A state of the art and a general formulation model of Hub Location-Routing Problems for LTL shipments

National audienceIn many logistic systems for less than truckload (LTL) shipments, transportation of goods from one origin to its destination is made through collection tours to a hub and delivery tours from the same or another hub, while the goods are shipped between two hubs using Full Truckload (FTL) shipments. Therefore, managers need to determine the location of the hubs, the allocation of non-hub nodes, and the optimal collection and delivery routes within the network. This problem, known as the hub location-routing problem (HLRP), is related to both the hub location problem (HLP) and the location-routing problem (LRP). The HLP involves the location of hub facilities concentrating flows in order to take advantage of economies of scale and through which flows are to be routed from origins to destinations. The objective of the HLRP is to minimize the total costs including hub costs, inter-hub transportation costs, and collection/distribution routing costs. Based on the literatures review, the aims of this paper are to analyze the state of the art, propose some generic mathematical models for the HLRP and implement some tests using a MIP solver

Demand Forecasting and Location Optimization of Recharging Stations for Electric Vehicles in Carsharing Industries

Carsharing is an alternative to private car usage. Using electric-vehicles as a substitute to fuel vehicles is a wiser option which leads to lower fuel emissions, more energy savings and decreased oil dependency. However, there are some barriers in using electric vehicles at large scale in carsharing companies. Battery power limitation and lack of sufficient infrastructures are some of them. Accurate demand forecasting is a must for this purpose. In the first part of this thesis, we investigate the demand forecasting problem for carsharing industries and apply four techniques namely simple linear regression, seasonally adjusted forecast, Winter's Model and artificial neural networks to decide the right number of vehicles to be made available at each station to meet the customer requests. The results on randomly generated test datasets show that artificial neural networks perform better over the other three. In the second part, we investigate the location planning problem of recharging stations for electric vehicles. The base model used for this study is the mathematical optimization model proposed by Wang & Lin (2013). Firstly, we improve their MIP model and solve it using AIMMS (Advanced Interactive Multidimensional Modeling System). Secondly, we propose Genetic Algorithm for the same problem and implement it in Matlab. The obtained results are compared with previous work done by Wang and Lin (2013). The comparisons show better performance of the proposed methods

A branch-and-cut algorithm for the partitioning-hub location-routing problem

We introduce the Partitioning-Hub-Location-Routing Problem (PHLRP), a hub location problem involving graph partitioning and routing features. The PHLRP consists of partitioning a given network into sub-networks, locating at least one hub in each sub-network and routing the traffic within the network at minimum cost. This problem finds applications in deployment of an Internet Routing Protocol called Intermediate SystemIntermediate System (ISIS), and strategic planning of LTL ground freight distribution systems. We present an Integer Programming (IP) model for solving exactly the PHLRP and explore possible valid inequalities to strengthen it. Computational experiments prove the effectiveness of our model which is able to tackle instances of PHLRP containing up to 20 vertices. © 2010 Elsevier Ltd. All rights reserved.SCOPUS: ar.jinfo:eu-repo/semantics/publishe