Solving the Fuzzy Job Shop Scheduling Problem via Learning Approaches
Keywords: Fuzzy job shop scheduling problem, neural combinatorial optimization, self-supervised learning
Abstract: The fuzzy job shop scheduling problem (FJSSP) emerges as an innovative extension to the conventional job shop scheduling problem (JSSP), incorporating a layer of uncertainty that aligns the model more closely with the complexities of real-world manufacturing environments. This enhancement, while enhancing its applicability, concurrently escalates the computational complexity of deriving solutions. In the domain of traditional scheduling, neural combinatorial optimization (NCO) has recently demonstrated remarkable efficacy. However, its application to the realm of fuzzy scheduling has been relatively unexplored. This paper aims to bridge this gap by investigating the feasibility of employing neural networks to assimilate and process fuzzy information for the resolution of FJSSP, thereby leveraging the advancements in NCO to enhance fuzzy scheduling methodologies. To this end, we present a self-supervised algorithm for the FJSSP (SS-FJSSP). This algorithm employs an iterative mechanism to refine pseudo-labels, progressively transitioning from suboptimal to optimal solutions. This innovative approach adeptly circumvents the significant challenge of procuring true labels, a common challenge in NCO frameworks. Experiments demonstrate that our SS-FJSSP algorithm yields results on a par with the state-of-the-art methods while achieving a remarkable reduction in computational time, specifically being two orders of magnitude faster.
Primary Area: optimization
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Submission Number: 6972
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