An Efficient ACO+RVND for Solving the Resource-Constrained Deliveryman Problem

Abstract

In this paper, Resource-Constrained Deliveryman Problem (RCDMP) is introduced. The RCDMP problem deals with finding a tour with minimum waiting time sum so that it consumes not more than the $R_{max}$ unites of the resources, where $R_{max}$ is some constant. Recently, an algorithm developed in a trajectory-based metaheuristic has been proposed. Since the search space of the problem is a combinatorial explosion, the trajectory-based sequential can only explore a subset of the search space, therefore, they easily fall into local optimal in some cases. To overcome the drawback of the current algorithms, we propose a population-based algorithm that combines an Ant Colony Algorithm (ACO), and Random Variable Neighborhood Descent (RVND). In the algorithm, ACO explores the promising solution areas while RVND exploits them with the hope of improving a solution. Extensive numerical experiments and comparisons with the state-of-the-art metaheuristic algorithms in the literature show that the proposed algorithm reaches better solutions in many cases.