Fleet electrification is essential to decarbonizing transportation. However, commercial adoption, particularly in heavy-duty segments, remains limited and falls short of zero-emission targets. As the primary decision makers, fleet operators face complex trade-offs that place this transition squarely within the scope of operations management (OM). This review adopts an OM perspective to synthesize and classify the expanding literature on fleet electrification, drawing also from operations research (OR), transportation, and energy domains. The contributions of this review are threefold:

 (i) Literature synthesis: We identify key decision factors at the strategic, tactical, and operational levels, connecting them to relevant academic work and supporting them with recent data and practical examples.

 (ii) Practice-oriented insights: We extract lessons learned to inform decision making by fleet operators and related stakeholders.

 (iii) Research agenda: We propose open research questions across decision levels to guide future studies in the OM and OR communities.

Publication

Begun in the early 2000s, mean field games have been a widespread research interest bothfor the theoretical difficulties concerning stochastic control and numerical analysis and for the promising application in economics, finance, and machine learning. The objective of this thesis is two-folded: firstly, to demonstrate the usage of two mainstream tools for solving mean field games, namely, the stochastic approach based on Pontryagin’s maximum principle and the analytic approach based on the coupled system of Hamiltonian-Jacob- Bellman (HJB) equation and Kolmogorov-Fokker-Planck (KFP) equation; secondly, to showcase the mean field games’ application in nature, including emergent behaviors and resource exploitation. To achieve this goal, we study the flocking model of birds and take the stochastic approach to compute explicitly the equilibria for both finite games and the linear quadratic mean field game, in order to prove that the convergence is well-established. Also, we provide Monte Carlo simulation for trajectories and velocities at the equilibrium. Moreover, since the analytic form of linear quadratic mean field games can be decomposed into Ricatti equations, we implement fixed point and Newton algorithms to tackle the forward-backward structure in the equation system. Following the analytic approach, we also review and compare several models of exhaustible resource production, such as oil. Furthermore, we extend mean field games to modeling renewables by incorporating the stochastic growth of the resource. To exemplify this application, we propose a mean field game model for the exploitation of fish stock and derive the associated HJB and KFP equations to characterize the mean field equilibrium.

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