assemblyflowshopscheduling problem (AFSP) in a single factory has attracted widespread attention over the past decades;however, the distributed AFSP with DPm -> 1 layout considering uncertainty is seldom investi...
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assemblyflowshopscheduling problem (AFSP) in a single factory has attracted widespread attention over the past decades;however, the distributed AFSP with DPm -> 1 layout considering uncertainty is seldom investigated. In this study, a distributed assembly flow shop scheduling problem with fuzzy makespan minimization (FDAFSP) is considered, and an efficient artificial bee colony algorithm (EABC) is proposed. In EABC, an adaptive population division method based on evolutionary quality of subpopulation is presented;a competitive employed bee phase and a novel onlooker bee phase are constructed, in which diversified combinations of global search and multiple neighborhood search are executed;the historical optimization data set and a new scout bee phase are adopted. The proposed EABC is verified on 50 instances from the literature and compared with some state-of-the-art algorithms. Computational results demonstrate that EABC performs better than the comparative algorithms on over 74% instances.
In this study, we investigate a classical distributed assembly flow shop scheduling problem with crane transportation. The objectives are to minimise the weighted value of the makespan and the energy consumptions. An ...
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In this study, we investigate a classical distributed assembly flow shop scheduling problem with crane transportation. The objectives are to minimise the weighted value of the makespan and the energy consumptions. An improved whale optimisation algorithm (IWOA) which is embedded with a simulated annealing (SA) algorithm is proposed to solve the considered problem. First, a clustering method is applied to divide the solutions to improve the performance of the algorithm. Then, a right shift heuristic is developed to reduce the number of machine switches, therefore decreasing the energy consumption. In addition, two novel crossover operators, namely, factory crossover and solution crossover, are designed to increase the overall performance of the proposed algorithm. Furthermore, a SA-based global search heuristic is embedded in the algorithm to enhance its exploration abilities. Finally, several real-world instances were generated to test the performance of the proposed algorithm. The experimental results show that this algorithm performs better than other comparable algorithms.
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