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Capacitated Fab Scheduling Approximation using Average Reward TD(${\lambda}$) Learning based on System Feature Functions  

Choi, Jin-Young (Division of Industrial and Information Systems Engineering, Ajou University)
Publication Information
Journal of Korean Society of Industrial and Systems Engineering / v.34, no.4, 2011 , pp. 189-196 More about this Journal
Abstract
In this paper, we propose a logical control-based actor-critic algorithm as an efficient approach for the approximation of the capacitated fab scheduling problem. We apply the average reward temporal-difference learning method for estimating the relative value functions of system states, while avoiding deadlock situation by Banker's algorithm. We consider the Intel mini-fab re-entrant line for the evaluation of the suggested algorithm and perform a numerical experiment by generating some sample system configurations randomly. We show that the suggested method has a prominent performance compared to other well-known heuristics.
Keywords
Fab Scheduling Problem; Actor-critic; Temporal-difference; Average Reward; Banker's Algorithm; Feature Functions;
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