Optical Application improved with Logistics of Artificial Intelligent and Electronic Systems

Computer science and electronics have a very big incidence in several research areas; optics and photonics are not the exception. The utilization of computers, electronic systems, and devices has allowed the authors to develop several projects to control processes. A description of the computer tool called Laser Micro-Lithography (LML) to characterize materials is realized. The Reasoning Based on Cases (RBC) and its implementation in the software using Java are presented. In order to guarantee the lithography precision, a control system based on a microcontroller was developed and coupled to the mechanical system. An alternative of LML, considering the use of a Personal Digital Assistant (PDA), instead of a Personal Computer (PC) is described. In this case, C language is used for programming. RBC optimizes the materials characterization, recovering information of materials previously characterized. The communication between the PDA and the displacement table is achieved by means of a system based on a micro-controller DSPIC. The developed computers tool permits obtaining lithography with channels narrower than an optical fiber with minimum equipment. The development of irradiance meters based on electronic automation is shown; this section includes the basic theoretical concepts, the experimental device design and the experimental results. Future research trends are presented, and as a consequence of the developed work, perspectives of micro drilling and cutting are also analyzed.

Decision Making Approaches for Advanced Manufacturing Technology Evaluation and Selection

Advanced Manufacturing Technology (AMT) constitutes one of the most important resources of manufacturing companies to achieve success in an extremely competitive world. Decision making processes for the Evaluation and Selection of AMT in these companies must lead to the best alternative available. Industry is looking for a combination of flexibility and high quality by doing significant investments in AMT. The proliferation of this technology has generated a whole field of knowledge related to the design, evaluation and management of AMT systems which includes a broad variety of methodologies and applications. This chapter presents a theoretical review of the term AMT, its diverse classification and a collection of the most effective multi-attribute models and methodologies available to support these processes. Relevant advantages are found in these models since they can manage complex decision making problems which involve large amount of information and attributes. These attributes frequently can be tangible and intangible when vagueness and uncertainty exist. There are several multi-attribute methodologies which are extensively known and used in literature; nevertheless, a new fuzzy multi-attribute axiomatic design approach is explained for an ergonomic compatibility evaluation of AMT.

An Evolutionary Algorithm for Graph Drawing with a Multiobjective Approach

In this chapter a hybrid algorithm is constructed, implemented and tested for the optimization of graph drawing employing a multiobjective approach. The multiobjective optimization problem for graph drawing consists of three objective functions: minimizing the number of edge crossing, minimizing the graph area, and minimizing the aspect ratio. The population of feasible solutions is generated using a hybrid algorithm and at each step a Pareto front is calculated. This hybrid algorithm combines a global search algorithm (EDA — Estimation of Distribution Algorithm) with a local search Algorithm (HC — Hill Climbing) in order to maintain a balance between the exploration and exploitation. Experiments were performed employing planar and non-planar graphs. A quality index of the obtained solutions by the hybrid MOEA-HCEDA (Multiobjective Evolutionary Algorithm - Hill Climbing & Univariate Marginal Distribution Algorithm) is constructed based on the Pareto front defined in this chapter. A factorial experiment using the algorithm parameters was performed. The factors are number of generations and population size, and the result is the quality index. The best combination of factors levels is obtained.

Grid Platform Applied to the Vehicle Routing Problem with Time Windows for the Distribution of Products

Around the world there have recently been new and more powerful computing platforms created that can be used to work with computer science problems. Some of these problems that are dealt with are real problems of the industry; most are classified by complexity theory as hard problems. One such problem is the vehicle routing problem with time windows (VRPTW). The computational Grid is a platform which has recently ventured into the treatment of hard problems to find the best solution for these. This chapter presents a genetic algorithm for the vehicle routing problem with time windows. The algorithm iteratively applies a mutation operator, first of the intelligent type and second of the restricting type. The algorithm takes advantage of Grid computing to increase the exploration and exploitation of the solution space of the problem. The Grid performance is analyzed for a genetic algorithm and a measurement of the latencies that affect the algorithm is studied. The convenience of applying this new computing platform to the execution of algorithms specially designed for Grid computing is presented.

Logistics for the Garbage Collection through the use of Ant Colony Algorithms

The contribution of this chapter is to present an approach to explain the Ant Colony System applied on the Waste Collection Problem, because waste management is moving up to the concern over health and environmental impacts. These algorithms are a framework for decision makers in order to analyze and simulate various spatial waste management problems. In the last decade, metaheuristics have become increasingly popular for effectively confronting difficult combinatorial optimization problems. In the present work, an individual metaheuristic Ant Colony System (ACS) algorithm is introduced, implemented and discussed for the identification of optimal routes in the case Solid Waste collection. This algorithm is applied to a waste collection and transport system, obtaining recollection routes with the less total distance with respect to the actual route utilized and to the solution obtained by a previously developed approach.

Heuristic Algorithms

This chapter approaches the Truck Loading Problem, which is formulated as a rich problem with the classic one dimensional Bin Packing Problem (BPP) and five variants. The literature review reveals that related work deals with three variants at the most. Besides, few efforts have been done to combine the Bin Packing Problem with the Vehicle Routing Problem. For the solution of this new Rich BPP a heuristic-deterministic algorithm, named DiPro, is proposed. It works together with a metaheuristic algorithm to plan routes, schedules and loads. The objective of the integrated problem, called RoSLoP, consists of optimizing the delivery process of bottled products in a real application. The experiments show the performance of three version of the Transportation System. The best version achieves a total demand satisfaction, an average saving of three vehicles and a reduction of the computational time from 3 hrs to two minutes regarding their manual solution. For the large scale the authors have develop a competitive genetic algorithm for BPP. As future work, it is intended integrate the approximation algorithm to the transportation system.

Optimization of the Impeller and Diffuser of Hydraulic Submersible Pump using Computational Fluid Dynamics and Artificial Neural Networks

Overall performance of hydraulic submersible pump is strongly linked to its geometry, impeller speed and physical properties of the fluid to be pumped. During the design stage, given a fluid and an impeller speed, the pump blades profiles and the diffuser shape has to be determined in order to achieve maximum power and efficiency. Using Computational Fluid Dynamics (CFD) to calculate pressure and velocity fields, inside the diffuser and impeller of pump, represents a great advantage to find regions where the behavior of fluid dynamics could be adverse to the pump performance. Several trials can be run using CFD with different blade profiles and different shapes and dimensions of diffuser to calculate the effect of them over the pump performance, trying to find an optimum value. However the optimum impeller and diffuser would never be obtained using lonely CFD computations, by this means are necessary the application of Artificial Neural Networks, which was used to find a mathematical relation between these components (diffusers and blades) and the hydraulic head obtained by CFD calculations. In the present chapter artificial neural network algorithms are used in combinations with CFD computations to reach an optimum in the pumps performance.

Hybrid Algorithm Applied to the Identification of Risk Factors on the Health of Newly Born in Mexico

This chapter presents the implementation of a Genetic Algorithm into a framework for machine learning that deals with the problem of identifying the factors that impact the health state of newborns in Mexico. Experimental results show a percentage of correct clustering for unsupervised learning of 89%, a real life training matrix of 46 variables, was reduced to only 25 that represent 54% of its original size. Moreover execution time is about one and a half minutes. Each risk factor (of neonatal health) found by the algorithm was validated by medical experts. The contribution to the medical field is invaluable, since the cost of monitoring these features is minimal and it can reduce neonatal mortality in our country.

Optimization of a Hybrid Methodology (CRISP-DM)

Data mining is a complex process that involves the interaction of the application of human knowledge and skills and technology. This must be supported by clearly defined processes and procedures. This Chapter describes CRISP-DM (Cross-Industry Standard Process for Data Mining), a fully documented, freely available, robust, and non proprietary data mining model. The chapter analyzes the contents of the official Version 1.0 Document, and it is a guide through all the implementation process. The main purpose of data mining is the extraction of hidden and useful knowledge from large volumes of raw data. Data mining brings together different disciplines like software engineering, computer science, business intelligence, human-computer interaction, and analysis techniques. Phases of these disciplines must be combined for data mining project outcomes. CRISP-DM methodology defines its processes hierarchically at four levels of abstraction allowing a project to be structured modularly, being more maintainable, scalable and the most important, to reduce complexity. CRISP-DM describes the life cycle of a data mining project consisting of six phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment.

Recombination Operators in Permutation-Based Evolutionary Algorithms for the Travelling Salesman Problem

The Travelling Salesman Problem (TSP) is one of the most widely studied optimization problems due to its many applications in domains such as logistics, planning, routing, and scheduling. Approximation algorithms to address this NP-hard problem include genetic algorithms, ant colony systems, and simulated annealing. This chapter concentrates on the evolutionary approaches to TSP based on permutation encoded individuals. A comparative analysis of several recombination operators is presented based on computational experiments for TSP instances and a generalized version of TSP. Numerical results emphasize a good performance of two proposed crossover schemes: best-worst recombination and best order recombination which take into account information from the global best and/or worst individuals besides the genetic material from parents.