A metaheuristic for the time-dependent pollution-routing problem

We propose a metaheuristic for the Time-Dependent Pollution-Routing Problem, which consists of routing a number of vehicles to serve a set of customers and determining their speed on each route segment with the objective of minimizing the cost of driver’s wage and greenhouse gases emissions. The vehicles face traffic congestion which, at peak periods, significantly restricts vehicle speeds and leads to increased emissions. Our algorithm is based on an adaptive large neighborhood search heuristic and uses new removal and insertion operators which significantly improve the quality of the solution. A previously developed departure time and speed optimization procedure is used as a subroutine to optimize departure times and vehicle speeds. Results from extensive computational experiments demonstrate the effectiveness of our algorithm

On green routing and scheduling problem

The vehicle routing and scheduling problem has been studied with much interest within the last four decades. In this paper, some of the existing literature dealing with routing and scheduling problems with environmental issues is reviewed, and a description is provided of the problems that have been investigated and how they are treated using combinatorial optimization tools

A matheuristic approach for the Pollution-Routing Problem

This paper deals with the Pollution-Routing Problem (PRP), a Vehicle Routing Problem (VRP) with environmental considerations, recently introduced in the literature by [Bektas and Laporte (2011), Transport. Res. B-Meth. 45 (8), 1232-1250]. The objective is to minimize operational and environmental costs while respecting capacity constraints and service time windows. Costs are based on driver wages and fuel consumption, which depends on many factors, such as travel distance and vehicle load. The vehicle speeds are considered as decision variables. They complement routing decisions, impacting the total cost, the travel time between locations, and thus the set of feasible routes. We propose a method which combines a local search-based metaheuristic with an integer programming approach over a set covering formulation and a recursive speed-optimization algorithm. This hybridization enables to integrate more tightly route and speed decisions. Moreover, two other "green" VRP variants, the Fuel Consumption VRP (FCVRP) and the Energy Minimizing VRP (EMVRP), are addressed. The proposed method compares very favorably with previous algorithms from the literature and many new improved solutions are reported.Comment: Working Paper -- UFPB, 26 page

Optimization of time-dependent routing problems considering dynamic paths and fuel consumption

Ces dernières années, le transport de marchandises est devenu un défi logistique à multiples facettes. L’immense volume de fret a considérablement augmenté le flux de marchandises dans tous les modes de transport. Malgré le rôle vital du transport de marchandises dans le développement économique, il a également des répercussions négatives sur l’environnement et la santé humaine. Dans les zones locales et régionales, une partie importante des livraisons de marchandises est transportée par camions, qui émettent une grande quantité de polluants. Le Transport routier de marchandises est un contributeur majeur aux émissions de gaz à effet de serre (GES) et à la consommation de carburant. Au Canada, les principaux réseaux routiers continuent de faire face à des problèmes de congestion. Pour réduire significativement l’impact des émissions de GES reliées au transport de marchandises sur l’environnement, de nouvelles stratégies de planification directement liées aux opérations de routage sont nécessaires aux niveaux opérationnel, environnemental et temporel. Dans les grandes zones urbaines, les camions doivent voyager à la vitesse imposée par la circulation. Les embouteillages ont des conséquences défavorables sur la vitesse, le temps de déplacement et les émissions de GES, notamment à certaines périodes de la journée. Cette variabilité de la vitesse dans le temps a un impact significatif sur le routage et la planification du transport. Dans une perspective plus large, notre recherche aborde les Problèmes de distribution temporels (Time-Dependent Distribution Problems – TDDP) en considérant des chemins dynamiques dans le temps et les émissions de GES. Considérant que la vitesse d’un véhicule varie en fonction de la congestion dans le temps, l’objectif est de minimiser la fonction de coût de transport total intégrant les coûts des conducteurs et des émissions de GES tout en respectant les contraintes de capacité et les restrictions de temps de service. En outre, les informations géographiques et de trafic peuvent être utilisées pour construire des multigraphes modélisant la flexibilité des chemins sur les grands réseaux routiers, en tant qu’extension du réseau classique des clients. Le réseau physique sous-jacent entre chaque paire de clients pour chaque expédition est explicitement considéré pour trouver des chemins de connexion. Les décisions de sélection de chemins complètent celles de routage, affectant le coût global, les émissions de GES, et le temps de parcours entre les nœuds. Alors que l’espace de recherche augmente, la résolution des Problèmes de distribution temporels prenant en compte les chemins dynamiques et les vitesses variables dans le temps offre une nouvelle possibilité d’améliorer l’efficacité des plans de transport... Mots clés : Routage dépendant du temps; chemins les plus rapides dépendant du temps; congestion; réseau routier; heuristique; émissions de gaz à effet de serre; modèles d’émission; apprentissage superviséIn recent years, freight transportation has evolved into a multi-faceted logistics challenge. The immense volume of freight has considerably increased the flow of commodities in all transport modes. Despite the vital role of freight transportation in the economic development, it also negatively impacts both the environment and human health. At the local and regional areas, a significant portion of goods delivery is transported by trucks, which emit a large amount of pollutants. Road freight transportation is a major contributor to greenhouse gas (GHG) emissions and to fuel consumption. To reduce the significant impact of freight transportation emissions on environment, new alternative planning and coordination strategies directly related to routing and scheduling operations are required at the operational, environmental and temporal dimensions. In large urban areas, trucks must travel at the speed imposed by traffic, and congestion events have major adverse consequences on speed level, travel time and GHG emissions particularly at certain periods of day. This variability in speed over time has a significant impact on routing and scheduling. From a broader perspective, our research addresses Time-Dependent Distribution Problems (TDDPs) considering dynamic paths and GHG emissions. Considering that vehicle speeds vary according to time-dependent congestion, the goal is to minimize the total travel cost function incorporating driver and GHG emissions costs while respecting capacity constraints and service time restrictions. Further, geographical and traffic information can be used to construct a multigraph modeling path flexibility on large road networks, as an extension to the classical customers network. The underlying physical sub-network between each pair of customers for each shipment is explicitly considered to find connecting road paths. Path selection decisions complement routing ones, impacting the overall cost, GHG emissions, the travel time between nodes, and thus the set of a feasible time-dependent least cost paths. While the search space increases, solving TDDPs considering dynamic paths and time-varying speeds may provide a new scope for enhancing the effectiveness of route plans. One way to reduce emissions is to consider congestion and being able to route traffic around it. Accounting for and avoiding congested paths is possible as the required traffic data is available and, at the same time, has a great potential for both energy and cost savings. Hence, we perform a large empirical analysis of historical traffic and shipping data. Therefore, we introduce the Time-dependent Quickest Path Problem with Emission Minimization, in which the objective function comprises GHG emissions, driver and congestion costs. Travel costs are impacted by traffic due to changing congestion levels depending on the time of the day, vehicle types and carried load. We also develop time-dependent lower and upper bounds, which are both accurate and fast to compute. Computational experiments are performed on real-life instances that incorporate the variation of traffic throughout the day. We then study the quality of obtained paths considering time-varying speeds over the one based only on fixed speeds... Keywords : Time-dependent routing; time-dependent quickest paths; traffic congestion; road network; heuristic; greenhouse gas emissions; emission models; supervised learning

Finding least fuel emission paths in a network with time-varying speeds

This article considers the problem of finding a route and schedule for a vehicle starting from a depot, visiting a set of customers, and returning to the depot, in a time-dependent network where the objective is to minimize the greenhouse gas emissions. In this formulation, the speeds of the vehicle as well as the routes chosen are decision variables subject to limits determined by the level of congestion on the roads at the time. Two methods are proposed to find the optimal strategy for a single route. One is a time-increment-based dynamic programming method, and the other is a new heuristic approach. In addition, a case study is carried out, which compares the performances of these methods, as well as the least polluting routes with the shortest time routes between two customer nodes

Optimization of route choice, speeds and stops in time-varying networks for fuel-efficient truck journeys

A method is presented for the real-time optimal control of the journey of a truck, travelling between a pair of pick-up/drop-off locations in a time-varying traffic network, in order to reduce fuel consumption. The method, when applied during the journey, encapsulates the choice of route, choice of speeds on the links, and choice of stop locations/durations; when applied pre-trip, it additionally incorporates choice of departure time. The problem is formulated by using a modified form of space-time extended network, in such a way that a shortest path in this network corresponds to an optimal choice of not only route, stops and (when relevant) departure time, but also of speeds. A series of simple illustrative examples are presented to illustrate the formulation. Finally, the method is applied to a realistic-size case study