Multi-objective decision-making methods for optimising CO₂ decisions in the automotive industry
Ibrahim, Nassir and Cox, Sharon and Mills, Robert and Aftelak, Andrew and Shah, Hanifa (2021) Multi-objective decision-making methods for optimising CO₂ decisions in the automotive industry. Journal of Cleaner Production, 314. p. 128037. ISSN 0959-6526
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Multi-objective decision-making methods for optimising CO2 decisions in the automotive industry (Ibrahim at al., 2021).pdf - Accepted Version Download (1MB) |
Abstract
Multi-objective optimisation (MOOP) methods are used heavily to support decision-makers in addressing problems with conflicting objectives. With global CO2 emission legislation becoming stringent, automotive OEMs face a challenge to balance conflicting commercial and environmental objectives simultaneously. Automotive OEMs seek to maximise profits by stimulating global sales volumes whilst also minimising CO2 management costs. MOOP methods can quantify CO2 management costs to optimise decisions in response to the increasingly regulated business environment. Whilst automotive OEMs are modelling the dynamic knock-on effects of pur-suing multiple objectives, there is also a need to formulate their decision objectives, decision criteria and decision options to be considered as part of CO2 management decisions first. A systematic literature review offers a detailed account of how automotive OEMs can optimise CO2 management decisions.
The multiple decision objectives, decision criteria and CO2 management decision options considered by automotive OEMs are first categorised. The systematic literature review reveals that evaluating decision criteria such as the vehicle fleet portfolio, customer demand, market requirements and financial cost can assist auto-motive OEMs select the optimal CO2 management decision in a given scenario. Next, reconfiguring vehicle features, investing in technology, restricting sales and paying CO2 tariffs are identified as the most common CO2 management decisions taken by automotive OEMs. Then MOOP methods are critiqued for their suitability, before a novel decision support model, which adopts an automotive OEMs’ perspective for mitigating CO2 management costs is proposed. It is found that interactive and objective decision making approaches such as MOOP opposed to classical Multi Criteria Decision Making (MCDM) methods can more precisely quantify the commercial impli-cations of the stricter global CO2 emission legislation now imposed on automotive OEMs. If automotive OEMs adopt the proposed model, they can effectively model future CO2 management scenarios and pre-emptively prevent counter-productive decisions by minimising CO2 management costs.
| Item Type: | Article | ||||||
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| Identification Number: | https://doi.org/10.1016/j.jclepro.2021.128037 | ||||||
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| Uncontrolled Keywords: | CO2 tariff minimisation Profit maximisation Financial cost Multi-objective optimisation Decision support model Automotive OEMs | ||||||
| Subjects: | CAH11 - computing > CAH11-01 - computing > CAH11-01-03 - information systems | ||||||
| Divisions: | Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology | ||||||
| Depositing User: | Nassir Ibrahim | ||||||
| Date Deposited: | 21 Oct 2021 09:20 | ||||||
| Last Modified: | 23 Jun 2023 03:00 | ||||||
| URI: | https://www.open-access.bcu.ac.uk/id/eprint/11836 |
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