Two-stage three-machine assembly scheduling problem with sum-of-processing-times-based learning effect

Zou, Yunqing; Wang, Dujuan; Lin, Win-Chin; Chen, Jiayang; Yu, Pay-Wen; Wu, Chin‐Chia; Chao, Yuan-Po; Wu, Chin-Chia

doi:10.1007/s00500-019-04301-y

Cited by 9 publications

(4 citation statements)

References 75 publications

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“…These approaches are presented in more than 40% of the short-listed papers that use metaheuristics. In this review, we found several authors who hybridize genetic algorithms with other methods such as tabu search [104][105][106], simulated annealing [107], variable neighborhood search [108,109], kangaroo algorithm [110] and differential evolution [111]. Among other approaches, we found the particle swarm optimization hybridized with local search [38], a hybrid CS-JADE algorithm combining improved cuckoo search algorithms and self-adaptive differential evolution [38], a hybrid SC-VNS mixing society and civilization algorithm with variable neighborhood search [112], and a hybrid VNS-ASHLO algorithm incorporating variable neighborhood search and adaptive simplified human learning optimization [113].…”

Section: Metaheuristicsmentioning

confidence: 99%

Taxonomy of Scheduling Problems with Learning and Deterioration Effects

2022

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In traditional scheduling problems, job processing times are considered constant and known in advance. This assumption is, however, a simplification when it comes to hand-intensive real-life production contexts because workers usually induce variability in the job processing times due to several factors such as learning, monotony, fatigue, psychological factors, etc. These effects can decrease or increase the actual processing time when workers execute a job. The academic literature has reported several modeling and resolution approaches to deal with the phenomenon in a variety of configurations. However, there is no comprehensive review of these research outputs to the best of our knowledge. In this paper, we follow a systematic approach to review relevant contributions addressing the scheduling problem with learning and deterioration effects. Modeling approaches for learning and deterioration effects, objective functions, and solution methods employed in the literature are the main topics for the taxonomy proposed in this review. A total of 455 papers from 1999 to 2021 are included and analyzed. Different areas of interest are presented, and some opportunities for future research are identified.

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Section: Metaheuristicsmentioning

confidence: 99%

Taxonomy of Scheduling Problems with Learning and Deterioration Effects

2022

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“…In 2020, Qian et al designed a heuristic algorithm to study the single-scheduling problem with release times and a learning factor [9]. In 2020, Zou et al studied a multi-machine scheduling problem with the sum-of-processing-times-based learning effect [10]. In 2021, Wu et al studied a flow-shop scheduling problem with a truncated learning function [11].…”

Section: Literature Reviewmentioning

confidence: 99%

The Due Date Assignment Scheduling Problem with Delivery Times and Truncated Sum-of-Processing-Times-Based Learning Effect

Qian

Zhan

2021

Mathematics

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This paper considers a single-machine scheduling problem with past-sequence-dependent delivery times and the truncated sum-of-processing-times-based learning effect. The goal is to minimize the total costs that comprise the number of early jobs, the number of tardy jobs and due date. The due date is a decision variable. There will be corresponding penalties for jobs that are not completed on time. Under the common due date, slack due date and different due date, we prove that these problems are polynomial time solvable. Three polynomial time algorithms are proposed to obtain the optimal sequence.

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“…Comparing with simulated annealing method, it proves that genetic algorithm is superior in solving such problems. Zou et al [ 22 ] studied the two-stage three machine assembly shop scheduling problem. Zhang et al [ 23 ] proposed an improved genetic algorithm to solve the flexible job shop scheduling problem considering the job transportation time between machines.…”

Section: Introductionmentioning

confidence: 99%

Research on multi-objective optimal scheduling considering the balance of labor workload distribution

Liu

Ling

et al. 2021

PLoS ONE

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In order to solve the problem of unbalanced workload of employees in parallel flow shop scheduling, a method of job standard balance is proposed to describe the work balance of employees. The minimum delay time of completion and the imbalance of employee work are taken as the two goals of the model. A bi-objective nonlinear integer programming model is proposed. NSGA-II-EDSP, NSGA-II-KES, and NSGA-II-QKES heuristic rule algorithms are designed to solve the problem. A number of computational experiments of different sizes are conducted, and compared with solutions generated by NSGA-II. The experimental results show the advantages of the proposed model and method, which error is reduced 14.56%, 15.16% and 15.67%.

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Two-stage three-machine assembly scheduling problem with sum-of-processing-times-based learning effect

Cited by 9 publications

References 75 publications

Taxonomy of Scheduling Problems with Learning and Deterioration Effects

Taxonomy of Scheduling Problems with Learning and Deterioration Effects

The Due Date Assignment Scheduling Problem with Delivery Times and Truncated Sum-of-Processing-Times-Based Learning Effect

Research on multi-objective optimal scheduling considering the balance of labor workload distribution

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