Improving the quality of the industrial enterprise management based on the network-centric approach

The article examines the network-centric approach to the industrial enterprise management to improve the ef ciency and effectiveness in the implementation of production plans and maximize responsiveness to customers. A network-centric management means the decentralized enterprise group management. A group means a set of enterprise divisions, which should solve by joint efforts a certain case that occurs in the production process. The network-centric management involves more delegation of authority to the lower elements of the enterprise’s organizational structure. The industrial enterprise is considered as a large complex system (production system) functioning and controlled amidst various types of uncertainty: information support uncertainty and goal uncertainty or multicriteria uncertainty. The information support uncertainty occurs because the complex system functioning always takes place in the context of incomplete and fuzzy information. Goal uncertainty or multicriteria uncertainty caused by a great number of goalsestablished for the production system. The network-centric management task de nition by the production system is formulated. The authors offer a mathematical model for optimal planning of consumers’ orders production with the participation of the main enterprise divisions. The methods of formalization of various types of uncertainty in production planning tasks are considered on the basis of the application of the fuzzy sets theory. An enterprise command center is offered as an effective tool for making management decisions by divisions. The article demonstrates that decentralized group management methods can improve the ef ciency and effectiveness of the implementation of production plans through the self-organization mechanisms of enterprise divisions.The work has been prepared with the financial support from the Russian Ministry of Education and Science (Contract No. 02.G25.31.0068 of 23.05.2013 as part of the measure to implement Decision of the Russian Government No. 218)

Face Validation Method Alternatives for Shiphandling Fuzzy Logic Difficulty Model

The development of shiphandling difficulty model for ferry is based on the empirical experience through the Master of Ro-Ro ferries. The SHDMF is consisted from two parts which are the Analytic Hierarchy Process (AHP) and Fuzzy Inference System. Both parts had been validated through internal validation in the form of consistency test for the first part and robustness test for the second part. Further, the external/face validation is required to compare the proposed model with similar model through benchmarking approach. The benchmarking approaches are elaborated for the reliability, validity, possibility, efficiency and effectiveness. Through fuzzy group decision making method, the questionnaire survey is performed to verify the most appropriate approach based on the shiphandling simulator as the most preferred benchmarking tool by experts. Next, the proposed scenario is overviewed and discussed especially related to the advantages and drawbacks of shiphandling simulator. Keywords: shiphandling difficulty, fuzzy group decision making, internal validation Model pengukuran kesulitan pengendalian feri didasarkan pada pengalaman empiris melalui pernyataan nahkoda kapal feri Ro-Ro. SHDMF terdiri atas dua bagian, yaitu Analytic Hierarchy Process dan Fuzzy Inference System. Kedua bagian ini telah divalidasi melalui validasi internal dalam bentuk uji konsistensi untuk bagian pertama dan uji kehandalan untuk bagian kedua. Selanjutnya validasi atau wajah eksternal diperlukan untuk membandingkan model yang diusulkan dengan model yang diperoleh dari benchmarking. Pendekatan benchmarking dijabarkan untuk kehandalan, validitas, kemungkinan, efisiensi, dan efektivitas. Melalui metode fuzzy kelompok pembuatan keputusan, survei kuesioner dilakukan untuk memverifikasi pendekatan yang paling tepat dengan simulator pengendalian kapal sebagai alat yang paling disukai oleh para ahli untuk benchmarking. Selanjutnya skenario yang ditinjau-ulang dan dibahas terutama terkait dengan keuntungan dan kelemahan simulator pengendalian kapal. Kata

Participatory Ecosystem Management Planning at Tuzla Lake (Turkey) Using Fuzzy Cognitive Mapping

A participatory environmental management plan was prepared for Tuzla Lake, Turkey. Fuzzy cognitive mapping approach was used to obtain stakeholder views and desires. Cognitive maps were prepared with 44 stakeholders (villagers, local decisionmakers, government and non-government organization (NGO) officials). Graph theory indices, statistical methods and "What-if" simulations were used in the analysis. The most mentioned variables were livelihood, agriculture and animal husbandry. The most central variable was agriculture for local people (villagers and local decisionmakers) and education for NGO & Government officials. All the stakeholders agreed that livelihood was increased by agriculture and animal husbandry while hunting decreased birds and wildlife. Although local people focused on their livelihoods, NGO & Government officials focused on conservation of Tuzla Lake and education of local people. Stakeholders indicated that the conservation status of Tuzla Lake should be strengthened to conserve the ecosystem and biodiversity, which may be negatively impacted by agriculture and irrigation. Stakeholders mentioned salt extraction, ecotourism, and carpet weaving as alternative economic activities. Cognitive mapping provided an effective tool for the inclusion of the stakeholders' views and ensured initial participation in environmental planning and policy making.Comment: 43 pages, 4 figure

Fuzzy logic as a decision-making support system for the indication of bariatric surgery based on an index (MAFOI) generated by the association between body fat and body mass index.

Background: A fuzzy obesity index (MAFOI) for use as an alternative to bariatric surgery indication (BSI) is presented. The search for a more accurate method to evaluate obesity and to indicate a better treatment is important in the world health context. BMI (body mass index) is considered the main criteria for obesity treatment and BSI. Nevertheless, the fat excess related to the percentage of Body Fat (%BF) is actually the principal harmful factor in obesity disease that is usually neglected. This paper presents a new fuzzy mechanism for evaluating obesity by associating BMI with %BF that yields a fuzzy obesity index for obesity evaluation and treatment and allows building up a Fuzzy Decision Support System (FDSS) for BSI. Methods: Seventy-two patients were evaluated for both BMI and %BF. These data are modified and treated as fuzzy sets. Afterwards, the BMI and %BF classes are aggregated yielding a new index (MAFOI) for input linguistic variable are considered the BMI and %BF, and as output linguistic variable is employed the MAFOI, an obesity classification with entirely new classes of obesity in the fuzzy context as well as is used for BSI. Results: There is gradual, smooth obesity classification and BSI when using the proposed fuzzy obesity index when compared with other traditional methods for dealing with obesity.
Conclusion: The BMI is not adequate for surgical indication in all the conditions and fuzzy logic becomes an alternative for decision making in bariatric surgery indication based on the MAFOI

Fuzzy logic as a decision-making support system for the indication of bariatric surgery based on an index (OBESINDEX) generated by the association between body fat and body mass index

Background: A Fuzzy Obesity Index (OBESINDEX) for use as an alternative in bariatric surgery indication (BSI) is presented. The search for a more accurate method to evaluate obesity and to indicate a better treatment is important in the world health context. BMI (body mass index) is considered the main criteria for obesity treatment and BSI. Nevertheless, the fat excess related to the percentage of Body Fat (%BF) is actually the principal harmful factor in obesity disease that is usually neglected. This paper presents a new fuzzy mechanism for evaluating obesity by associating BMI with %BF that yields a fuzzy obesity index for obesity evaluation and treatment and allows building up a Fuzzy Decision Support System (FDSS) for BSI.

Methods: Seventy-two patients were evaluated for both BMI and %BF. These data are modified and treated as fuzzy sets. Afterwards, the BMI and %BF classes are aggregated yielding a new index (OBESINDEX) for input linguistic variable are considered the BMI and %BF, and as output linguistic variable is employed the OBESINDEX, an obesity classification with entirely new classes of obesity in the fuzzy context as well is used for BSI.

Results: There is a gradual, smooth obesity classification and BSI when using the proposed fuzzy obesity index when compared with other traditional methods for dealing with obesity.

Conclusion: The BMI is not adequate for surgical indication in all the conditions and fuzzy logic becomes an alternative for decision making in bariatric surgery indication based on the OBESINDEX