Adaptive memetic algorithm for minimizing distance in the vehicle routing problem with time windows

Abstract

This paper presents an adaptive memetic algorithm to solve the vehicle routing problem with time windows (VRPTW). It is a well-known NP-hard discrete optimization problem with two objectives-to minimize the number of vehicles serving a set of geographically dispersed customers, and to minimize the total distance traveled in the routing plan. Although memetic algorithms have been proven to be extremely efficient in solving the VRPTW, their main drawback is an unclear tuning of their numerous parameters. Here, we introduce the adaptive memetic algorithm (AMA-VRPTW) for minimizing the total travel distance. In AMA-VRPTW, a population of solutions evolves with time. The parameters of the algorithm, including the selection scheme, population size and the number of child solutions generated for each pair of parents, are adjusted dynamically during the search. We propose a new adaptive selection scheme to balance the exploration and exploitation of the solution space. Extensive experimental study performed on the well-known Solomon's and Gehring and Homberger's benchmark sets confirms the efficacy and convergence capabilities of the proposed AMA-VRPTW. We show that it is very competitive compared with other state-of-the-art techniques. Finally, the influence of the proposed adaptive schemes on the AMA-VRPTW behavior and performance is investigated in a thorough sensitivity analysis. This analysis is complemented with

An Adaptive Data-Driven Approach to Solve Real-World Vehicle Routing Problems in Logistics

“…The results do not show a clear prevalence of any of them, but confirming the significance of those additional costs and externalities. In [13] the adaptive memetic algorithm for minimizing distance in vehicle routing problem with time windows (VRPTW) is described. In [14], different metaheuristic approaches for solving VRPTW are described, such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) or Artificial Bee Colony (ABC).…”

“… The unloading time for the customer which will be used in the following steps of the proposed algorithm is equal to: (12)  The time window of the customer for use in later steps of the algorithm is equal to: (13)  When all customers are checked, the iterations are stopped Besides the listed transformations, other data preparations are done before the start of the algorithm's execution. The listed preparations of the algorithm's input data will be explained later in the following sections of this work.…”

A discrete firefly algorithm to solve a rich vehicle routing problem modelling a newspaper distribution system with recycling policy

“…Finally, a multiperior VRP with profit is addressed by a memetic algorithm in [27]. Works cited in this paragraph are some recent and interesting examples of the whole literature, some other recent examples can be found in, for example, [28,29,30]. Therefore, in this paper one metaheuristic proposed a few years ago is used to solve the presented problem.…”

Optimal routing and scheduling in E-commerce logistics using crowdsourcing strategies

“…Besides,[59][60][61] investigated the Cumulative CVRP in which the objective is to minimize the sum of arrival times at customers rather than total routing cost or distance.The authors concluded that this problem constitutes a good way to model situations where arrival time at customers is very important such as after natural disasters.Moreover, Lei et al[62] and Eufinger et al[63] incorporated some uncertainty in their studies; the former authors developed a neighborhood search heuristic to solve the problem with stochastic demands while the latter authors came up with a robust approach that takes travel times uncertainty into account. Results confirmed the superiority of the proposed heuristics over an alternative solution approaches.…”