Assigning multiple job types to parallel specialized servers

Dinard van der Laan*

*Corresponding author for this work

    Research output: Contribution to JournalArticleAcademicpeer-review


    In this paper methods of mixing decision rules are investigated and applied to the so-called multiple job type assignment problem with specialized servers. This problem is modeled as continuous time Markov decision process. For this assignment problem performance optimization is in general considered to be difficult. Moreover, for optimal dynamic Markov decision policies the corresponding decision rules have in general a complicated structure not facilitating a smooth implementation. On the other hand optimization over the subclass of so-called static policies is known to be tractable. In the current paper a suitable static decision rule is mixed with dynamic decision rules which are selected such that these rules are relatively easy to describe and implement. Some mixing methods are discussed and optimization is performed over corresponding classes of so-called mixing policies. These mixing policies maintain the property that they are easy to describe and implement compared to overall optimal dynamic Markov decision policies. Besides for all investigated instances the optimized mixing policies perform substantially better than optimal static policies.

    Original languageEnglish
    Pages (from-to)471-507
    Number of pages37
    JournalDiscrete Event Dynamic Systems
    Issue number4
    Publication statusPublished - Dec 2018


    • Assignment
    • Implementation
    • Markov decision process
    • Mixing decision rules
    • Specialized servers


    Dive into the research topics of 'Assigning multiple job types to parallel specialized servers'. Together they form a unique fingerprint.

    Cite this