Computational Evaluation of Hierarchical Production Control Policies for Stochastic Manufacturing Systems
Chand Samaratunga, Suresh P. Sethi, Xun Yu Zhou
Operations Research · 1997 · Volume 45 · Issue 2
Abstract
This paper is concerned with near-optimal control of manufacturing systems consisting of two unreliable machines in tandem and having the objective of minimizing the total discounted cost of inventories/shortages over an infinite horizon. Asymptotic optimal feedback controls are constructed with respect to the rate of machine breakdown/repair as compared to the given discount rate. Performance of these controls, known as hierarchical controls , is compared with the optimal cost (when possible) and the costs obtained with two well-known heuristics, known as Kanban controls and two boundary controls . It is shown that hierarchical controls perform better than Kanban controls in some cases and no worse in others. Costs of hierarchical and two boundary controls are not significantly different, although the former is a simpler policy than the latter. Also examined computationally is the asymptotic nature of hierarchical controls.
Keywords
production/scheduling, approximations/heuristics, hierarchical decision making, asymptotic optimality, dynamic programming/optimal control, finite state markov, manufacturing with unreliable machines, computation of MDP, simulation, applications, performance comparison of different policies
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