Solving the vehicle routing problem with adaptive memory programming methodology

In this paper we develop an adaptive memory programming method for solving the capacitated vehicle routing problem called Solutions' Elite Parts Search (SEPAS). This iterative method first generates initial solutions via a systematic diversification technique and stores their routes in an adaptive memory. Subsequently, a constructive heuristic merges route components (called elite parts) from those in the adaptive memory. Finally, a tabu search approach improves the heuristically constructed solution and the adaptive memory is appropriately updated. SEPAS has been tested on two benchmark data sets and provides high quality solutions in short computational times for all problem instances. The method reaches several new best solutions for benchmark instances with a large number of customers.