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

Abstract

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.

Emissions and Total Cost of Ownership for Diesel and Battery Electric Freight Pickup and Delivery Trucks in New Zealand: Implications for Transition

“…Furthermore, it allows various levels of complexity in modelling representation. For example, models may be relatively complex intersection-based models that represent route segments [32] or simple two-tier architecture models [33], depending on the purposes of the simulation.…”

Optimising Urban Freight Logistics Using Discrete-Event Simulation and Cluster Analysis: A Stochastic Two-Tier Hub-and-Spoke Architecture Approach

“…Nevertheless, due to the effort involved in the development of the model, it is not well-suited for representing the intricate activities that occur within urban regions. A two-tier architecture was proposed to simulate freight operations for a large cluster incorporating cluster analysis [29]. The study outlines the methodology for modelling freight operations and how the cluster approach can be utilised to construct a DES model.…”

“…Specifically, the hubs in this architecture refer to cluster centres rather than physical logistics hubs. To compute the first-tier distance (𝐷 𝑓 ), the cluster mean centre should be determined first [29]. Subsequently, the second-tier distance (𝐷 𝑠 ) for each customer location exhibits a stochastic nature, which can be computed by multiplying the Euclidean distance (𝐷 𝑒 ) between the cluster centre and the customer locations by a factor R. Hence, the total travel distance (𝐷 𝑡𝑜𝑡𝑎𝑙 ) for the journey can be computed as: To compute the first-tier distance (D f , the cluster mean centre should be determined first [29].…”

“…To compute the first-tier distance (𝐷 𝑓 ), the cluster mean centre should be determined first [29]. Subsequently, the second-tier distance (𝐷 𝑠 ) for each customer location exhibits a stochastic nature, which can be computed by multiplying the Euclidean distance (𝐷 𝑒 ) between the cluster centre and the customer locations by a factor R. Hence, the total travel distance (𝐷 𝑡𝑜𝑡𝑎𝑙 ) for the journey can be computed as: To compute the first-tier distance (D f , the cluster mean centre should be determined first [29]. Subsequently, the second-tier distance (D s ) for each customer location exhibits a stochastic nature, which can be computed by multiplying the Euclidean distance (D e ) between the cluster centre and the customer locations by a factor R. Hence, the total travel distance (D total ) for the journey can be computed as:…”

“…The result of this process produced ten groups of R values (see Equation ( 2)), which represented the variability in R as a probability distribution. For this study, the normal distribution was chosen due to its compatibility with positive R values and its lower bound per [29]. The spoke distances were established by using gamma distributions.…”