Locating Two Transfer Points on a Network with a Trip Covering Criterion and Mixed Distances

In this paper we consider a set of origin-destination pairs in a mixed model in which a network embedded in the plane represents an alternative high-speed transportation system, and study a trip covering problem which consists on locating two points in the network which maximize the number of covered pairs, that is, the number of pairs which use the network by acceding and exiting through such points. To deal with the absence of convexity of this mixed distance function we propose a decomposition method based on formulating a collection of subproblems and solving each of them via discretization of the solution set.Ministerio de Educación, Ciencia e Innovación MTM2009-14243Ministerio de Economía y Competitividad MTM2012-37048Junta de Andalucía P09-TEP-5022Junta de Andalucía P10-FQM-584

A general approach for the location of transfer points on a network with a trip covering criterion and mixed distances

In this paper we consider a trip covering location model in a mixed planar-network space. An embed- ded network in the plane represents an alternative transportation system in which traveling is fasterthan traveling within the plane. We assume that the demand to be covered is given by a set of origin- destination pairs in the plane, with some traffic between them. An origin-destination pair is covered bytwo facility points on the network (or transfer points), if the travel time from the origin to destinationby using the network through such points is not higher than a given acceptance level related to the traveltime without using the network. The facility location problems studied in this work consist of locatingone or two transfer points on the network such that, under several objective functions, the traffic throughthe network is maximized. Due to the continuous nature of these problems, a general approach is pro- posed for discretizing them. Since the non-convexity of the distance function on cyclic networks alsoimplies the absence of convexity of the mixed distance function, such an approach is based on a decom- position process which leads to a collection of subproblems whose solution set can be found by adaptingthe general strategy to each problem considered.Ministerio de Economía y Competitividad MTM2012-37048Ministerio de Economía y Competitividad MTM2015-67706-PJunta de Andalucía P10-FQM-584

Median problems in networks

The P-median problem is a classical location model “par excellence”. In this paper we, first examine the early origins of the problem, formulated independently by Louis Hakimi and Charles ReVelle, two of the fathers of the burgeoning multidisciplinary field of research known today as Facility Location Theory and Modelling. We then examine some of the traditional heuristic and exact methods developed to solve the problem. In the third section we analyze the impact of the model in the field. We end the paper by proposing new lines of research related to such a classical problem.P-median, location modelling

Efficient heuristic algorithms for location of charging stations in electric vehicle routing problems

Indexación: Scopus.This work has been partially supported by CONICYT FONDECYT by grant 11150370, FONDEF IT17M10012 and the “Grupo de Logística y Transporte” at the Universidad del Bío-Bío.. This support is gratefully acknowledged.Eco-responsible transportation contributes at making a difference for companies devoted to product delivery operations. Two specific problems related to operations are the location of charging stations and the routing of electric vehicles. The first one involves locating new facilities on potential sites to minimise an objective function related to fixed and operational opening costs. The other one, electric vehicle routing problem, involves the consolidation of an electric-type fleet in order to meet a particular demand and some guidelines to optimise costs. It is determined by the distance travelled, considering the limited autonomy of the fleet, and can be restored by recharging its battery. The literature provides several solutions for locating and routing problems and contemplates restrictions that are closer to reality. However, there is an evident lack of techniques that addresses both issues simultaneously. The present article offers four solution strategies for the location of charging stations and a heuristic solution for fleet routing. The best results were obtained by applying the location strategy at the site of the client (relaxation of the VRP) to address the routing problem, but it must be considered that there are no displacements towards the recharges. Of all the other three proposals, K-means showed the best performance when locating the charging stations at the centroid of the cluster. © 2012-2018. National Institute for R and D in Informatics.https://sic.ici.ro/wp-content/uploads/2018/03/Art.-8-Issue-1-2018-SIC.pd

Moving Walkways, Escalators, and Elevators

We study a simple geometric model of transportation facility that consists of two points between which the travel speed is high. This elementary definition can model shuttle services, tunnels, bridges, teleportation devices, escalators or moving walkways. The travel time between a pair of points is defined as a time distance, in such a way that a customer uses the transportation facility only if it is helpful. We give algorithms for finding the optimal location of such a transportation facility, where optimality is defined with respect to the maximum travel time between two points in a given set.Comment: 16 pages. Presented at XII Encuentros de Geometria Computacional, Valladolid, Spai

Location models for airline hubs behaving as M/D/c queues

Models are presented for the optimal location of hubs in airline networks, that take into consideration the congestion effects. Hubs, which are the most congested airports, are modeled as M/D/c queuing systems, that is, Poisson arrivals, deterministic service time, and {\em c} servers. A formula is derived for the probability of a number of customers in the system, which is later used to propose a probabilistic constraint. This constraint limits the probability of {\em b} airplanes in queue, to be lesser than a value $\alpha$. Due to the computational complexity of the formulation. The model is solved using a meta-heuristic based on tabu search. Computational experience is presented.Hub location, congestion, tabu-search