Flexible job-shop scheduling was studied,which frequently surfaced in real-world industrial production and involved deterioration effects and multiple time constraints.An integer programming model was established to optimize the maximum completion time,considering both transportation times between machines and job arrival times.An improved genetic tabu algorithm was proposed for problem-solving.The algorithm used segment chain encoding based on operations and machines,and the active left-shift insertion decoding informed by problem characteristics such as deterioration effects and time constraints.To increase population diversity,an opposition-based learning rule and a modified NEH heuristic were introduced to generate initial segment sets.Then,according to the operation and machine segments,a mixed crossover operation consisting of an IPOX based on job numbers,an IMPX based on machine positions,and a combination mutation operation based on gene positions were utilized to update the segment chain.To enhance the search ability of the genetic algorithm,a tabu search algorithm that combined with the movement rules of job insertion or exchange for neighborhood solutions was designed.Lastly,simulation experiments compared the proposed algorithm with CPLEX and several existing algorithms.The results demonstrate the effectiveness of the proposed improved genetic tabu algorithm.
维普
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