Abstract
This paper aims to address current manufacturing problems such as high buffers and energy waste by considering a novel performance measure, the sum of the core and front idle time of each machine and the core waiting time of each job, within the permutation flow shop scheduling optimization. Four new mixed-integer linear programming (MILP) models and three warm-start procedures were implemented in 240 instances of a widely used benchmark. The results of computational experiments were evaluated using performance profiles, considering computing times and optimality gaps. Among the models, the solver achieved better results with the position-based models than with the sequence-based models, achieving 25% more optimal solutions, being more efficient and robust. Regarding the warm-start strategies, we highlight the procedure with the longest processing time (LPT) heuristic as the best method among the seven studied.
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