Tools
Tools
Zhang, L, Lu, J and Yang, Z (2020) Dynamic optimization of emergency resource scheduling in a large-scale maritime oil spill accident. Computers and Industrial Engineering, 152. ISSN 0360-8352
|
Text
Dynamic optimization of emergency resource scheduling in a large-scale maritime oil spill accident.pdf - Accepted Version Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (1MB) | Preview |
Abstract
Current maritime emergency logistics studies on oil spills are largely conducted based on static analysis to optimize resource scheduling. This does not suitably address the practical industrial demand, where the oil spill risk in nature dynamically depends on the motion of oil films. To better simulate the reality, this paper aims to conduct a study on a novel dynamic multi-objective location-routing model with split delivery considering practical characteristics, such as the time-varying demands of contaminated areas, uncertainty in the state of associated transportation networks and interrelationship between the changes in spilled oil films and emergency operations, which all result from the dynamic motion of oil films at sea. To address model complexity, we propose a two-stage optimization model, whereby a hybrid heuristic algorithm is developed to obtain Pareto solutions. To demonstrate the proposed model and approaches, a case study involving a series of sensitivity analyses is presented to highlight the importance of the proposed model and determines its implications.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | 01 Mathematical Sciences, 08 Information and Computing Sciences, 09 Engineering |
| Subjects: | H Social Sciences > HD Industries. Land use. Labor > HD61 Risk Management T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Engineering |
| Publisher: | Elsevier |
| Related URLs: | |
| Date Deposited: | 28 Apr 2021 10:22 |
| Last Modified: | 15 Jun 2022 00:50 |
| DOI or ID number: | 10.1016/j.cie.2020.107028 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/14880 |
 |
View Item |