| Summary: | 碩士 === 逢甲大學 === 統計學系統計與精算碩士班 === 107 === In this study we address a single-machine two-agent multitasking scheduling problem where the measurement criterion is to find an optimal schedule to minimize the total tardiness of the first agent subject to the total completion time of the second agent is less than a given upper bound. To solve this problem, we apply a branch-and-bound method incorporating with some propositions and a lower bound for finding the optimal solution for small-sized jobs. Meanwhile, we also propose three metaheuristics, including genetic algorithm (GA), simulated annealing algorithm (SA), and cloud simulated annealing algorithm (CSA) for finding the near-optimal solutions for large-sized jobs. Finally, we carry on some computational experiments and apply statistical analysis methods to measure the performances for the proposed algorithms and compare the performances of these algorithms, respectively.
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