Internalizing negative externalities in vehicle routing problems through green taxes and green tolls

Road freight transportation includes various internal and external costs that need to be accounted for in the construction of efficient routing plans. Typically, the resulting optimization problem is formulated as a vehicle routing problem in any of its variants. While the traditional focus of the vehicle routing problem was the minimization of internal routing costs such as travel distance or duration, numerous approaches to include external factors related to environmental routing aspects have been recently discussed in the literature. However, internal and external routing costs are often treated as competing objectives. This paper discusses the internalization of external routing costs through the consideration of green taxes and green tolls. Numeric experiments with a biased-randomization savings algorithm, show benefits of combining internal and external costs in delivery route planning.Peer Reviewe

Quantifying the benefits of vehicle pooling with shareability networks

Taxi services are a vital part of urban transportation, and a considerable contributor to traffic congestion and air pollution causing substantial adverse effects on human health. Sharing taxi trips is a possible way of reducing the negative impact of taxi services on cities, but this comes at the expense of passenger discomfort quantifiable in terms of a longer travel time. Due to computational challenges, taxi sharing has traditionally been approached on small scales, such as within airport perimeters, or with dynamical ad-hoc heuristics. However, a mathematical framework for the systematic understanding of the tradeoff between collective benefits of sharing and individual passenger discomfort is lacking. Here we introduce the notion of shareability network which allows us to model the collective benefits of sharing as a function of passenger inconvenience, and to efficiently compute optimal sharing strategies on massive datasets. We apply this framework to a dataset of millions of taxi trips taken in New York City, showing that with increasing but still relatively low passenger discomfort, cumulative trip length can be cut by 40% or more. This benefit comes with reductions in service cost, emissions, and with split fares, hinting towards a wide passenger acceptance of such a shared service. Simulation of a realistic online system demonstrates the feasibility of a shareable taxi service in New York City. Shareability as a function of trip density saturates fast, suggesting effectiveness of the taxi sharing system also in cities with much sparser taxi fleets or when willingness to share is low.Comment: Main text: 6 pages, 3 figures, SI: 24 page

The Relationship between Vehicle Routing & Scheduling and Green Logistics - A Literature Survey

The basic Vehicle Routing and Scheduling Problem (VRSP) is described followed by an outline of solution approaches. Different variations of the basic VRSP are examined that involve the consideration of additional constraints or other changes in the structure of the appropriate model. An introduction is provided to Green Logistics issues that are relevant to vehicle routing and scheduling including discussion of the environmental objectives that should be considered. Particular consideration is given to VRSP models that relate to environmental issues including the timedependent VRSP, the transportation of hazardous materials and dynamic VRSP models. Finally some conclusions are drawn about further research needs in this area and the relation to road pricing

Simulating The Impact of Emissions Control on Economic Productivity Using Particle Systems and Puff Dispersion Model

A simulation platform is developed for quantifying the change in productivity of an economy under passive and active emission control mechanisms. The program uses object-oriented programming to code a collection of objects resembling typical stakeholders in an economy. These objects include firms, markets, transportation hubs, and boids which are distributed over a 2D surface. Firms are connected using a modified Prim’s Minimum spanning tree algorithm, followed by implementation of an all-pair shortest path Floyd Warshall algorithm for navigation purposes. Firms use a non-linear production function for transformation of land, labor, and capital inputs to finished product. A GA-Vehicle Routing Problem with multiple pickups and drop-offs is implemented for efficient delivery of commodities across multiple nodes in the economy. Boids are autonomous agents which perform several functions in the economy including labor, consumption, renting, saving, and investing. Each boid is programmed with several microeconomic functions including intertemporal choice models, Hicksian and Marshallian demand function, and labor-leisure model. The simulation uses a Puff Dispersion model to simulate the advection and diffusion of emissions from point and mobile sources in the economy. A dose-response function is implemented to quantify depreciation of a Boid’s health upon contact with these emissions. The impact of emissions control on productivity and air quality is examined through a series of passive and active emission control scenarios. Passive control examines the impact of various shutdown times on economic productivity and rate of emissions exposure experienced by boids. The active control strategy examines the effects of acceptable levels of emissions exposure on economic productivity. The key findings on 7 different scenarios of passive and active emissions controls indicate that rate of productivity and consumption in an economy declines with increased scrutiny of emissions from point sources. In terms of exposure rates, the point sources may not be the primary source of average exposure rates, however they significantly impact the maximum exposure rate experienced by a boid. Tightening of emissions control also negatively impacts the transportation sector by reducing the asset utilization rate as well as reducing the total volume of goods transported across the economy

A chance-constrained programming model for airport ground movement optimisation with taxi time uncertainties

Airport ground movement remains a major bottleneck for air traffic management. Existing approaches have developed several routing allocation methods to address this problem, in which the taxi time traversing each segment of the taxiways is fixed. However, taxi time is typically difficult to estimate in advance, since its uncertainties are inherent in the airport ground movement optimisation due to various unmodelled and unpredictable factors. To address the optimisation of taxi time under uncertainty, we introduce a chance-constrained programming model with sample approximation, in which a set of scenarios is generated in accordance with taxi time distributions. A modified sequential quickest path searching algorithm with local heuristic is then designed to minimise the entire taxi time. Working with real-world data at an international airport, we compare our proposed method with the state-of-the-art algorithms. Extensive simulations indicate that our proposed method efficiently allocates routes with smaller taxiing time, as well as fewer aircraft stops during the taxiing process

How to anticipate the level of activity of a sustainable collaborative network: the case of urban freight delivery through logistics platforms

International audienceIn this paper, we elaborate a methodology to study a particular case of collaborative network: city logistics. We identify that many solutions for urban logistics are, most of time, badly evaluated. Indeed, the theory often predicts a positive effect but the reality is most of time counterbalanced. We tried to fill this gap by making use of innovative methods. To do so, we mobilize several domains of knowledge: operational research, game theory and transportation studies on real cases. We suggest a solution to anticipate the level of activity of an Urban Consolidation Center and determine the condition under which it generates benefit for a carrier using or not, the collaborative network. We present the result obtained by application of our method on the real case of the city of Saint-Etienne

Congestion based Truck Drone intermodal delivery optimization

Commerce companies have experienced a rise in the number of parcels that need to be delivered each day. The goal of this study is to provide a decision-making procedure to assist carriers in taking a more significant role in selecting cost and risk-efficient truck-drone intermodal delivery routing plan. The congestion-based model is developed to select the method of parcel delivery utilizing a truck and a drone for optimizing cost and time. A study also has been conducted to compare drone-only and truck-only delivery routing plan. The proposed A* Heuristic algorithm and the OSRM application generate the travel path for drone and a truck along with the time of travel. Case studies have been conducted by varying the weight provided to cost and risk variable, studies indicate that there is a significant change in drone delivery travel time and cost with increase of cost weightage

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