Optimizing the Replication of Multi-Quality Web Applications Using ACO and WoLF

This thesis presents the adaptation of Ant Colony Optimization to a new NP-hard problem involving the replication of multi-quality database-driven web applications (DAs) by a large application service provider (ASP). The ASP must assign DA replicas to its network of heterogeneous servers so that user demand is satisfied and replica update loads are minimized. The algorithm proposed, AntDA, for solving this problem is novel in several respects: ants traverse a bipartite graph in both directions as they construct solutions, pheromone is used for traversing from one side of the bipartite graph to the other and back again, heuristic edge values change as ants construct solutions, and ants may sometimes produce infeasible solutions. Experiments show that AntDA outperforms several other solution methods, but there was room for improvement in the convergence rates of the ants. Therefore, in an attempt to achieve the goals of faster convergence and better solution values for larger problems, AntDA was combined with the variable-step policy hill-climbing algorithm called Win or Learn Fast (WoLF). In experimentation, the addition of this learning algorithm in AntDA provided for faster convergence while outperforming other solution methods

Biological inspired algorithm for Storage Area Networks (ACOSAN)

The routing algorithms like Storage Area Networks (SAN) algorithms are actually deterministic algorithms, but they may become heuristics or probabilistic just because of applying biological inspired algorithms like Ant Colony Optimization (ACO) of Dorigo. A variant suggested by Navarro and Sinclair in the University of Essex in UK, it is called MACO and it may open new paths for adapting routing algorithms to changes in the environment of any network. A new algorithm is anticipated in this paper to be applied in routing algorithms for SAN Fibre Channel switches, it is called ACOSAN.IFIP International Conference on Artificial Intelligence in Theory and Practice - Integration of AI with other TechnologiesRed de Universidades con Carreras en Informática (RedUNCI

The tabu ant colony optimizer and its application in an energy market

A new ant colony optimizer, the \u27tabu ant colony optimizer\u27 (TabuACO) is introduced, tested, and applied to a contemporary problem. The TabuACO uses both attractive and repulsive pheromones to speed convergence to a solution. The dual pheromone TabuACO is benchmarked against several other solvers using the traveling salesman problem (TSP), the quadratic assignment problem (QAP), and the Steiner tree problem. In tree-shaped puzzles, the dual pheromone TabuACO was able to demonstrate a significant improvement in performance over a conventional ACO. As the amount of connectedness in the network increased, the dual pheromone TabuACO offered less improvement in performance over the conventional ACO until it was applied to fully-interconnected mesh-shaped puzzles, where it offered no improvement. The TabuACO is then applied to implement a transactive energy market and tested with published circuit models from IEEE and EPRI. In the IEEE feeder model, the application was able to limit the sale of power through an overloaded transformer and compensate by bringing downstream power online to relieve it. In the EPRI feeder model, rapid voltage changes due to clouds passing over PV arrays caused the PV contribution to outstrip the ability of the substation to compensate. The TabuACO application was able to find a manageable limit to the photovoltaic energy that could be contributed on a cloudy day --Abstract, page iii

Prometno-tehnološka optimizacija faze uručenja poštanskih pošiljaka

Technological phase end-delivery represents the most expensive activity of postal traffic. By identifying user requirements and separating it as a distinguished holistic system and process it is possible to identify factors which affect efficiency and effectiveness regarding handing over and delivery of postal items. End-delivery has decisive effect on quality beacuse as a service subsystem directly represents postal service to end users. Postal services are performed by using public or autonomous postal network. Systematic modeling of delivery districts, application of mathematical and heuristic methods with adaptation of intelligent transportation solutions optimize network and everyday operational costs.Tehnološka faza uručenja predstavlja najskuplju aktivnost odvijanja poštanskog prometa. Prepoznavanjem korisničkih zahtjeva uz postavljanje uručenja kao izdvojenog holističkog sustava i procesa moguće je utvrditi čimbenike koji utječu na učinkovitost te djelotvornost dostave i isporuke poštanskih pošiljaka. Uručenje ima presudan utjecaj na kvalitetu jer kao uslužni podsustav direktno predstavlja poštansku djelatnost krajnjim korisnicima. Poštanske usluge se obavljaju korištenjem javne ili autonomne poštanske mreže. Sustavno modeliranje dostavnih rajona, primjena matematičkih i heurističkih metoda uz adaptaciju inteligentnih transportnih rješenja optimiziraju mrežu te svakodnevne operativne troškove

Improving inbound logistic planning for large-scale real-world routing problems: a novel ant-colony simulation-based optimization

Abstract This paper presents the first results of an agent-based model aimed at solving a Capacitated Vehicle Routing Problem (CVRP) for inbound logistics using a novel Ant Colony Optimization (ACO) algorithm, developed and implemented in the NetLogo multi-agent modelling environment. The proposed methodology has been applied to the case study of a freight transport and logistics company in South Italy in order to find an optimal set of routes able to transport palletized fruit and vegetables from different farms to the main depot, while minimizing the total distance travelled by trucks. Different scenarios have been analysed and compared with real data provided by the company, by using a set of key performance indicators including the load factor and the number of vehicles used. First results highlight the validity of the method to reduce cost and scheduling and provide useful suggestions for large-size operations of a freight transport service

Shared Mobility Optimization in Large Scale Transportation Networks: Methodology and Applications

abstract: Optimization of on-demand transportation systems and ride-sharing services involves solving a class of complex vehicle routing problems with pickup and delivery with time windows (VRPPDTW). Previous research has made a number of important contributions to the challenging pickup and delivery problem along different formulation or solution approaches. However, there are a number of modeling and algorithmic challenges for a large-scale deployment of a vehicle routing and scheduling algorithm, especially for regional networks with various road capacity and traffic delay constraints on freeway bottlenecks and signal timing on urban streets. The main thrust of this research is constructing hyper-networks to implicitly impose complicated constraints of a vehicle routing problem (VRP) into the model within the network construction. This research introduces a new methodology based on hyper-networks to solve the very important vehicle routing problem for the case of generic ride-sharing problem. Then, the idea of hyper-networks is applied for (1) solving the pickup and delivery problem with synchronized transfers, (2) computing resource hyper-prisms for sustainable transportation planning in the field of time-geography, and (3) providing an integrated framework that fully captures the interactions between supply and demand dimensions of travel to model the implications of advanced technologies and mobility services on traveler behavior.Dissertation/ThesisDoctoral Dissertation Civil, Environmental and Sustainable Engineering 201