Abstract
Considering green scheduling and sustainable manufacturing, the energy-efficient hybrid flow shop scheduling problem (EHFSP) with a variable speed constraint is investigated, and a novel multi-population artificial bee colony algorithm (MPABC) is developed to minimize makespan, total tardiness and total energy consumption (TEC), simultaneously. It is necessary for manufacturers to fully understand the notion of symmetry in balancing economic and environmental indicators. To improve the search efficiency, the population was randomly categorized into a number of subpopulations, then several groups were constructed based on the quality of subpopulations. A different search strategy was executed in each group to maintain the population diversity. The historical optimization data were also used to enhance the quality of solutions. Finally, extensive experiments were conducted. The results demonstrate that MPABC can achieve an outstanding performance on three metrics DIR, c and nd for the considered EHFSP.
Highlights
Shop scheduling is an essential subject and an effective way to improve resource utilization [1]
efficient hybrid flow shop scheduling problem (EHFSP) often consists of green constraints, green objectives and three sub-problems including job permutation, machine assignment and speed selection, which is more complicated than energy-efficient flow shop scheduling problems and apparently NP-hard
The contributions of this study can be summarized as follows: (1) EHFSP with a variable speed constraint is addressed to optimize makespan, total tardiness and total energy consumption (TEC) simultaneously, where total tardiness is a vital index for manufactures and equals the sum of tardiness of all jobs
Summary
Shop scheduling is an essential subject and an effective way to improve resource utilization [1]. In the typical flow shop scheduling problem, sequencing jobs are processed in predetermined orders, and each stage contains only one machine, which has been proved to be NP-hard [7]. EHFSP often consists of green constraints, green objectives and three sub-problems including job permutation, machine assignment and speed selection, which is more complicated than energy-efficient flow shop scheduling problems and apparently NP-hard. Wu et al [22] applied a hybrid non-dominated sorting genetic algorithm (NSGA-II) with variable local search to EHFSP considering renewable energy. The contributions of this study can be summarized as follows: (1) EHFSP with a variable speed constraint is addressed to optimize makespan, total tardiness and TEC simultaneously, where total tardiness is a vital index for manufactures and equals the sum of tardiness of all jobs.
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