Modeling and solving the fuel distribution problem with unloading precedence and loading sequence considerations

Androutsopoulos, Konstantinos N.; Zografos, Konstantinos G. · 2023 · Crossref

DOI: 10.1007/s10479-023-05752-1

archive: archived pipeline: cataloged verified

Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)

Summary

This paper addresses the Fuel Distribution Problem with Unloading Precedence and Loading Sequence considerations (FDP-UPLS), a complex vehicle routing challenge in the liquid fuel industry. The research is motivated by the need to enhance safety and efficiency in transporting hazardous materials. Specifically, it tackles two underexplored operational constraints: maintaining truck stability through balanced loading during multi-stop routes and managing bottlenecks at depot loading facilities where limited infrastructure causes significant delays. Unbalanced loading can lead to accidents, while congestion at loading docks reduces fleet utilization. The study aims to integrate these factors into a unified optimization model to minimize total traveled distance while ensuring safe loading practices and efficient scheduling. The authors propose a Mixed Integer Programming (MIP) formulation and develop an Adaptive Large Neighborhood Search (ALNS) heuristic algorithm to solve real-world instances. The model incorporates a heterogeneous fleet of multi-compartment vehicles and enforces a specific unloading precedence rule (Middle-Rear-Front) to maintain weight balance throughout the route. Additionally, it models the sequencing of loading activities at the depot, assigning trucks to discrete time slots based on limited facility capacity. The ALNS algorithm features novel components, including a giant-tour heuristic for generating initial feasible solutions and a specialized repair operator that utilizes an enhanced version of this heuristic. A separate MIP formulation, termed the Route Loading Problem, is employed within the algorithm to assign fuel orders to specific vehicle compartments while satisfying the unloading precedence constraints. Computational experiments on benchmark problems and real-life test cases demonstrate the effectiveness of the proposed approach. The results indicate that incorporating balanced loading constraints has a manageable impact on operational efficiency, increasing the total traveled distance by a maximum of 4.37%. The study confirms that the ALNS algorithm provides high-quality solutions for this hard-constrained routing problem. Furthermore, the experiments highlight the significance of the interplay between loading scheduling and routing decisions, showing that ignoring depot bottlenecks can lead to considerable delays and increased costs. The significance of this work lies in its comprehensive modeling of real-world fuel distribution complexities that previous literature has largely overlooked. It is the first study to explicitly incorporate both unloading precedence constraints for en-route stability and the scheduling of loading activities at the depot into a vehicle routing problem. The developed computational tools offer practical value for dispatchers, enabling the generation of efficient and safe delivery routes while effectively managing depot congestion. This contribution aids in mitigating the risks associated with hazardous material transport and improving overall fleet utilization in the fuel distribution sector.

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed.

Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• last mile delivery