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On the Efficiency Impacts of Berthing Priority Provision Transportation Science (IF 4.6) Pub Date : 2024-02-29 Xi Lin, Xinyue Pu, Xiwen Bai
Facing intensified interport competition in the global container shipping market, an increasing number of ports choose to offer berthing priority for carriers to increase their attractiveness. This study is the first to theoretically analyze the efficiency impacts of such prioritization. Specifically, this study models the steady-state dynamics for each terminal in a biterminal port as a prioritized
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Optimal Service Time Windows Transportation Science (IF 4.6) Pub Date : 2024-02-27 Marlin W. Ulmer, Justin C. Goodson, Barrett W. Thomas
Because customers must usually arrange their schedules to be present for home services, they desire an accurate estimate of when the service will take place. However, even when firms quote large service time windows, they are often missed, leading to customer dissatisfaction. Wide time windows and frequent failures occur because time windows must be communicated to customers in the face of several
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Data-Driven Optimization for Air Traffic Flow Management with Trajectory Preferences Transportation Science (IF 4.6) Pub Date : 2024-02-27 Luigi De Giovanni, Carlo Lancia, Guglielmo Lulli
In this paper, we present a novel data-driven optimization approach for trajectory-based air traffic flow management (ATFM). A key aspect of the proposed approach is the inclusion of airspace users’ trajectory preferences, which are computed from traffic data by combining clustering and classification techniques. Machine learning is also used to extract consistent trajectory options, whereas optimization
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Combinatorial Optimization-Enriched Machine Learning to Solve the Dynamic Vehicle Routing Problem with Time Windows Transportation Science (IF 4.6) Pub Date : 2024-02-14 Léo Baty, Kai Jungel, Patrick S. Klein, Axel Parmentier, Maximilian Schiffer
With the rise of e-commerce and increasing customer requirements, logistics service providers face a new complexity in their daily planning, mainly due to efficiently handling same-day deliveries. Existing multistage stochastic optimization approaches that allow solving the underlying dynamic vehicle routing problem either are computationally too expensive for an application in online settings or—in
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A New Simheuristic Approach for Stochastic Runway Scheduling Transportation Science (IF 4.6) Pub Date : 2024-02-13 Rob Shone, Kevin Glazebrook, Konstantinos G. Zografos
We consider a stochastic, dynamic runway scheduling problem involving aircraft landings on a single runway. Sequencing decisions are made with knowledge of the estimated arrival times (ETAs) of all aircraft due to arrive at the airport, and these ETAs vary according to continuous-time stochastic processes. Time separations between consecutive runway landings are modeled via sequence-dependent Erlang
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Robust Charging Network Planning for Metropolitan Taxi Fleets Transportation Science (IF 4.6) Pub Date : 2024-02-09 Gregor Godbersen, Rainer Kolisch, Maximilian Schiffer
We study the robust charging station location problem for a large-scale commercial taxi fleet. Vehicles within the fleet coordinate on charging operations but not on customer acquisition. We decide on a set of charging stations to open to ensure operational feasibility. To make this decision, we propose a novel solution method situated between the location routing problems with intraroute facilities
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Reliable Routing Strategies on Urban Transportation Networks Transportation Science (IF 4.6) Pub Date : 2024-02-08 Daniel Yamín, Andrés L. Medaglia, Arun Prakash Akkinepally
The problem of finding the most reliable routing strategy on urban transportation networks refers to determining the time-adaptive routing policy that maximizes the probability of on-time arrival at a destination given an arrival time threshold. The problem is defined on a stochastic and time-dependent network that captures real-world transportation systems’ inherent uncertainty and dynamism. To solve
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Hybrid Value Function Approximation for Solving the Technician Routing Problem with Stochastic Repair Requests Transportation Science (IF 4.6) Pub Date : 2024-01-24 Dai T. Pham, Gudrun P. Kiesmüller
We investigate the combined planning problem involving the routing of technicians and the stocking of spare parts for servicing geographically distributed repair tasks. The problem incorporates many operational uncertainties, such as future repair requests and the required spare parts to replace malfunctioned components. We model the problem as a sequential decision problem where decisions are made
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Demand Steering in a Last-Mile Delivery Problem with Home and Pickup Point Delivery Options Transportation Science (IF 4.6) Pub Date : 2024-01-19 Albina Galiullina, Nevin Mutlu, Joris Kinable, Tom Van Woensel
To increase the efficiency of last-mile delivery, online retailers can adopt pickup points in their operations. The retailer may then incentivize customers to steer them from home to pickup point delivery to reduce costs. However, it is usually uncertain whether the customer accepts this incentive to switch to pickup delivery. This setup gives rise to a new last-mile delivery problem with integrated
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Stochastic Cyclic Inventory Routing with Supply Uncertainty: A Case in Green-Hydrogen Logistics Transportation Science (IF 4.6) Pub Date : 2024-01-04 Umur Hasturk, Albert H. Schrotenboer, Evrim Ursavas, Kees Jan Roodbergen
Hydrogen can be produced from water, using electricity. The hydrogen can subsequently be kept in inventory in large quantities, unlike the electricity itself. This enables solar and wind energy generation to occur asynchronously from its usage. For this reason, hydrogen is expected to be a key ingredient for reaching a climate-neutral economy. However, the logistics for hydrogen are complex. Inventory
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Dual Bounds from Decision Diagram-Based Route Relaxations: An Application to Truck-Drone Routing Transportation Science (IF 4.6) Pub Date : 2023-12-20 Ziye Tang, Willem-Jan van Hoeve
For vehicle routing problems, strong dual bounds on the optimal value are needed to develop scalable exact algorithms as well as to evaluate the performance of heuristics. In this work, we propose an iterative algorithm to compute dual bounds motivated by connections between decision diagrams and dynamic programming models used for pricing in branch-and-cut-and-price algorithms. We apply techniques
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Electric Vehicle Scheduling in Public Transit with Capacitated Charging Stations Transportation Science (IF 4.6) Pub Date : 2023-12-12 Marelot H. de Vos, Rolf N. van Lieshout, Twan Dollevoet
This paper considers the scheduling of electric vehicles in a public transit system. Our main innovation is that we take into account that charging stations have limited capacity, while also considering partial charging. To solve the problem, we expand a connection-based network in order to track the state of charge of vehicles and model recharging actions. We then formulate the electric vehicle scheduling
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A Real-Time Control Policy to Achieve Maximum Throughput of an Online Order Fulfillment Network Transportation Science (IF 4.6) Pub Date : 2023-12-12 Michael Levin
Several major companies operate large online order fulfillment systems to ship goods from fulfillment centers through a distribution network to customer destinations in response to purchase orders. These networks make several types of decisions in real-time to serve customers. First, when a customer places an order, when and where (which fulfillment center) is it fulfilled from? Second, once an order
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Introduction to the Special Issue on Machine Learning Methods and Applications in Large-Scale Route Planning Problems Transportation Science (IF 4.6) Pub Date : 2023-12-12 Matthias Winkenbach, Stefan Spinler, Julian Pachon, Karthik Konduri
In this paper, we introduce the Special Issue on Machine Learning Methods and Applications in Large-Scale Route Planning Problems, which draws its inspiration from the academic community’s positive reception of the 2021 Amazon Last Mile Routing Research Challenge. We provide a structured overview of the papers featured in this special issue, and briefly discuss other noteworthy contributions to the
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Neural Network Estimators for Optimal Tour Lengths of Traveling Salesperson Problem Instances with Arbitrary Node Distributions Transportation Science (IF 4.6) Pub Date : 2023-12-12 Taha Varol, Okan Örsan Özener, Erinç Albey
It is essential to solve complex routing problems to achieve operational efficiency in logistics. However, because of their complexity, these problems are often tackled sequentially using cluster-first, route-second frameworks. Unfortunately, such two-phase frameworks can suffer from suboptimality due to the initial phase. To address this issue, we propose leveraging information about the optimal tour
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Decomposing the Train-Scheduling Problem into Integer-Optimal Polytopes Transportation Science (IF 4.6) Pub Date : 2023-12-07 Masoud Barah, Abbas Seifi, James Ostrowski
This paper presents conditions for which the linear relaxation for the train-scheduling problem is integer optimal. These conditions are then used to identify how to partition a general problem’s feasible region into integer-optimal polytopes. Such an approach yields an extended formulation that contains far fewer binary variables. Our computational experiments show that this approach results in significant
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Empowering the Capillary of the Urban Daily Commute: Battery Deployment Analysis for the Locker-Based E-bike Battery Swapping Transportation Science (IF 4.6) Pub Date : 2023-12-06 Xiaolei Xie, Xu Dai, Zhi Pei
In densely populated Asian countries, e-bikes have become a new supernova in daily urban transportation. To facilitate the operations of e-bike-based mobility, the present paper studies the management of the battery deployment for the e-bike battery-swapping system, where the unique features of e-bike riding are considered. Given the pedal-assisted mode, e-bike users could abandon waiting and return
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Dynamic Courier Capacity Acquisition in Rapid Delivery Systems: A Deep Q-Learning Approach Transportation Science (IF 4.6) Pub Date : 2023-12-04 Ramon Auad, Alan Erera, Martin Savelsbergh
With the recent boom of the gig economy, urban delivery systems have experienced substantial demand growth. In such systems, orders are delivered to customers from local distribution points respecting a delivery time promise. An important example is a restaurant meal delivery system, where delivery times are expected to be minutes after an order is placed. The system serves orders by making use of
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The Orienteering Problem with Drones Transportation Science (IF 4.6) Pub Date : 2023-11-29 Nicola Morandi, Roel Leus, Hande Yaman
We extend the classical problem setting of the orienteering problem (OP) to incorporate multiple drones that cooperate with a truck to visit a subset of the input nodes. We call this problem the OP with multiple drones (OP-mD). Drones have a limited battery endurance, and thus, they can either move together with the truck at no energy cost for the battery or be launched by the truck onto short flights
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Matching vs. Individual Choice: How to Counter Regional Imbalance of Carsharing Demand Transportation Science (IF 4.6) Pub Date : 2023-11-21 Nils Boysen, Dirk Briskorn, Rea Röntgen, Michael Dienstknecht
Among the most crucial organizational challenges of free-floating carsharing is the question how to cope with regional demand imbalance. Because users are allowed to leave a rented car anywhere in the service district, it regularly occurs that too many cars are left behind in low-demand regions whereas other regions face a demand surplus. In this paper, we consider a countermeasure that has been overlooked
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Customer-Centric Dynamic Pricing for Free-Floating Vehicle Sharing Systems Transportation Science (IF 4.6) Pub Date : 2023-11-06 Christian Müller, Jochen Gönsch, Matthias Soppert, Claudius Steinhardt
Free-floating vehicle sharing systems such as car or bike sharing systems offer customers the flexibility to pick up and drop off vehicles at any location within the business area and, thus, have become a popular type of urban mobility. However, this flexibility has the drawback that vehicles tend to accumulate at locations with low demand. To counter these imbalances, pricing has proven to be an effective
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Solving a Continent-Scale Inventory Routing Problem at Renault Transportation Science (IF 4.6) Pub Date : 2023-10-31 Louis Bouvier, Guillaume Dalle, Axel Parmentier, Thibaut Vidal
This paper is the fruit of a partnership with Renault. Their reverse logistic requires solving a continent-scale multiattribute inventory routing problem (IRP). With an average of 30 commodities, 16 depots, and 600 customers spread across a continent, our instances are orders of magnitude larger than those in the literature. Existing algorithms do not scale, so we propose a large neighborhood search
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Electric Vehicle Charge Scheduling with Flexible Service Operations Transportation Science (IF 4.6) Pub Date : 2023-10-26 Patrick S. Klein, Maximilian Schiffer
Operators who deploy large fleets of electric vehicles often face a challenging charge scheduling problem. Specifically, time-ineffective recharging operations limit the profitability of charging during service operations such that operators recharge vehicles off duty at a central depot. Here, high investment cost and grid capacity limit available charging infrastructure such that operators need to
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Machine Learning for Data-Driven Last-Mile Delivery Optimization Transportation Science (IF 4.6) Pub Date : 2023-10-24 Sami Serkan Özarık, Paulo da Costa, Alexandre M. Florio
In the context of the Amazon Last-Mile Routing Research Challenge, this paper presents a machine-learning framework for optimizing last-mile delivery routes. Contrary to most routing problems where an objective function is clearly defined, in the real-world setting considered in the challenge, an objective is not explicitly specified and must be inferred from data. Leveraging techniques from machine
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Load Factor Optimization for the Auto Carrier Loading Problem Transportation Science (IF 4.6) Pub Date : 2023-10-17 Christian Jäck, Jochen Gönsch, Hans Dörmann-Osuna
The distribution of passenger vehicles is a complex task and a high cost factor for automotive original equipment manufacturers (OEMs). On the way from the production plant to the customer, vehicles travel long distances on different carriers, such as ships, trains, and trucks. To save costs, OEMs and logistics service providers aim to maximize their loading capacities. Modern auto carriers are extremely
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A Stochastic Optimization Approach to Energy-Efficient Underground Timetabling Under Uncertain Dwell and Running Times Transportation Science (IF 4.6) Pub Date : 2023-09-20 Patrick Gemander, Andreas Bärmann, Alexander Martin
We consider a problem from the context of energy-efficient underground railway timetabling, in which an existing timetable draft is improved by slightly changing departure and running times. In practice, synchronization between accelerating and braking trains to utilize regenerative braking plays a major role for the energy efficiency of a timetable. Because deviations from a planned timetable may
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Understanding Origin-Destination Ride Demand with Interpretable and Scalable Nonnegative Tensor Decomposition Transportation Science (IF 4.6) Pub Date : 2023-09-20 Xiaoyue Li, Ran Sun, James Sharpnack, Yueyue Fan
This paper focuses on the estimation and compression of ride demand from origin-destination (OD) trip event data. By representing the OD event data as a three-way tensor (origin, destination, and time), we model the data as a Poisson process with an intensity tensor that can be decomposed according to a Tucker decomposition. We establish and justify a specific form of nonnegative Tucker-like tensor
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An Exact Approach for Solving Pickup-and-Delivery Traveling Salesman Problems with Neighborhoods Transportation Science (IF 4.6) Pub Date : 2023-08-31 Cai Gao, Ningji Wei, Jose L. Walteros
This paper studies a variant of the traveling salesman problem, called the pickup-and-delivery traveling salesman problem with neighborhoods, that combines traditional pickup and delivery requirements with the flexibility of visiting the customers at locations within compact neighborhoods of arbitrary shape. We derive two optimality conditions for the problem, a local condition that verifies whether
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ULTRA: Unlimited Transfers for Efficient Multimodal Journey Planning Transportation Science (IF 4.6) Pub Date : 2023-08-30 Moritz Baum, Valentin Buchhold, Jonas Sauer, Dorothea Wagner, Tobias Zündorf
We study a multimodal journey planning scenario consisting of a public transit network and a transfer graph that represents a secondary transportation mode (e.g., walking, cycling, e-scooter). The objective is to compute Pareto-optimal journeys with respect to arrival time and the number of used public transit trips. Whereas various existing algorithms can efficiently compute optimal journeys in either
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Optimal Condition-Based Maintenance via a Mobile Maintenance Resource Transportation Science (IF 4.6) Pub Date : 2023-08-29 Shadi Sanoubar, Bram de Jonge, Lisa M. Maillart, Oleg A. Prokopyev
We consider the problem of performing condition-based maintenance on a set of geographically distributed assets via a single maintenance resource that travels between the assets’ locations. That is, we dynamically determine the optimal positioning of the maintenance resource and the optimal timing of condition-based maintenance interventions that the maintenance resource performs. These decisions are
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A Three-Front Parallel Branch-and-Cut Algorithm for Production and Inventory Routing Problems Transportation Science (IF 4.6) Pub Date : 2023-08-16 Cleder Marcos Schenekemberg, Thiago André Guimarães, Antonio Augusto Chaves, Leandro C. Coelho
Production and inventory routing problems consider a single-product supply chain operating under a vendor-managed inventory system. A plant creates a production plan and vehicle routes over a planning horizon to replenish its customers at minimum cost. In this paper, we present two- and three-index formulations, implement a branch-and-cut algorithm based on each formulation, and introduce a local search
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A Linear-Parameter-Varying Formulation for Model Predictive Perimeter Control in Multi-Region MFD Urban Networks Transportation Science (IF 4.6) Pub Date : 2023-08-14 Anastasios Kouvelas, Mohammadreza Saeedmanesh, Nikolas Geroliminis
An alternative approach for real-time network-wide traffic control in cities that has recently gained attention is perimeter flow control. Many studies have shown that this method is more efficient than state-of-the-art adaptive signal control strategies for heterogeneously congested urban networks. The basic concept of such an approach is to partition heterogeneous cities into a small number of homogeneous
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A New Family of Route Formulations for Split Delivery Vehicle Routing Problems Transportation Science (IF 4.6) Pub Date : 2023-08-14 Isaac Balster, Teobaldo Bulhões, Pedro Munari, Artur Alves Pessoa, Ruslan Sadykov
We propose a new family of formulations with route-based variables for the split delivery vehicle routing problem with and without time windows. Each formulation in this family is characterized by the maximum number of different demand quantities that can be delivered to a customer during a vehicle visit. As opposed to previous formulations in the literature, the exact delivery quantities are not always
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Server Routing-Scheduling Problem in Distributed Queueing System with Time-Varying Demand and Queue Length Control Transportation Science (IF 4.6) Pub Date : 2023-08-03 Zerui Wu, Ran Liu, Ershun Pan
We study a server routing-scheduling problem in a distributed queueing system, where the system consists of multiple queues at different locations. In a distributed queueing system, servers are shared among multiple queues, and they travel between queues in response to stochastic and time-varying demands. Although server traveling can improve service levels and shorten queue lengths, server routing
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From Corridor to Network Macroscopic Fundamental Diagrams: A Semi-Analytical Approximation Approach Transportation Science (IF 4.6) Pub Date : 2023-08-01 Gabriel Tilg, Lukas Ambühl, Sérgio F. A. Batista, Mónica Menéndez, Ludovic Leclercq, Fritz Busch
The design of network-wide traffic management schemes or transport policies for urban areas requires computationally efficient traffic models. The macroscopic fundamental diagram (MFD) is a promising tool for such applications. Unfortunately, empirical MFDs are not always available, and semi-analytical estimation methods require a reduction of the network to a corridor that introduces substantial inaccuracies
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The Traveling Salesman Problem with Stochastic and Correlated Customers Transportation Science (IF 4.6) Pub Date : 2023-07-28 Pascal L. J. Wissink
It is well-known that the cost of parcel delivery can be reduced by designing routes that take into account the uncertainty surrounding customers’ presences. Thus far, routing problems with stochastic customer presences have relied on the assumption that all customer presences are independent from each other. However, the notion that demographic factors retain predictive power for parcel-delivery efficiency
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A Bilevel Model for Robust Network Design and Biomass Pricing Under Farmers’ Risk Attitudes and Supply Uncertainty Transportation Science (IF 4.6) Pub Date : 2023-07-24 Qiaofeng Li, Halit Üster, Zhi-Hai Zhang
This paper addresses an integrated biomass pricing and logistics network design problem. A bilevel design and pricing model is proposed to capture the dynamic decision process between a biofuel producer as a Stackelberg leader and farmers as Stackelberg followers. The bilevel optimization model is transformed into a tractable single-level formulation by using optimality constraints. Other unique characteristics
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The Traveling Salesman Problem with Drones: The Benefits of Retraversing the Arcs Transportation Science (IF 4.6) Pub Date : 2023-07-11 Nicola Morandi, Roel Leus, Jannik Matuschke, Hande Yaman
In the traveling salesman problem with drones (TSP-mD), a truck and multiple drones cooperate to serve customers in the minimum amount of time. The drones are launched and retrieved by the truck at customer locations, and each of their flights must not consume more energy than allowed by their batteries. Most problem settings in the literature restrict the feasible truck routes to cycles (i.e., closed
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Charging Station Location and Sizing for Electric Vehicles Under Congestion Transportation Science (IF 4.6) Pub Date : 2023-06-16 Ömer Burak Kınay, Fatma Gzara, Sibel A. Alumur
This paper studies the problem of determining the strategic location of charging stations and their capacity levels under stochastic electric vehicle flows and charging times taking into account the route choice response of users. The problem is modeled using bilevel optimization, where the network planner or leader minimizes the total infrastructure cost of locating and sizing charging stations while
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Hub Network Design Problem with Capacity, Congestion, and Stochastic Demand Considerations Transportation Science (IF 4.6) Pub Date : 2023-06-16 Vedat Bayram, Barış Yıldız, M. Saleh Farham
Our study introduces the hub network design problem with congestion, capacity, and stochastic demand considerations (HNDC), which generalizes the classical hub location problem in several directions. In particular, we extend state-of-the-art by integrating capacity acquisition decisions and congestion cost effect into the problem and allowing dynamic routing for origin-destination (OD) pairs. Connecting
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Efficient Algorithms for Stochastic Ride-Pooling Assignment with Mixed Fleets Transportation Science (IF 4.6) Pub Date : 2023-06-15 Qi Luo, Viswanath Nagarajan, Alexander Sundt, Yafeng Yin, John Vincent, Mehrdad Shahabi
Ride-pooling, which accommodates multiple passenger requests in a single trip, has the potential to substantially enhance the throughput of mobility-on-demand (MoD) systems. This paper investigates MoD systems that operate mixed fleets composed of “basic supply” and “augmented supply” vehicles. When the basic supply is insufficient to satisfy demand, augmented supply vehicles can be repositioned to
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Benders Adaptive-Cuts Method for Two-Stage Stochastic Programs Transportation Science (IF 4.6) Pub Date : 2023-06-14 Cristian Ramírez-Pico, Ivana Ljubić, Eduardo Moreno
Benders decomposition is one of the most applied methods to solve two-stage stochastic problems (TSSP) with a large number of scenarios. The main idea behind the Benders decomposition is to solve a large problem by replacing the values of the second-stage subproblems with individual variables and progressively forcing those variables to reach the optimal value of the subproblems, dynamically inserting
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Acknowledgment to Referees (2022) Transportation Science (IF 4.6) Pub Date : 2023-06-12
We wish to thank the following individuals who acted as referees for Transportation Science in 2022. We express our apologies to those whose names we may have missed.
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Formation and Routing of Worker Teams for Airport Ground Handling Operations: A Branch-and-Price-and-Check Approach Transportation Science (IF 4.6) Pub Date : 2023-06-02 Giacomo Dall’Olio, Rainer Kolisch
We address workforce optimization for ground handling operations at the airport, focusing on baggage loading and unloading. Teams of skilled workers have to be formed and routed across the apron to unload the baggage from the aircraft after a landing and to load it before takeoff. Such tasks must be performed within time windows and require a team of workers with different skill levels. The goal is
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Cross-Border Capacity Planning in Air Traffic Management Under Uncertainty Transportation Science (IF 4.6) Pub Date : 2023-05-31 Jan-Rasmus Künnen, Arne K. Strauss, Nikola Ivanov, Radosav Jovanović, Frank Fichert, Stefano Starita
In European air traffic management (ATM), it is an important decision how much capacity to provide for each airspace, and it has to be made weeks or even months in advance of the day of operation. Given the uncertainty in demand that may materialize until then along with variability in capacity provision (e.g., due to weather), Airspace Users could face high costs of displacements (i.e., delays and
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Uncertainty Estimation of Connected Vehicle Penetration Rate Transportation Science (IF 4.6) Pub Date : 2023-05-22 Shaocheng Jia, S. C. Wong, Wai Wong
Knowledge of the connected vehicle (CV) penetration rate is crucial for realizing numerous beneficial applications during the prolonged transition period to full CV deployment. A recent study described a novel single-source data penetration rate estimator (SSDPRE) for estimating the CV penetration rate solely from CV data. However, despite the unbiasedness of the SSDPRE, it is only a point estimator
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Human-Robot Cooperation: Coordinating Autonomous Mobile Robots and Human Order Pickers Transportation Science (IF 4.6) Pub Date : 2023-05-05 Maximilian Löffler, Nils Boysen, Michael Schneider
In the e-commerce era, efficient order fulfillment processes in distribution centers have become a key success factor. One novel technology to streamline these processes is robot-assisted order picking. In these systems, human order pickers are supported by autonomous mobile robots (AMRs), which carry bins for collecting picking orders, autonomously move through the warehouse, and wait in front of
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Multimodal Vaccine Distribution Network Design with Drones Transportation Science (IF 4.6) Pub Date : 2023-04-25 Shakiba Enayati, Haitao Li, James F. Campbell, Deng Pan
Childhood vaccines play a vital role in social welfare, but in hard-to-reach regions, poor transportation, and a weak cold chain limit vaccine availability. This opens the door for the use of vaccine delivery by drones (uncrewed aerial vehicles, or UAVs) with their fast transportation and reliance on little or no infrastructure. In this paper, we study the problem of strategic multimodal vaccine distribution
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The Electric Dial-a-Ride Problem on a Fixed Circuit Transportation Science (IF 4.6) Pub Date : 2023-04-25 Yves Molenbruch, Kris Braekers, Ohad Eisenhandler, Mor Kaspi
Shared mobility services involving electric autonomous shuttles have increasingly been implemented in recent years. Because of various restrictions, these services are currently offered on fixed circuits and operated with fixed schedules. This study introduces a service variant with flexible stopping patterns and schedules. Specifically, in the electric dial-a-ride problem on a fixed circuit (eDARP-FC)
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Parking Search in the Physical World: Calculating the Search Time by Leveraging Physical and Graph Theoretical Methods Transportation Science (IF 4.6) Pub Date : 2023-04-05 Nilankur Dutta, Thibault Charlottin, Alexandre Nicolas
Parking plays a central role in transport policies and has wide-ranging consequences: While the average time spent searching for parking exceeds dozens of hours per driver every year in many Western cities, the associated cruising traffic generates major externalities, by emitting pollutants and contributing to congestion. However, the laws governing the parking search time remain opaque in many regards
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Humanitarian Relief Distribution Problem: An Adjustable Robust Optimization Approach Transportation Science (IF 4.6) Pub Date : 2023-03-23 Farzad Avishan, Milad Elyasi, İhsan Yanıkoğlu, Ali Ekici, O. Örsan Özener
Management of humanitarian logistics operations is one of the most critical planning problems to be addressed immediately after a disaster. The response phase covers the first 12 hours after the disaster and is prone to uncertainties because of debris and gridlock traffic influencing the dispatching operations of relief logistics teams in the areas affected. Moreover, the teams have limited time and
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Heatmap-Based Decision Support for Repositioning in Ride-Sharing Systems Transportation Science (IF 4.6) Pub Date : 2023-03-22 Jarmo Haferkamp, Marlin W. Ulmer, Jan Fabian Ehmke
In ride-sharing systems, platform providers aim to distribute the drivers in the city to meet current and potential future demand and to avoid service cancellations. Ensuring such distribution is particularly challenging in the case of a crowdsourced fleet, as drivers are not centrally controlled but are free to decide where to reposition when idle. Thus, providers look for alternative ways to ensure
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Transportation Asset Acquisition under a Newsvendor Model with Cutting-Stock Restrictions: Approximation and Decomposition Algorithms Transportation Science (IF 4.6) Pub Date : 2023-03-21 Joris Wagenaar, Ioannis Fragkos, W. L. C. Faro
Logistics service providers use transportation assets to offer services to their customers. To cope with demand variability, they may acquire additional assets on a one-off (spot) basis. The planner’s problem is to determine the optimal level of assets acquired upfront, such that their cost is minimized, for a given planning horizon. Our formulation captures a nontrivial complication: Although ordering
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Branch-Price-and-Cut-Based Solution of Order Batching Problems Transportation Science (IF 4.6) Pub Date : 2023-03-09 Julia Wahlen, Timo Gschwind
Given a set of customer orders each comprising one or more individual items to be picked, the order batching problem (OBP) in warehousing consists of designing a set of picking batches such that each customer order is assigned to exactly one batch, all batches satisfy the capacity restriction of the pickers, and the total distance traveled by the pickers is minimal. In order to collect the items of
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Electric Vehicle Fleets: Scalable Route and Recharge Scheduling Through Column Generation Transportation Science (IF 4.6) Pub Date : 2023-03-07 Axel Parmentier, Rafael Martinelli, Thibaut Vidal
The rise of battery-powered vehicles has led to many new technical and methodological hurdles. Among these, the efficient planning of an electric fleet to fulfill passenger transportation requests still represents a major challenge. This is because of the specific constraints of electric vehicles, bound by their battery autonomy and necessity of recharge planning, and the large scale of the operations
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Optimal Retrieval in Puzzle-Based Storage Systems Using Automated Mobile Robots Transportation Science (IF 4.6) Pub Date : 2023-02-23 Tal Raviv, Yossi Bukchin, René de Koster
Puzzle-based storage (PBS) systems store unit loads at very high density, without consuming space for transport aisles. In such systems, each load is stored on a moving device (conveyor module or transport vehicle), making these systems very expensive to build and maintain. This paper studies a new type of PBS system where loads are moved by a small number of autonomous mobile robots (AMRs). The AMRs
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Using Machine Learning to Include Planners’ Preferences in Railway Crew Scheduling Optimization Transportation Science (IF 4.6) Pub Date : 2023-01-18 Theresa Gattermann-Itschert, Laura Maria Poreschack, Ulrich W. Thonemann
In crew scheduling, optimization models can become complex when a large number of penalty terms is included in the objective function to take planners’ preferences into account. Planners’ preferences often include nonmonetary aspects for which both the mathematical formulation and the assignment of appropriate penalty costs can be difficult. We address this problem by using machine learning to learn
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Two-Sided Deep Reinforcement Learning for Dynamic Mobility-on-Demand Management with Mixed Autonomy Transportation Science (IF 4.6) Pub Date : 2023-01-17 Jiaohong Xie, Yang Liu, Nan Chen
Autonomous vehicles (AVs) are expected to operate on mobility-on-demand (MoD) platforms because AV technology enables flexible self-relocation and system-optimal coordination. Unlike the existing studies, which focus on MoD with pure AV fleet or conventional vehicles (CVs) fleet, we aim to optimize the real-time fleet management of an MoD system with a mixed autonomy of CVs and AVs. We consider a realistic
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Simultaneous Production and Transportation Problem: A Case of Additive Manufacturing Transportation Science (IF 4.6) Pub Date : 2023-01-16 Gourav Dwivedi, Shuvabrata Chakraborty, Yogesh K. Agarwal, Rajiv K. Srivastava
Additive manufacturing (AM) promises considerable advantages over conventional manufacturing to meet the growing demand for customized products and faster delivery times. Consider a mobile mini-factory, that is, a vehicle equipped with an AM facility, which can simultaneously produce and transport the final products to the customers. The overlapping of production and transportation processes allows
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The Parallel Drone Scheduling Traveling Salesman Problem with Collective Drones Transportation Science (IF 4.6) Pub Date : 2023-01-13 Minh Anh Nguyen, Minh Hoàng Hà
In this paper, we study a new variant of the parallel drone scheduling traveling salesman problem that aims to increase the utilization of drones, particularly for heavy item deliveries. The system under consideration adopts a technology that combines multiple drones to form a collective drone (c-drone) capable of transporting heavier items. The innovative concept is expected to add further flexibility