A clustering approach for vehicle routing problems with hard time windows

Dissertação para obtenção do Grau de Mestre em Logica ComputicionalThe Vehicle Routing Problem (VRP) is a well known combinatorial optimization problem and many studies have been dedicated to it over the years since solving the VRP optimally or near-optimally for very large size problems has many practical applications (e.g. in various logistics systems). Vehicle Routing Problem with hard TimeWindows (VRPTW) is probably the most studied variant of the VRP problem and the presence of time windows requires complex techniques to handle it. In fact, finding a feasible solution to the VRPTWwhen the number of vehicles is fixed is an NP-complete problem. However, VRPTW is well studied and many different approaches to solve it have been developed over the years. Due to the inherent complexity of the underlying problem VRPTW is NP-Hard. Therefore, optimally solving problems with no more than one hundred requests is considered intractably hard. For this reason the literature is full with inexact methods that use metaheuristics, local search and hybrid approaches which are capable of producing high quality solutions within practical time limits. In this work we are interested in applying clustering techniques to VRPTWproblem. The idea of clustering has been successfully applied to the basic VRP problem. However very little work has yet been done in using clustering in the VRPTW variant. We present a novel approach based on clustering, that any VRPTW solver can adapt, by running a preprocessing stage before attempting to solve the problem. Our proposed method, tested with a state of the art solver (Indigo), enables the solver to find solutions much faster (up to an order of magnitude speed-up). In general this comes with at slightly reduced solution quality, but in somes types of problems, Indigo is able to obtain better solutions than those obtained with no clustering

A Hybrid Multicast-Unicast Infrastructure for Efficient Publish-Subscribe in Enterprise Networks

One of the main challenges in building a large scale publish-subscribe infrastructure in an enterprise network, is to provide the subscribers with the required information, while minimizing the consumed host and network resources. Typically, previous approaches utilize either IP multicast or point-to-point unicast for efficient dissemination of the information. In this work, we propose a novel hybrid framework, which is a combination of both multicast and unicast data dissemination. Our hybrid framework allows us to take the advantages of both multicast and unicast, while avoiding their drawbacks. We investigate several algorithms for computing the best mapping of publishers' transmissions into multicast and unicast transport. Using extensive simulations, we show that our hybrid framework reduces consumed host and network resources, outperforming traditional solutions. To insure the subscribers interests closely resemble those of real-world settings, our simulations are based on stock market data and on recorded IBM WebShpere subscriptions