A two-stage three-machine assembly scheduling problem with a position-based learning effect

Wu, Chin‐Chia; Wang, Dujuan; Cheng, Shuenn‐Ren; Chung, I-Hong; Lin, Win-Chin

doi:10.1080/00207543.2017.1401243

Cited by 39 publications

(14 citation statements)

References 45 publications

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“…The normal job processing times on machines 1 , 2 , and 3 , in line with the scheme of Lee et al [5], were generated at random from a discrete uniform distribution (1, 100), respectively. All the parameters used in CSA adopted the settings of Wu et al [1] while all the parameters in IG used the results of previous tests. In the computational experiments for small number of jobs, was set at 8, 9, 10, and 11.…”

Section: Computational Experiments and Resultsmentioning

confidence: 99%

“…No matter what the number of jobs ( ) or the learning index ( ), all instances are solvable within 10 8 nodes. As for the impact of particular dominant properties on the efficiency of branch and bound (B&B), it can be easily done in the same way as that of Wu et al [1]. For the proposed algorithms, CSA and IGLS, the AEPs are presented in Table 2.…”

Section: Computational Experiments and Resultsmentioning

confidence: 99%

“…Wu et al [1] studied the makespan minimization in the two-stage three-machine flow shop problem with positionbased learning consideration based on Biskup [2]. Wu etal.…”

Section: Introductionmentioning

confidence: 99%

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Minimizing the Makespan for a Two-Stage Three-Machine Assembly Flow Shop Problem with the Sum-of-Processing-Time Based Learning Effect

Lin

2018

Discrete Dynamics in Nature and Society

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Two-stage production process and its applications appear in many production environments. Job processing times are usually assumed to be constant throughout the process. In fact, the learning effect accrued from repetitive work experiences, which leads to the reduction of actual job processing times, indeed exists in many production environments. However, the issue of learning effect is rarely addressed in solving a two-stage assembly scheduling problem. Motivated by this observation, the author studies a two-stage three-machine assembly flow shop problem with a learning effect based on sum of the processing times of already processed jobs to minimize the makespan criterion. Because this problem is proved to be NP-hard, a branch-and-bound method embedded with some developed dominance propositions and a lower bound is employed to search for optimal solutions. A cloud theory-based simulated annealing (CSA) algorithm and an iterated greedy (IG) algorithm with four different local search methods are used to find near-optimal solutions for small and large number of jobs. The performances of adopted algorithms are subsequently compared through computational experiments and nonparametric statistical analyses, including the Kruskal–Wallis test and a multiple comparison procedure.

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Section: Computational Experiments and Resultsmentioning

confidence: 99%

Section: Computational Experiments and Resultsmentioning

confidence: 99%

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Minimizing the Makespan for a Two-Stage Three-Machine Assembly Flow Shop Problem with the Sum-of-Processing-Time Based Learning Effect

Lin

2018

Discrete Dynamics in Nature and Society

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“…For the AFSP, Wu et al. (2018) and Kazemi et al. (2017) considered machining and assembly from the perspective of the flow‐shop scheduling problem.…”

Section: Introductionmentioning

confidence: 99%

An effective algorithm for flexible assembly job‐shop scheduling with tight job constraints

Lin

Deng

Han

et al. 2020

Int Trans Operational Res

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Thus far, the available works on the flexible assembly job‐shop scheduling problem (FAJSP) consider job processing and assembly separately. However, in some real production systems, if equipment is composed of thousands of jobs and assembled in many stages, some jobs and assemblies cannot be processed simultaneously. Therefore, this work proposes an FAJSP with tight job constraints (FAJSP‐JC) in which jobs and assemblies can be processed simultaneously, and each assembly is treated as an operation. A job constraint genetic algorithm (JCGA) is presented to solve the proposed FAJSP‐JC with the goal of minimizing the makespan. In the JCGA, a novel two‐dimensional encoding method (2D‐encoding) is designed to conveniently express the operating constraints and tight job constraints, and an effective decoding method is proposed to decode the 2D‐encoded information. Furthermore, a crossover operator and a mutation operator are designed to improve the computational efficiency and expand the solution space. Ten benchmark instances of the FAJSP‐JC are constructed to test the JCGA. The Taguchi method is used to obtain the best combination of the key parameters that are used in the JCGA. Computational experiments carried out confirm that the JCGA is able to easily obtain better solutions compared to the genetic algorithm (GA) with a division encoding method and the classical GA, demonstrating its superior performance over these algorithms in terms of both solution quality and computational efficiency.

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“…Besides, there are some two-stage models also with the learning effect consideration. Wu et al [13] adopted the learning model developed by Biskup [2] to solve a two-stage flow shop scheduling problem with three machines to minimize the makespan. ey devised three simulated annealing (SA) algorithms and three cloud theory-based SA algorithms.…”

Section: Introductionmentioning

confidence: 99%

A Two-Stage Three-Machine Flow Shop Assembly Problem Mixed with a Controllable Number and Sum-of-Processing Times-Based Learning Effect by Simulated Annealing Algorithms

Liu

2020

Discrete Dynamics in Nature and Society

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The two-stage assembly scheduling problem is widely used in industrial and service industries. This study focuses on the two-stage three-machine flow shop assembly problem mixed with a controllable number and sum-of-processing times-based learning effect, in which the job processing time is considered to be a function of the control of the truncation parameters and learning based on the sum of the processing time. However, the truncation function is very limited in the two-stage flow shop assembly scheduling settings. Thus, this study explores a two-stage three-machine flow shop assembly problem with truncated learning to minimize the makespan criterion. To solve the proposed model, we derive several dominance rules, lemmas, and lower bounds applied in the branch-and-bound method. On the other hand, three simulated annealing algorithms are proposed for finding approximate solutions. In both the small and large size number of job situations, the SA algorithm is better than the JS algorithm in this study. All the experimental results of the proposed algorithm are presented on small and large job sizes, respectively.

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A two-stage three-machine assembly scheduling problem with a position-based learning effect

Cited by 39 publications

References 45 publications

Minimizing the Makespan for a Two-Stage Three-Machine Assembly Flow Shop Problem with the Sum-of-Processing-Time Based Learning Effect

Minimizing the Makespan for a Two-Stage Three-Machine Assembly Flow Shop Problem with the Sum-of-Processing-Time Based Learning Effect

An effective algorithm for flexible assembly job‐shop scheduling with tight job constraints

A Two-Stage Three-Machine Flow Shop Assembly Problem Mixed with a Controllable Number and Sum-of-Processing Times-Based Learning Effect by Simulated Annealing Algorithms

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