Tabu Search Optimization for Vehicle Routing Problems

This program implements a tabu search algorithm to optimize vehicle routing paths for solving dynamic multi-vehicle scheduling problems. Tabu search is a metaheuristic algorithm based on local search that effectively avoids getting trapped in local optima during the optimization process. The core implementation maintains a tabu list to record forbidden moves and states throughout the search procedure, enabling dynamic scheduling of multiple vehicles for optimal route planning. The algorithm incorporates several heuristic techniques including neighborhood reduction strategies to limit search space and random perturbation methods to escape local minima. Key functional components involve movement evaluation functions, aspiration criteria handling, and solution fitness calculation. The implementation features adaptive tabu tenure management and diversification mechanisms to balance exploration and exploitation. Through these enhancements, the program significantly improves search efficiency and solution accuracy, effectively optimizing vehicle routes while enhancing scheduling efficiency and transportation safety.