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  1. An Improved Genetic-Shuffled Frog-Leaping Algorithm for Permutation Flowshop Scheduling.Peiliang Wu, Qingyu Yang, Wenbai Chen, Bingyi Mao & Hongnian Yu - 2020 - Complexity 2020:1-15.
    Due to the NP-hard nature, the permutation flowshop scheduling problem is a fundamental issue for Industry 4.0, especially under higher productivity, efficiency, and self-managing systems. This paper proposes an improved genetic-shuffled frog-leaping algorithm to solve the permutation flowshop scheduling problem. In the proposed IGSFLA, the optimal initial frog in the initialized group is generated according to the heuristic optimal-insert method with fitness constrain. The crossover mechanism is applied to both the subgroup and the global group to avoid the local optimal (...)
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  • Solution Algorithms for Single-Machine Group Scheduling with Learning Effect and Convex Resource Allocation.Wanlei Wang, Jian-Jun Wang & Ji-Bo Wang - 2021 - Complexity 2021:1-13.
    This paper deals with a single-machine resource allocation scheduling problem with learning effect and group technology. Under slack due-date assignment, our objective is to determine the optimal sequence of jobs and groups, optimal due-date assignment, and optimal resource allocation such that the weighted sum of earliness and tardiness penalties, common flow allowances, and resource consumption cost is minimized. For three special cases, it is proved that the problem can be solved in polynomial time. To solve the general case of problem, (...)
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