The ever-increasing size of offshore wind turbines, combined with the development of wind farms at more remote sites with deeper waters and more extreme weather conditions, presents significant logistical challenges. Furthermore, the offshore wind sector faces reduced government subsidies, narrow profit margins, and a lack of industry guidelines, all while striving to lower the levelised cost of energy in order to remain competitive in the market. Developers must navigate complex decisions regarding foundation selection and installation strategies. However, existing research lacks systematic approaches to optimise these decisions, with particular gaps in studies addressing multi-vessel operations, vessel compatibility with component size, alternative transport vessels, and the specific challenges of substructure installation. This lack of structured frameworks hinders evidence-based decision-making in foundation selection and installation planning. This research develops a framework to address the question: How can a multi-vessel optimisation framework integrating alternative transport vessels and accounting for weather uncertainty improve foundation selection and installation scheduling to minimise costs?  The first phase of this study develops a deterministic decision tree model that evaluates foundation selection based on environmental parameters and identifies cost-optimal Transport and Installation (T&I) strategies. Analysis of 23 previous installation projects highlights water depth as the primary decision factor, with a 50-metre threshold distinguishing between monopile foundations for shallower waters and suction bucket jackets for deeper waters. A 127-nautical-mile threshold marks the transition from shuttling to feeder strategies. Cost sensitivity analysis reveals that water depth significantly impacts cost, while seabed conditions show minimal influence. The decision tree model’s foundation type prediction accuracy is 43.5%, reflecting the complexity of real-world decision-making. This phase provides a visualisation of pathways for early-stage project decision-making, emphasising the importance of more detailed installation schedule optimisation using Mixed Integer Linear Programming (MILP) and stochastic approaches.  The second phase employs MILP to optimise installation scheduling for a case study and assess the installation performance of monopiles versus pin-pile jackets using the assembly-line installation strategy. The deterministic model incorporates constraints (e.g., task precedence, operational, deck capacity) and weather limitations using historical site weather data, while a stochastic extension models weather uncertainty using Weibull distributions. The performance of monopile-transition piece (MP-TP) strategies is compared with pin-pile jacket (PP-JK) strategies, focusing on weather sensitivity, costs, and computational performance. In addition, logistic setups are evaluated by varying the number and type of vessels used, from three to five. The results show that MP-TP strategies outperform PP-JK strategies, with MP-TP installations preferring shuttling over feeding when a 2.5-metre wave height limit is applied. For PP-JK installations, Heavy Transport Vessels (HTVs) are preferred over barges due to their higher deck capacity, despite the lower installation rate of the associated installation vessel. The use of a single installation vessel capable of installing all component sizes is found to be more cost-effective than using smaller and cheaper additional vessels. Weather uncertainty significantly influences installation scheduling, as shown by both deterministic and stochastic models.  The developed decision support tool provides a basis for further research in offshore wind logistics and other industries. Although the findings are applicable within the scope of this study, future research should explore additional factors such as stochastic risk assessments for pile refusal and assess the impact of larger wind farm sizes and dynamic port-to-farm distances.