Dynamic optimisation of preventative and corrective maintenance schedules for a large scale urban drainage system
- Submitting institution
-
University of Keele
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 349
- Type
- D - Journal article
- DOI
-
10.1016/j.ejor.2016.07.027
- Title of journal
- European Journal of Operational Research
- Article number
- -
- First page
- 494
- Volume
- 257
- Issue
- 2
- ISSN
- 0377-2217
- Open access status
- Compliant
- Month of publication
- July
- Year of publication
- 2016
- URL
-
https://www.sciencedirect.com/science/article/pii/S0377221716305641
- 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
- Yes
- Number of additional authors
-
4
- Research group(s)
-
-
- Citation count
- 15
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Chen's research significantly extends that of her EngD supervisors (Polack and Cowling), presenting novel hyperheuristic approaches to facilitate maintenance planning of urban drainage. The case study is a client of her industrial sponsor, Gaist Solutions (author Remde). The paper extends Chen's "best paper award" from 5th International Conference on Operations Research and Enterprise Systems (https://doi.org/ffm7). It has been recognised in operations research e.g. as "a notable approach that pays attention to risk, planning, scheduling and execution of maintenance operations" (https://doi.org/ffm8).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -