, a new decoding method is developed for the optimization of both spindle speed and scheduling scheme simultaneously, and an estimation of the distribution algorithm adopting the new decoding method is proposed to solve large-size problems. The parameters of this algorithm are determined by statistics from a simplified practical case. Validation results of the proposed method show that the makespan is shortened to a large extent, and the consumed energy is significantly saved. These results demonstrate the effectiveness of the proposed mathematical model and algorithm.

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“…In this research, each factor has three levels: Psize (30,50,80), η% (10%, 20%, 30%), and a s (10%, 20%, 30%).…”

Section: Parameter Calibration Of Edamentioning

confidence: 99%

“…A large number of research groups have paid efforts to improve the performance of EDA algorithm [47][48][49]. The algorithm has been successfully applied to solve flow shop scheduling problems [50][51][52] and flexible flow shop scheduling problems, and it has obtained promising scheduling results. For its advantages, EDA is used to solve the energy-efficient scheduling problems in flexible flow shops in this paper.…”

Section: Eda Algorithmmentioning

confidence: 99%

Estimation of Distribution Algorithm for Energy-Efficient Scheduling in Turning Processes

Wang

Rao

Zhang

et al. 2016

Preprint

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With the increasing concern for the environment, energy-efficient scheduling of the manufacturing industry is becoming urgent and popular. In turning processes, both spindle speed and processing time affect the final energy consumption and thus the spindle speed and scheduling scheme need to be optimized simultaneously. Since the turning workshop can be regarded as the flexible flow shop, this paper formulates a mixed integer nonlinear programming model for the energy-efficient scheduling of the flexible flow shop. Accordingly, a new decoding method is developed for the optimization of both spindle speed and scheduling scheme simultaneously, and an estimation of the distribution algorithm adopting the new decoding method is proposed to solve large-size problems. The parameters of this algorithm are determined by statistics from a simplified practical case. Validation results of the proposed method show that the makespan is shortened to a large extent, and the consumed energy is significantly saved. These results demonstrate the effectiveness of the proposed mathematical model and algorithm.

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A hybrid estimation of distribution algorithm for simulation-based scheduling in a stochastic permutation flowshop

Cited by 26 publications

References 48 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

Estimation of Distribution Algorithm for Energy-Efficient Scheduling in Turning Processes

Estimation of Distribution Algorithm for Energy-Efficient Scheduling in Turning Processes

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