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In recent years, with the increase of emerging pick-up requests during service, logistics companies have been driven to integrate delivery and pick-up service in a dynamic environment. To provide a balanced and robust approach to cope with delivery requests and emerging pick-up requests, this article aims at considering and modeling a practically useful service principle as preemptive services. To our knowledge, most existing studies assume that the dynamically arriving requests are handled in a non-preemptive processing sequence; that is, once the delivery person is allocated to a task, the process is noninterruptible till it gets completed. In the preemptive service, a service suspension of the delivery process (with low service utility) is allowed to satisfy the pick-up requests (with high service utility) first. To provide a systematic assessment on the value of preemptive service for evolving urban logistics systems, a dynamic vehicle routing problem with preemptive pick-up service (VRPPS) is proposed to systematically describe the problem with potentially complex dynamic priorities among different tasks. Based on a dynamically constructed space-time network, this study formulates a multicommodity flow model that aims at optimizing the generalized service utility and the operating cost simultaneously. To provide a fast value approximation, we present a solution framework deploying the augmented Lagrangian relaxation approach with embedded dynamic programming algorithms. This framework jointly integrates the processes of updating request information and obtaining optimal routes. Finally, the validity and effectiveness of the proposed methods are evaluated on an illustrative network and a real-world last-mile delivery network operated by a logistics company. [ABSTRACT FROM AUTHOR]

This paper considers a high-speed rail corridor that requires high fidelity scheduling of train speed for a large number of trains with both tight power supply and temporal capacity constraints. This research aims to systematically integrate problems of macroscopic train timetabling and microscopic train trajectory calculations. We develop a unified modeling framework using three-dimensional space-time-speed grid networks to characterize both second-by-second train trajectory and segment-based timetables at different space and time resolutions. The discretized time lattices can approximately track the train position, speed, and acceleration solution through properly defined spacing and modeling time intervals. Within a Lagrangian relaxation-based solution framework, we propose a dynamic programming solution algorithm to find the speed/acceleration profile solutions with dualized train headway and power supply constraints. The proposed numerically tractable approach can better handle the non-linearity in solving the differential equations of motion, and systematically describe the complex connections between two problems that have been traditionally handled in a sequential way. We further use a real-world case study in the Beijing-Shanghai high-speed rail corridor to demonstrate the effectiveness and computational efficiency of our proposed methods and algorithms. [ABSTRACT FROM AUTHOR]

• Developing new arc- and path-based models for the activity-based VRP. • Utilizing a Lagrangian relaxation and dynamic programming solution approach. • Proposing an algorithm to efficiently solve the path-based model on large datasets. In a world of ever-changing travel behavior and ever-increasing modal options, is vital to have integrated models that could capture the interactions between supply and demand layers of travel. Addressing this need, we propose three different versions of network representation and mathematical models for the activity-based vehicle routing problem to connect activity-travel graphs of passengers (demand layer) to spatio-temporal networks of vehicles (supply layer). Versions I and II are arc-based, while version III is path-based. In version I, we introduce the concept of activity-travel graphs for passengers. For vehicles, we construct space–time networks and add a new dimension, called "under-service state", to track the execution status of trip requests at any location and time. In version II, we reduce the complexity of the network structure by eliminating the state dimension and some other modifications in the structure of the passengers' and vehicles' network. Although both versions can capture various behavioral constraints of the activity-based vehicle routing problem (e.g., mandatory and optimal activities, duration of activities, chain of activities, preferred starting and ending times of activities), due to the high level of complexity of the network structure, both versions can only solve small-sized problems. To tackle the computational complexity, we propose a path-based network representation in version III, and to make a balance between the disutility of passengers and vehicles, we present a tolled user equilibrium problem. Mathematical models are coded in C and GAMS and implemented on real-world Phoenix regional transportation network with more than 39 million trip requests, which demonstrate the effectiveness of the proposed solution for the original and restricted master problems. [ABSTRACT FROM AUTHOR]

• Study the electric logistics vehicle recharging station location–routing problem. • Develop a multi-commodity network flow model in space–time–state–energy network. • Propose a hybrid Lagrangian relaxation-ADMM solution framework. • Conduct case studies based on the Sioux-Falls network and West Jordan network. The widespread application of electric vehicles for city last-mile logistics has been enhanced by the emerging trend of urban sustainable mobility, which intends to reduce vehicle emissions and the dependence on fossil fuels. The recharging facility location is critical for electric logistics network planning, because it significantly affects future operation costs and efficiency. This study proposes an integrated electric logistics vehicle recharging station location–routing problem with mixed backhauls and recharging strategies, which is formulated as a time-discretized multicommodity network flow optimization model based on a space–time–state–energy representation network. This study aims to optimize the selection of the recharging station location considering route planning with complicated constraints of recharging capacity, facility construction budget, vehicle loading capacity, battery remaining capacity, spatial structure of real road networks, mixed pickup and delivery requests, and service time windows. A hybrid Lagrangian relaxation and alternating direction method of multipliers (LR-ADMM) decomposition solution framework is constructed to decouple the proposed integrated problem into a recharging station location problem for strategic planning and an electric vehicle routing problem with mixed backhauls, time windows, and recharging strategies for operational decisions. These two subproblems are solved alternately by time-dependent forward dynamic programming algorithms embedded into the LR-ADMM framework. The solution quality is guaranteed by calculating the optimality gap between the best lower and upper bounds for each iteration. The experimental results based on the Sioux-Falls network and real-world West Jordan network prove the computational effectiveness and optimization quality of the proposed solution approach. [ABSTRACT FROM AUTHOR]

• Study an electric vehicle routing problem with mixed backhauls for city logistics. • Formulate a multi-commodity network flow model in a space–time-state network. • Develop a solution approach based on the alternating direction multiplier methods. • Conduct the experiments based on the real large-scale Yizhuang road network. Electric vehicles are environmental transportation modes that are widely applied in green logistics systems. To guarantee the energy efficiency, the impacts of customer service modes and recharging strategies need to be integrated into the optimization of electric logistics resource. This paper proposes an electric vehicle routing problem with mixed backhauls, time windows, and recharging strategies (EVRPMBTW-RS), minimizing the total travel cost with sophisticated constraints on the time-dependent pickup and delivery requests, limited recharging station capacity, and battery remaining capacity of electric vehicles. Mixed service sequences of linehaul and backhaul customers is allocated for the routing planning, with the synchronous optimization of recharging strategies including the selection of recharging stations and determination of recharging time. A time-discretized multi-commodity network flow model is constructed based on an extended space–time-state modeling framework, which is formulated as a quadratic 0–1 programming model by using the augmented Lagrangian relaxation technique. After the dualization and linearized transformation, we decompose the model into a sequence of least-cost path subproblems based on the alternating direction multiplier method (ADMM). The subproblems are alternately minimized and solved using the time-dependent forward dynamic programming algorithm. The solution quality can be guaranteed through calculating the optimality gap between the best lower bound and upper bound for each iteration. The proposed solution approach is examined on examples of a simple 7-node network and real-world Yizhuang road network. This paper provides a theoretical foundation for the route optimization method of electric logistics vehicles, and contributes to improve the operational efficiency of electric logistics systems. [ABSTRACT FROM AUTHOR]