Showing papers in "Transportation Science in 2016"

50th Anniversary Invited Article—City Logistics: Challenges and Opportunities

Abstract: Today, around 54% of the world’s population lives in urban areas. By 2050, this share is expected to go up significantly. As a result, city logistics, which focuses on the efficient and effective transportation of goods in urban areas while taking into account the negative effects on congestion, safety, and environment, is critical to ensuring continued quality of life in cities. We review and discuss a variety of current and anticipated challenges and opportunities of city logistics. We hope this helps shaping an appropriate research agenda and stimulates more researchers to enter this exciting field.

50th Anniversary Invited Article—Autonomous Vehicles and Connected Vehicle Systems: Flow and Operations Considerations

Abstract: The impacts of autonomous vehicles, coupled with greater inter-vehicle and system connectivity, may be far-reaching on several levels. They entail changes to (1) the demand and behavior side, (2) the supply of mobility services, and (3) network and facility operational performance. We focus here on their impact on traffic flow and operations, especially in mixed traffic situations in which autonomous vehicles share the road with regular, human-driven vehicles, along with connected vehicles that may also have some automated functions. These mixed traffic situations correspond to likely deployment scenarios of the technologies, especially in the long transition towards 100% deployment. We explain using elementary traffic science concepts how autonomous vehicles and connected vehicles are expected to increase the throughput of highway facilities, as well as improve the stability of the traffic stream. A microsimulation framework featuring varying behavioral mechanisms for the three classes of vehicles is int...

50th Anniversary Invited Article-Goods Distribution with Electric Vehicles: Review and Research Perspectives

Abstract: Since the mid-2000s, electric vehicles have gained popularity in several countries even though their market share is still relatively low. However, most gains have been made in the area of passenger vehicles and most technical and scientific studies have been devoted to this case. By contrast, the potential of electric vehicular technology for goods distribution has received less attention. The issues related to the use of electric vehicles for goods distribution reveal a wide range of relevant research problems. The aims of this survey paper are to provide transportation researchers an overview of the technological and marketing background needed to conduct research in this area, to present a survey of the existing research in this field, and to offer perspectives for future research.

A Railway Timetable Rescheduling Approach for Handling Large-Scale Disruptions

Abstract: On a daily basis, large-scale disruptions require infrastructure managers and railway operators to reschedule their railway timetables together with their rolling stock and crew schedules. This research focuses on timetable rescheduling for passenger train services on a macroscopic level in a railway network. An integer linear programming model is formulated for solving the timetable rescheduling problem, which minimizes the number of cancelled and delayed train services while adhering to infrastructure and rolling stock capacity constraints. The possibility of rerouting train services to reduce the number of cancelled and delayed train services is also considered. In addition, all stages of the disruption management process from the start of the disruption to the time the normal situation is restored are taken into account. Computational tests of the described model on a heavily used part of the Dutch railway network show that the model is able to find optimal solutions in short computation times. This makes the approach applicable for use in practice.

Choice-Based Demand Management and Vehicle Routing in E-Fulfillment

Abstract: Attended home delivery services face the challenge of providing narrow delivery time slots to ensure customer satisfaction, while keeping the significant delivery costs under control. To that end, a firm can try to influence customers when they are booking their delivery time slot so as to steer them toward choosing slots that are expected to result in cost-effective schedules. We estimate a multinomial logit customer choice model from historic booking data and demonstrate that this can be calibrated well on a genuine e-grocer data set. We propose dynamic pricing policies based on this choice model to determine which and how much incentive (discount or charge) to offer for each time slot at the time a customer intends to make a booking. A crucial role in these dynamic pricing problems is played by the delivery cost, which is also estimated dynamically. We show in a simulation study based on real data that anticipating the likely future delivery cost of an additional order in a given location can lead to significantly increased profit as compared with current industry practice.

50th Anniversary Invited Article—Future Research Directions in Stochastic Vehicle Routing

Abstract: Stochastic vehicle routing, which deals with routing problems in which some of the key problem parameters are not known with certainty, has been an active, but fairly small research area for almost 50 years. However, over the past 15 years we have witnessed a steady increase in the number of papers targeting stochastic versions of the vehicle routing problem (VRP). This increase may be explained by the larger amount of data available to better analyze and understand various stochastic phenomena at hand, coupled with methodological advances that have yielded solution tools capable of handling some of the computational challenges involved in such problems. In this paper, we first briefly sketch the state-of-the-art in stochastic vehicle routing by examining the main classes of stochastic VRPs (problems with stochastic demands, with stochastic customers, and with stochastic travel or service times), the modeling paradigms that have been used to formulate them, and existing exact and approximate solution meth...

A Stochastic Optimization Model for Designing Last Mile Relief Networks

Abstract: In this study, we introduce a distribution network design problem that determines the locations and capacities of the relief distribution points in the last mile network, while considering demand-and network-related uncertainties in the post-disaster environment. The problem addresses the critical concerns of relief organizations in designing last mile networks, which are providing accessible and equitable service to beneficiaries. We focus on two types of supply allocation policies and propose a hybrid version considering their different implications on equity and accessibility. Then, we develop a two-stage stochastic programming model that incorporates the hybrid allocation policy and achieves high levels of accessibility and equity simultaneously. We devise a branch-and-cut algorithm based on Benders decomposition to solve large problem instances in reasonable times and conduct a numerical study to demonstrate the computational effectiveness of the solution method. We also illustrate the application of our model on a case study based on real-world data from the 2011 Van earthquake in Turkey.

A Branch-Price-and-Cut Algorithm for the Inventory-Routing Problem

Abstract: The inventory-routing problem IRP integrates two well-studied problems, namely, inventory management and vehicle routing. Given a set of customers to service over a multiperiod horizon, the IRP consists of determining when to visit each customer, which quantity to deliver in each visit, and how to combine the visits in each period into feasible routes such that the total routing and inventory costs are minimized. In this paper, we propose an innovative mathematical formulation for the IRP and develop a state-of-the-art branch-price-and-cut algorithm for solving it. This algorithm incorporates known and new families of valid inequalities, including an adaptation of the well-known capacity inequalities, as well as an ad hoc labeling algorithm for solving the column generation subproblems. Through extensive computational experiments on a widely used set of 640 benchmark instances involving between two and five vehicles, we show that our branch-price-and-cut algorithm clearly outperforms a state-of-the-art branch-and-cut algorithm on the instances with four and five vehicles. In this instance set, 238 were still open before this work and we proved optimality for 54 of them.

Inventory-Allocation Distribution Models for Postdisaster Humanitarian Logistics with Explicit Consideration of Deprivation Costs

Abstract: This paper develops novel mathematical models to maximize the benefits derived from the distribution of critical supplies to populations in need after a disaster. The formulations are based on welfare economics and the use of social costs, which are incurred by the segments of society involved in, and impacted by, the relief distribution strategy. The costs to the relief group are assessed as logistical costs, whereas the impacts to the beneficiaries are measured as the effects that the relief distribution has on their deprivation costs, which are the economic value of human suffering resulting from the lack of access to a good or service. The impacts on beneficiaries take into account two components: the reduction in deprivation costs for the recipients of the aid and the increase in deprivation costs for those individuals who do not receive the aid at a delivery epoch (the opportunity costs of the delivery strategy). The paper develops an inventory-allocation-routing model for the optimal assignment of ...

Optimizing Bus Bridging Services in Response to Disruptions of Urban Transit Rail Networks

Abstract: With growing dependence of many cities on urban mass transit, even limited disruptions of public transportation networks can lead to widespread confusion and significant productivity losses. A need exists for systematic approaches to developing efficient responses to minimize such negative impacts. We present an optimization-based approach that responds to degradations of urban transit rail networks by introducing smartly designed bus bridging services that take into consideration commuter travel demand at the time of the disruption. The approach consists of three fundamental steps, namely, 1 a column generation procedure to dynamically generate demand-responsive candidate bus routes, 2 a path-based multicommodity network flow model to identify the most effective combination of these candidate bus routes, and 3 another optimization-based procedure to determine simultaneously the optimal allocation of available vehicle resources among the selected routes and corresponding headways. The approach is applied to two case studies defined using actual data. The results show that the proposed approach can be carried out efficiently and that adding nonintuitive bus routes to the standard bus bridging services can significantly reduce the average travel delay. Moreover, the approach distributes delay more equitably. Many realistic operating constraints can also be handled.

The multi-trip vehicle routing problem with time windows and release dates

Abstract: The multi-trip vehicle routing problem with time windows and release dates is a variant of the multi-trip vehicle routing problem where a time window and a release date are associated with each customer. The release date represents the date when the merchandise requested by a customer becomes available at the depot. The interest for this problem comes from the field of city logistics and the study of delivery systems involving City Distribution Centers (CDC). In these systems, goods are first delivered to a CDC before being transferred to eco-friendly vehicles for final delivery. We propose to address the problem through a population-based algorithm, with a giant tour representation for individuals. An efficient labeling procedure allows turning giant tours into solutions. Experiments demonstrate the effectiveness of the method.

Models and Algorithms for Stochastic and Robust Vehicle Routing with Deadlines

Abstract: We consider the vehicle routing problem with deadlines under travel time uncertainty in the contexts of stochastic and robust optimization. The problem is defined on a directed graph where a fleet of vehicles is required to visit a given set of nodes and deadlines are imposed at a subset of nodes. In the stochastic vehicle routing problem with deadlines (SVRP-D), the probability distribution of the travel times is assumed to be known and the problem is solved to minimize the sum of probability of deadline violations. In the robust vehicle routing problem with deadlines (RVRP-D), however, the exact probability distribution is unknown but it belongs to a certain family of distributions. The objective of the problem is to optimize a performance measure, called lateness index, which represents the risk of violating the deadlines. Although novel mathematical frameworks have been proposed to solve these problems, the size of the problem that those approaches can handle is relatively small. Our focus in this pap...

The One-Dimensional Dynamic Dispatch Waves Problem

Abstract: We study same-day delivery systems by formulating the dynamic dispatch waves problem (DDWP), which models a depot where delivery requests arrive dynamically throughout a service day. At any dispatch epoch (wave), the information available to the decision maker is (1) a set of known, open requests that remain unfulfilled, and (2) a set of potential requests that may arrive later in the service day. At each wave, the decision maker decides whether or not to dispatch a vehicle, and if so, which subset of open requests to serve, with the objective of minimizing expected vehicle operating costs and penalties for unserved requests. We consider the DDWP with a single delivery vehicle and request destinations on a line, where vehicle operating times and costs depend only on the distance between points. We propose an efficient dynamic programming approach for the deterministic variant, and leverage it to design an optimal a priori policy with predetermined routes for the stochastic case. We then show that fully dy...

Service Network Design with Resource Constraints

Abstract: We first present a new service network design model for freight consolidation carriers, one that selects services and routes both commodities and resources needed to support the services that transport them, while explicitly recognizing that there are limits on how many resources are available at each terminal. We next present a solution approach that combines column generation, meta-heuristic, and exact optimization techniques to produce high-quality solutions. We demonstrate the efficacy of the approach with an extensive computational study and benchmark its performance against both a leading commercial solver and a column generation-based heuristic.

An Inventory-Routing Problem with Pickups and Deliveries Arising in the Replenishment of Automated Teller Machines

Abstract: The purpose of this paper is to introduce, model, and solve a rich multiperiod inventory-routing problem with pickups and deliveries motivated by the replenishment of automated teller machines in the Netherlands. Commodities can be brought to and from the depot, as well as being exchanged among customers to efficiently manage their inventory shortages and surpluses. A single customer can both provide and receive commodities at different periods, since its demand changes dynamically throughout the planning horizon and can be either positive or negative. In the case study, new technology provides these machines with the additional functionality of receiving deposits and reissuing banknotes to subsequent customers. We first formulate the problem as a very large-scale mixed-integer linear programming model. Given the size and complexity of the problem, we first decompose it into several more manageable subproblems by means of a clustering procedure, and we further simplify the subproblems by fixing some variables. The resulting subproblems are strengthened through the generation of valid inequalities and solved by branch and cut. We assess the performance of the proposed solution methodology through extensive computational experiments using real data. The results show that we are able to obtain good lower and upper bounds for this new and challenging practical problem.

Intermediate Facilities in Freight Transportation Planning: A Survey

Abstract: Consolidation of freight and merging operations are essential for transportation companies to reduce costs and improve the level of service provided to customers. Such operations take place in intermediate facilities or terminals located between the origins and the destinations of freight. This survey reviews the main contributions from the operations research literature on freight transportation planning problems where the presence of intermediate facilities has a strong impact on the cost of the system and on how goods are delivered. In particular, we focus on the tactical planning issues arising in this context. We have identified three classes of problems with intermediate facilities: vehicle routing problems, transshipment problems, and service network design problems. For each class of problems we provide an overview of the main problem variants, survey the methods used for their solution, and indicate open research directions.

An Exact Algorithm for the Elementary Shortest Path Problem with Resource Constraints

Abstract: The elementary shortest path problem with resource constraints ESPPRC is an NP-hard problem that often arises in the context of column generation for vehicle routing problems. We propose an exact solution method that relies on implicit enumeration with a novel bounding scheme that dramatically narrows the search space. We embedded our algorithm within a column generation to solve the linear relaxation root node of the vehicle routing problem with time windows VRPTW and found that the proposed algorithm performs well when compared against state-of-the-art algorithms for the ESPPRC on the well-known Solomon's test bed for the VRPTW.

Approximating the performance of a last mile transportation system

Abstract: The Last Mile Problem refers to the provision of travel service from the nearest public transportation node to a home or office. We study the supply side of this problem in a stochastic setting, with batch demands resulting from the arrival of groups of passengers who request last-mile service at urban rail stations or bus stops. Closed-form approximations are derived for the performance of Last Mile Transportations Systems (LMTS) as a function of the fundamental design parameters of such systems. An initial set of results is obtained for the case wherein a fleet of vehicles of unit capacity provides the Last-Mile service, and each delivery route consists of a simple round trip between the rail station or bus stop and a single passenger’s destination. These results are then extended to the general case in which the capacity of a vehicle is a small number (up to 20). It is shown through comparisons with simulation results that the approximations perform consistently well for a broad and realistic range of ...

Fairness and Collaboration in Network Air Traffic Flow Management: An Optimization Approach

Abstract: Air traffic flow management ATFM attempts to maintain a safe and efficient flow of aircraft given demand-capacity mismatches while ensuring an equitable distribution of delays among stakeholders. There has been extensive research addressing network effects such as the presence of multiple airports, sectors, and connectivity requirements in ATFM, but it has not explicitly incorporated the equitable distribution of delays, as well as work on the equitable distribution of delays in a single-airport setting, such as ration-by-schedule RBS as introduced under the collaborative decision-making paradigm. In this paper, we develop a two stage approach for network ATFM that incorporates fairness and airline collaboration. In Stage 1, we propose a discrete optimization model that attempts to incorporate an equitable distribution of delays among airlines by introducing a notion of fairness in network ATFM models-controlling the number of reversals and total amount of overtaking, which is a natural generalization of RBS. For two flights f and f', a reversal occurs when flight f' arrives before f, when f was scheduled to arrive before f'. In the event a reversal occurs, the number of time periods between the arrival times constitutes overtaking. In Stage 2, we allow for airline collaboration by proposing a network model for slot reallocation. We provide extensive empirical results of the proposed optimization models on national-scale, real-world data sets spanning six days that show interesting trade-offs between fairness and efficiency. We report computational times of less than 30 minutes for up to 25 airports and provide theoretical evidence that illuminates the strength of our approach.

A Queuing Model of the Airport Departure Process

Abstract: This paper presents an analytical model of the aircraft departure process at an airport. The modeling procedure includes the estimation of unimpeded taxi-out time distributions and the development of a queuing model of the departure runway system based on the transient analysis of D/E/1 queuing systems. The parameters of the runway service process are estimated using operational data. Using the aircraft pushback schedule as input, the model predicts the expected runway schedule and takeoff times. It also estimates the expected taxi-out time, queuing delay, and its variance for each flight in addition to the congestion level of the airport, sizes of the departure runway queues, and the departure throughput. The proposed approach is illustrated using a case study based on Newark Liberty International Airport. The model is trained using data from 2011 and is subsequently used to predict taxi-out times in 2007 and 2010. The predictions are compared with actual data to demonstrate the predictive capabilities of the model.

Storage Yard Management in Maritime Container Terminals

Abstract: This paper studies the daily storage yard manage problem arising in maritime container terminals, which integrates the space allocation and yard crane deployment decisions together with the consideration of container traffic congestion in the storage yard. The integrated problem is formulated as an integer linear programming model with the objective of minimizing the yard crane operating cost and the yard crane interblock movement cost. A divide-and-conquer solution approach is designed to solve the problem in an efficient manner in which harmony search and constraint satisfaction techniques are employed. Numerical experiments are conducted to validate the performance of the solution approach and the improvement from the integrated optimization method.

Enabling Urban Logistics Services at La Poste through Multi-Echelon Location-Routing

Abstract: We present a large-scale static and deterministic mixed-integer linear programming (MILP) model solving a two-echelon capacitated location-routing problem (2E-CLRP) with modal choice in the context of urban logistics services (ULS). This model aims to support the development of profitable ULS by guiding the strategic decision making of postal operators as they design an optimal facility network and vehicle fleet for the centralized consolidation and transportation of inbound and outbound urban freight flows. After comprehensively analyzing operating data from La Poste, we identify the key determinants of an optimal infrastructure and fleet design for the centralized coordination and consolidation of urban freight flows under a global service time constraint. Further, we discuss the optimal design’s sensitivity to changes in the input data and parameters of the 2E-CLRP model. The main theoretical contributions of this work are an optimal routing cost estimation formula and an optimization heuristic. Togeth...

An Adaptive Memory Programming Framework for the Robust Capacitated Vehicle Routing Problem

Abstract: We present an adaptive memory programming (AMP) metaheuristic to address the robust capacitated vehicle routing problem under demand uncertainty. Contrary to its deterministic counterpart, the robust formulation allows for uncertain customer demands, and the objective is to determine a minimum cost delivery plan that is feasible for all demand realizations within a prespecified uncertainty set. A crucial step in our heuristic is to verify the robust feasibility of a candidate route. For generic uncertainty sets, this step requires the solution of a convex optimization problem, which becomes computationally prohibitive for large instances. We present two classes of uncertainty sets for which route feasibility can be established much more efficiently. Although we discuss our implementation in the context of the AMP framework, our techniques readily extend to other metaheuristics. Computational studies on standard literature benchmarks with up to 483 customers and 38 vehicles demonstrate that the proposed ap...

Integrated Optimization of Rolling Stock Rotations for Intercity Railways

Abstract: This paper proposes a highly integrated solution approach for rolling stock planning problems in the context of long distance passenger traffic between cities. The main contributions are a generic hypergraph-based mixed-integer programming model for the considered rolling stock rotation problem and an integrated algorithm for its solution. The newly developed algorithm is able to handle a large spectrum of industrial railway requirements, such as vehicle composition, maintenance constraints, infrastructure capacities, and regularity aspects. We show that our approach has the power to produce rolling stock rotations that can be implemented in practice. In this way, the rolling stock rotations at the largest German long distance operator Deutsche Bahn Fernverkehr AG could be optimized by an automated system utilizing advanced mathematical programming techniques.

Large Neighborhoods with Implicit Customer Selection for Vehicle Routing Problems with Profits

Abstract: We consider several vehicle routing problems (VRP) with profits, which seek to select a subset of customers, each one being associated with a profit, and to design service itineraries. When the sum of profits is maximized under distance constraints, the problem is usually called the team orienteering problem. The capacitated profitable tour problem seeks to maximize profits minus travel costs under capacity constraints. Finally, in the VRP with a private fleet and common carrier, some customers can be delegated to an external carrier subject to a cost. Three families of combined decisions must be taken: customer’s selection, assignment to vehicles, and sequencing of deliveries for each route. We propose a new neighborhood search for these problems, which explores an exponential number of solutions in pseudo-polynomial time. The search is conducted with standard VRP neighborhoods on an exhaustive solution representation, visiting all customers. Since visiting all customers is usually infeasible or suboptim...

Restocking-Based Rollout Policies for the Vehicle Routing Problem with Stochastic Demand and Duration Limits

Abstract: We develop restocking-based rollout policies to make real-time, dynamic routing decisions for the vehicle routing problem with stochastic demand and duration limits. Leveraging dominance results, we develop a computationally tractable method to estimate the value of an optimal restocking policy along a fixed route. Embedding our procedure in rollout algorithms, we show restocking-based rollout outperforms a priori-based rollout, demonstrating the value of explicitly considering preemptive capacity replenishment in a rollout approach for dynamic routing. We also demonstrate the effectiveness of basic local search versus more sophisticated mechanisms for the heuristic component of the rollout procedure.

Extension of Traffic Flow Pattern Dynamic Classification by a Macroscopic Model Using Multivariate Clustering

Abstract: In this paper, we evaluate the performance of a dynamic approach to classifying flow patterns reconstructed by a switching-mode macroscopic flow model considering a multivariate clustering method. To remove noise and tolerate a wide scatter of traffic data, filters are applied before the overall modeling process. Filtered data are dynamically and simultaneously input to the density estimation and traffic flow modeling processes. A modified cell transmission model simulates traffic flow to explicitly account for flow condition transitions considering wave propagations throughout a freeway test stretch. We use flow dynamics specific to each of the cells to determine the mode of prevailing traffic conditions. Flow dynamics are then reconstructed by neural methods. By using two methods in part, i.e., dynamic classification and nonhierarchical clustering, classification of flow patterns over the fundamental diagram is obtained by considering traffic density as a pattern indicator. The fundamental diagram of speed-flow is dynamically updated to specify the current corresponding flow pattern. The dynamic classification approach returned promising results in capturing sudden changes on test stretch flow patterns as well as performance compared to multivariate clustering. The dynamic methods applied here are open to use in practice within intelligent management strategies, including incident detection and control and variable speed management.

A Branch-Cut-and-Price Algorithm for the Energy Minimization Vehicle Routing Problem

Abstract: We study a variant of the capacitated vehicle routing problem where the cost over each arc is defined as the product of the arc length and the weight of the vehicle when it traverses that arc We propose two new mixed-integer linear programming formulations for the problem: an arc-load formulation and a set partitioning formulation based on q-routes with additional constraints A family of cycle elimination constraints are derived for the arc-load formulation We then compare the linear programming LP relaxations of these formulations with the two-index one-commodity flow formulation proposed in the literature In particular, we show that the arc-load formulation with the new cycle elimination constraints gives the same LP bound as the set partitioning formulation based on 2-cycle-free q-routes, which is stronger than the LP bound given by the two-index one-commodity flow formulation We propose a branch-and-cut algorithm for the arc-load formulation, and a branch-cut-and-price algorithm for the set partitioning formulation strengthened by additional constraints Computational results on instances from the literature demonstrate that a significant improvement can be achieved by the branch-cut-and-price algorithm over other methods

Day-to-Day Flow Dynamics and Congestion Control

Abstract: For a predetermined set of an upper bound of link flows, this paper proposes a price-based congestion control scheme for achieving such a restraint target of traffic flow that evolves from day to day. On each day, drivers have to pay a toll selected from a feasible set. The tolls on each day are determined by the link flows and toll charges on the previous day and the predetermined upper bound of link flows. Several properties of the dynamical system model with the control scheme are analyzed, including the invariance of its evolutionary trajectories; the equivalence between its stationary state and user equilibrium under toll charge; the uniqueness, existence, and boundedness of its stationary state; and the convergence of its evolutionary trajectories. A special case of the model and implementation of the control scheme for several alternative targets are also given. Finally, application of the model to a traffic network is demonstrated with a numerical example. The study is helpful for better understanding the mechanism of congestion control under day-to-day traffic flow dynamics.

Vehicle Longitudinal Control and Traffic Stream Modeling

Abstract: A simple yet efficient traffic flow model, in particular one that describes vehicle longitudinal operational control and further characterizes a traffic flow fundamental diagram, is always desirable. Though many models have been proposed in the past with each having its own merits, research in this area is far from conclusive. This paper contributes a new model, i.e., the longitudinal control model, to the arsenal with a unique set of properties. The model is suited for a variety of transportation applications, among which a concrete example is provided in this paper.