Matías Alvarado

Optimization to Manage Supply Chain Disruptions Using the NSGA-II

Disruption on a supply chain provokes lost that can be minimized through an alternative solution. This solution involves a strategy to manage the impact of the disruption and thus to recuperate the supply chain. Difficulty of this management is the diversity of factors such that becomes complex to provide or choice a solution among the possible ones. Depending on the objective(s) to optimize are the strategy to follow and the solution to choice. In this work the Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization NSGA-II is used as the strategy to generate and optimize (minimize) solutions (lost) in front of a disruption. The included objectives are cost, risk and the place of facilities supporting the supply chain recuperation. These objectives are combined to generate possible solutions and to choice one such that it provides a proposal to minimize the disruption impact on a delimited period of time. Advantage of NSGA-II utilization is the provision of a practical formal and computational tool to analyze different scenarios without simplifies the complexity of a standard real supply chain. The illustrative exercise presents recovery scenarios for a crude oil refinery supply chain.

Decision-making on pipe stress analysis enabled by knowledge-based systems

This paper presents engineering decision-making on pipe stress analysis through the application of knowledge-based systems (KBS). Stress analysis, as part of the design and analysis of process pipe networks, serves to identify whether a given pipe arrangement can cope with weight, thermal, and pressure stress at safe operation levels. An iterative process of design and analysis cycle is done routinely by engineers while analyzing the existing networks or while designing the process pipe networks. In our proposal, the KBS establishes a bidirectional communication with the current engineering software for pipe stress analysis, so that the user benefits from this integration. The stress analysis knowledge base is constructed by registering the senior engineers’ know-how. The engineers’ overall strategy to follow up during the pipe stress analysis, to some extent contained by the KBS, is presented. Advantages in saving engineering man-hours and usefulness in guiding experts in pipe stress analysis are the major services for the process industry.

A corporative memory based on the user profiles

Abstract In this paper, each position in the organization has a well-delimited profile defined by, the assigned tasks as well as for the engaged relationships during the process and the organizational domain. Ontologies for organization positions, tasks and application domains are introduced in order to model an Organizational Memory. This Memory is designed/specified through UML/XML diagrams and it is exemplified by a Customer Relationship Management information system. The organizationals memory reuses the resulting knowledge from experiences abstraction of organization members while laboring at their positions.

Autonomous agents and computational intelligence: the future of AI application for petroleum industry

The special issue is dedicated to artificial intelligence (AI) applications in the petroleum industry. The papers in this issue are extended versions of selected papers presented at the First Workshop Intelligent Computing in the Petroleum Industry held in Mexico-city, November 24– 25, 2002. About 20 papers were presented at the Workshop by the authors from 7 countries, from which 10 papers were carefully selected by the International Program Committee for this special issue. The primary goal of the issue is to present to the petroleum community a selection of applications based on more recent AI models and techniques meant to solve the petroleum industry problems. The selected examples could also serve as a starting point or as an opening out, in the AI techniques application to a wider range of problems in industry. Both traditional symbolic and also computational intelligence applications are contained in this special issue. The petroleum industry has been among the first to use AI techniques: DIPMETER ADVISOR and PROSPECTOR are often mentioned as being examples of early classic systems. However, AI techniques should still achieve their stay as a strategic technology within this industry, such that the usefulness of various AI techniques is well-understood and largely used there. The applications discussed in this volume fall into the implemented AI methods, motivating in turn novel methodologies to deal with classical and recent problems. Agents’ papers contain applications, which the modeling of reasoning, inspired by the mechanisms of logical thinking, is essential for. Soft Computing contains applications usually treated by numerical algorithms and shows the advantages of methods inspired by the domain evolution, i.e. neural networks and genetic algorithms (GAs), as well as the applications based on fuzzy logic also used for modeling of human reasoning, which is highly dependent on the computational aspects. The Knowledge Management papers use workflow techniques to deal with the enterprise modeling; in addition, the content languages and the semantic of the web techniques are applied to deal with the huge volume of complex available information/knowledge related with energetic/oil industries. With the maturing of AI, many researchers and developers have come to use hybrid systems, which employ several AI techniques. Hybridization means the use of distinct AI techniques in the same global system, each technique for what it is good to, such that each of these techniques may correctly solve part of the problem, and support the working out of a coherent solution to the overall problem. Hybrid systems are currently receiving a lot of attention, and appear to yield promising results. So, this special issue has several articles devoted to hybrid systems being of interest to refer and present the applications which the special issue makes a selection of. The combinations of various techniques being used to achieve this goal includes, but not reduces to:

Non-uniform sampling for improved appearance-based models

This work proposes the usage of non-uniform sampling to construct appearance-based models. Through this form of sampling, we shall have a guideline to spend less time for model construction and diminish storage usage, when pose estimation no matters. The sampling depends on the object class to be recognized and it is done by a simple proposed technique. This technique is based on a scheme of linear interpolation and sum-of-squared-difference to determine the strictly necessary images to build the objects model with an e precision, and has been used in conjunction with the eigenspaces technique for object recognition. Experimental results about precision of Columbia object image library applying the proposed technique are exposed as well. In addition, the proposed technique allows controlling the movements of a motorized turntable, to automatically carry out the process of image acquisition and diminish the buffered images.

TEAKS: simulation of human performance at work to support team configuration

The management of a complex engineering project is a difficult task that initially involves the division of the project into tasks; the selection of the right people; and the correct allocation of those tasks for the selected people. Team configuration process is typically performed by a manager based on his/her past experience and the available (though frequently scarce, uncertain and dynamic) information about the cognitive, personal and social characteristics of the potential team members. To support this decision-making process we propose TEAKS, a knowledge-based tool that given an initial team configuration and a set of tasks, simulates the most possible team performance. Its formal bases are Fuzzy Set Theory and Fuzzy Logic and it is implemented using Multi-Agent Systems technology in Java, JADE, JESS and FuzzyJESS. This tool was validated with an industrial project involving 23 team members and 23 tasks (ranging from task assignment to SAP administration, SQL programming and equipment connectivity).

Nash equilibrium for collective strategic reasoning

In multi-player games, the Nash Equilibrium (NE) profile concept deserves a team for selecting strategies during a match, so no player - except in own prejudice - individually deviates from the team selected strategy. By using NE strategy profiles, the way a baseball team increases the possibilities to a match victory is payoff-matrices-based analyzed in this paper. Each matrix entry arrange each players strategies by regarding the ones from mates and adversaries, and posterior to a NE-profile-selection, the matrix from all players strategies can support the managers strategic decision-making in the course of a match. A finite state machine, a formal grammar and a generator of random plays are the algorithmic fundament for this collective strategic reasoning automation. The relationships to e-commerce, social and political scopes, as well as to computing issues are reviewed.

Similarity Measures for Substituting Web Services

Web service substitution is one of the most advanced tasks that a composite Web service developer must achieve. Substitution occurs when, in a composite scenario, a service operation is replaced to improve the composition performance or fix a disruption caused by a failing service. To move the automation of substitution forward, a set of measures, considering structure and functionality of Web services, are provided. Most of current proposals for the discovery and matchmaking of Web services are based on the semantic perspective, which lacks the precise information that is needed toward Web service substitution. This paper describes a set of similarity measures to support this substitution. Similarity measurement accounts the differences or similarities by the syntax comparison of names and data types, followed by the comparison of input and output parameters values of Web service operations. Calculation of these measures was implemented using a filtering process. To evaluate this approach, a software architecture was implemented, and experimental tests were carried on both private and public available Web services. Additionally, as is discussed, the application of these measures can be extended to other Web services tasks, such as classification, clustering and composition.

Mobile nested transactions for nomadic teams

Abstract This paper introduces the Mobile Nested Transactions (MNT) model for transaction processing coordination in groups of mobile devices. The model extends traditional approaches—centralized and distributed—for distributed transaction processing over mobile devices. Under the Nested Transactions approach, sub-transactions—logical work units—processing control is highly flexible and fault-tolerant. Hence, transaction processing with mobile groups lacking of access to a wired network or the Internet becomes possible. A MNT additional advantage is that sub-transactions could be distributed among mobile devices. This is, whenever some sub-transactions are successfully committed, they cannot be affected by failed or pending sub-transactions.

Fruit Classification by Extracting Color Chromaticity, Shape and Texture Features: Towards an Application for Supermarkets

In this paper we present an approach for fruit recognition using artificial vision, towards to employ it as an application for supermarkets. The fruits visual features extracted are shape, texture and -we focus on- the extraction of the color chromaticity. From the few related works on fruit recognition, the color extraction is performed in the RGB (Red, Green, Blue) space. We claim as necessary to employ the chromaticity of colors to characterize the color of the fruits. Thus, we propose to use the HSV (Hue, Saturation, Value) space because it is possible to extract and process the chromaticity data, but without the undesirable intensity effects of the RGB space. On the other hand, before the color is characterized, we complete a selection of the chromaticities that contribute with important data about the fruits. Our approachs strength is validated by performing test on 20 common fruit classes: this proposal outperforms the color characterization of related work.