Systems Modeling and Optimization of Container Ship Berthing with Various Enterprise Risks

Scheduling for logistics systems, such as assigning of container vessels to marine port terminals, is subject to uncertainty in product arrival times, resource availability, efficiency of available resources, product volume, and others. A model-based approach is developed to create schedule options that are evaluated on multiple performance objectives. Mixed-integer linear programming minimizing the schedule cost is coupled with Monte Carlo simulation that samples inputs represented by random variables. This results in probabilistic assignments of vessels to terminals. Based on the likelihood of assignments, multiple schedule options are created and filtered. The schedule options are subsequently evaluated for the random variables and multiple performance criteria are observed. This allows stakeholders to negotiate a schedule that balances cost, delay, and other objectives. The approach is demonstrated for a marine container port in the United States. The research contributes to the methods and applications of scheduling for systems with stochastic elements. The results and methods are of interest to enterprises whose schedules face significant uncertainty, including the fields of logistics, construction, manufacturing, and others.