EUROPT 2025
Abstract Submission

609. Optimizing decision-making framework under uncertainty for fleet replacement problem

Invited abstract in session WC-12: Optimisation under uncertainty for sustainability, stream Applications: AI, uncertainty management and sustainability.

Wednesday, 14:00-16:00
Room: B100/8009

Authors (first author is the speaker)

1. Parisa Ahani
Mathematics, NOVA Universuty Lisbon, FCT
2. Maria Isabel Gomes
CMA - Universidade Nova de Lisboa

Abstract

This study explores the fleet composition challenge faced by transportation operators over a designated planning horizon, accounting for uncertainties in energy prices, vehicle acquisition costs and operational expenditures. Within the constraints of a limited budget, the decision-maker must select an optimal mix of vehicle types, each offering unique trade-offs in terms of cost and performance. For example, diesel-powered vehicles often have lower upfront costs but incur higher fuel and maintenance expenses, whereas electric vehicles tend to be more expensive initially but offer reduced operating costs over time. Given the fluctuating nature of energy markets particularly oil factoring uncertainty into the fleet planning process becomes essential for avoiding high long-term ownership costs. A strategy that prioritizes vehicles with lower purchase prices may prove more expensive when considering total cost of ownership. To address these complexities, this research introduces a novel multi-objective mixed-integer quadratic programming model. The model aims to simultaneously minimize the overall cost encompassing purchase price, energy use, maintenance, depreciation and emissions and the financial risks tied to uncertain factors such as fuel prices and vehicle cost variability. By generating a Pareto front, the model equips decision-makers with a robust framework to assess trade-offs between cost and risk. This enables the development of cost-efficient and resilient strategies.

Keywords

Status: accepted

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