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作者： Margolis, Joshua T. Clemson University

学位级别：Ph.D., Doctor of Philosophy

We study vehicle routing for target surveillance and consider several extensions to present a holistic account of military-operational experiences. These problems are variations of the multi-vehicle covering tour problem which has been well-studied within the optimization community. Although many formulations and solution methodologies have been presented, they cannot be directly applied to our problems due to specific problem structures under study. We provide a review of the relevant literature and propose several different optimization models and algorithms for solving our problems of interest. First, we consider routing a fleet of vehicles for target surveillance within a deterministic setting with speed optimization under covering constraints; we present both (i) a branch-and-price-based exact algorithm and an effective labeling algorithm with an innovative set of dominance rules for solving the resulting pricing problem, and (ii) effective heuristic approaches. Next, we consider dynamic routing plans for target surveillance due to the sudden materialization of new targets. We propose a Markov decision process model to adapt to the changing information state and develop a two-stage stochastic programming-based look-ahead approach within a rolling horizon procedure. Lastly, we investigate the effect that routing markers' locations have on the vehicle set's coverage capabilities in the presence of an unknown target set. We formulate a two-stage stochastic program for marker selection and use scenario-decomposition techniques to avoid solving the large-scale mixed-integer program. Collectively, our research can be used by military and security personnel to analyze and improve their processes and to help these decision-makers implement economical, sustainable, and successful policies.

关键词： branch-and-price algorithm Covering tour Rolling-horizon Speed optimization Target surveillance Vehicle routing

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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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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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Tramp ship routing and speed optimization with tidal berth time windows

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

作者： Gao, Yuan Sun, Zhuo Dalian Maritime Univ 1 Linghai Rd Dalian 116026 Liaoning Peoples R China

Tramp shipping companies need to schedule shipping routes and decide on appropriate speeds based on short-term demand. This task differs from traditional vehicle routing problems (VRPs) in that the ship may wait for the tide, which changes with time. The wait time is a nonlinear function of the load, and in this paper, we describe this kind of wait as the ship following a tidal berth time windows. Additionally, the speed of the ship affects both the wait time and the sailing cost. This paper proposes a mixed-integer nonlinear programming model to tackle this problem. A branchand-price framework is applied to solve the model efficiently, decomposing the model into a set partitioning master problem and an elementary shortest path subproblem. A labeling algorithm incorporating ship speed is developed to handle the subproblem, and further enhancements are made by optimizing the speed separately. Computational experiments show the effectiveness and accuracy of the proposed solution approach for large-scale instances. Moreover, considering the tidal time windows allows for exploiting the practical benefits of raising tides, which benefits the tramp shipping industry.

关键词： Tramp Shipping Vehicle Routing Problem Tidal Berth Time Windows branch-and-price algorithm

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Infectious waste management during a pandemic: A stochastic location-routing problem with chance-constrained time windows

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COMPUTERS & INDUSTRIAL ENGINEERING 2023年第1期177卷 109066页

作者： Hassanpour, Saeed Tasouji Ke, Ginger Y. Zhao, Jiahong Tulett, David M. Mem Univ Newfoundland Fac Business Adm St John NF A1B 3X5 Canada Guangdong Univ Technol Sch Civil & Transportat Engn Guangzhou 510006 Peoples R China

The COVID-19 pandemic has presented tremendous challenges to the world, one of which is the management of infectious waste generated by healthcare activities. Finding cost-efficient services with minimum threats to public health has become a top priority. The pandemic has induced extreme uncertainties, not only in the amount of generated waste, but also in the associated service times. With this in mind, the present study develops a mixed-integer linear programming (MILP) model for the location-routing problem with time windows (LRPTW). To handle the uncertainty in the amount of generated waste, three scenarios are defined respectively reflecting different severity levels of a pandemic. Furthermore, chance constraints are applied to deal with the variation of the service times at small generation nodes, and time windows at the transfer facilities. The complexity of the resulting mathematical model motivated the application of a branch-and-price (B&P) algorithm along with an -constraint technique. A case study of the situation of Wuhan, China, during the initial COVID-19 outbreak is employed to examine the performance and applicability of the proposed model. Our numerical tests indicate that the B&P algorithm outperforms CPLEX in the computational times by more than 83% in small-sized problem instances and reduces the gaps by at least 70% in large-scale ones. Through a comparison with the current and deterministic systems, our proposed stochastic system can timely adjust itself to fulfill nearly four times the demand of other systems in an extreme pandemic scenario, while maintaining a cost-efficient operation with no outbreak.

关键词： Infectious waste Location-routing problem Time windows Stochastic programming Chance constrained programming branch-and-price algorithm

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Routing optimization of shared autonomous electric vehicles under uncertain travel time and uncertain service time

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2022年第0期157卷 102548-102548页

作者： Zhang, Li Liu, Zhongshan Yu, Lan Fang, Ke Yao, Baozhen Yu, Bin Beihang Univ Sch Transportat Sci & Engn Beijing 100191 Beijing Peoples R China Dalian Maritime Univ Coll Transportat Engn Dalian 116026 Peoples R China Dalian Univ Technol Sch Automot Engn State Key Lab Struct Anal Ind Equipment Dalian 116024 Peoples R China BDBC Beijing 100191 Peoples R China

The emerging autonomous electric vehicles have facilitated the implementation of the shared autonomous electric vehicle (SAEV) service. In real-life applications, the operations of SAEVs may be affected by various uncertain factors, such as uncertain travel time caused by traffic congestion and uncertain service time caused by unpredictable customer delay. It is essential to consider uncertain factors to design conservative and robust routes for SAEVs. In this paper, we study a routing optimization problem of SAEVs, where the charging schedules, uncertain travel time, and uncertain service time are considered. The objective of the problem is to minimize the total operational cost that consists of fixed cost and travel cost of SAEVs. A branch and-price algorithm is developed to solve the problem. Specifically, a tailored label setting algorithm is introduced to identify the robust feasible routes with accessible charging schedules for the pricing subproblem. The proposed algorithm is tested on a set of generated instances. The computational results indicate that the proposed algorithm outperforms the commercial solver CPLEX in terms of both solution quality and computational time. Besides, based on the sensitivity analyses, we show the impact of the budgets of uncertainty, maximum deviations of uncertain parameters, battery capacity, fixed cost, and charging speed on the SAEV service. This work can also provide inspirations of both models and algorithms for many other application areas, such as urban logistics.

关键词： Shared autonomous electric vehicles Routing optimization Charging schedules Travel time uncertainty Service time uncertainty branch-and-price algorithm

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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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Joint routing and charging optimization for eVTOL aircraft recovery

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AEROSPACE SCIENCE AND TECHNOLOGY 2022年第0期126卷

作者： Guo, Zhen Hao, Mengyan Liu, Jiaming Yu, Bin Jiang, Yu Beihang Univ Sch Transportat Sci & Engn Beijing 100191 Peoples R China Beijing Capital Int Airport Co Ltd Beijing Peoples R China Beihang Univ Beijing Adv Innovat Ctr Big Data & Brain Comp Beijing Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Civil Aviat Nanjing Peoples R China

We study the eVTOL aircraft recovery problem (eVTOL-ARP) raised in the use cases of airport shuttles and intercity flights in Urban Air Mobility (UAM). One distinctive feature of the eVTOL-ARP is that charging tasks are allowed to be deferred and canceled. An optional charging scheme is proposed to improve recovery flexibility. Jointly handling routing and charging in an integrated manner makes the problem different from classical aircraft recovery literature and much more challenging. We formulate an integer linear program model and propose a branch-and-price algorithm to solve the eVTOL-ARP exactly. A tailored label setting algorithm that exploits the structure of the problem is proposed for the parallel pricing subproblems. Extensive experiments are conducted on the instances based on the Beijing-Tianjin-Hebei airport cluster and the Zhejiang Province airport cluster. Experimental results show that the proposed algorithms can provide efficient and timely recovery solutions in real-world operations. The recovery cost gap is over 9% between non-flexible charging and the proposed flexible charging. We conduct sensitivity analysis on a key factor, i.e. charging time that may affect the recovery cost and highlight the decreasing marginal value of the charging time.

关键词： Urban air mobility eVTOL aircraft Optional charging scheme branch-and-price algorithm

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Stochastic multistage co-planning of integrated energy systems considering power-to-gas and the cap-and-trade market

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2020年 119卷 105817-000页

作者： Liang, Junkai Tang, Wenyuan North Carolina State Univ Raleigh NC 27695 USA

This paper develops an expansion co-planning model for integrated power and gas systems where uncertainties in both systems are considered. The power-to-gas technology is modeled and considered as a potential solution for some worldwide societal goals such as decarbonized economy and 100% renewable penetration. Compared with expansion planning models for power systems, the complexity of expansion co-planning models is severely exacerbated by random variables in gas systems. A scenario based decomposition scheme called branch-and-price is presented. The numerical results demonstrate the effectiveness of the proposed method and show the economic value of power-to-gas technology.

关键词： branch-and-price algorithm Multistage stochastic programming Expansion co-planning for integrated power and gas systems Power-to-gas

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