Paper Info
Title
An extended epsilon-constraint method for a multiobjective finite-horizon Markov decision process
Abstract
A Markov decision process (MDP) is an appropriate mathematical framework for analysis and modeling a large class of sequential decision-making problems. Real-world applications necessitate the evaluation of the value of a decision according to several conflicting objectives. This paper presents an extended epsilon-constraint method for a multiobjective finite-horizon MDP. This study integrates the epsilon-constraint method with the K-best policies algorithm to find the nondominated deterministic Markovian policies on the Pareto-optimal frontier. The proposed algorithm is evaluated on biobjective maintenance scheduling and machine running speed selection problems, and its performance is compared with a classic approach in the literature (weighted-sum, WS, method). Satisfying results show that the proposed algorithm obtains a good-quality Pareto frontier and has advantages over the WS method.
Year
DOI
Venue
2022
10.1111/itor.12989
INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH
Keywords
DocType
Volume
K&#8208, best policies algorithm, Markov decision process, weighted&#8208, sum method, &#1013, &#8208, constraint method
Journal
29
Issue
ISSN
Citations 
5
0969-6016
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Maryam Eghbali-Zarch100.34
Reza Tavakkoli-Moghaddam258572.31
Amir Azaron318520.39
Kazem Dehghan-Sanej400.34