An extended smart "predict, and optimize"(SPO) framework based on similar sets for ship inspection planning

This study aims to address one critical issue in ship inspection planning optimization, where the first step is to accurately predict ship risk, and the second step is to assign scarce port inspection resources, aiming to identify as much non-compliance from the inspected ships as possible. A traditional decision tree is first developed as the benchmark. Then, to go from a good prediction to a good decision, the structure and performance of the following optimization problem are integrated in the prediction model, which we denote by integrated decision trees. Three modes are proposed to develop integrated decision trees with different combination ways and degrees. Especially, we innovatively propose the concept of "similar set'' in data sets, and use the similar sets to select the hyperparameter tuple leading to the best decision optimization problem in mode 1. Then, the structure of the decision problem is considered into the decision tree construction facilitated by similar sets in mode 2. Finally, similar sets are used to integrate the performance of the following decision optimization problem directly into the decision tree construction process in mode 3. Numerical experiments show that mode 3 can achieve the best performance in the decision optimization model. Conservative estimations show that the proposed models can save at least millions to tens of millions inspection cost in Hong Kong dollars for the Hong Kong port each year, and up to 837 million inspection cost in Hong Kong dollars all over the world per year.