What question did this study set out to answer?

The aim is to minimize total delivery time by optimizing both truck routes and loading arrangements.

March 26, 2026The Proceedings of Mechanical Engineering Congress Japan0 citationsOpen Access

Environment-adaptive genetic algorithm-based optimization method considering truck loading and transportation time

YNYuki NakadanKindai University TSTatsuhiko Sakaguchi

Key Points

The aim is to minimize total delivery time by optimizing both truck routes and loading arrangements.
Developed an Environment-Adaptive Genetic Algorithm (EAGA) for optimization.
Jointly optimize route planning and package loading.
Considered the trade-off between loading more items and unloading time.
Achieved reduced total delivery time compared to traditional methods.
Improved efficiency in last-mile delivery operations.
Optimized package placement minimized unloading delays.

Abstract

In Japan, truck transport handles 90% of freight, but a 2024 legal cap on overtime has worsened driver shortages. As delivery demands rise, improving last-mile efficiency is vital. This study minimizes total delivery time—truck travel plus unloading—by jointly optimizing route planning and package placement. While loading more items per trip saves travel time, it can increase unloading time due to package rearrangement. To solve this trade-off, we extend our Environment-Adaptive Genetic Algorithm (EAGA) to optimize both delivery order and 3D loading layout.

问 AI

Bookmark

View Full Paper