Abstract
Due to global challenges and disruptions—such as increasing energy costs and limited availability of resources and workforce—multi-objective production planning and scheduling has recently attracted significant attention from both academia and industry. Solving scheduling problems based on multiple objectives, rather than focusing solely on makespan reduction, has become one of the key challenges in sustainable manufacturing. The job shop scheduling problem is a well-known optimization problem belonging to the class of NP-hard combinatorial optimization problems. In addition to the inherent complexity of scheduling itself, the difficulty is further increased by the various criteria (objectives) that must be considered, such as electrical energy consumption, machine workload, and tardiness. Consequently, job shop scheduling becomes a multi-objective optimization problem. This paper presents the MOFJSP-HUMANT algorithm for solving the multi-objective flexible job shop scheduling problem (MOFJSP). The proposed algorithm is based on the ant colony optimization metaheuristic and the PROMETHEE multi-criteria decision-making method, resulting in an “a priori” approach to multi-objective optimization. As mentioned, the proposed methodology and algorithm enable a search for the best compromise production schedule based on three criteria—makespan time, electrical energy consumption, and machine workload—to support sustainable manufacturing. The algorithm has been tested on a MOFJSP instance, and the results are discussed to evaluate the proposed MOFJSP-HUMANT framework before its application in more complex industrial environments.