Population Initialisation Methods for Fuzzy Job-Shop Scheduling Problems: Issues and Future Trends

Abstract

Scheduling job shops in the real-world manufacturing environment is a multifarious task that involved various and multiple components, solutions and approach. Fuzzy Job-Shop Scheduling Problems (Fuzzy JSSPs) are most commonly addressed by the population-based Meta-heuristic algorithms. These algorithms usually derive and develop near-optimum results within credible computational times, almost by two main steps; the initialisation and then improvement step. Numerous theoretical studies pointed out that a Meta-heuristic performance is mainly affected by the performance of its initialisation method. The main motivation of this work is to perceive the existing pattern and concerns on population initialisation issues for Fuzzy JSSPs current work by scrutinizing the published articles. Furthermore, this paper focusing on providing comprehension insight and future direction on these methods. Therefore, this paper determined to review and classify the existing literature on Fuzzy JSSPs and analyse the performance of the initialisation methods used to identify their possible limitations. In consequence, previous works outlined three potential methods for initial solutions generation, which are Random-based, priority rules-based, and heuristic methods. However, the current analysis showed that Heuristic-based initialisation approach remains lacking in the Fuzzy JSSPs domain in spite of its successful performance in the crisp JSSP domain, especially, its capability to generate high-quality initial population that consists of optimal or near optimal solutions. Furthermore, this paper identifies probable gaps and reveals several performance limitations in the existing methods, which demands for an urgent solution to develop alternatives. Promising suggestions for future studies are also provided that may lead to new Heuristic Initialisation methods that can be proposed to solve current weaknesses.