Electric Commercial Vehicles in Mid-Haul Logistics Networks

Schiffer, Maximilian; Stütz, Sebastian; Walther, Grit

doi:10.1007/978-3-319-69950-9_7

Cited by 9 publications

(4 citation statements)

References 28 publications

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“…The TCO of a fleet of vehicles owned by a company must account for various fixed costs (such as capital cost, insurance, and taxes) and variable costs that depend on the duration of operation (such as maintenance and repair costs and energy consumption cost). Rogge et al (2018) and Schiffer et al (2018) performed TCO analysis for commercial mixed electric fleets in the context of public transit and mid-haul logistics respectively. In the current paper, we consider a TCO that comprises of one-time purchase costs at the strategic level and energy and maintenance costs that are proportional to the travel time in addition to penalty for not serving some customer in every operational period.…”

Section: Fleet Size and MIX Optimizationmentioning

confidence: 99%

Stochastic Fleet Mix Optimization: Evaluating Electromobility in Urban Logistics

Malladi¹,

Christensen²,

Ramrez³

et al. 2020

Preprint

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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.

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Section: Fleet Size and MIX Optimizationmentioning

confidence: 99%

Stochastic Fleet Mix Optimization: Evaluating Electromobility in Urban Logistics

Malladi¹,

Christensen²,

Ramrez³

et al. 2020

Preprint

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show abstract

“…Due to their relatively short driving range, EVs may need to detour to CSs to replenish their battery. This is especially critical in the context of mid-haul or long-haul routing (Schiffer et al, 2018b;Villegas et al, 2018). Therefore, key decisions in E-VRPs concern not only the assignment of the customers to the vehicles and their sequencing, but also where and how much to charge the vehicles.…”

Section: Introductionmentioning

confidence: 99%

The Electric Vehicle Routing Problem with Capacitated Charging Stations

Froger

Jabali

Mendoza

et al. 2022

Transportation Science

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Electric vehicle routing problems (E-VRPs) deal with routing a fleet of electric vehicles (EVs) to serve a set of customers while minimizing an operational criterion, for example, cost or time. The feasibility of the routes is constrained by the autonomy of the EVs, which may be recharged along the route. Much of the E-VRP research neglects the capacity of charging stations (CSs) and thus implicitly assumes that an unlimited number of EVs can be simultaneously charged at a CS. In this paper, we model and solve E-VRPs considering these capacity restrictions. In particular, we study an E-VRP with nonlinear charging functions, multiple charging technologies, en route charging, and variable charging quantities while explicitly accounting for the number of chargers available at privately managed CSs. We refer to this problem as the E-VRP with nonlinear charging functions and capacitated stations (E-VRP-NL-C). We introduce a continuous-time model formulation for the problem. We then introduce an algorithmic framework that iterates between two main components: (1) the route generator, which uses an iterated local search algorithm to build a pool of high-quality routes, and (2) the solution assembler, which applies a branch-and-cut algorithm to combine a subset of routes from the pool into a solution satisfying the capacity constraints. We compare four assembly strategies on a set of instances. We show that our algorithm effectively deals with the E-VRP-NL-C. Furthermore, considering the uncapacitated version of the E-VRP-NL-C, our solution method identifies new best-known solutions for 80 of 120 instances.

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“…Due to their limited autonomy, EVs may need to detour to CSs in order to recharge their battery. This is particularly true in medium and long range routes, like in [11]. A key decision in this context is where and how much to charge the EVs.…”

Section: Introductionmentioning

confidence: 99%

The electric vehicle shortest path problem with time windows and prize collection

Cassia¹,

Jabali²,

Malucelli³

et al. 2022

Annals of Computer Science and Information Systems

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The Electric Vehicle Shortest Path Problem (EVSPP) aims at finding the shortest path for an electric vehicle (EV) from a given origin to a given destination. During long trips, the limited autonomy of the EV may imply several stops for recharging its battery. We consider combining such stops with visiting points of interest near charging stations (CSs). Specifically, we address a version of the EVSPP in which the charging decisions are harmonized with the driver's preferences. The goal is to maximize the total gained score (assigned by the driver to the CSs), while respecting the time windows and the EV autonomy constraints. We define the problem as a MILP and develop an A* search heuristic to solve it. We evaluate the method by means of extensive computational experiments on realistic instances.

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Electric Commercial Vehicles in Mid-Haul Logistics Networks

Cited by 9 publications

References 28 publications

Stochastic Fleet Mix Optimization: Evaluating Electromobility in Urban Logistics

Stochastic Fleet Mix Optimization: Evaluating Electromobility in Urban Logistics

The Electric Vehicle Routing Problem with Capacitated Charging Stations

The electric vehicle shortest path problem with time windows and prize collection

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