Solving the flexible job shop scheduling problem using an improved Jaya algorithm

The classical job shop scheduling problem (JSSP) has been a subject of extensive research for the past many years. Due to its high computational complexity, it is considered to be NP-hard (Non-deterministic polynomial time) in nature. The flexible job shop scheduling problem (FJSSP) which is a classification of basic JSSP further increases the complexity of the problem by considering a job to be processed on more than one machine. Hence a routing problem along with the sequencing problem needs to be considered. Considering the NP-hard nature of the problem the research has sailed through the extensive use of meta-heuristics to find near-optimal solutions. However, these meta-heuristics tend to get trapped in the local optimum and also contain algorithm-specific tuning parameters which need to be tuned to obtain an optimal solution. To overcome this, an improved Jaya algorithm is proposed in this work. To improve the solution quality and maintain diversity, an efficient initialization mechanism, a local search technique and acceptance criterion is incorporated into the algorithm. The performance of the improved Jaya algorithm is compared using makespan criteria with other well-known meta-heuristics on 203 benchmark instances. Results demonstrate the effectiveness of the proposed algorithm in solving the FJSSP.