An Improved Prescriptive Tree-Based Model for Stochastic Parallel Machine Scheduling

2024-11-18

Chen, Siping

Li, Debiao

Noman, Nasimul

Harrison, Kyle

Chiong, Raymond

Editor(s): Mingming Gong, Yun Sing Koh, Derui Wang, Yiliao Song and Wei Xiang

Conference Publication

en

Publication

Springer

Singapore

Lecture Notes in Computer Science

10.1007/978-981-96-0348-0_26

une:1959.11/64026

Machine scheduling serves as a vital function for industrial and service operations, and uncertainties always pose a significant challenge in real-world scheduling practices. In this paper, we propose to solve the stochastic machine scheduling problems with uncertain processing times by an improved prescriptive tree-based (IPTB) model. Our approach includes a novel way of combining historical processing time data with current scheduling constraints to strengthen the quality of historical decisions. We apply these improved historical decisions and incorporate an improved model for calculating the optimisation loss and accelerate the training of our IPTB model. Our trained model can directly prescribe downstream scheduling solutions with high robustness in the face of uncertainties. We evaluate the proposed IPTB method on a stochastic parallel machine scheduling problem originating from printed circuit board assembly lines. Through a series of comparative experiments, our findings demonstrate the IPTB method’s superior accuracy and robustness, highlighting its resilience in noisy data environments. Additionally, we interpret the model through feature importance analysis and examine the model’s behaviours under noisy conditions.

link

37th Australasian Joint Conference on Artificial Intelligence, AI 2024 Melbourne, VIC, Australia, November 25–29, 2024 Proceedings, Part I, p. 354-365

9789819603480

9789819603473

354

365