An Improved Bi-Objective Randomized Local Search Heuristic for a Resource-Constrained Joint Production & Maintenance Scheduling Problem

Production scheduling, maintenance planning, and resource allocation are three critical tasks managers must address before implementing decisions on the shop floor. This paper examines a bi-objective permutation flow shop scheduling problem that integrates these three aspects. The goal is to determine a production schedule for various jobs, along with maintenance planning and resource supply rate decisions, to minimize both the makespan and total production costs, which encompass maintenance, resource consumption, and resource inventory costs. An improved bi-objective randomized local search (IBORLS) heuristic was developed to generate multiple non-dominated solutions throughout its search process. Computational experiments were conducted to evaluate the heuristic's performance across different problem sizes. The results demonstrate that the developed improvement significantly enhance the heuristic's performance in terms of convergence and the number of solutions obtained.