Advanced routing for city logistics service providers based on time-dependent travel times

Ehmke, Jan Fabian; Steinert, Andre F.; Mattfeld, Dirk C.

doi:10.1016/j.jocs.2012.01.006

Cited by 85 publications

(44 citation statements)

References 29 publications

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“…Cattaruzza et al [18] provide a complete review of routing problems arising in city logistics, identifying four main challenges that capture the complexity of the urban environment: the consideration of time-dependent travel times [19,20], multilevel distribution [21,22], dynamic routing [23,24], and multitrip routing [25]. Other recent VRP applications for urban freight include the formulation of an inverse vehicle routing approach to estimate urban delivery vehicle flows [26], or the consideration of travel times as random variables, thus formulating the routing approach in order to maximize the probability of serving customers within their assigned time windows [27].…”

Section: Routing For Urban Freight: Access Time Windowsmentioning

confidence: 99%

Mathematical Formulation and Comparison of Solution Approaches for the Vehicle Routing Problem with Access Time Windows

et al. 2018

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The application of the principles of sustainability to the implementation of urban freight policies requires the estimation of all the costs and externalities involved. We focus here on the case of access time windows, which ban the access of freight vehicles to central urban areas in many European cities. Even though this measure seeks to reduce congestion and emissions in the most crowded periods of the day, it also imposes additional costs for carriers and results in higher emissions and energy consumption. We present here a mathematical model for the Vehicle Routing Problem with Access Time Windows, a variant of the VRP suitable for planning delivery routes in a city subject to this type of accessibility restriction. We use the model to find exact solutions to small problem instances based on a case study and then compare the performance over larger instances of a modified savings algorithm, a genetic algorithm, and a tabu search procedure, with the results showing no clear prevalence of any of them, but confirming the significance of those additional costs and externalities.

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Section: Routing For Urban Freight: Access Time Windowsmentioning

confidence: 99%

Mathematical Formulation and Comparison of Solution Approaches for the Vehicle Routing Problem with Access Time Windows

et al. 2018

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“…When the influence of delivery trucks on road traffic cannot be ignored due to the recurring congestion and sophisticated e-commerce business models [37], VRP problem should be treated as a traffic problem at the macro level rather than a logistics issue at the micro level. Then, we must design the delivery loops/paths from the view of total traffic equilibrium, while considering the interactions among all vehicles and traffic generation/attraction in traffic zones.…”

Section: Motivation and Contributionmentioning

confidence: 99%

Multiple Depots Vehicle Routing Problem in the Context of Total Urban Traffic Equilibrium

Chen

Yang

2017

Journal of Advanced Transportation

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A multidepot VRP is solved in the context of total urban traffic equilibrium. Under the total traffic equilibrium, the multidepot VRP is changed to GDAP (the problem of Grouping Customers + Estimating OD Traffic + Assigning traffic) and bilevel programming is used to model the problem, where the upper model determines the customers that each truck visits and adds the trucks' trips to the initial OD (Origin/Destination) trips, and the lower model assigns the OD trips to road network. Feedback between upper model and lower model is iterated through OD trips; thus total traffic equilibrium can be simulated.

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“…In real settings, millions of components and nets may need to be integrated on a single chip, making the problem extremely challenging. Similar routing problems are encountered in other domains including routing-design of hydraulic systems [5], complex pipe system routing in ships [6], urban water distribution systems [7,8], and routing of city logistic services [9,10].…”

Section: Introductionmentioning

confidence: 97%

A Deep Reinforcement Learning Approach for Global Routing

Liao

Zhang

Dong

et al. 2019

Journal of Mechanical Design

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Global routing has been a historically challenging problem in electronic circuit design, where the challenge is to connect a large and arbitrary number of circuit components with wires without violating the design rules for the printed circuit boards or integrated circuits. Similar routing problems also exist in the design of complex hydraulic systems, pipe systems and logistic networks. Existing solutions typically consist of greedy algorithms and hard-coded heuristics. As such, existing approaches suffer from a lack of model flexibility and non-optimum solutions. As an alternative approach, this work presents a deep reinforcement learning method for solving the global routing problem in a simulated environment. At the heart of the proposed method is deep reinforcement learning that enables an agent to produce an optimal policy for routing based on the variety of problems it is presented with leveraging the conjoint optimization mechanism of deep reinforcement learning. Conjoint optimization mechanism is explained and demonstrated in details; the best network structure and the parameters of the learned model are explored. Based on the fine-tuned model, routing solutions and rewards are presented and analyzed. The results indicate that the approach can outperform the benchmark method of a sequential A* method, suggesting a promising potential for deep reinforcement learning for global routing and other routing or path planning problems in general. Another major contribution of this work is the development of a global routing problem sets generator with the ability to generate parameterized global routing problem sets with different size and constraints, enabling evaluation of different routing algorithms and the generation of training datasets for future data-driven routing approaches.

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Advanced routing for city logistics service providers based on time-dependent travel times

Cited by 85 publications

References 29 publications

Mathematical Formulation and Comparison of Solution Approaches for the Vehicle Routing Problem with Access Time Windows

Mathematical Formulation and Comparison of Solution Approaches for the Vehicle Routing Problem with Access Time Windows

Multiple Depots Vehicle Routing Problem in the Context of Total Urban Traffic Equilibrium

A Deep Reinforcement Learning Approach for Global Routing

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