Abstract
AbstractA 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 ϵ‐constraint method for a multiobjective finite‐horizon MDP. This study integrates the ϵ‐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.
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