Hybrid heuristic algorithm for multi-objective scheduling problem

This research provides academic and practical contributions. From a theoretical standpoint, a hybrid harmony search (HS) algorithm, namely the oppositional global-based HS (OGHS), is proposed for solving the multi-objective flexible job-shop scheduling problems (MOFJSPs) to minimize makespan, total machine workload and critical machine workload. An initialization program embedded in opposition-based learning (OBL) is developed for enabling the individuals to scatter in a well-distributed manner in the initial harmony memory (HM). In addition, the recursive halving technique based on opposite number is employed for shrinking the neighbourhood space in the searching phase of the OGHS. From a practice-related standpoint, a type of dual vector code technique is introduced for allowing the OGHS algorithm to adapt the discrete nature of the MOFJSP. Two practical techniques, namely Pareto optimality and technique for order preference by similarity to an ideal solution (TOPSIS), are implemented for solving the MOFJSP. Furthermore, the algorithm performance is tested by using different strategies, including OBL and recursive halving, and the OGHS is compared with existing algorithms in the latest studies. Experimental results on representative examples validate the performance of the proposed algorithm for solving the MOFJSP.