An analysis of the Taguchi method for tuning a memetic algorithm with reduced computational time budget

Determining the best initial parameter values for an algorithm, called parameter tuning, is crucial to obtaining better algorithm performance; however, it is often a time-consuming task and needs to be performed under a restricted computational budget. In this study, the results from our previous work on using the Taguchi method to tune the parameters of a memetic algorithm for cross-domain search are further analysed and extended. Although the Taguchi method reduces the time spent finding a good parameter value combination by running a smaller size of experiments on the training instances from different domains as opposed to evaluating all combinations, the time budget is still larger than desired. This work investigates the degree to which it is possible to predict the same good parameter setting faster by using a reduced time budget. The results in this paper show that it was possible to predict good combinations of parameter settings with a much reduced time budget. The good final parameter values are predicted for three of the parameters, while for the fourth parameter there is no clear best value, so one of three similarly performing values is identified at each time instant

An effective memetic algorithm for the cumulative capacitated vehicle routing problem

The cumulative capacitated vehicle routing problem (CCVRP) is a transportation problem which occurs when the objective is to minimize the sum of arrival times at customers, instead of the classical route length, subject to vehicle capacity constraints. This type of challenges arises whenever priority is given to the satisfaction of the customer need, e.g. vital goods supply or rescue after a natural disaster. The CCVRP generalizes the NP-hard traveling repairman problem (TRP), by adding capacity constraints and a homogeneous vehicle fleet. This paper presents the first upper and lower bounding procedures for this new problem. The lower bounds are derived from CCVRP properties. Upper bounds are given by a memetic algorithm using non-trivial evaluations of cost variations in the local search. Good results are obtained not only on the CCVRP, but also on the special case of the TRP, outperforming the only TRP metaheuristic published

Lower and upper bounds for the m-peripatetic vehicle routing problem

The m-Peripatetic Vehicle Routing Problem (m-PVRP) consists in finding a set of routes of minimum total cost over m periods so that two customers are never sequenced consecutively during two different periods. It models for example money transports or cash machines supply, and the aim is to minimize the total cost of the routes chosen. The m-PVRP can be considered as a generalization of two well-known NP-hard problems: the Vehicle Routing Problem (VRP or 1-PVRP) and the m-Peripatetic Salesman Problem (m-PSP). In this paper we discuss some complexity results of the problem before presenting upper and lower bounding procedures. Good results are obtained not only on the m-PVRP in general, but also on the VRP and the m-PSP using classical VRP instances and TSPLIB instances

Multistart Evolutionary Local Search for a Disaster Relief Problem

International audienceThis paper studies the multitrip cumulative capacitated vehicle routing problem (mt-CCVRP), a variant of the classical capacitated vehicle routing problem (CVRP). In the mt-CCVRP the objective function becomes the minimization of the sum of arrival times at required nodes and each vehicle may perform more than one trip. Applications of this NP-Hard problem can be found in disaster logistics. This article presents a Multistart Evolutionary Local Search (MS-ELS) that alternates between giant tour and mt-CCVRP solutions, and uses an adapted split procedure and a variable neighborhood descent (VND). The results on two sets of instances show that this approach finds very good results in relatively short computing time compared with a multistart iterated local search which works directly on the mt-CCVRP solution space

The multi-vehicle cumulative covering tour problem

International audienceThis paper introduces the multi-vehicle cumulative covering tour problem whose motivation arises from humanitarian logistics. The objective is to determine a set of tours that must be followed by a fleet of vehicles in order to minimize the sum of arrival times (latency) at each visited location. There are three types of locations: mandatory, optional, and unreachable. Each mandatory location must be visited, and optional locations are visited in order to cover the unreachable locations. To guarantee the vehicle autonomy, the duration of each tour should not exceed a given time limit. A mixed integer linear formulation and a greedy randomized adaptive search procedure are proposed for this problem. The performance of the algorithm is assessed over a large set of instances adapted from the literature. Computational results confirm the efficiency of the proposed algorithm

A Metaheuristic Approach for the Cumulative Capacitated Arc Routing Problem

In this paper we propose a new variant of the capacitated arc routing problem (CARP). In this new problem the objective function becomes a cumulative objective computed as the traveled distance multiplied by the vehicle load. A metaheuristic approach is proposed which is based on the hybridization of three known procedures: GRASP, VND and Set covering model. The metaheuristic is tested with some benchmark instances from CARP. The results allow to evaluate the performance with the different metaheuristic components and to compare the solutions with the classical objective function. © 2018, Springer Nature Switzerland AG