Energy-ef fi cient scheduling for a permutation fl ow shop with variable transportation time using an improved discrete whale swarm optimization

As environmental issues become more serious, energy-efficient scheduling has emerged as a research hotspot. Although permutation flow shop scheduling has attracted substantial research attention, practical cases that consider conveyor speed control energy-saving strategies have rarely been studied. Motivated by this gap, this paper addresses a permutation flow shop scheduling problem with sequence dependent setup time considering a novel conveyor speed control energy-saving strategy. The aim is to find the optimal processing sequence of jobs and conveyor belt speed setting scheme between any two nodes (i.e., machine or warehouse). A mixed-integer linear programming model is established to minimize both the makespan and total energy consumption. To solve such a bi-objective model, an improved discrete whale swarm optimization (IDWSO) is designed that combines differential evolution, augmented search and job-swapped mutation to enhance performance. Numerical experiments are carried out to compare the performance of the IDWSO with existing algorithms, including the non dominated sorting genetic algorithm-II (NSGA-II) and the multiobjective evolutionary algorithm based on decomposition (MOEA/D). Sensitivity analyses on energy-saving strategies under different scale problems/alternative conveyor speeds are carried out to verify the effectiveness of the model and algorithm. The experimental results show that the IDWSO is superior to the NSGA-II and MOEA/D for this case. In addition, the proposed conveyor speed control energy-saving strategy is found to be an effective way for manufacturers to achieve green production. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

This paper discusses a novel energy-saving conveyor speed control strategy in permutation flow shop scheduling, aiming to find the optimal processing sequence of jobs and conveyor speed setting scheme with a mixed-integer linear programming model and an improved whale swarm optimization algorithm. Experimental results demonstrate the superiority of this method over existing algorithms and its effectiveness in helping manufacturers achieve green production.