Inland and short-sea container shipping in Northwestern Europe relies on manual planning by experienced logistics operators. This process, while effective for routine operations, is time-consuming, difficult to scale, and limited in its ability to globally optimize fleet utilization. The underlying scheduling problem is classified as a Heterogeneous Vehicle Routing Problem with Pickup and Delivery and Time Windows (VRPPDTW), incorporating domain-specific constraints such as minimum call sizes, forbidden terminals, terminal opening hours, and mandatory vessel breaks.

This thesis presents Orion, a constraint-based optimization solver built on the Google OR-Tools Routing Library, designed to automate and improve this vessel scheduling process. The problem is formulated using Constraint Programming, which allows complex business rules to be expressed as logical predicates rather than linearized inequalities. The solver pipeline includes a data preprocessing stage that aggregates individual container orders into compound orders, reducing problem size by approximately 90\% while preserving solution quality.

The evaluation follows a three-phase methodology. First, a feasibility analysis filters 14 construction heuristics down to two viable candidates: Local Cheapest Insertion and Parallel Cheapest Insertion. Second, a parameter tuning phase identifies Local Cheapest Insertion combined with Tabu Search as the best-performing configuration, achieving an Average Relative Percentage Deviation of 2.72\% across all scenarios. Third, a benchmarking phase compares Orion against human planners and the company's existing Simulated Annealing solver (Baseline SA) on six real-world scenarios from three distinct logistics operators.

The results show that Orion achieves travel cost reductions of 6.7\% to 32.8\% compared to human planners on five of six scenarios and outperforms Baseline SA's average result on five of six scenarios. A key structural advantage is determinism: Orion produces identical results across repeated runs, whereas Baseline SA exhibits variance of up to 37.5\% between its best and worst runs. Convergence analysis indicates that 96--99.97\% of objective improvement occurs within the first 30 seconds, making a 5-minute time budget sufficient for operational use.

The thesis concludes with recommendations for production deployment and identifies future research directions, including dynamic water level constraints, improved rolling-horizon replanning, custom fleet reduction operators, and hub-based consolidation strategies.