Papers › Self-Labeling the Job Shop Scheduling Problem

Self-Labeling the Job Shop Scheduling Problem

22 Jan 2024arXiv:2401.11849archive 2025-07-28

Andrea Corsini, Angelo Porrello, Simone Calderara, Mauro Dell'Amico

This work proposes a self-supervised training strategy designed for combinatorial problems. An obstacle in applying supervised paradigms to such problems is the need for costly target solutions often produced with exact solvers. Inspired by semi- and self-supervised learning, we show that generative models can be trained by sampling multiple solutions and using the best one according to the problem objective as a pseudo-label. In this way, we iteratively improve the model generation capability by relying only on its self-supervision, eliminating the need for optimality information. We validate this Self-Labeling Improvement Method (SLIM) on the Job Shop Scheduling (JSP), a complex combinatorial problem that is receiving much attention from the neural combinatorial community. We propose a generative model based on the well-known Pointer Network and train it with SLIM. Experiments on popular benchmarks demonstrate the potential of this approach as the resulting models outperform constructive heuristics and state-of-the-art learning proposals for the JSP. Lastly, we prove the robustness of SLIM to various parameters and its generality by applying it to the Traveling Salesman Problem.

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JobShopStates andreacorsini1/selflabelingjobshop/sampling.py official repository unverified no licence file found · pointer only · 3ae62533564065ec · report
sample_training andreacorsini1/selflabelingjobshop/sampling.py official repository unverified no licence file found · pointer only · 66002223004233de · report

Tasks

Job Shop SchedulingPseudo LabelSchedulingSelf-Supervised LearningTraveling Salesman Problemreinforcement-learning

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LSTMPointer NetworkSigmoid ActivationSoftmaxTanh Activation

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