Quantum-Inspired Algorithms for Maritime Logistics Optimization

Maritime logistics involves closely connected decisions in vessel operations, port calls, cargo handling, and container transport. Many of these decisions give rise to optimization problems that combine continuous and discrete variables under operational, resource, time, and cost constraints. This paper reviews the application of quantum-inspired algorithms to maritime logistics optimization, focusing on quantum-behaved particle swarm optimization and quantum genetic algorithms. Three application areas are considered: vessel speed optimization, berth allocation and vessel scheduling, and multi-resource scheduling at container terminals. Rather than comparing reported results directly, the review considers how these problems are formulated, how quantum-inspired mechanisms are introduced into the solution procedures, and how the resulting methods are tested. Existing studies suggest that such algorithms can be adapted to both continuous and combinatorial problems in maritime logistics. Their reported performance, however, is sensitive to encoding schemes, parameter choices, problem size, and the treatment of constraints. The available results are also difficult to compare because the studies use different datasets, objectives, benchmarks, and evaluation criteria. In addition, empirical validation is often limited to a particular route, port, or predefined operating scenario, while uncertainties arising from vessel arrivals, weather, and equipment availability are less frequently represented. More evidence from operational data, together with consistent comparisons against established optimization methods, is therefore needed to assess when quantum-inspired approaches offer practical advantages for maritime logistics problems.