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Reinforcement learning approaches for the parallel machines job shop scheduling problem Host Publication: Proceedings of the International conference on interdisciplinary research on technology, education and communication, itec2010 Authors: Y. Martinez Jimenez, W. Tony, A. Nowé, D. Causmaecker Patrick and K. Verbeeck Publication Date: May. 2010 Number of Pages: 2
Abstract: This paper addresses the application of AI techniques in a practical OR problem, i.e. scheduling. Scheduling is a scientific domain concerning the allocation of tasks to a limited set of resources over time. The goal of scheduling is to maximize (or minimize) different optimization criteria such as the makespan (i.e. the completion time of the last operation in the schedule), the occupation rate of a machine or the total tardiness. In this area, the scientific community usually classifies the problems according to the characteristics of the systems studied. Important characteristics are: the number of machines available (one machine, parallel machines), the shop type (Job Shop, Open Shop or Flow Shop), the job characteristics (such as preemption allowed or not, equal processing times or not) and so on [1]. In this work we present two Reinforcement Learning approaches for the Parallel Machines Job Shop Scheduling Problem (JSP-PM).
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