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A STATE AGGREGATION APPROACH TO MANUFACTURING SYSTEMS HAVING MACHINE STATES WITH WEAK AND STRONG-INTERACTIONS
被引:12
作者:
JIANG, J
SETHI, SP
机构:
关键词:
DYNAMIC PROGRAMMING;
OPTIMAL CONTROL;
STOCHASTIC;
CONTINUOUS TIME;
PROBABILITY;
MARKOV PROCESSES;
HIERARCHICAL CONTROL OF MARKOV PROCESS DRIVEN SYSTEMS;
PRODUCTION SCHEDULING;
HIERARCHICAL PLANNING;
MANUFACTURING WITH UNRELIABLE MACHINES;
D O I:
10.1287/opre.39.6.970
中图分类号:
C93 [管理学];
学科分类号:
12 ;
1201 ;
1202 ;
120202 ;
摘要:
A hierarchical approach to control a manufacturing system, subject to multiple machine states modeled by a Markov process with weak and strong interactions, is suggested. The idea is to aggregate strongly interacting or high transition probability states within a group of states and consider only the transition between these groups for the analysis of the system in the long run. We show that such an aggregation results in a problem of reduced size, whose solution can be modified in a simple way to obtain an asymptotically optimal feedback solution to the original problem. Also, an example is solved to illustrate the results developed in the paper,
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页码:970 / 978
页数:9
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