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 Survey of Network Design Problems

Network design problems arise in many different application areas such as air freight, highway traffic, and communication systems. The intention of this survey is to present a coherent unified view of a number of papers in the network design literature. We discuss suggested solution procedures, computational experience, relations between various network models, and potential application areas. Promising topics of research for improving, solving, and extending the models reviewed in this survey are also indicated.Supported in part by the U.S. Department of Transportation under contract DOT-TSC-1058, Transportation Advanced Research Program (TARP)

Agent-Based Model of Price Competition and Product Differentiation on Congested Networks

Using consistent agent-based techniques, this research models the decision-making processes of users and infrastructure owner/operators to explore the welfare consequence of price competition, capacity choice, and product differentiation on congested transportation networks. Component models include: (1) An agent-based travel demand model wherein each traveler has learning capabilities and unique characteristics (e.g. value of time); (2) Econometric facility provision cost models; and (3) Representations of road authorities making pricing and capacity decisions. Different from small-network equilibrium models in prior literature, this agent-based model is applicable to pricing and investment analyses on large complex networks. The subsequent economic analysis focuses on the source, evolution, measurement, and impact of product differentiation with heterogeneous users on a mixed ownership network (with tolled and untolled roads). Two types of product differentiation in the presence of toll roads, path differentiation and space differentiation, are defined and measured for a base case and several variants with different types of price and capacity competition and with various degrees of user heterogeneity. The findings favor a fixed-rate road pricing policy compared to complete pricing freedom on toll roads. It is also shown that the relationship between net social benefit and user heterogeneity is not monotonic on a complex network with toll roads.Network dynamics, road pricing, autonomous links, privatization, price competition, product differentiation, agent-based transportation model

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

Minimum cost VRP with time-dependent speed data and congestion charge

A heuristic algorithm, called LANCOST, is introduced for vehicle routing and scheduling problems to minimize the total travel cost, where the total travel cost includes fuel cost, driver cost and congestion charge. The fuel cost required is influenced by the speed. The speed for a vehicle to travel along any road in the network varies according to the time of travel. The variation in speed is caused by congestion which is greatest during morning and evening rush hours. If a vehicle enters the congestion charge zone at any time, a fixed charge is applied. A benchmark dataset is designed to test the algorithm. The algorithm is also used to schedule a fleet of delivery vehicles operating in the London area

Minimizing the carbon emissions on road networks

The models and algorithms developed for transportation planning, vehicle routing, path finding and the software that utilize them are usually based on distance and constant travel times between the relevant locations and aim at minimizing total distance or travel time . However, constant travel time assumption is not realistic on road networks as the traffic conditions may vary from morning/evening rush hours to off-peak noon/night hours, from the weekends to business days, even from one season to another. Thus, distance/time based optimization does not exactly reflect the real fuel consumptions, hence the actual costs; neither can they be used to accurately account for the greenhouse gas (GHG) emissions. A distance/constant time based optimization model may even yield an infeasible solution when time-windows exist or the route length is time limited. In this study, we first analyze the peculiar characteristics of the Greenest Path Problem (GPP) where the objective is to find the least GHG generating path from an origin to a destination on the road network. We then propose a fast heuristic method for determining the greenest path, by incorporating fuel consumption and GHG emission objectives. Finally, we integrate the proposed algorithm into the Green Vehicle Routing Problem that minimizes the GHG emissions rather than the total distance or travel time. The developed heuristic is benchmarked against the existing algorithms by using synthetic traffic data on a real road network to illustrate potential savings and sustainability benefits

Modelling dynamic stochastic user equilibrium for urban road networks

In this study a dynamic assignment model is developed which estimates travellers' route and departure time choices and the resulting time varying traffic patterns during the morning peak. The distinctive feature of the model is that it does not restrict the geometry of the network to specific forms. The proposed framework of analysis consists of a travel time model, a demand model and a demand adjustment mechanism. Two travel time models are proposed. The first is based on elementary relationships from traffic flow theory and provides the framework for a macroscopic simulation model which calculates the time varying flow patterns and link travel times given the time dependent departure rate distributions; the second is based on queueing theory and models roads as bottlenecks through which traffic flow is either uncongested or fixed at a capacity independent of traffic density. The demand model is based on the utility maximisation decision rule and defines the time dependent departure rates associated with each reasonable route connecting, the O-D pairs of the network, given the total utility associated with each combination of departure time and route. Travellers' choices are assumed to result from the trade-off between travel time and schedule delay and each individual is assumed to first choose a departure time t, and then select a reasonable route, conditional on the choice of t. The demand model has therefore the form of a nested logit. The demand adjustment mechanism is derived from a Markovian model, and describes the day-to-day evolution of the departure rate distributions. Travellers are assumed to modify their trip choice decisions based on the information they acquire from recent trips. The demand adjustment mechanism is used in order to find the equilibrium state of the system, defined as the state at which travellers believe that they cannot increase their utility of travel by unilaterally changing route or departure time. The model outputs exhibit the characteristics of real world traffic patterns observed during the peak, i. e., time varying flow patterns and travel times which result from time varying departure rates from the origins. It is shown that increasing the work start time flexibility results in a spread of the departure rate distributions over a longer period and therefore reduces the level of congestion in the network. Furthermore, it was shown that increasing the total demand using the road network results in higher levels of congestion and that travellers tend to depart earlier in an attempt to compensate for the increase in travel times. Moreover, experiments using the queueing theory based travel time model have shown that increasing the capacity of a bottleneck may cause congestion to develop downstream, which in turn may result in an increase of the average travel time for certain O-D pairs. The dynamic assignment model is also applied to estimate the effects that different road pricing policies may have on trip choices and the level of congestion; the model is used to demonstrate the development of the shifting peak phenomenon. Furthermore, the effect of information availability on the traffic patterns is investigated through a number of experiments using the developed dynamic assignment model and assuming that guided drivers form a class of users characterised by lower variability of preferences with respect to route choice

The role of operational research in green freight transportation

Recent years have witnessed an increased awareness of the negative external impacts of freight transportation. The field of Operational Research (OR) has, particularly in the recent years, continued to contribute to alleviating the negative impacts through the use of various optimization models and solution techniques. This paper presents the basic principles behind and an overview of the existing body of recent research on ‘greening’ freight transportation using OR-based planning techniques. The particular focus is on studies that have been described for two heavily used modes for transporting freight across the globe, namely road (including urban and electric vehicles) and maritime transportation, although other modes are also briefly discussed