Heterogeneous fixed fleet vehicle routing considering carbon emission

The paper considers heterogeneous fixed fleet vehicle routing with carbon emission to minimizing the sum of variable operation costs. A cost-benefit assessment of the value of purchasing or selling of carbon emission rights, using a mixed integer-programming model to reflect heterogeneous vehicle routing, is incorporated. Essentially, the use of a carbon market as a means of introducing more flexibility into an environmentally constrained network is considered. Tabu search algorithms are used to obtain solutions within a reasonable amount of computation time. In particular, we show the possibility that the amount of carbon emission can be reduced significantly without sacrificing the cost due to the benefit obtained from carbon trading.     

Vehicle routing problem and driver behaviour: a review and framework for analysis

Vehicle routing problems (VRPs) whose typical objective is to minimise total travel costs over a tour have evolved over the years with objectives ranging from minimising travel times and distances to minimising pollution and fuel consumption. However, driver behaviour continues to be neglected while planning for vehicle routes. Factors such as traffic congestion levels, monotonous drives and fatigue have an impact on the behaviour of drivers, which in turn might affect their speed-choice and route-choice behaviours. The behaviour of drivers and their subsequent decision-making owing to these factors impact the revenue of transport companies and could lead to huge losses in extreme cases. There have been studies on the behaviour of drivers in isolation, without inclusion of the objectives and constraints of the traditional routing problem. This paper presents a review of existing models of VRP, planner behaviour models in the VRP context and driver behaviour models and provides a motivation to integrate these models in a stochastic traffic environment to produce practical, economic and driver-friendly logistics solutions. The paper provides valuable insights on the relevance of behavioural issues in logistics and highlights the modelling implications of incorporating planner and driver behaviour in the framework of routing problems.     

The performance of route modification and demand stabilization strategies in stochastic vehicle routing

A key concern in managing vehicle routing operations under stochastic demands is whether, on the basis of travel distance, route modification yields materially greater logistical efficiency than fixed routes. This research uses statistical calibration as the primary technique to develop a robust and tractable model for estimating this difference in logistical efficiency. Based on features such as the models predictive accuracy and generalizability, it constitutes a substantive improvement over existing models. The present study also expands the range of predictive models relevant to vehicle routing under stochastic demands with models to estimate the transportation and inventory effects of persuading customers to stabilize their ordering patterns.     

Optimal solution to the vehicle routing problem by adopting a meta-heuristic algorithm

The delivery service provided by large-scale retailers continues to grow as online sales occupy an increasingly large share of the market. This study aims to tease out efficient vehicle scheduling times as well as optimal delivery routes by applying meta-heuristic algorithms. Monthly data on existing routes were obtained from a branch of Korea’s leading large-scale online retailer. The first task was to examine the status of existing routes by comparing delivery routes created using Dijkstra’s algorithm with existing delivery routes and their vehicle scheduling. The second task was to identify optimal delivery routes through a comparative analysis of the genetic algorithm and Tabu search algorithm, known for its superior applicability amongst other meta-heuristic algorithms. These findings demonstrate that the optimal vehicle routing problem not only has the potential to reduce distribution costs for operators and expedite delivery for consumers, but also the added social benefit of reduced carbon emissions.     

Impact of vehicle usage on consumer choice of hybrid electric vehicles

We analyze the vehicle usage and consumer profile attributes extracted from both National Household Travel Survey and Vehicle Quality Survey data to understand the impact of vehicle usage upon consumers’ choices of hybrid electric vehicles in the US. In addition, the key characteristics of hybrid vehicle drivers are identified to determine the market segmentations of hybrid electric vehicles and the critical attributes to include in the choice model. After a compatibility test of two datasets, a pooled choice model combining both data sources illustrates the significant influences of vehicle usage upon consumers’ choices of hybrid electric vehicles. Even though the data-bases have in the past been used independently to study travel behavior and vehicle quality ratings, here we use them together.     

Methods of analysis for vehicle soak time data

Vehicle soak time, the duration of time a vehicle’s engine is at rest prior to being started, and its distribution function are important transportation activity data inputs for mobile emissions inventory estimation due to their impacts on vehicle start and evaporative emissions. This paper provides vehicle emission researchers with an overview of statistical analysis methods relevant to analyzing vehicle soak time data. Many of these methods are already in use in emissions research and have appeared in the literature. These methods are reviewed and further details regarding the implementation and interpretation of these methods are provided. Statistical methods relevant to the analysis of soak time data that have yet to appear in the emissions literature, including kernel density estimation and generalized linear models, are also introduced. Advantages and disadvantages of the methods are compared and theoretical justification is provided. Issues of correlated observations and censored data are discussed. General guidelines for the analysis of soak time data, such as stratification by start type and geographical region, are established. Finally, a subset of the statistical methods discussed is used to analyze the US Environmental Protection Agency’s 3-city data.     

Heuristic search for the coupled runway sequencing and taxiway routing problem

This paper presents the first local search heuristic for the coupled runway sequencing (arrival & departure) and taxiway routing problems, based on the receding horizon (RH) scheme that takes into account the dynamic nature of the problem. As test case, we use Manchester Airport, the third busiest airport in the UK. From the ground movement perspective, the airport layout requires that departing aircraft taxi across the arrivals runway. This makes it impossible to separate arrival from departure sequencing in practice. Operationally, interactions between aircraft on the taxiways could prevent aircraft from taking off from, or landing on, runways during the slots assigned to them by an algorithm optimizing runway use alone. We thus consider the interactions between arrival and departure aircraft on the airport surface. Compared to sequentially optimized solutions, the results obtained with our approach indicate a significant decrease in the taxiway routing delay, with generally no loss in performance in terms of the sequencing delay for a regular day of operations. Another benefit of such a simultaneous optimization approach is the possibility of holding aircraft at the stands for longer, without the engines running. This significantly reduces the fuel burn, as well as bottlenecks and traffic congestion during peak hours that are often the cause of flight delays due to the limited amount of airport surface space available. Given that the maximum computing time per horizon is around 95 s, real-time operation might be practical with increased computing power.     

An analysis of the effects of French vehicle inspection program and grant for scrappage on household vehicle transaction

The French government has implemented a periodical vehicle inspection program, which aims at maintaining proper functioning of the vehicle and ensuring the emissions control systems installed on the vehicle work properly. Also, an incentive program for scrapping old vehicles was introduced in 1994 through 1996 to promote the replacement of those vehicles with higher emissions by newer vehicles with lower emissions. A hazard-based duration model of household vehicle transaction behavior has been developed in this study to examine the effects of the inspection program and the grant for scrappage on vehicle transaction timing. The model is developed as a competing risks model assuming the following three types of competing risks: replacing one of the vehicles in the household fleet, disposing of one vehicle in the fleet, and acquiring one vehicle to add to the fleet. The empirical analysis is carried out using the panel data of French households' vehicle ownership from 1984 to 1998, obtained by the panel survey called Parc-Auto, which has been conducted by a French marketing firm, SOFRES, since 1976. The long panel observation period facilitates the introduction of macro-economic indicators into the model, enabling the analysis to distinguish the effects of policy measures from macro-economic factors. The empirical results indicate that the expected vehicle holding duration becomes 1.3 years longer under the inspection program than before the program commenced, given that the vehicle is replaced by another vehicle at the end of the holding duration; and that the conditional probability of replacing a vehicle aged 10 years and over becomes 1.2 times higher, and the average holding duration becomes shorter by 3.3 years, when the grant for scrappage is available.     

Development of computerized vehicle scheduling system for specialized transportation: delaware case

A computer‐aided vehicle scheduling system is developed for Delaware's state‐wide specialized transportation system for elderly and handicapped persons. This paper presents the model, computer program package, system implementation, evaluation and the lessons learned from the project. The project which spanned two and a half years consists of five phases: (1) examine the manual method of vehicle scheduling (prior to the computerization); (2) develop a scheduling model; (3) develop a computer program package for the operation of the model; (4) install and operate the system; and (5) evaluate the system performance. The scheduling model consolidates passengers by block of time, origin and destination zones and along the direction of vehicle travel, first; second, minimizes the empty vehicle travel; and, lastly, assigns drivers to route considering assignment priority among the types of drivers and passengers. The product of the model is a set of driver log‐sheets which specify the sequence of passenger pick‐ups and drop‐offs. The computer program package includes necessary auxiliary data management functions such as registration of trip reservation, operating statistics and parameter value changes, as well as the execution of the model. It also allows the dispatcher to override the computer generated log‐sheet based on his discretion. The system has been operational for more than one year and it has brought about a number of changes in the operation and the role of the dispatcher. A before‐and‐after comparison of the operation and the lessons learned are also shown.     

带时间窗的混和车辆路径问题的优化算法研究

文章针对带时间窗约束的混合车辆路径问题的特点,建立了带时间窗的混合车辆路径问题的数学模型,并设计了变邻域禁忌搜索算法对该问题进行求解。通过标准算例测试及与现有文献计算结果的比较,验证了该算法的有效性。     

Constraints to green vehicle ownership: A focus group study

We consider constraints that prevent people with environmental concerns from buying “green” vehicles that are smaller, more fuel-efficient, and less polluting by using a series of focus group discussions. We find that the features of vehicles currently on the market, family and work responsibilities, residential choices, and routines and preferences all act as constraints. Serious misunderstandings about the environmental impacts of owning and using vehicles also were noted, making it difficult for many to accurately assess their alternatives. For some, environmental concerns are unlikely to influence future vehicle purchase decisions, even if constraints were removed altogether; other priorities have taken and will take precedence over the environmental impacts of their choices.     

A decision support system for integrated hazardous materials routing and emergency response decisions

Hazardous materials routing constitutes a critical decision in mitigating the associated transportation risk. This paper presents a decision support system for assessing alternative distribution routes in terms of travel time, risk and evacuation implications while coordinating the emergency response deployment decisions with the hazardous materials routes. The proposed system provides the following functionalities: (i) determination of alternative non-dominated hazardous materials distribution routes in terms of cost and risk minimization, (ii) specification of the hazardous materials first-response emergency service units locations in order to achieve timely response to an accident, and (iii) determination of evacuation paths from the impacted area to designated shelters and estimation of the associated evacuation time. The proposed system has been implemented, used and evaluated for assessing alternative hazardous materials routing decisions within the heavily industrialized area of Thriasion Pedion of Attica, Greece. The implementation of the aforementioned functionalities is based on two new integer programming models for the hazardous materials routing and the emergency response units location problems, respectively. A simplified version of the routing model is solved by an existing heuristic algorithm developed by the authors. A new Lagrangean relaxation heuristic algorithm has been developed for solving the emergency response units location problem. The focus of this paper is on the exposition of the proposed decision support system components and functionalities. Special emphasis is placed on the presentation of the two new mathematical models and the new solution method for the location model.     

The time-dependent pollution-routing problem

The Time-Dependent Pollution-Routing Problem (TDPRP) consists of routing a fleet of vehicles in order to serve a set of customers and determining the speeds on each leg of the routes. The cost function includes emissions and driver costs, taking into account traffic congestion which, at peak periods, significantly restricts vehicle speeds and increases emissions. We describe an integer linear programming formulation of the TDPRP and provide illustrative examples to motivate the problem and give insights about the tradeoffs it involves. We also provide an analytical characterization of the optimal solutions for a single-arc version of the problem, identifying conditions under which it is optimal to wait idly at certain locations in order to avoid congestion and to reduce the cost of emissions. Building on these analytical results we describe a novel departure time and speed optimization algorithm for the cases when the route is fixed. Finally, using benchmark instances, we present results on the computational performance of the proposed formulation and on the speed optimization procedure.