Balancing resources for dynamic vehicle routing with stochastic customer requests

Ninja Soeffker (), Marlin W. Ulmer () and Dirk C. Mattfeld ()
Additional contact information
Ninja Soeffker: University of Vienna
Marlin W. Ulmer: Chair of Management Science, Otto-von-Guericke-Universität Magdeburg
Dirk C. Mattfeld: Technische Universität Braunschweig

OR Spectrum: Quantitative Approaches in Management, 2024, vol. 46, issue 2, No 4, 373 pages

Abstract: Abstract We consider a service provider performing pre-planned service for initially known customers with a fleet of vehicles, e.g., parcel delivery. During execution, new dynamic service requests occur, e.g., for parcel pickup. The goal of the service provider is to serve as many dynamic requests as possible while ensuring service of all initial customers. The allocation of initial services impacts the potential of serving dynamic requests. An allocation aiming on a time-efficient initial routing leads to minimal overall workload regarding the initial solution but may congest some vehicles that are unable to serve additional requests along their routes. An even workload division is less efficient but grants all vehicles flexibility for additional services. In this paper, we investigate the balance between efficiency and flexibility. For the initial customers, we modify a routing algorithm to allow a shift between efficient initial routing and evenly balanced workloads. For effective dynamic decision making with respect to the dynamic requests, we present value function approximations with different feature sets capturing vehicle workload in different levels of detail. We show that sacrificing some initial routing efficiency in favor of a balanced vehicle workload is a key factor for a flexible integration of later customer requests that leads to an average improvement of 10.75%. Further, we show when explicitly depicting heterogeneity in the vehicle workload by features of the value function approximation provides benefits and that the best choice of features leads to an average improvement of 5.71% compared to the worst feature choice.

Keywords: Dynamic vehicle routing; Same-day service; Approximate dynamic programming; Value function approximation (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s00291-024-00747-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:orspec:v:46:y:2024:i:2:d:10.1007_s00291-024-00747-1

Ordering information: This journal article can be ordered from
http://www.springer. ... research/journal/291

DOI: 10.1007/s00291-024-00747-1

Access Statistics for this article

OR Spectrum: Quantitative Approaches in Management is currently edited by Rainer Kolisch

More articles in OR Spectrum: Quantitative Approaches in Management from Springer, Gesellschaft für Operations Research e.V.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().