Balancing resources for dynamic vehicle routing with stochastic customer requests

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 possibl...

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Vydané v:OR Spectrum Ročník 46; číslo 2; s. 331 - 373
Hlavní autori: Soeffker, Ninja, Ulmer, Marlin W, Mattfeld, Dirk C
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Berlin, Heidelberg Springer 01.06.2024
Springer Berlin Heidelberg
Springer Nature B.V
Predmet:
Algorithms
Approximate dynamic programming
Approximation
Business and Management
Calculus of Variations and Optimal Control; Optimization
Customer services
Customers
Dynamic vehicle routing
Flexibility
Heterogeneity
Operations Research/Decision Theory
Original Article
Same-day service
Value function approximation
Vehicle routing
Vehicles
Workload
Workloads
Dynamic vehicle routing
Approximate dynamic programming
Value function approximation
Same-day service
ISSN:1436-6304, 0171-6468, 1436-6304
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
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content type line 14
ISSN:1436-6304
0171-6468
1436-6304
DOI:10.1007/s00291-024-00747-1