Home > Research > Publications & Outputs > Performance evaluation of scheduling policies f...

Associated organisational unit

Electronic data

  • Performance evaluation of scheduling policies for the Dynamic and Stochastic Resource-Constrained Multi-Project Scheduling Problem

    Rights statement: This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Production Research on 26 Dec 2020, available online: https://www.tandfonline.com/doi/abs/10.1080/00207543.2020.1857450

    Accepted author manuscript, 370 KB, PDF document

    Embargo ends: 26/12/21

    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

Links

Text available via DOI:

View graph of relations

Performance evaluation of scheduling policies for the Dynamic and Stochastic Resource-Constrained Multi-Project Scheduling Problem

Research output: Contribution to journalJournal articlepeer-review

E-pub ahead of print
<mark>Journal publication date</mark>26/12/2020
<mark>Journal</mark>International Journal of Production Research
Publication StatusE-pub ahead of print
Early online date26/12/20
<mark>Original language</mark>English

Abstract

In this study, we consider the dynamic and stochastic resource-constrained multi-project scheduling problem where projects generate rewards at their completion, completions later than a due date cause tardiness costs, task duration is uncertain, and new projects arrive randomly during the ongoing project execution both of which disturb the existing project scheduling plan. We model this problem as a discrete-time Markov decision process and explore the performance and computational limitations of solving the problem by dynamic programming. We run and compare five different solution approaches, which are: a dynamic programming algorithm to determine a policy that maximises the time-average profit, a genetic algorithm and an optimal reactive baseline algorithm, both generate a schedule to maximise the total profit of ongoing projects, a rule-based algorithm which prioritises processing of tasks with the highest processing durations, and a worst decision algorithm to seek a non-idling policy that minimises the time-average profit. The performance of the optimal reactive baseline algorithm is the closest to the optimal policies of the dynamic programming algorithm, but its results are suboptimal, up to 37.6%. Alternative scheduling algorithms are close to optimal with low project arrival probability but quickly deteriorate their performance as the probability increases.

Bibliographic note

This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Production Research on 26 Dec 2020, available online: https://www.tandfonline.com/doi/abs/10.1080/00207543.2020.1857450