Combining Monte-Carlo and hyper-heuristic methods for the multi-mode resource-constrained multi-project scheduling problem
- Submitting institution
-
University of Nottingham, The
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1321863
- Type
- D - Journal article
- DOI
-
10.1016/j.ins.2016.09.010
- Title of journal
- Information Sciences
- Article number
- -
- First page
- 476
- Volume
- 373
- Issue
- -
- ISSN
- 0020-0255
- Open access status
- Compliant
- Month of publication
- September
- Year of publication
- 2016
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
4
- Research group(s)
-
-
- Citation count
- 24
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper presents a hybrid optimisation algorithm for multi-mode resource-constrained multi-project scheduling. It's significant because the approach proved to be resilient to varying characteristics of unseen problem instances; achieving this using an adaptive control mechanism and a diverse set of components – in contrast to the common reliance on a few heuristics and with non-adaptive control. The algorithm was state-of-the-art, easily winning first place at MISTA 2013’s Challenge on Multi-mode resource-constrained multi-project scheduling, producing the best solution for 85% of the test instances. It lies within the top 10% of the highly-cited papers in Computer Science (InCites Essential Science Indicators).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
