Optimization of energy-efficient open shop scheduling with an adaptive multi-objective differential evolution algorithm

There have been growing interests in the energy-efficient production scheduling recently because of the growing shortage of energy. Open shop scheduling problem (OSSP) is a kind of common but seldom concerned production scheduling problem. This paper focuses on energy-efficient OSSP (EOSSP), where the effect of setup operation on production efficiency and energy consumption is considered. A multi-objective energy-efficient model based on machine speed scaling mechanism is proposed. To handle this multi-objective problem, we propose an effective adaptive multi-objective differential evolution (AMODE) algorithm. The AMODE uses a new fitness evaluation mechanism (FEM) based on dynamic reference point and fuzzy correlation entropy analysis to assess the solutions in evolution population. It also uses an adaptive opposition-based learning (AOBL) to improve its local search ability. Taguchi method is utilized to obtain the best combination of critical parameters of the AMODE. The proposed mathematical model is validated with CPLEX, and a lexicographic method is used to determine the preferable solution. Experimental results show that both our proposed FEM and AOBL can improve the performance of AMODE. Extensive experiments reveal that the performance of AMODE is superior to the other three well-known algorithms in addressing the EOSSP. (C) 2022 Elsevier B.V. All rights reserved.

Automated summary

This paper focuses on the energy-efficient open shop scheduling problem (EOSSP) and proposes a multi-objective energy-efficient model based on machine speed scaling mechanism. An adaptive multi-objective differential evolution algorithm is used to solve the problem. Experimental results demonstrate that the proposed algorithm outperforms the other three well-known algorithms in addressing the EOSSP.