Modified biology migration algorithm for dual-resource energy-saving flexible job shop scheduling problem

Energy-saving scheduling is a workshop scheduling problem oriented to green manufacturing, which has become a research hot spot in the manufacturing field. Aiming at the flexible job shop with dual resource constraints of machine and worker, the effects of worker learning and job transportation time were considered simultaneously to minimize the energy consumption of the workshop, and a Modified Biology Migration Algorithm (MBMA) was proposed. In the algorithm, a job-machine-worker based three-segment encoding method was adopted to represent the scheduling solution, and a population initialization approach was design to improve the quality of initial scheduling solutions. Considering that the basic biology migration algorithm cannot be directly applied to the discrete workshop scheduling problem, a discrete biological migration operator based on crossover operations was proposed, by which the algorithm could search directly in the discrete scheduling domain. Furthermore, a dynamic adjustment strategy of the conversion probability was introduced into the migration operator to balance exploration and exploitation of the algorithm, and a memory pool mechanism was added to avoid the premature convergence. For the individual updating operator, a local search algorithm was designed and embedded to enhance the local search ability of the algorithm. Finally, a large number of experimental results showed that the computational results of MBMA were superior to other algorithms. © 2024 CIMS. All rights reserved.