Data underlying the publication: Simulation-optimization configurations for real-time decision-making in fugitive interception

This repository is part of the Ph.D. thesis of Irene S. van Droffelaar, Delft University of Technology.<br>This repository accompanies the paper "Simulation-optimization configurations for real-time decision-making in fugitive interception" (https://doi.org/10.1016/j.simpat.2024.102923).<br>The optimization algorithm can be found under<em> platypus-fork.zip</em>.<br>In<em> simopt-configs.zip</em>, the folders _cleaned data_ and _models_ contain all results and software for:- Sequential Simulation Optimization (abbreviated as _seq_ in file names), solved with MIP solver CBC (https://github.com/coin-or/Cbc)- Sequential Simulation Optimization (_seq_), solved with metaheuristic Borg- Simulation Model Optimization (abbreviated as _smo_ in file names), solved with metaheuristic Borg

本仓库是代尔夫特理工大学（Delft University of Technology）Irene S. van Droffelaar博士论文的组成部分。
本仓库随附论文《面向逃逸目标拦截的实时决策仿真优化配置》（https://doi.org/10.1016/j.simpat.2024.102923）。
优化算法可于<em>platypus-fork.zip</em>中获取。
在<em>simopt-configs.zip</em>中，_cleaned data_（清理后数据集）与_models_（模型）文件夹包含了全部结果与软件工具，涵盖以下三类任务：
1. 序列仿真优化（文件名中缩写为_seq），采用混合整数规划（Mixed Integer Programming, MIP）求解器CBC进行求解（https://github.com/coin-or/Cbc）；
2. 序列仿真优化（文件名中缩写为_seq），采用元启发式算法Borg进行求解；
3. 仿真模型优化（文件名中缩写为_smo），采用元启发式算法Borg进行求解。