New Decision Support System for Optimization of Rail Track Maintenance Planning Based on Adaptive Neurofuzzy Inference System

2008 ◽  
Vol 2043 (1) ◽  
pp. 49-54 ◽  
Author(s):  
Mauro Dell'Orco ◽  
Michele Ottomanelli ◽  
Leonardo Caggiani ◽  
Domenico Sassanelli
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Maintenance Planning ◽  
Inference System ◽  
Track Maintenance ◽  
Rail Track ◽  
Adaptive Neurofuzzy Inference System

scholarly journals Decision Support System for Evaluating Suitable Job Applicants

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1773
Author(s):  
Bogdan Walek ◽  
Ondrej Pektor ◽  
Radim Farana
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Real Data ◽  
Job Applicants ◽  
Inference System ◽  
Selection Procedures ◽  
Novel Approach ◽  
Fuzzy Expert Systems ◽  
Takagi Sugeno

This paper describes a novel approach in the area of evaluating suitable job applicants for various job positions, and specifies typical areas of requirement and their usage. Requirements for this decision-support system are defined in order to be used in middle-size companies. Suitable tools chosen were fuzzy expert systems, primarily the inference system Takagi-Sugeno type, which were then supplied with implementation of methods of variant multi-criteria analysis. The resulting system is a variable tool with the possibility to simply set the importance of individual selection criteria so that it can be used in various situations, primarily in repeated selection procedures for similar job positions. A strong emphasis is devoted to the explanatory module, which enables the results of the expert system to be used easily. Verification of the system on real data in cooperation with a collaborating company has proved that the system is easily usable.

scholarly journals A decision support system for strategic maintenance planning in offshore wind farms

2016 ◽  
Vol 99 ◽  
pp. 784-799 ◽  
Author(s):  
Xiaodong Li ◽  
Djamila Ouelhadj ◽  
Xiang Song ◽  
Dylan Jones ◽  
Graham Wall ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Wind Farms ◽  
Offshore Wind ◽  
Maintenance Planning ◽  
Offshore Wind Farms

scholarly journals PENENTUAN JUMLAH PRODUKSI SHAMPO DENGAN FUZZY INFERENCE SYSTEM SUGENO: STUDI KASUS PT. GUARDIAN PHARMATAMA TANGERANG

2017 ◽  
Vol 1 (2) ◽  
Author(s):  
Ri Sabti Septarini
Keyword(s):  
Fuzzy Logic ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Fuzzy Inference ◽  
Production Plan ◽  
Inference System ◽  
Fuzzy Variables ◽  
Fuzzy Input ◽  
A Company

ABSTRACTHuman are always faced with taking a decision. It also happens to a company in the process of determining which employees. In determination the production plan required a lot of considerations in case of taking decisions. Beside that, the number of employees in a company is to determine who get the production plan of the achievement. System is made to determine employees who will get benefits achievement based on the some criteria have been determined by the company. These criterias will be used as fuzzy input which also process a called fuzzy variables. In this research will construct decision support system by using fuzzy logic with fuzzy variables input that are productivity, quality tabbing and discipline. In of fuzzy logic method there are three stages, namely stage fuzzification, inference and deffuzification. At this stage of the fuzzy inference used the Sugeno method. The results of this experiments has performed that the system is able to display the production planning data for the calculation of the value of production that have been determined based on fuzzy logic with fuzzy variables. Keyword: Decision Support System, Fuzzy Logic,  Sugeno.

scholarly journals Decision Support System for Variable Rate Irrigation Based on UAV Multispectral Remote Sensing

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2880 ◽  
Author(s):  
Xiang Shi ◽  
Wenting Han ◽  
Ting Zhao ◽  
Jiandong Tang
Keyword(s):  
Decision Support ◽  
Water Stress ◽  
Decision Support System ◽  
Support System ◽  
Duty Cycle ◽  
Variable Rate ◽  
Crop Water Stress Index ◽  
Inference System ◽  
Control Command ◽  
Variable Rate Irrigation

Rational utilization of water resources is one of the major methods of water conservation. There are significant differences in the irrigation needs of different agricultural fields because of their spatial variability. Therefore, a decision support system for variable rate irrigation (DSS-VRI) by center pivot was developed. This system can process multi-spectral images taken by unmanned aerial vehicles (UAVs) and obtain the vegetation index (VI). The crop evapotranspiration model (ETc) and crop water stress index (CWSI) were obtained from their established relationships with the VIs. The inputs to the fuzzy inference system were constituted with ETc, CWSI and precipitation. To provide guidance for users, the duty-cycle control map was outputted using ambiguity resolution. The control command contained in the map adjusted the duty cycle of the solenoid valve, and then changed the irrigation amount. A water stress experiment was designed to verify the rationality of the DSS-VRI. The results showed that the more severe water stress is, the more irrigation is obtained, consistent with the expected results. Meanwhile, a user-friendly software interface was developed to implement the DSS-VRI function.

scholarly journals Fuzzy Approach to Decision Support System Design for Inventory Control and Management

2019 ◽  
Vol 28 (4) ◽  
pp. 549-557
Author(s):  
Mahuya Deb ◽  
Prabjot Kaur ◽  
Kandarpa Kumar Sarma
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Inventory Control ◽  
Inventory Management ◽  
Support System ◽  
Large Scale ◽  
Fuzzy Inference ◽  
Neural System ◽  
Inference System ◽  
Neuro Fuzzy

Abstract The ubiquitous nature of inventory and its reliance on a reliable decision support system (DSS) is crucial for ensuring continuous availability of goods. The DSS needs to be designed in a manner that enables it to highlight its present status. Further, the DSS should be able to provide indications about subtle and large-scale variations that are likely to occur in the supply chain within the context of the decision-making framework and inventory management. However, while dealing with the parameters of the system, it is observed that its operations and mechanisms are surrounded by uncertain, imprecise, and vague environments. Fuzzy-based approaches are best suited for such situations; however, these require assistance from learning systems like artificial neural network (ANN) to facilitate automated decision support. When ANN and fuzzy are combined, the fuzzy neural system and the neuro-fuzzy system (NFS) are formulated. The model of the DSS reported here is based on a framework commonly known as adaptive neuro-fuzzy inference system (ANFIS), which is a version of NFS. The configured model has the advantages of both the ANN and fuzzy systems, and has been tested for the design of a DSS for use as part of inventory control. In this work, we report the design of an ANFIS-based DSS configured to work as DSS for inventory management. The system accepts demand as input and generates procurement, ordering, and holding cost to control production and supply. The system deals with a certain profitability rating required to quantify the changes in the input and is combined with the day-to-day inventory records and demand-available cycle. The effectiveness of the system has been checked in terms of number and types of membership used, accuracy generated, and computational efficiency accounted by the computation cycles required.

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