Swarm intelligence-based hyper-heuristic for the vehicle routing problem with prioritized customers

The vehicle routing problem (VRP) is a combinatorial optimization management problem that seeks the optimal set of routes traversed by a vehicle to deliver products to customers. A recognized problem in this domain is to serve ‘prioritized’ customers in the shortest possible time where customers with known demands are supplied by one or several depots. This problem is known as the Vehicle Routing with Prioritized Customers (VRPC). The purpose of this work is to present and compare two artificial intelligence-based novel methods that minimize the traveling distance of vehicles when moving cargo to prioritized customers. Various studies have been conducted regarding this topic; nevertheless, up to now, few studies used the Cuckoo Search-based hyper-heuristic. This paper modifies a classical mathematical model that represents the VRPC, implements and tests an evolutionary Cuckoo Search-based hyper-heuristic, and then compares the results with those of our proposed modified version of the Clarke Wright (CW) algorithm. In this modified version, the CW algorithm serves all customers per their preassigned priorities while covering the needed working hours. The results indicate that the solution selected by the Cuckoo Search-based hyper-heuristic outperformed the modified Clarke Wright algorithm while taking into consideration the customers’ priority and demands and the vehicle capacity.