Developing a Stochastic Two-Tier Architecture for Modelling Last-Mile Delivery and Implementing in Discrete-Event Simulation

Modelling freight logistics is challenging due to the variable consignments and diverse customers. Discrete-event Simulation (DES) is an approach that can model freight logistics and incorporate stochastic events. However, the flexible delivery routes of Pickup and Delivery (PUD) are still problemat...

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Vydáno v:Systems (Basel) Ročník 10; číslo 6; s. 214
Hlavní autoři: Lyu, Zichong, Pons, Dirk, Chen, Jiasen, Zhang, Yilei
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.12.2022
Témata:
Case studies
Computer simulation
Customers
Delivery of goods
Discrete event systems
discrete-event simulation
Discrete-time systems
freight operations
Geographic information systems
last-mile delivery
Literature reviews
Logistics
Mathematical models
Methods
Modelling
Planning
Simulation
Simulation methods
Stochastic processes
Traveling salesman problem
New Zealand
ISSN:2079-8954, 2079-8954
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Shrnutí:Modelling freight logistics is challenging due to the variable consignments and diverse customers. Discrete-event Simulation (DES) is an approach that can model freight logistics and incorporate stochastic events. However, the flexible delivery routes of Pickup and Delivery (PUD) are still problematic to simulate. This research aims to develop last-mile delivery architecture in DES and evaluate the credibility of the model. A two-tier architecture was proposed and integrated with a DES model to simulate freight operations. The geographic foundation of the model was determined using Geographic Information Systems (GIS), including identifying customer locations, finding cluster centres, and implementing Travelling Salesman Problem (TSP) simulation. This complex model was simplified to the two-tier architecture with stochastic distances, which is more amenable to DES models. The model was validated with truck GPS data. The originality of the work is the development of a novel and simple methodology for developing a logistics model for highly variable last-mile delivery.
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ISSN:2079-8954
2079-8954
DOI:10.3390/systems10060214