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

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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Two-stage assembly scheduling problem for processing products with dynamic component-sizes and a setup time

Computers & Industrial Engineering ◽

10.1016/j.cie.2016.12.030 ◽

2017 ◽

Vol 104 ◽

pp. 98-113 ◽

Cited By ~ 14

Author(s):

Sunwoong Jung ◽

Young-Bin Woo ◽

Byung Soo Kim

Keyword(s):

Setup Time ◽

Scheduling Problem ◽

Two Stage ◽

Dynamic Component ◽

Assembly Scheduling

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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

Discrete Dynamics in Nature and Society ◽

10.1155/2018/8170294 ◽

2018 ◽

Vol 2018 ◽

pp. 1-15 ◽

Cited By ~ 2

Author(s):

Win-Chin Lin

Keyword(s):

Learning Effect ◽

Flow Shop ◽

Optimal Solutions ◽

Work Experiences ◽

Two Stage ◽

Processing Times ◽

Production Environments ◽

Flow Shop Problem ◽

Iterated Greedy ◽

Machine Assembly

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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