Introduction

The field of combinatorial optimization is rapidly advancing, driven by the development of innovative heuristic algorithms and learning-based approaches. Recent research has focused on improving the efficiency and effectiveness of optimization methods for complex problems, such as vehicle routing and inventory management.

General Direction

The current trend in the field is towards the integration of machine learning and combinatorial optimization techniques. This has led to the development of novel frameworks that can learn to solve complex optimization problems. Additionally, there is a growing interest in using local search heuristics and metaheuristics to improve the performance of optimization algorithms.

Noteworthy Papers

• A new heuristic algorithm combines beam search and iterated local search to solve the maritime inventory routing problem, improving the best-known solution for several instances.
• The Learning to Insert for Constructive Neural Vehicle Routing Solver proposes a novel learning-based method for constructive neural combinatorial optimization, achieving superior performance on various problem sizes.
• The integration of curriculum learning into genetic programming guided local search has been proposed to improve the performance of large-scale vehicle routing problems.
• A bandit-based dynamic candidate edge selection method has been developed to enhance the performance of the Lin-Kernighan-Helsgaun algorithm for the traveling salesman problem.