MODELING AND EMPIRICAL STUDY OF 3D LOADING OPTIMIZATION FOR A SINGLE VEHICLE TYPE BASED ON MULTI-CONSTRAINT HEURISTIC ALGORITHMS

Abstract

Addressing the complex demands for space utilization and safe loading in logistics transportation, this study focuses on optimizing 3D loading decisions for a single vehicle type. After systematically organizing eight key rigid constraints—spatial non-overlap, truck compartment boundaries, cargo support, load-bearing capacity per unit area, stacking stability, placement of special cargo, orthogonal placement, and total vehicle load—this paper constructs a single-vehicle loading maximization model with a weighted objective score based on space utilization and load utilization, as well as a scaled-up transportation model aimed at minimizing the total number of vehicles. To address the combinatorial explosion problem in large-scale cargo loading, this study introduces a grid-based dimensionality reduction strategy, transforming continuous spatial constraints into discrete grid load-bearing constraints, thereby significantly reducing the scale of the problem. At the algorithmic level, a multi-strategy greedy heuristic loading algorithm based on an extreme point generation mechanism was designed. By alternately testing various sorting rules—such as volume, weight, and priority—the algorithm achieves efficient verification of complex constraints and space filling. Empirical results show that Vehicle Type 2 outperforms Vehicle Type 1 in both single-vehicle loading performance and overall vehicle scheduling efficiency, with a maximum space utilization rate of 91.45%. This study provides theoretical support and algorithmic references for logistics enterprises to formulate scientific loading plans under stringent physical constraints.