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

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...

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Vydáno v:Discrete dynamics in nature and society Ročník 2018; číslo 2018; s. 1 - 15
Hlavní autor: Lin, Win-Chin
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cairo, Egypt Hindawi Publishing Corporation 01.01.2018
Hindawi
John Wiley & Sons, Inc
Wiley
Témata:
Algorithms
Assembly
Branch and bound methods
Computer simulation
Cybernetics
Employment
Greedy algorithms
Heuristic
Industrial engineering
Job shops
Learning
Lower bounds
Manufacturing
Mathematical problems
Operations research
Production scheduling
Sequential scheduling
Simulated annealing
Statistical analysis
ISSN:1026-0226, 1607-887X
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Shrnutí: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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ISSN:1026-0226
1607-887X
DOI:10.1155/2018/8170294