A Memetic Algorithm Approach for the Job-Shop Scheduling Problem with Variable Machine Efficiency and Maintenance Activities

Variable machine efficiency (VME) and maintenance activities (MA) are critical factors often unexplored in job scheduling problems. This paper introduces a new problem termed the job-shop scheduling problem with variable machine efficiency and maintenance activities (JSSP-VME-MT), wherein, unlike the traditional JSSP, machine efficiency and maintenance activities are explicitly incorporated into the scheduling process. The study proposes a novel memetic algorithm (MA) underpinned by a variable neighborhood descent (VND) local search strategy to address this complex problem. This methodology demonstrates significant improvements, achieving mean makespan reductions ranging from 2.22% to 5.77% across diverse problem instances with varying numbers of machines and jobs. Key contributions include the development of an encoding scheme to model maintenance activities and machine-specific constraints, along with the design of a hybrid metaheuristic framework combining global exploration and local refinement. This work provides a foundation for future comparative studies, algorithm enhancements, and practical industrial applications. The approach offers a scalable and flexible solution to job-shop scheduling challenges involving dynamic efficiency and planned maintenance activities.