JournalTOCs

Journal on Vehicle Routing Algorithms
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Hybrid journal (It can contain Open Access articles)
ISSN (Print) 2367-3591 - ISSN (Online) 2367-3605
Published by Springer-Verlag

[2469 journals]

Allocating limited deicing resources in winter snow events
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  Abstract: Abstract As the number and severity of snowfall events continue to grow, the need to direct road maintenance intelligently during these snowfall events will also grow. In several locations, local governments lack the resources to treat all roadways completely during snow events. Furthermore, some governments utilize only traffic data to determine which roads should be treated. As a result, many schools, businesses, and government offices must be unnecessarily closed, which directly impacts the social, educational, and economic well-being of citizens and institutions. In this work, we propose a mixed integer programming (MIP) formulation to allocate resources to manage snowfall on roads using meteorological, geographical, and environmental parameters. Given that MIPs often scale poorly with problem size, we also present a constructive heuristic designed for the problem. Additionally, we evaluate the impacts of an increase in budget for winter road maintenance on snow control resources.
  PubDate: 2019-12-01
  DOI: 10.1007/s41604-019-00015-4
On a multi-trip vehicle routing problem with time windows integrating
European and French driver regulations
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  Abstract: Abstract In the European Union, truck drivers must comply with Regulation (EC) No 561/2006, which provides a set of rules on the daily or weekly working time and on break or rest periods after specific working times. Truck drivers can be back to the depot much earlier than the end of their working period, and be able to perform other trips. The resulting problem is a Multi-Trip Vehicle Routing Problem with Time Windows, where truck drivers have the possibility to accomplish, during their time horizon, more than one trip, combined with a Truck Driver Scheduling Problem. It aimed to find and assign a set of routes to a fleet of vehicles allowing each customer to be visited within its given time window, and the accumulated load to be delivered (or collected) not exceeding the capacity of the vehicle. Each truck driver must comply with applicable hours of service regulations, and the objective is to minimize total transportation cost. To solve this problem, we have developed a hybrid method composed of a genetic algorithm and a local neighborhood search. Experiments have been conducted on instances from the literature and real-life cases from SYSTRANS, a specialized French software company.
  PubDate: 2019-12-01
  DOI: 10.1007/s41604-019-00016-3
A dial-a-ride problem using private vehicles and alternative nodes
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  Abstract: Abstract This paper addresses the dial-a-ride problem (DARP) using private vehicles and alternative nodes (DARP-PV-AN). The DARP consists of creating vehicle routes in order to ensure a set of users’ transportation requests. Each request corresponds to a client needing to be transported from his/her origin to his/her destination. Routing costs have to be minimized while respecting a set of constraints like time windows and maximum route length. In the classical DARP, vehicles have to start from a depot and come back to it at the end of their route. In the DARP-PV-AN, the on-demand transportation service can be done either by a public fleet or by clients that use their private vehicles. The use of these vehicles adds more flexibility and unclog the public transportation fleet by allowing clients to organize their own transportation. However, it also raises some privacy concerns. The DARP-PV-AN addresses these concerns and focuses on location privacy, i.e., the ability to prevent the third parties from learning clients’ locations, by keeping both original and final locations private. This is addressed by setting several pickup/delivery nodes for the transportation requests, thus masking the private address. A compact mixed integer linear model is presented, and an evolutionary local search (ELS) is proposed to compute solutions of good quality for the problem. These methods are benchmarked on a modified set of benchmark instances. A new set of realistic instances is also provided to test the ELS in a more realistic way.
  PubDate: 2019-12-01
  DOI: 10.1007/s41604-019-00014-5
Solving a real-life roll-on–roll-off waste collection problem with
column generation
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  Abstract: Abstract In this paper, we develop an intelligent solution to a complex, real-life vehicle routing problem in the waste management sector. Waste management companies deliver various types of empty waste containers to industrial customers, to be filled with different types of waste, after which the containers are picked up again. The full containers are transported to a so-called waste management depot or waste handling depot, where they are emptied, after which they are reused. Empty containers are stocked in various stock depots. Time windows in which containers may be picked up and delivered at the customers and opening hours of the different depots have to be taken into account additionally. Very importantly, there are two types of waste collection trucks that can, respectively, carry one or two containers. The resulting vehicle routing problem belongs to a class of so-called roll-on–roll-off problems, characterized by unit demand. Additionally, the problem discussed in this paper has several characteristics (multiple waste types, multiple container types, multiple depots, a heterogeneous fleet, pickup and delivery, time window constraints, and other) that make solving it a difficult task. To solve this problem, we develop a novel column generation scheme that incorporates a heuristic approach to generate new columns. The master problem is solved by linear relaxation of a set partitioning problem. New routes (columns) are generated by a constructive heuristic loosely based on Solomon’s insertion heuristic for the vehicle routing problem with time windows. The construction heuristic operates on a reformulated version of the roll-on–roll-off problem, i.e., a generalized vehicle routing problem with time windows. The algorithm—called wmpopt—is submitted to an extensive sensitivity analysis to determine its response to different parameter settings. After this, we test it on four real-life problem instances and compare its results to those obtained by a commercial solver. We show that wmpopt achieves much better solutions than the commercial solver in similar computing times.
  PubDate: 2019-05-10
  DOI: 10.1007/s41604-019-00013-6
Emergent intelligence technique-based transport depot resource management
in a metropolitan area
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  Abstract: Abstract A metropolitan area experiences transport resource scarcities and dynamic resource allocation problems due to huge number of commuters’ population, dynamic commuters’ arrival rates, etc. The progress of these problems results into economic loss, traffic congestion, inefficient utilization of resources and increase in indefinite waiting time of commuters. Hence, we propose a dynamic transport resource allocation scheme. The scheme efficiently manages number of vehicles over time and space using emergent intelligence (EI) technique. Based on the historical information, commuters’ arrival rates, resource availability, deficit resources, surplus resources of neighbor depots, etc., the EI technique with agents is used for collecting, analyzing, sharing and allocating resources, for optimal utilization of resources in a metropolitan area. Analytical model of EI technique-based transport resource allocation scheme is developed using Markov chain, and derived the closed-form expression of optimal utilization of resources. Performance evaluation is carried out with varying number of commuters’ arrival rates, vehicles, requests and number of neighborhood depots. We observe that results obtained in the simulation and analysis reflect the effectiveness of the proposed scheme in a metropolitan area.
  PubDate: 2019-03-18
  DOI: 10.1007/s41604-019-00012-7
The VeRoLog Solver Challenge 2019
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  Abstract: Abstract The VeRoLog Solver Challenge 2019 is the \({4{\mathrm{th}}}\) solver challenge facilitated by VeRoLog, the EURO Working Group on Vehicle Routing and Logistics. The challenge is organized in cooperation with ORTEC. On behalf of the organizing committee, the authors report on the problem and organization of the challenge.
  PubDate: 2019-03-13
  DOI: 10.1007/s41604-019-00011-8
On automatic algorithm configuration of vehicle routing problem solvers
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  Abstract: Abstract Many of the algorithms for solving vehicle routing problems expose parameters that strongly influence the quality of obtained solutions and the performance of the algorithm. Finding good values for these parameters is a tedious task that requires experimentation and experience. Therefore, methods that automate the process of algorithm configuration have received growing attention. In this paper, we present a comprehensive study to critically evaluate and compare the capabilities and suitability of seven state-of-the-art methods in configuring vehicle routing metaheuristics. The configuration target is the solution quality of eight metaheuristics solving two vehicle routing problem variants. We show that the automatic algorithm configuration methods find good parameters for the vehicle route optimization metaheuristics and clearly improve the solutions obtained over default parameters. Our comparison shows that despite some observable differences in configured performance there is no single configuration method that always outperforms the others. However, largest gains in performance can be made by carefully selecting the right configurator. The findings of this paper may give insights on how to effectively choose and extend automatic parameter configuration methods and how to use them to improve vehicle routing solver performance.
  PubDate: 2019-02-22
  DOI: 10.1007/s41604-019-00010-9