A hybrid differential evolution algorithm for job shop scheduling problems with expected total tardiness criterion

Zhang, Rui; Song, Shiji; Wu, Cheng

doi:10.1016/j.asoc.2012.02.024

Cited by 38 publications

(9 citation statements)

References 30 publications

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“…Furthermore, in was applied in job shop production. Zhang et al [16] adapted this method to deal with the job shop problem in order to minimize the total tardiness. For applications in the supply chain management problem, in particular, for the vehicle routing problem in the supply chain, the relevant studies have been carried out with different objectives and constraints under various conditions.…”

Section: Differential Evolution Algorithm For Solving Other Problemsmentioning

confidence: 99%

Improved Differential Evolution Algorithm for Flexible Job Shop Scheduling Problems

Sriboonchandr

Kriengkorakot

2019

MCA

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This research project aims to study and develop the differential evolution (DE) for use in solving the flexible job shop scheduling problem (FJSP). The development of algorithms were evaluated to find the solution and the best answer, and this was subsequently compared to the meta-heuristics from the literature review. For FJSP, by comparing the problem group with the makespan and the mean relative errors (MREs), it was found that for small-sized Kacem problems, value adjusting with “DE/rand/1” and exponential crossover at position 2. Moreover, value adjusting with “DE/best/2” and exponential crossover at position 2 gave an MRE of 3.25. For medium-sized Brandimarte problems, value adjusting with “DE/best/2” and exponential crossover at position 2 gave a mean relative error of 7.11. For large-sized Dauzere-Peres and Paulli problems, value adjusting with “DE/best/2” and exponential crossover at position 2 gave an MRE of 4.20. From the comparison of the DE results with other methods, it was found that the MRE was lower than that found by Girish and Jawahar with the particle swarm optimization (PSO) method (7.75), which the improved DE was 7.11. For large-sized problems, it was found that the MRE was lower than that found by Warisa (1ST-DE) method (5.08), for which the improved DE was 4.20. The results further showed that basic DE and improved DE with jump search are effective methods compared to the other meta-heuristic methods. Hence, they can be used to solve the FJSP.

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Section: Differential Evolution Algorithm For Solving Other Problemsmentioning

confidence: 99%

Improved Differential Evolution Algorithm for Flexible Job Shop Scheduling Problems

Sriboonchandr

Kriengkorakot

2019

MCA

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“…Scheduling problem, Shahnazari-Shahrezaei et al (2012) present DE to generate the fair shift Nurse Scheduling problem base on a real case study. Zhang et al (2013) adapted DE method dealing with the job shop problem in order to minimize total tardiness. In the line balancing problems, (Sresracoo et al, 2018) presented the DE algorithm for solving the U-shaped Assembly Line Balancing Problem Type 1 (UALBP-1) with the goal was minimize the number of workstations.…”

Section: Literature Reviewmentioning

confidence: 99%

The Improved Differential Evolution Algorithm for Multi-Floor Facility Layout Problems

Ayutthaya¹,

Kriengkorakot²,

Sriboonchandr³

2019

J. Eng. Appl. Sci.

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This research aimed to study the Improved Differential Evolution (IDE) for solving the Multi-floor Facility Layout Problem (MFLP) with the target of minimize the transporting material costand maximize adjacency requirement between the facilities. The IDE algorithm had been evaluated and compared with MULTIPLE, SABLE and the basic DE algorithm by using DE/rand/1/bin and DE/rand/2/bin. For MFLP, the IDE methods were tested with 6 data sets as following:

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“…The warehouse transport vehicles match multiple parallel execution of tasks of the processor, in order to achieve optimal utilization of resources, the scheduler wants as much as possible use of the vehicle [10], the car transporters are assigned task to achieve vehicle resources full use of [9]. Therefore, the algorithm in the final increase the penalty function when seeking the fitness value [11], the punishment is for the uneven distribution of individual fitness. The calculated fitness aspects of process can be subdivided into:…”

Section: Greedy Strategy Improved Analysis Of the Genetic Algorithmmentioning

confidence: 99%

Research of job-shop scheduling problem based on improved crossover strategy genetic algorithm

Liu

Jiao

et al. 2013

Proceedings of 2013 3rd International Conference on Computer Science and Network Technology

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The article proposes a crossover strategy improved genetic algorithm to solve the shop scheduling problemfor the traditional genetic algorithm.The algorithm uses coding method, based on the process and the introduction of the linear weighted fitness function, the greedy count sub-fitness ratio algorithm combined select operation. The improved algorithm is able to improve search efficiency, accuracy, and avoid premature shortcomings.

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A hybrid differential evolution algorithm for job shop scheduling problems with expected total tardiness criterion

Cited by 38 publications

References 30 publications

Improved Differential Evolution Algorithm for Flexible Job Shop Scheduling Problems

Improved Differential Evolution Algorithm for Flexible Job Shop Scheduling Problems

The Improved Differential Evolution Algorithm for Multi-Floor Facility Layout Problems

Research of job-shop scheduling problem based on improved crossover strategy genetic algorithm

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