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Routing Optimization by Considering Multiple Uses of Vehicles and Demand Uncertainty: A Real-World Case Study

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第12期23卷 24991-25003页

作者： Zhang, Li Chen, Tingting Ding, Chuan Yu, Bin Beihang Univ Sch Transportat Sci & Engn Beijing 100191 Peoples R China Beijing Jiaotong Univ Sch Traff & Transportat Beijing 100044 Peoples R China

This paper studies a novel routing optimization problem motivated by a practical application of urban corrugated box transportation. The problem involves several features originating from the practical application, such as multi-trip, demand uncertainty, and demand-dependent loading time. The robust arc-flow formulation based on the budget uncertainty set is given. Then, we have recourse to the branch-and-price algorithm to solve the problem. Specifically, the pricing subproblem is to find the robust feasible routes with several unique features, including demand uncertainty, trip duration limitation, and demand-dependent loading time. A tailored labeling algorithm with recursive resource extension functions is involved to identify the robust feasible routes. Several numerical experiments are conducted on a real-world case study and generated instances based on the real road network. The computational results indicate that the proposed algorithm is more efficient than the commercial solver, and the proposed algorithm can fit various scenarios in practical applications to generate conservative routes efficiently.

关键词： Routing optimization problem multiple uses of vehicles demand uncertainty branch-and-price algorithm robust optimization

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Capacity expansion strategies for electric vehicle charging networks: Model, algorithms, and case study

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NAVAL RESEARCH LOGISTICS 2022年第3期69卷 442-460页

作者： Chen, Qianqian Huang, Kai Ferguson, Mark R. McMaster Univ Sch Computat Sci & Engn Hamilton ON Canada McMaster Univ DeGroote Sch Business Hamilton ON L8S 4M4 Canada McMaster Univ McMaster Inst Transportat & Logist Hamilton ON Canada

Governments in many jurisdictions are taking measures to promote the use of electric vehicles. As part of this goal, it is crucial to provide a sufficient number of charging stations to alleviate drivers' anxieties associated with the range of the vehicle. The goal of this research is to help governments develop vehicle charging networks for public use via the application of multistage stochastic integer programming model that determines both the locations and capacities of charging facilities over finite planning horizons. The logit choice model is used to estimate drivers' choices of nearby charging stations. Moreover, we characterize the charging demand as a function of the charging station quantity to reflect the range anxiety of consumers. The objective of the model is to minimize the expected total cost of installing and operating the charging facilities. An approximation algorithm, a heuristic algorithm, and a branch-and-price algorithm are designed to solve the model. We conduct numerical experiments to test the efficiency of these algorithms. Importantly, each algorithm has advantages over the CPLEX MIP solver. Finally, the City of Oakville in Ontario, Canada, is used to demonstrate the effectiveness of this model.

关键词： branch-and-price algorithm capacity expansion charging networks electric vehicle facility location logit choice model multistage stochastic integer programming

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A novel predict-then-optimize method for sustainable bike-sharing management: a data-driven study in China

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ANNALS OF OPERATIONS RESEARCH 2022年 1-33页

作者： Zhou, Yu Li, Qin Yue, Xiaohang Nie, Jiajia Guo, Qiang Chongqing Univ Sch Econ & Business Adm Chongqing Peoples R China Chongqing Univ Chongqing Key Lab Logist Chongqing Peoples R China Univ Wisconsin Sheldon B Lubar Sch Business Milwaukee WI 53201 USA Southwest Jiaotong Univ Sch Econ & Management Chengdu Peoples R China Key Lab Serv Sci & Innovat Sichuan Prov Chengdu Peoples R China

Sustainable operations management will appeal to the post-pandemic world. As the economy recovers, the surging demand for low-carbon bike-sharing has led to exacerbated mismatch in urban transportation. It is a serious challenge to optimize the reallocation schedule of sharing bikes among multiple positions in a network. To address the problem, we develop a novel predict-then-optimize method consisting of a data-driven robust optimization model and a branch-and-price algorithm. The optimization model derives the predicted demand surplus of each position based on historical data, enabling the optimal reallocation schedule in the network at minimum operational costs. Based on the prediction, the branch-and-price algorithm can find out the best routes of assigning bikes to specific positions that further improves transportation efficiency. Finally, we deploy the predict-then-optimize method to a realistic bike-sharing network in one major city of China. The computational results demonstrate that our method can significantly save the cost of operations and reduce the waste of resources. Therefore, the novel predict-then-optimize method has a great potential to facilitate the sustainable development of bike-sharing systems in urban transportation.

关键词： Bike-sharing system Reallocation scheduling Predict-then-optimize method Data-driven models branch-and-price algorithm

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Predicting flight arrival times with deep learning: A strategy for minimizing potential conflicts in gate assignment

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2024年 169卷

作者： Cao, Feng Tang, Tieqiao Gao, Yunqi Michler, Oliver Schultz, Michael Beihang Univ Sch Transportat Sci & Engn Beijing 100191 Peoples R China Tech Univ Dresden Inst Traff Telemat D-01069 Dresden Germany Bundeswehr Univ Munich Inst Flight Syst D-85577 Neubiberg Germany Beihang Univ Hangzhou Int Innovat Inst Hangzhou 311115 Peoples R China

Air transportation is frequently disrupted by factors such as weather and air traffic control, making it difficult for flights to strictly adhere to schedules, leading to frequent early arrivals or delays. These disruptions pose challenges to airport operations management, particularly in gate assignments, where potential conflicts and adjustments are often required. Unlike traditional methods that focus on enhancing robustness to reduce conflicts, this study adopts a Predict- then-Optimize (PO) framework, using predicted flight arrival times for gate assignments to avoid the need for robustness-related objectives. In the prediction phase, a CNN-LSTM-Attention deep learning model is developed to predict flight arrival times based on the historical data of a single airport, enhancing data availability and model practicality. In the optimization phase, a bi-objective gate assignment model is constructed, using predicted arrival times instead of scheduled times as input. An epsilon-constrained branch-and-price algorithm is developed to obtain non-dominated Pareto optimal solutions. Analysis using actual operational data from Beijing Capital International Airport shows that the prediction model achieves an accuracy of 93.27% for early arrivals and 83.6% for on-time flights. The epsilon-constrained branch-and- price algorithm outperforms heuristic algorithms in both the quantity and quality of Pareto solutions. Notably, the gate assignment strategy based on predicted arrival times significantly reduces potential conflicts, with a maximum reduction of 25.33% compared to the schedule- based strategy. This study demonstrates that the proposed gate assignment method, based on flight arrival time prediction, effectively mitigates the impact of arrival time uncertainty on gate assignments, providing a new approach to reducing potential conflicts without relying on robustness.

关键词： Airport operations management Gate assignment problem Deep learning branch-and-price algorithm Predict-then-Optimize framework

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A ridesharing routing problem for airport riders with electric vehicles

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2024年 184卷

作者： Zhang, Li Liu, Zhongshan Yu, Bin Long, Jiancheng Beihang Univ Sch Transportat Sci & Engn Beijing 100191 Peoples R China Beihang Univ Key Lab Intelligent Transportat Technol & Syst Minist Educ Beijing 100191 Peoples R China Hefei Univ Technol Sch Automot & Transportat Engn Hefei 230009 Peoples R China Engn Res Ctr Intelligent Transportat & Cooperat Ve Hefei 230009 Anhui Peoples R China

This paper proposes a ridesharing routing problem for airport riders motivated by a practical application. This problem designs routes for electric vehicles owned by an airport to provide pickup and delivery service for riders both departing from and traveling to the airport. To maintain a high quality of ridesharing service, it is assumed that drivers should ensure that all riders on board from the airport are completely delivered before they pick up new riders to the airport. The departure time from the airport and the return time to the airport of electric vehicles are affected by the flight schedules of riders. Additionally, due to the limited driving range of electric vehicles, the partial recharging at the airport is considered. The proposed ridesharing routing problem is formulated as a trip -based mixed integer linear programming model. An efficient method is proposed to enumerate all non -dominated feasible trips to form a trip -based graph for the model. A branch -and -price algorithm is developed to solve the proposed problem, in which a tailored labeling algorithm with feasibility checking process is designed to solve the pricing subproblem. The numerical experiments indicate that the trip -based model is more efficient compared with the rider -based model, and the proposed branch -and -price algorithm could solve large scale instances efficiently. The effects of acceptable extra time ratio, trip duration, capacity, and battery capacity on both airport and rider sides are discussed by sensitivity analysis.

关键词： Ridesharing Airport riders Electric vehicles Trip-based model Trip-enumeration algorithm branch-and-price algorithm

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Modeling and Optimization of the Inland Container Transportation Problem Considering Multi-Size Containers, Fuel Consumption, and Carbon Emissions

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PROCESSES 2024年第10期12卷 2231-2231页

作者： Song, Yujian Zhang, Yuting Shanghai Lixin Univ Accounting & Finance Sch Business Adm Shanghai 201620 Peoples R China Tongji Univ Sch Econ & Management Shanghai 200092 Peoples R China

This paper investigates the inland container transportation problem with a focus on multi-size containers, fuel consumption, and carbon emissions. To reflect a more realistic situation, the depot's initial inventory of empty containers is also taken into consideration. To linearly model the constraints imposed by the multiple container sizes and the limited number of empty containers, a novel graphical representation is presented for the problem. Based on the graphical representation, a mixed-integer programming model is presented to minimize the total transportation cost, which includes fixed, fuel, and carbon emission costs. To efficiently solve the model, a tailored branch-and-price algorithm is designed, which is enhanced by improvement schemes including a heuristic label-setting algorithm, decremental state-space relaxation, and the introduction of a high-quality upper bound. Results from a series of computational experiments on randomly generated instances demonstrate that (1) the proposed branch-and-price algorithm demonstrates a superior performance compared to the tabu search algorithm and the genetic algorithm;(2) each additional empty container in the depot reduces the total transportation cost by less than 1%, with a diminishing marginal effect;(3) the rational configuration of different types of trucks improves scheduling flexibility and reduces fuel and carbon emission costs as well as the overall transportation cost;and (4) extending customer time windows also contributes to lower the total transportation cost. These findings not only deepen the theoretical understanding of inland container transportation optimization but also provide valuable insights for logistics companies and policymakers to improve efficiency and implement more sustainable operational practices. Additionally, our research paves the way for future investigations into the integration of dynamic factors and emerging technologies in this field.

关键词： inland container transportation multi-size containers limited empty containers fuel consumption carbon emissions branch-and-price algorithm

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Suburban Demand Responsive Transit Service With Rental Vehicles

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2021年第4期22卷 2391-2403页

作者： Zhang, Li Chen, Tingting Yu, Bin Wang, Chenyu Beihang Univ Sch Transportat Sci & Engn Beijing 100191 Peoples R China Beijing Jiaotong Univ Sch Traff & Transportat Beijing 100044 Peoples R China Beihang Univ BDBC Beijing 100191 Peoples R China Eindhoven Univ Technol Dept Urban Sci & Syst NL-5600 Eindhoven Netherlands

Demand responsive transit (DRT) services in suburban regions serve residents as the complements of the public transportation. Since suburban DRT service usually faces demand fluctuation in some special situations, such as terrible weather, DRT operators have recourse to temporal rental vehicles to satisfy all requests. There are two types of rental contracts. In the first case, the costs of rental vehicles are similar to the costs of owned vehicles, and the multi-trip vehicle routing problem is studied. In the second case, the costs of outbound trips are not covered by DRT companies, and the open vehicle routes are introduced. In this paper, a mixed integer linear programming formulation is proposed to model the route optimization problem of the suburban DRT service, in which the number of rental vehicles and two classes of routes should be decided simultaneously. To solve the proposed model, a branch-and-price algorithm is designed, which has two pricing subproblems to generate two classes of routes, respectively. From the results of experiments, it can be found that the service sequence is introduced, when open routes are involved. Renting vehicles can help DRT companies save operational costs even in daily operations.

关键词： Companies Routing Heuristic algorithms Analytical models Public transportation Pricing Contracts Demand responsive transit rental vehicle multi-trip vehicle routing problem open vehicle routing problem branch-and-price algorithm

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A column generation approach for an emission-oriented vehicle routing problem on a multigraph

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2021年第3期288卷 794-809页

作者： Behnke, Martin Kirschstein, Thomas Bierwirth, Christian Martin Luther Univ Halle Wittenberg Sch Econ & Business Gr Steinstr 73 D-06108 Halle Germany

In this work, an emission-minimizing vehicle routing problem with heterogeneous vehicles and a heterogeneous road and traffic network is considered as it is typical in urban areas. Depending on the load of the vehicle, there exist multiple emission-minimal arcs for traveling between two locations. To solve the vehicle routing problem efficiently, a column generation approach is presented. At the core of the procedure an emission-oriented elementary shortest path problem on a multigraph is solved by a backward labeling algorithm. It is shown that the labeling algorithm can be sped up by adjusting the dual master program and by restricting the number of labels propagated in the sub-problem. The column generation technique is used to setup a fast heuristic as well as a branch-and-price algorithm. Both procedures are evaluated based on test instances with up to 100 customers. It turns out that the heuristic approach is very effective and generates near-optimal solutions with gaps below 0.1% on average while only requiring a fraction of the runtime of the exact approach. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Routing Green logistics Emission minimization Multigraph Column generation branch-and-price algorithm

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Reliable reserve-crew scheduling for airlines

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2023年 178卷

作者： Schrotenboer, Albert H. Wenneker, Rob Ursavas, Evrim Zhu, Stuart X. Eindhoven Univ Technol Sch Ind Engn Operat Planning Accounting & Control Grp Eindhoven Netherlands CGI Netherlands Space Transport & Logist Rotterdam Netherlands Univ Groningen Fac Econ & Business Dept Operat Groningen Netherlands

We study the practical setting in which regular-and reserve-crew schedules are dynamically maintained up to the day of executing the schedule. At each day preceding the execution of the schedule, disruptions occur due to sudden unavailability of personnel, making the planned regular and reserve-crew schedules infeasible for its execution day. This paper studies the fundamental question how to repair the schedules' infeasibility in the days preceding the execution, taking into account labor regulations. We propose a robust repair strategy that maintains flexibility in order to cope with additional future disruptions. The flexibility in reserve-crew usage is explicitly considered through evaluating the expected shortfall of the reserve-crew schedule based on a Markov chain formulation. The core of our approach relies on iteratively solving a set-covering formulation, which we call the Robust Crew Recovery Problem, which encapsulates this flexibility notion for reserve crew usage. A tailored branch-and-price algorithm is developed for solving the Robust Crew Recovery Problem to optimality. The corresponding pricing problem is efficiently solved by a newly developed pulse algorithm. Based on actual data from a medium-sized hub-and-spoke airline, we show that embracing our approach leads to fewer flight cancellations and fewer last-minute alterations, compared to repairing disrupted schedules without considering our robust measure.

关键词： Airline operations Disruption management branch-and-price algorithm Reserve crew Robust optimization Scheduling

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Robust optimization for the electric vehicle pickup and delivery problem with time windows and uncertain demands

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COMPUTERS & OPERATIONS RESEARCH 2023年 151卷

作者： Liu, Xiaochang Wang, Dujuan Yin, Yunqiang Cheng, T. C. E. Univ Elect Sci & Technol China Sch Management & Econ Chengdu 611731 Peoples R China Sichuan Univ Business Sch Chengdu 610064 Peoples R China Hong Kong Polytech Univ Dept Logist & Maritime Studies Hung Hom Kowloon Hong Kong Peoples R China

Recently, electric vehicles have become a viable option for city logistics to reduce air pollution and operating noise. In this study we consider the pickup and delivery problem with time windows involving battery-powered electric vehicles under demand uncertainly, where the uncertain demands fall within a budget polytope uncertainty set. We develop a two-stage adaptive robust model to find solutions that are insusceptible to a certain number of deviations in demands, where the routing, and the service start times and remaining battery capacities along a route are fixed before the realization of uncertain demands, while the quantities to load and unload along the route are adjustable to the demand scenario. To solve the model, we develop an exact two-phase decomposition method based on column-and-row generation to alleviate the computational difficulty arising from the large number of demand scenario-related variables and constraints, which decomposes the robust model into a master problem comprising only partial demand scenario-related variables and constraints, and an adversarial separation problem that verifies whether there are infeasible demand scenarios for the solution to the master problem. We develop a tailored branch-and-price-and-cut algorithm incorporating various acceleration strategies to solve the master problem, and a dynamic programming algorithm to solve the adversarial separation problem. We also conduct extensive numerical studies to evaluate the performance of the developed algorithm, ascertain the benefits of the robust model over the deterministic model, and analyze the effect of the charge consumption rate of the battery on the optimal solution.

关键词： Scheduling Pickup and delivery problem Robust optimization Electric vehicles branch-and-price algorithm

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