A Multi-Period MILP for Strategic Transportation Electrification under Incentive Expiry and Fuel Price Volatility | ||||
International Journal of Advanced Engineering and Business Sciences | ||||
Volume 6, Issue 1, April 2025, Page 19-35 PDF (532.39 K) | ||||
Document Type: Original Article | ||||
DOI: 10.21608/ijaebs.2025.372170.1102 | ||||
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Author | ||||
Mohamed H. Abdelati ![]() ![]() | ||||
Automotive and Tractors Department, Faculty of Engineering, Minia university, Egypt | ||||
Abstract | ||||
This paper presents a multi-period optimization model for strategic fleet electrification under conditions of fuel price volatility, time-limited subsidies, and vehicle performance degradation. The model is formulated as a mixed-integer linear program that allows decision-makers to determine the optimal timing and scale of electric vehicle acquisitions, balancing operational cost, environmental penalties, and investment constraints. Rather than assuming static inputs or single-period trade-offs, the formulation captures how evolving economic and policy signals shape long-term replacement strategies. Key features include scenario-based pricing, degradation-adjusted fleet capacity, and flexible treatment of emission costs. The model is designed to support both theoretical exploration and applied decision-making. It can be used with synthetic inputs to evaluate transition behavior under different assumptions. A numerical illustration demonstrates how policy design, cost trajectories, and degradation rates interact to determine investment timing. The framework is extendable to incorporate stochastic vehicle lifetimes, uncertain demand, and mixed-technology fleets. By structuring these interdependencies explicitly, the model offers a planning tool that is transparent, adaptable, and grounded in the real trade-offs facing fleet operators today. | ||||
Keywords | ||||
Fleet Electrification; Multi-Period Optimization; Mixed Integer Linear Programming (MILP); Policy Incentives and Subsidies; Sustainable Transportation Policy | ||||
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