AI summaryⓘ
The authors looked at how delivery trucks decide when to leave the depot on fixed routes based on a threshold of waiting parcels or items. They studied two ways of setting these thresholds using real data and a simulation to see how cost and delivery speed trade off. For the volume-based method, the current settings were already very efficient, meaning no other settings did better in cost or speed. But for the item-count method, the authors found better threshold settings that can save about 5% in cost without slowing down deliveries. Their suggested fix is simple, adjusting two numbers in the threshold formula to work better with truck capacity, leading to ongoing savings without new investment.
parcel networksfixed routesload-accumulation rulethreshold parameterizationPareto frontierdiscrete-event simulationoperating costparcel lead timequeueing policydata-driven optimization
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.