Stochastic Fleet Mix Optimization: Evaluating Electromobility in Urban Logistics

TL;DR

This paper presents a stochastic optimization framework for determining the optimal mix of electric and conventional vehicles in urban logistics fleets, incorporating detailed power consumption models and uncertainty in customer requests.

Contribution

It introduces a novel two-stage stochastic programming model with a comprehensive vehicle power consumption model and a heuristic solution approach for fleet planning.

Findings

Effective fleet mix optimization under demand uncertainty

Inclusion of climate control and auxiliary power in energy modeling

Demonstrated applicability through urban logistics case studies

Abstract

In this paper, we study the problem of optimizing the size and mix of a mixed fleet of electric and conventional vehicles owned by firms providing urban freight logistics services. Uncertain customer requests are considered at the strategic planning stage. These requests are revealed before operations commence in each operational period. At the operational level, a new model for vehicle power consumption is suggested. In addition to mechanical power consumption, this model accounts for cabin climate control power, which is dependent on ambient temperature, and auxiliary power, which accounts for energy drawn by external devices. We formulate the problem of stochastic fleet size and mix optimization as a two-stage stochastic program and propose a sample average approximation based heuristic method to solve it. For each operational period, an adaptive large neighborhood search algorithm is used to determine the operational decisions and associated costs. The applicability of the approach is demonstrated through two case studies within urban logistics services.