A green scheduling algorithm for the distributed flowshop problem

In recent years, sustainable development and green manufacturing have attracted widespread attention to environmental problems becoming increasingly serious. Meanwhile, affected by the intensification of market competition and economic globalization, distributed manufacturing systems have become increasingly common. This paper addresses the energy-efficient scheduling of the distributed permutation flowshop (EEDPFSP) with the criteria of minimizing both total flow time and total energy consumption. Considering the distributed and multi-objective optimization complexity, an improved NSGAII algorithm (INSGAII) is proposed. First, we analyze the problem-specific characteristics and designed new operators based on the knowledge of the problem. Second, four constructive heuristic algorithms are proposed to produce high-quality initial solutions. Third, inspired by the artificial bee colony algorithm, we propose a new colony generation method using the operators designed. Fourth, a local intensification is designed for exploiting better non-dominated solutions. The influence of parameter settings is investigated by experiments to determine the optimal parameter configuration of the INSGAII. Finally, a large number of computational tests and comparisons have been carried out to verify the effectiveness of the proposed INSGAII in solving EEDPFSP. (c) 2021 Elsevier B.V. All rights reserved. Superscript/Subscript Available

Automated summary

This paper addresses the energy-efficient scheduling of the distributed permutation flowshop with an improved NSGAII algorithm. Problem-specific characteristics are analyzed and new operators are designed to optimize the solution. Constructive heuristic algorithms, inspired by the artificial bee colony algorithm, are proposed to generate high-quality initial solutions and a local intensification method is designed to exploit better non-dominated solutions. The effectiveness of the proposed INSGAII in solving EEDPFSP is verified through computational tests and comparisons.