112 research outputs found
Heuristics for a green orienteering problem
We address a routing problem where a vehicle with limited time, loading capacity and battery autonomy can optionally serve a set of customers, each providing a profit. Such a problem is of particular relevance both because of its practical implications in sustainable transportation and its use as a sub-problem in Green Vehicle Routing column generation algorithms. We propose a dynamic programming approach to obtain both primal and dual bounds to the value of the optimal solutions, a fast greedy heuristics and a very large scale neighbourhood search procedure
Optimal Routing of Energy-aware Vehicles in Networks with Inhomogeneous Charging Nodes
We study the routing problem for vehicles with limited energy through a
network of inhomogeneous charging nodes. This is substantially more complicated
than the homogeneous node case studied in [1]. We seek to minimize the total
elapsed time for vehicles to reach their destinations considering both
traveling and recharging times at nodes when the vehicles do not have adequate
energy for the entire journey. We study two versions of the problem. In the
single vehicle routing problem, we formulate a mixed-integer nonlinear
programming (MINLP) problem and show that it can be reduced to a lower
dimensionality problem by exploiting properties of an optimal solution. We also
obtain a Linear Programming (LP) formulation allowing us to decompose it into
two simpler problems yielding near-optimal solutions. For a multi-vehicle
problem, where traffic congestion effects are included, we use a similar
approach by grouping vehicles into "subflows". We also provide an alternative
flow optimization formulation leading to a computationally simpler problem
solution with minimal loss in accuracy. Numerical results are included to
illustrate these approaches.Comment: To appear in proceeding of 22nd Mediterranean Conference on Control
and Automation, MED'1
A fast algorithm to optimize meeting-point-based electric first-mile feeder services with capacitated charging stations
peer reviewedR-AGR-3802 - C20/SC/14703944/M-EVRST (01/04/2021 - 31/03/2024) - CONNORS Richar
A green logistics solution for last-mile deliveries considering e-vans and e-cargo bikes
Abstract The environmental challenges and the initiatives for sustainable development in urban areas are mainly focused on eco-friendly transportation systems. Therefore, we introduce a new green logistics solution for last-mile deliveries considering synchronization between e-vans and e-cargo bikes, developed as a Two-Echelon Electric Vehicle Routing Problem with Time Windows and Partial Recharging (2E-EVRPTW-PR). The first echelon represents an urban zone, and the second echelon represents a restricted traffic zone (e.g., historical center) in which e-vans in the first and e-cargo bikes in the second echelon are used for customers' deliveries. The proposed 2E-EVRPTW-PR model aims to minimize the total costs in terms of travel costs, initial vehicles' investment costs, drivers' salary costs, and micro-depot cost. The effectiveness of the proposed solution has been demonstrated comparing two different cases, i.e., the EVRPTW-PR considering e-vans for the first case, and the 2E-EVRPTW-PR considering e-vans and e-cargo bikes for the second case. The comparison has been carried out on existing EVRPTW-PR instances for the first case, and on novel 2E-EVRPTW-PR instances for the second case, in which customers of initial EVRPTW-PR instances have been divided into two zones (urban and restricted traffic zones) by using Fuzzy C-mean clustering. Moreover, results encourage logistics companies to adopt zero-emission strategies for last-mile deliveries, especially in restricted traffic zones
A simheuristic for routing electric vehicles with limited driving ranges and stochastic travel times
Green transportation is becoming relevant in the context of smart cities, where the use of electric vehicles represents a promising strategy to support sustainability policies. However the use of electric vehicles shows some drawbacks as well, such as their limited driving-range capacity. This paper analyses a realistic vehicle routing problem in which both driving-range constraints and stochastic travel times are considered. Thus, the main goal is to minimize the expected time-based cost required to complete the freight distribution plan. In order to design reliable Routing plans, a simheuristic algorithm is proposed. It combines Monte Carlo simulation with a multi-start metaheuristic, which also employs biased-randomization techniques. By including simulation, simheuristics extend the capabilities of metaheuristics to deal with stochastic problems. A series of computational experiments are performed to test our solving approach as well as to analyse the effect of uncertainty on the routing plans.Peer Reviewe
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