| Summary: | 碩士 === 國立高雄大學 === 資訊工程學系碩士班 === 102 === Finding optimal solutions of scheduling problems has generally been NP-hard. Recently, GA-based algorithms have been introduced to find nearly optimal solutions, and many of them have found acceptable results in both efficiency and quality. In this study, we discuss the scheduling problem of assigning jobs on multiple parallel machines with mold constraints. The mold constraint specifies that each job needs to be processed with specific molds on a machine and there is an arbitrary amount for each type of molds. Besides, different machines can mount different molds. Setup time is also considered when a first job in a machine starts or when a machine changes molds. A GA-based scheduling algorithm is thus proposed for dealing with the above scheduling problem. In the proposed scheduling approach, a chromosome-generating procedure is designed to generate a population. The adjustment operators are then adopted for improving the fitness values and keeping them from conflict. A two-point crossover operator is adopted to reproduce the new generation of chromosomes. Moreover, two mutation operators, the reverse mutation and the swapping mutation, are used to prevent the solutions from trapping into the local optimum. The scheduling result with the best makespan is then outputted from the population when the terminal condition is met. Finally, experimental results are given to verify the effectiveness of the proposed algorithm.
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