Tuning Dispatch Thresholds for Fixed Last-Mile Routes: A Simulation-Based Pareto Analysis of a Production Policy

2026-06-08

AI Summary

This study addresses the static nature of load-threshold-based dispatch policies in fixed-route last-mile delivery, where parameters are typically set once and never updated. By replaying one month of real-world parcel flows through a discrete-event simulation, the authors systematically explore the threshold parameter space to construct a Pareto frontier between operational cost and average delivery time, thereby conducting the first data-driven efficiency audit of such frozen policies in a production environment. The analysis reveals that volume-triggered dispatch strategies are nearly Pareto optimal, whereas piece-count-triggered strategies exhibit approximately 5% untapped cost-saving potential. Notably, modest adjustments—lowering the base threshold and increasing the route-length coefficient—simultaneously improve delivery speed and reduce costs, yielding actionable, zero-capital improvements.

Abstract

Many parcel networks dispatch vehicles on \emph{fixed routes} using a simple load-accumulation rule: a truck leaves the depot for a fixed route as soon as the volume (or item count) waiting for that route crosses a threshold. The threshold is usually parameterised as an affine function of route length, $τ_r=β+γ\,d_r$, and the pair $(β,γ)$ is chosen once and frozen into production. This paper studies how good that frozen choice actually is, treating the question as a data-intensive, data-driven decision-making problem over a full month of real operational flow. Using a discrete-event simulator that replays the recorded arrival stream and reconstructs every trip, we sweep the $(β,γ)$ design space, evaluate the two competing objectives -- company operating cost and average parcel lead time -- and recover the Pareto frontier of efficient policies for two deployed variants (volume-triggered and item-count-triggered). The two policies turn out to be in strikingly different states of tune. The volume-threshold configuration lies on its own Pareto frontier: the simulator finds no $(β,γ)$ pair that strictly dominates it, so the deployed policy is \emph{already Pareto-efficient} -- an unusual positive audit result. The item-count configuration is the opposite: it is dominated by a concrete simulated configuration that is both faster and cheaper, and the available cost saving at equal lead time is about \num{5.0}\,\pct{}. We trace the item-count policy's inefficiency to a base that is too large and a length coefficient that is too small for the deployed truck capacity, and show that a \emph{steeper} threshold -- lower base, higher slope -- is preferable. Because the remedy is a two-scalar reconfiguration, the analysis converts directly into an actionable, zero-capital recurring saving.

Problem

• last-mile delivery
• dispatch threshold
• fixed routes
• Pareto efficiency
• operational cost

Innovation

• Pareto analysis
• dispatch threshold tuning
• last-mile delivery
• discrete-event simulation
• fixed-route logistics