Recent advances in integrating demand management and vehicle routing: A methodological review

Fleckenstein, David; Klein, Robert; Steinhardt, Claudius

doi:10.1016/j.ejor.2022.04.032

Cited by 23 publications

(5 citation statements)

References 94 publications

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“…In terms of commercial categories, vehicle tasks encompass a wide range, such as "attended home delivery", "same-day delivery", and "mobility-on-demand". However, from the actual subject of the task, where tasks can be broadly categorized into transportation tasks [45] and vehicle supply and maintenance tasks [46]. The information corresponding to these two types of tasks is shown in Table 1.…”

Section: Vehicle Task Classification Modelmentioning

confidence: 99%

Real-Time Multi-Vehicle Scheduling in Tasks With Dependency Relationships Using Multi-Agent Reinforcement Learning

Zhang,

Shi,

Zhang

et al. 2024

IEEE Access

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With the advancement of technology in vehicle-road collaboration and autonomous driving, new commercial applications have surfaced. These include autonomous ride-hailing vehicles and unmanned delivery vehicles. As a result of the challenges presented by commercial applications, dispatching systems are moving towards being maintenance-free, centralized, multitasking, and real-time. Yet, most existing dispatching systems have been designed for single-task purposes and cannot tackle multitasking issues. Moreover, traditional optimization algorithms make it difficult to achieve timeliness in real-time changing traffic conditions. Therefore, this paper innovatively proposes a task allocation method based on Multi-Agent Reinforcement Learning (MARL). Firstly, this study introduces a classification model of task relationships through the binary assumption model of geographical areas and vehicles. Secondly, the study matches the classification model's task cost state transition process with the Markov Decision Process, constructing a Multi-Agent Reinforcement Learning framework. Finally, the study constructs a simulation environment suitable for reinforcement learning based on Simulation of Urban Mobility (SUMO). Simulation results indicate that the task allocation system based on MARL can effectively improve the system's overall efficiency by determining the order of task allocation and the matching relationships between tasks.

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Section: Vehicle Task Classification Modelmentioning

confidence: 99%

Real-Time Multi-Vehicle Scheduling in Tasks With Dependency Relationships Using Multi-Agent Reinforcement Learning

Zhang,

Shi,

Zhang

et al. 2024

IEEE Access

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“…The incorporation of demand management strategies into vehicle routing issues is mostly examined in dynamic and stochastic environments (cf. Fleckenstein et al 2022;Snoeck et al 2020). With application areas such as attended home delivery, same-day delivery, and ridesharing, the research focus has also been on vehicle routing problems heavily constrained by time windows.…”

Section: Demand Managementmentioning

confidence: 99%

Multitrip vehicle routing with delivery options: a data-driven application to the parcel industry

Janinhoff

Klein

Scholz³

2023

OR Spectrum

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To make the last mile of parcel delivery more efficient, service providers offer an increasing number of modes of delivery as alternatives to the traditional and often cost-intensive home delivery service. Parcel lockers and pickup stations can be utilized to reduce the number of stops and avoid costly detours. To design smart delivery networks, service providers must evaluate different business models. In this context, a multitrip vehicle routing problem with delivery options and location-dependent costs arises. We present a data-driven framework to evaluate alternative delivery strategies, formulate a corresponding model and solve the problem heuristically using adaptive large neighborhood search. By examining large, real-life instances from a major European parcel service, we determine the potential and benefits of different delivery options. Specifically, we show that delivery costs can be mitigated by consolidating orders in pickup stations and illustrate how pricing can be applied to steer customer demand toward profitable, eco-friendly products.

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“…In each of these sections, we start by briefly discussing the most related research on pure online tour-planning approaches to show how the discussed integrated approaches evolve from them. Integrated approaches exceed basic feasibility control by (at least) allowing feasible customer requests to be rejected if the expected contribution to the objective is negative ( Fleckenstein, Klein, & Steinhardt, 2022 ). Further, our discussion is structured along groups of publications that follow comparable ideas.…”

Section: Solution Approaches For the Sdd-dmtpmentioning

confidence: 99%

“…Since the delivery deadlines are typically narrow in SDD, the service period in which customer orders are being served starts with, or shortly after, the first realized customer order and ends when the last customer order of a day has been served. Hence, a particularity of SDD is that the booking and service periods overlap ( Fleckenstein et al, 2022 ).…”

Section: Problem Descriptionmentioning

confidence: 99%

“…Integrated perspective -The most recent and comprehensive survey of related literature that integrates both of the previously discussed perspectives, is the survey by Fleckenstein et al (2022) . The authors provide a generalized problem definition and outline AHD and SDD applications, as well as literature on mobilityon-demand (MOD).…”

Section: Appendix B Tabular Overview Of Anticipatory Solution Approac...mentioning

confidence: 99%

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Dynamic Demand Management and Online Tour Planning for Same-Day Delivery

Klein

Steinhardt

2021

SSRN Journal

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Recent advances in integrating demand management and vehicle routing: A methodological review

Cited by 23 publications

References 94 publications

Real-Time Multi-Vehicle Scheduling in Tasks With Dependency Relationships Using Multi-Agent Reinforcement Learning

Real-Time Multi-Vehicle Scheduling in Tasks With Dependency Relationships Using Multi-Agent Reinforcement Learning

Multitrip vehicle routing with delivery options: a data-driven application to the parcel industry

Dynamic Demand Management and Online Tour Planning for Same-Day Delivery

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