A Heuristic Algorithm Based on Beam Search and Iterated Local Search for the Maritime Inventory Routing Problem

TL;DR

This paper introduces a heuristic combining Beam Search and Iterated Local Search to efficiently solve large Maritime Inventory Routing Problems, achieving improved solutions on several benchmark instances without relying on exact optimization methods.

Contribution

The study presents a novel heuristic approach that effectively solves MIRP instances, outperforming existing solutions and facilitating benchmarking without using mixed integer programming.

Findings

Improved solutions for 19 out of 72 benchmark instances.

Achieved high-quality results within acceptable CPU times.

Demonstrated effectiveness of the combined heuristic approach.

Abstract

Maritime Inventory Routing Problem (MIRP) plays a crucial role in the integration of global maritime commerce levels. However, there are still no well-established methodologies capable of efficiently solving large MIRP instances or their variants due to the high complexity of the problem. The adoption of exact methods, typically based on Mixed Integer Programming (MIP), for daily operations is nearly impractical due to the CPU time required, as planning must be executed multiple times while ensuring high-quality results within acceptable time limits. Non-MIP-based heuristics are less frequently applied due to the highly constrained nature of the problem, which makes even the construction of an effective initial solution challenging. Papageorgiou et al. (2014) introduced a single-product MIRP as the foundation for MIRPLib, aiming to provide a collection of publicly available benchmark instances. However, only a few studies that propose new methodologies have been published since then. To encourage the use of MIRPLib and facilitate result comparisons, this study presents a heuristic approach that does not rely on mathematical optimization techniques to solve a deterministic, finite-horizon, single-product MIRP. The proposed heuristic combines a variation of a Beam Search algorithm with an Iterated Local Search procedure. Among the 72 instances tested, the developed methodology can improve the best-known solution for 19 instances within an acceptable CPU time.