Search

In this work a new type of hybrid PI controller is presented using the Paraconsistent Logic (PL) as the basis for the mathematical and logical treatment of the signals corresponding to the control variables. This control has the conventional actions PI (Proportional and Integral) together with the logical actions structured in the concepts of Paraconsistent Logic. Compared to conventional techniques, this new type of Hybrid PI Controller with Paraconsistent Logic responded well to the various tests performed in the control loop. The tests and the technical validations were done with evidences that prove the efficiency of the paraconsistent concepts.

This paper presents a computational method based on non-classical logic dedicated to routing management and information stream control in communication networks. Paraconsistent logic (PL) was used to create an algorithmic structure whose main property is to accept contradiction. Moreover, a computational structure, the denominated paraconsistent data analyzer (PDAPAL2v), was constructed to perform routing management in communication networks. Direct comparisons of PDAPAL2v with a classical logic system that simulates routing conditions were made in the laboratory. In the conventional system, the paraconsistent algorithms were considered as binary logic gates, and in the tests, the same adjustment limits of PDAPAL2v were applied. Using a database with controlled insertion of noise, we obtained an efficacy of 97% in the detection of deteriorated packets with PDAPAL2v and 72% with the conventional simulation system. Functional tests were carried out, showing that PDAPAL2v is able to assess the conditions and degradation of links and perform the analysis and correlation of various inputs and variables, even if the signals have contradictory values. From practical tests in the laboratory, the proposed method represents a new way of managing and controlling communication network routes with good performance.

The Paraconsistent Annotated Logic (PAL) is one type of non-classical logics that, differently than classical logic, allows the processing of contradictory signals in its theoretical structure. The Paraconsistent Artificial Neural Cell (PANcell) is the basic block of a set of algorithms that utilizes the interpretation of the lattice of the Paraconsistent Annotated Logic with annotation of two values (PAL2v). The Paraconsistent Artificial Neural Cell of Learning (LPANcell) is a type of PANcell that uses the output as a feedback to the input of the unfavorable degree of evidence in time. The resulting behavior is to learn any real value within a normalized closed range (interval values [0, 1]). This cell can be used in signal analysis, processing, average estimator and as a filter, called PAL2vFilter. The proposed article presents the results of the analysis of a PAL2vFilter, when using the simplified resulting degree of evidence - uE or the resulting degree of real evidence - uRE (when contradiction effects are extracted) as output.

In general, the performance of productive systems considers the efficient use of technological transformation resources (such as machines and raw materials), information processing, and handling/transportation operations. However, there are no normalized criteria or rules to evaluate the performance of a productive system in the context of sustainability. Thus, this paper introduces an approach to identify and evaluate the performance indicators related to the sustainability of productive systems, specifically for geographically dispersed cases, i.e., dispersed productive system (DPS), in which the processes are in a distributed and dispersive architecture. The proposed approach is based on a framework aimed to measure sustainability key performance indicators (SuKPIs) that evaluate the sustainability of a system. The framework considers the ANSI/ISA-95 standard, and the sustainability assessment methodology considers the balance of sustainability indicators, which depend on economic, environmental, social, and technological aspects. The Petri net and derived techniques are used to model and to verify the main functionalities of the proposed framework, and also to monitor the productive processes of DPS for data acquisition of the SuKPIs. An application example is also presented to show the feasibility and validity of the proposal.

With the advent of globalization, the number of geographically dispersed firms through the global supply chain increased. The partnership between those firms contributed to the supply chain interdependence among different levels of the supply chain. In a positive way, the interdependence of the supply chains encourages all players of the supply chains to engage in planning, forecasting, replenishment, information sharing, resource sharing and incentive sharing. In a negative way, if a disruption happens in any point of the chain and the firm doesn't have any contingence plan, the disruption effect easily propagates and amplifies through the others in the chain. In this sense, the supply chain interdependence must be modeled for a better understanding. Therefore, this paper has the aim to model the supply chain interdependence through the combination of Inoperability Input-output Modeling (IIM) and Petri net (PN) technique. From that point, it was created a place node and a transition node. One example demonstrated the proposed place-transition Petri net class, where it is more generic and easy to apply technique.

This paper presents a method to design a reconfigurable distributed manufacturing control systems which integrates heterogeneous technologies of facilities that are geographically distributed. Due to the heterogeneity of the environment, the method explores concepts of different techniques and takes the best practices from each one. Mechanisms for reconfiguration are proposed using holonic and multi-agent systems techniques. The service-oriented architecture concept is applied to define the interfaces for communication. A combined top-down and bottom- up approach is described using Petri net models. An application example demonstrates the characteristics and advantages of the proposed method, such as autonomy, reactivity, reuse models, interoperability and reconfiguration flexibility.

Productive systems (PS) should currently consider the efficient use of resources (such as technological transformation, handling and material transport) besides the dispersed distribution of productive plants and sustainability concepts to allow the adequate use of environmental resources and reduce costs through local facilities. However, in the literature there are not widely accepted criteria to evaluate this class of Sustainable and Dispersed Productive Systems (SDPS). Therefore, this work introduces a framework for SDPS performance evaluation by extending the system requirements in the ANSI/ISA-95 standard specifications. Petri net (PN) technique and its extension, Production Flow Schema (PFS), are used to systematize the collection of production data and indexes to evaluating the SDPS performance. Cloud computing technology is also considered to provide resources to access information from SDPS. The presented method allows obtaining and choosing the better indicator for sustainable SDPS.

APS Systems (Advanced planning and scheduling) can provide production plans that enable a detailed analysis on the impact and possibility of analyzing different scenarios for a plant. The present work proposes an allocation heuristics of the type used in resource-based (bottleneck) APS Systems and uses task time windows in situations where product delivery time is important. The heuristics uses loading information from the equipment to dynamically assign (at each step) the equipment and the bottleneck time interval. Based on this information, it allocates the lots assigned to this equipment in the manner with the lowest influence on the bottleneck, so as to reduce its importance in the bottleneck problem without increasing its gravity. This heuristics enables successive and correct handling of all existing bottlenecks by updating the information at each step via constrained propagation mechanisms. The problem addressed is the scheduling of production in lot plants. Short-term problems with constraints on shared intermediate product storage resources (equipment) and plant operation in on-demand mode are considered.

Innovation challenges for handling supply chain risks have become one of the most important drivers in business competitiveness and differentiation. Furthermore, supply chain risk evaluation is of a great importance in emerging areas such as biomanufacturing, nanotechnology, and energy. However, with the advent of globalization and the advance of new technologies, the global and lean supply chains are more than ever at risk. In this scenario, this paper has the purpose to develop a general supply chain business continuity framework, which is an extension of the model developed by [1]. The fact that this model has already been implemented in the pharmaceutical supply chain risks in business units of Iran [2], demonstrates the potential of its replication to different field of industry all over the world. In addition, this paper shows that this framework can be extended to the financial and marketing operational constructs.

With the advent of globalization, mitigating supply chain disruptions is the differential for any company. However, mitigating supply chain disruption-related risks in companies is a difficult task. There are some ISO norms that provide standards that can make systems to deal with supply chain disruptions and there are some modeling techniques that ensure that supply chain disruptions be mitigated. Although both approaches do not eliminate supply chain disruptions, they have a fundamental importance for supply chain mitigation. In this context, this paper presents an agent-based with the support of some supply chain and risk norms to mitigate supply chain disruptions. The objective of the proposed method is to identify possible hidden supply chain risks, often highlighting previous hidden or overlooked areas of high risk. An example application for a manufacturing firm demonstrates that this model is feasible and can be replicated to different sectors.

Analysis of the Raman data to obtain results in discrimination models is usually done with multivariate statistics based on principal component analysis (PCA). In this work, we present a technique based on a non-classical logic called paraconsistent logic (PL). The aim of this work is to use computational procedures capable of generating efficient expert systems to discriminate cutaneous tissue samples obtained by Raman spectroscopy. First, a set of algorithms originating from PL is presented, and then its application in discrimination analyses is described; the discrimination analysis was conducted using a database of skin tissue samples obtained ex vivo by Raman spectroscopy of spectrum range of 400–1800 cm−1 wavelengths. Data processing, pattern creation, and comparisons were performed using the set of paraconsistent algorithms (SPA-PAL2v). The total number of samples was divided into four histopathological groups, with 115 spectra of basal cell carcinoma (BCC), 21 spectra of squamous cell carcinoma (SCC), 57 spectra of actinic keratosis (AK), and 30 normal skin (NO) spectra. An arrangement type was created for this study, and the samples were randomly selected and analyzed, and the NO group was compared with the group of non-melanoma cancer lesions (BCC + SCC) and the AK tumor lesion. Two analyses were performed. The first (SPA-PAL2v) Mode 1 (no cross-validation) achieved 76% of hits, and the second (SPA-PAL2v) Mode 2 (with cross-validation) achieved 75.78% of hits. These results were compared with discrimination using PCA statistical methods (PCA/DA) and presented superior percentages of hits, which proves the robustness of the SPA-PAL2v, confirming its potential for Raman spectrum data analysis.

O presente trabalho tem por objetivo realizar um estudo dos processos que envolvem a producao de trabalhos academicos e apresentar uma proposta de arquitetura de uma plataforma de sistema da informacao de automacao para edicao, gerenciamento de projeto com controle do fluxo de producao de documentos tecnicos institucionais. Para alcancar tal objetivo, foi realizado um mapeamento dos processos que envolvem a producao de trabalhos de conclusao de curso e producao de artigos academicos para periodicos cientificos e congressos. Tambem e apresentado os requisitos funcionais, tecnicos e metodologias de analises dentro da perspectiva de gestao de riscos, assim como o detalhamento das ferramentas tecnologicas, sistemas e padroes que compoem a arquitetura da plataforma proposta.

This paper presents the modeling of fault diagnosis mechanisms extending a method to design reconfigurable and distributed manufacturing control system. The method combines different techniques: service-oriented architecture, holonic and multi-agent system, production flow schema and input-output place-transition Petri net. An application example demonstrates advantages of the proposal, such as at cloud-based engineering, reuse, implementation and reconfiguration flexibility.

In an era of worldwide economic downturn, managing supply chain disruptions is the best option in the success of any firm. In this context, this paper presents a supply chain risk mitigation framework for manufacturing firms. It was used agent-based model to create a framework to deal with the supply chain disruption. To validate the proposed model, it was used Petri net to assess the structural properties of the agent-based supply chain risk framework. It was given an example application of the proposed framework and findings demonstrated that this model is feasible and can be used as a mitigation tool to deal with the supply chain disruption of the manufacturing firms.

The main objective of this note is to complement the excellent work on supply chain risk classification by Rangel et al. [International Journal of Production Research, vol. 52 (7), 2014]. In this note, we consider the use of ISO/IEC (International Organisation for Standardisation/International Electrotechnical Commission) norms to support the supply chain risk classification. Its purpose is to develop, maintain and promote standards in the fields of information technology and information communications technology. Therefore, to improve on the work by Rangel et al. (2014), ISO 27036 (Information Security for Supplier Relationship) and ISO 28000 (Specification for Security Management Systems for the Supply Chain) are aligned with ISO 31000 (Risk Management–Risk Assessment Techniques). Furthermore, since supply chain risk management does not have a standardised process, these norms, particularly ISO 31000, can serve as a guide to improve its implementation.

Manufacturing control systems must quickly react to variations of product, process specifications, fault occurrence, changes in the resources functional capabilities, and other operational demands. Besides, to gain competitive advantages, dispersed manufacturing systems must cooperate with each other. The combination of holonic control system and service-oriented architecture techniques can be effective to integrate these heterogeneous environments since the agents must also cooperate to achieve their services. Therefore, this paper introduces a service-oriented and holonic control architecture using Petri net to represent the workflow of design method itself, structure and dynamic of the entities. The modeling of an example is presented to demonstrate application at self-organization and adaptation, fault treatment, degeneration, and relatively shorter implementing time.