Energy Saving in Single-Machine Scheduling Management: An Improved Multi-Objective Model Based on Discrete Artificial Bee Colony Algorithm

A Multi-Objective Cellular Memetic Optimization Algorithm for Green Scheduling in Flexible Job Shops

In the last 30 years, a flexible job shop scheduling problem (FJSP) has been extensively explored. Production efficiency is a widely utilized objective. With the rise in environmental awareness, green objectives (e.g., energy consumption) have received a lot of attention. Nevertheless, energy consumption has received little attention. Furthermore, controllable processing times (CPT) should be considered in the field of scheduling, because they are closer to some real production. Therefore, this work investigates a FJSP with CPT (i.e., FJSP-CPT) where asymmetrical conditions and symmetrical constraints increase the difficulty of problem solving. The objectives of FJSP-CPT are to minimize simultaneously the maximum completion time (i.e., makespan) and total energy consumption (TEC). First of all, a mathematical model of this multi-objective FJSP-CPT was formulated. To optimize this problem, a novel multi-objective cellular memetic optimization algorithm (MOCMOA) was presented. The proposed MOMOA combined the advantages of cellular structure for global exploration and variable neighborhood search (VNS) for local exploitation. At last, MOCMOA was compared against other multi-objective optimization approaches by performing experiments. Numerical experiments reveal that the presented MOCMOA is superior to its competitors in 15 instances regarding three commonly used performance metrics.