Approximate policy iteration for dynamic resource-constrained project scheduling

被引：6
|
作者：
Parizi, Mahshid Salemi [1 ]
Gocgun, Yasin [2 ]
Ghate, Archis [1 ]
机构：
[1] Univ Washington, Ind & Syst Engn, BOX 352650, Seattle, WA 98195 USA
[2] Altinbas Univ, Dept Ind Engn, Istanbul, Turkey
来源：
OPERATIONS RESEARCH LETTERS | 2017年 / 45卷 / 05期
关键词：
Markov decision processes; Approximate dynamic programming; Queueing;
D O I：
10.1016/j.orl.2017.06.002
中图分类号：
C93 [管理学]; O22 [运筹学];
学科分类号：
070105 ; 12 ; 1201 ; 1202 ; 120202 ;
摘要：
We study non-preemptive scheduling problems where heterogeneous projects stochastically arrive over time. The projects include precedence-constrained tasks that require multiple resources. Incomplete projects are held in queues. When a queue is full, an arriving project must be rejected. The goal is to choose which tasks to start in each time-slot to maximize the infinite-horizon discounted expected profit. We provide a weakly coupled Markov decision process (MDP) formulation and apply a simulation-based approximate policy iteration method. Extensive numerical results are presented. (C) 2017 Elsevier B.V. All rights reserved.
引用
收藏
页码：442 / 447
页数：6
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