Abstract
The dynamic vehicle routing problem (DVRP) is a variant of the Vehicle Routing Problem (VRP) in which customers appear dynamically. The objective is to determine a set of routes that minimizes the total travel distance. In this paper, we propose a monarch butterfly optimization (MBO) algorithm to solve DVRPs, utilizing a greedy strategy. Both migration operation and the butterfly adjusting operator only accept the offspring of butterfly individuals that have better fitness than their parents. To improve performance, a later perturbation procedure is implemented, to maintain a balance between global diversification and local intensification. The computational results indicate that the proposed technique outperforms the existing approaches in the literature for average performance by at least 9.38%. In addition, 12 new best solutions were found. This shows that this proposed technique consistently produces high-quality solutions and outperforms other published heuristics for the DVRP.
Highlights
The dynamic vehicle routing problem (DVRP) is a hard combinatorial optimization problem which is advanced by information and communication technologies that allow information to be obtained and processed in real time, typically used in distribution logistics and transportation systems.This system facilitates quick updating of transportation system plans in unexpected or uncertain events, for example if roads between two customers are blocked off, customers can modify their orders, or when the travel time for some routes is increased due to bad weather conditions or traffic congestion, etc
Because Vehicle Routing Problem (VRP) can be regarded as special cases of dynamic VRPs, dynamic VRPs are at least as hard as VRPs; research efforts mainly focus on metaheuristics and intelligent optimization algorithms
The modified monarch butterfly optimization (MBO) algorithm we propose includes three important parts: (1) an insert heuristic are different from standard static VRPs—that is, vehicles start from their position in the last slice
Summary
The dynamic vehicle routing problem (DVRP) is a hard combinatorial optimization problem which is advanced by information and communication technologies that allow information to be obtained and processed in real time, typically used in distribution logistics and transportation systems. MBO has been successfully applied to a great variety of hard combinatorial optimization problems ([6,7,8,9]), this paper, as far as we know, proposes the first MBO-based algorithm for the DVRP. To the best of our knowledge, this is the first MBO implementation for the DVRP It is tested using data sets introduced in Kilby et al [14] and Montemanni et al [15], and compared to other well-known meta-heuristics.
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