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

页码：442 / 447

页数：6

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