scholarly journals Constructing a model for the automated operative planning of local operations at railroad technical stations

2021 ◽  
Vol 3 (3 (111)) ◽  
pp. 32-41
Author(s):  
Artem Prokopov ◽  
Viktor Prokhorov ◽  
Tetiana Kalashnikova ◽  
Tetiana Golovko ◽  
Hanna Bohomazova
Keyword(s):  
Fuzzy Inference ◽  
Turnover Time ◽  
Operating Costs ◽  
Inference System ◽  
Railroad Network ◽  
Operative Planning ◽  
Speed Up ◽  
Freight Car ◽  
Network Speed ◽  
The Cost

This paper has investigated the technology of forwarding local wagons at railroad technical stations and established the need to improve it given the extra downtime of local wagons. The main issue relates to the considerable combinatorial complexity of the tasks of operational planning. Another problem is that as part of the conventional approach, planning a station operation and planning a local operation at it is considered separately. Another planning issue is the lack of high-quality models for the preparation of initial data, in particular, data on the duration of technological operations, such as, for example, shunting operations involving local wagons forwarding. To resolve these issues, a new approach has been proposed, under which the tasks of operative planning of a technical station’s operation and its subsystem of local operations are tackled simultaneously, based on a single model. To this end, a mathematical model of vector combinatoric optimization has been built, which uses the criteria of total operating costs and wagon-hours spent at a station when forwarding local wagon flows, in the form of separate objective functions. Within this model, a predictive model was constructed in the form of a fuzzy inference system. This model is designed to determine the duration of shunting half-runs when executing the spotting/picking operations for delivering local wagons to enterprises’ goods sheds. The model provides for the accuracy level that would suffice at planning, in contrast to classical methods. A procedure has been devised for optimizing the planning model, which employs the modern genetic algorithm of vector optimization NSGA-III. This procedure is implemented in the form of software that makes it possible to build a rational operative plan for the operation of a technical station, including a subsystem of local operations, in graphic form, thereby reducing the operating costs by 5 % and the duration of maintenance of a local wagon by 8 %. The resulting effect could reduce the turnover time of a freight car in general on the railroad network, speed up the delivery of goods, and reduce the cost of transportation

scholarly journals Identification of Fuzzy Inference Systems by Means of a Multiobjective Opposition-Based Space Search Algorithm

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Wei Huang ◽  
Sung-Kwun Oh
Keyword(s):  
Fuzzy Inference ◽  
Search Algorithm ◽  
Fuzzy Model ◽  
Parametric Identification ◽  
Fuzzy Inference Systems ◽  
Inference System ◽  
Fuzzy Models ◽  
Opposition Based Learning ◽  
Speed Up ◽  
Inference Systems

We introduce a new category of fuzzy inference systems with the aid of a multiobjective opposition-based space search algorithm (MOSSA). The proposed MOSSA is essentially a multiobjective space search algorithm improved by using an opposition-based learning that employs a so-called opposite numbers mechanism to speed up the convergence of the optimization algorithm. In the identification of fuzzy inference system, the MOSSA is exploited to carry out the parametric identification of the fuzzy model as well as to realize its structural identification. Experimental results demonstrate the effectiveness of the proposed fuzzy models.

Providing Successful Scat Data Management Support During Spill Response

1999 ◽  
Vol 1999 (1) ◽  
pp. 943-945 ◽  
Author(s):  
Alain Lamarche ◽  
Jack Ion ◽  
Edward H. Owens ◽  
Peter Rubec
Keyword(s):  
Data Management ◽  
Electronic System ◽  
Turnover Time ◽  
Decision Makers ◽  
Prototype System ◽  
Management Support ◽  
Speed Up ◽  
Oil Spill Response ◽  
The Cost ◽  
Computerized System

ABSTRACT Shoreline Cleanup Assessment Teams (SCAT) are now used worldwide to assess oiled shorelines as part of response cleanup activities. The amount of SCAT information gathered during surveys can be very large, with the possibility of overwhelming decision makers. New tools are now available to automate the processing of SCAT information. For example, dedicated computerized SCAT data management systems have been used during the Iron Baron (Tasmania) and Kure (California) incidents. More recently, a prototype system was developed by the State of Florida to electronically support all the steps involved in the cleanup phase of an oil spill response. Given this, when should computerized SCAT data management be used and at what level? An analysis of the work performed during recent spills involving SCAT activity provided answers to these questions. Some of the main findings include the following: (1) computerized systems can decrease the time necessary to gather data and increase the accuracy of the captured data; (2) computerized systems decrease the data turnover time and speed up the decision-making cycle; (3) an all-electronic computerized system can become essential in cases where the length of oiled shoreline is very large with respect to the number of SCAT survey teams; (4) for large spills, the increased cost of an all-electronic system may outweigh the cost of not being prepared.

scholarly journals Release planning for multi-tenant software as a service (SaaS) applications

2021 ◽  
Author(s):  
Mubarak Alrashoud
Keyword(s):  
Planning Process ◽  
Fuzzy Inference ◽  
Optimization Method ◽  
Software As A Service ◽  
Inference System ◽  
Service Levels ◽  
Release Planning ◽  
Development Lifecycle ◽  
Speed Up ◽  
Exact Optimization

In multi-tenant Software as a Service (SaaS) applications, the providers are required to regularly deliver new releases of the software in order to satisfy the evolving requirements of tenants. The first step in a release development lifecycle is the release planning process. This thesis formulates the problem of the "next release" planning for multi-tenant Software as a Service (SaaS) applications. Two variables that influence release planning in SaaS applications are introduced: the degree of commonality of features and the contractual constraints. The commonality of a feature denotes the number of tenants that have requested that feature. The contractual constraints denote the effects of service levels to which tenants have subscribed on the release planning process. Furthermore, this thesis proposes three novel approaches in order to tackle the problem of the "next release" planning for multi-tenant SaaS applications. The first one is a prioritization approach that employs a Fuzzy Inference System (FIS) engine in order to speed up the release planning process and overcome the uncertainty associated with the human judgment. In this approach, the human expertise, which is represented by fuzzy rules, is considered automatically in the release planning process. The second and third approaches consider release planning as an optimization problem. The second approach uses an exact optimization method (Binary Linear Programming (BLP)) in order to generate an optimal release plan, while the third approach uses heuristic optimization method (Genetic Algorithm (GA)). All of the three approaches aim to generate a plan for the next release that maximizes the degree of overall tenants’ satisfaction, maximizes the degree of commonality, and minimizes the potential risk while taking into account contractual, effort, and dependencies constraints. Moreover, the thesis presents an experimental study of the proposed approaches in order to determine which approach is best suited to different sets of scenarios. In this experiment, the performance of the proposed approaches is evaluated using four criteria: the overall tenants’ satisfaction, the commonality, the adherence to the risk, and the running time. Additionally, the thesis presents an experiment that compares the proposed approaches with a compared model that is selected from the literature.

The Additive Manufacturing Process Setting Feasible Space Exploration and Association With Variable Product Platform Design

2015 ◽  
Author(s):  
Xiling Yao ◽  
Seung Ki Moon ◽  
Guijun Bi
Keyword(s):  
Additive Manufacturing ◽  
Production Cost ◽  
Fuzzy Inference ◽  
Design Parameters ◽  
Product Platform ◽  
Activity Based Costing ◽  
Inference System ◽  
Wide Range ◽  
The Cost ◽  
Feasible Space

Additive manufacturing (AM) has evolved from prototyping to functional part fabrication for a wide range of applications. AM process settings have significant impact to both part quality and production cost, which makes the process setting adjustment a key consideration during product development and manufacturing. This research aims to investigate the relationship among process setting adjustments, costs, and component design parameters. Platform-based product family design and process family planning are used in this research as the strategy to provide product diversity while controlling cost. In this paper, the concept of a variable product platform and its corresponding AM process setting variants are proposed to describe the characteristics of additive manufactured platform modules. AM production cost drivers are identified. A Fuzzy Time-Driven Activity-Based Costing (FTDABC) approach is proposed to estimate the cost increment due to process setting adjustments. Time equations in the FTDABC are computed in a trained Adaptive Neuro-Fuzzy Inference System (ANFIS). The process setting adjustment’s feasible space boundary searching is formulated as an optimization problem, with minimizing the cost increment and maximizing the design parameters’ variability as objective functions. The upper and lower limits of variable platform module’s design parameters are mapped from process setting adjustments in a Mamdani-type expert system. The proposed methodology is illustrated in the analysis of a honeycomb-shaped bumper, which is taken as a variable platform module for a family of R/C racing cars. The result provides boundaries for design parameters, which confines the AM-enabled design space for product platform modules.

scholarly journals An Adaptive Location-Based Tracking Algorithm Using Wireless Sensor Network for Smart Factory Environment

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Po-Chih Chiu ◽  
Kuo-Wei Su ◽  
Tsung-Yin Ou ◽  
Chih-Lung Yu ◽  
Chen-Yang Cheng ◽  
...  
Keyword(s):  
Fuzzy Inference ◽  
Signal Transmission ◽  
Signal Strength ◽  
Localization Algorithm ◽  
Inference System ◽  
Accurate Localization ◽  
Localization Algorithms ◽  
New Type ◽  
The Cost ◽  
Smart Factories

In recent years, how to improve the performance of smart factories and reduce the cost of operation has been the focus of industry attention. This study proposes a new type of location-based service (LBS) to improve the accuracy of location information delivered by self-propelled robots. Traditional localization algorithms based on signal strength cannot produce accurate localization results because of the multipath effect. This study proposes a localization algorithm that combines the Kalman filter (KF) and the adaptive-network-based fuzzy inference system (ANFIS). Specifically, the KF was adopted to eliminate noise during the signal transmission process. Through the learning of the ANFIS, the environment parameter suitable for the target was generated, to overcome the deficiency of traditional localization algorithms that cannot obtain real signal strength. In this study, an experiment was conducted in a real environment to compare the proposed localization algorithm with other commonly used algorithms. The experimental results show that the proposed localization algorithm produces minimal errors and stable localization results.

scholarly journals Release planning for multi-tenant software as a service (SaaS) applications

2021 ◽  
Author(s):  
Mubarak Alrashoud
Keyword(s):  
Planning Process ◽  
Fuzzy Inference ◽  
Optimization Method ◽  
Software As A Service ◽  
Inference System ◽  
Service Levels ◽  
Release Planning ◽  
Development Lifecycle ◽  
Speed Up ◽  
Exact Optimization

In multi-tenant Software as a Service (SaaS) applications, the providers are required to regularly deliver new releases of the software in order to satisfy the evolving requirements of tenants. The first step in a release development lifecycle is the release planning process. This thesis formulates the problem of the "next release" planning for multi-tenant Software as a Service (SaaS) applications. Two variables that influence release planning in SaaS applications are introduced: the degree of commonality of features and the contractual constraints. The commonality of a feature denotes the number of tenants that have requested that feature. The contractual constraints denote the effects of service levels to which tenants have subscribed on the release planning process. Furthermore, this thesis proposes three novel approaches in order to tackle the problem of the "next release" planning for multi-tenant SaaS applications. The first one is a prioritization approach that employs a Fuzzy Inference System (FIS) engine in order to speed up the release planning process and overcome the uncertainty associated with the human judgment. In this approach, the human expertise, which is represented by fuzzy rules, is considered automatically in the release planning process. The second and third approaches consider release planning as an optimization problem. The second approach uses an exact optimization method (Binary Linear Programming (BLP)) in order to generate an optimal release plan, while the third approach uses heuristic optimization method (Genetic Algorithm (GA)). All of the three approaches aim to generate a plan for the next release that maximizes the degree of overall tenants’ satisfaction, maximizes the degree of commonality, and minimizes the potential risk while taking into account contractual, effort, and dependencies constraints. Moreover, the thesis presents an experimental study of the proposed approaches in order to determine which approach is best suited to different sets of scenarios. In this experiment, the performance of the proposed approaches is evaluated using four criteria: the overall tenants’ satisfaction, the commonality, the adherence to the risk, and the running time. Additionally, the thesis presents an experiment that compares the proposed approaches with a compared model that is selected from the literature.

Compensation of Voltage Sags with Phase-Jumps through DVR with Minimum VA Rating Using PSO based ANFIS Controller

2010 ◽  
Vol 1 (3) ◽  
pp. 19-33
Author(s):  
Anil Kumar Ramakuru ◽  
Siva G. Kumar ◽  
Kalyan B. Kumar ◽  
Mahesh K. Mishra
Keyword(s):  
Distribution System ◽  
Fuzzy Inference ◽  
Voltage Sags ◽  
Sinusoidal Voltage ◽  
Dynamic Voltage Restorer ◽  
Inference System ◽  
Detailed Simulation ◽  
Dynamic Voltage ◽  
In Series ◽  
The Cost

Dynamic Voltage Restorer (DVR) restores the distribution system load voltage to a nominal balanced sinusoidal voltage, when the source voltage has distortions, sag/swell and unbalances. DVR has to inject a required amount of Volt-Amperes (VA) into the system to maintain a nominal balanced sinusoidal voltage at the load. Keeping the cost effectiveness of DVR, it is desirable to have a minimum VA rating of the DVR, for a given system without compromising compensation capability. In this regard, a methodology has been proposed in this work to minimize VA rating of DVR. The optimal angle at which DVR voltage has to be injected in series to the line impedance so as to have minimum VA loading on DVR as well as the removal of phase jumps in the three-phases is computed by the Particle Swarm Optimization (PSO) technique. The proposed method is able to compensate voltage sags with phase jumps by keeping the DVR voltage and power ratings minimum, effectively. The proposed PSO methodology together with adaptive neuro–fuzzy inference system used to make the DVR work online with minimum VA loading. The proposed method has been validated through detailed simulation studies.

scholarly journals Organization and Provision of Buses Operation on the Route Taking Into Account the Expenditures of Participants of the Transportation Process

2018 ◽  
Vol 7 (4.3) ◽  
pp. 206 ◽  
Author(s):  
M М. Мoroz ◽  
V L. Khorolskyi ◽  
O V. Мoroz ◽  
K V. Vasylkovska ◽  
V V. Herasymchuk
Keyword(s):  
Turnover Time ◽  
Optimal Number ◽  
Economic Feasibility ◽  
Operating Costs ◽  
Local Council ◽  
Passenger Traffic ◽  
Passenger Transportation ◽  
Transportation Process ◽  
The Cost

The purpose of research. Determination of the optimal number of buses, taking into account the opposite economic factors and economic feasibility in new routes of passenger transport when changing the parameters of transport system using legislative methods and norms.Methods: The article defines the total expenditures of participants of passenger transportation process, which is influenced greatly by the number of buses on the route: on one hand, a large number of them provides the least amount of waiting time, on the other hand, with such quantity of buses carrier’s expenditures increase, as well as the estimate and the cost account of the bus operation on the route using regulatory documents.Results: Using of the proposed methodology allows organizers and performers, in accordance with the current legislation, to overestimate (underestimate) costs deliberately (unintentionally), determine the economic expediency of opening new routes and calculating fares on existing routes and optimal number of buses on the route taking into account the influence of opposite factors. The compromise of the interests of the carrier and the passenger is influenced by the transport service time cost, operating costs and turnover time. Daily operating costs of the route studied and the volume of passenger traffic flow indicate that at the socially preferential (specified) by local council fare the carrier can cover expenditures by increasing the fare or targeted subsidies (subventions).   

scholarly journals Using Adaptive Neuro Fuzzy Inference System to Predict Rate of Penetration from Dynamic Elastic Properties

2020 ◽  
Vol 26 (7) ◽  
pp. 45-61
Author(s):  
Yasser Abbas Khudaier ◽  
Fadhil Sarhan Kadhim ◽  
Yousif Khalaf Yousif
Keyword(s):  
Elastic Properties ◽  
Fuzzy Inference ◽  
Oil Field ◽  
Anfis Model ◽  
Inference System ◽  
Rate Of Penetration ◽  
A Value ◽  
Neuro Fuzzy ◽  
The Cost ◽  
Dynamic Elastic Properties

Rate of penetration plays a vital role in field development process because the drilling operation is expensive and include the cost of equipment and materials used during the penetration of rock and efforts of the crew in order to complete the well without major problems. It’s important to finish the well as soon as possible to reduce the expenditures. So, knowing the rate of penetration in the area that is going to be drilled will help in speculation of the cost and that will lead to optimize drilling outgoings. In this research, an intelligent model was built using artificial intelligence to achieve this goal.  The model was built using adaptive neuro fuzzy inference system to predict the rate of penetration in Mishrif formation in Nasiriya oil field for the selected wells. The mean square error for the results obtained from the ANFIS model was 0.015. The model was trained and simulated using MATLAB and Simulink platform. Laboratory measurements were conducted on core samples selected from two wells. Ultrasonic device was used to measure the transit time of compressional and shear waves and to compare these results with log records. Ten wells in Nasiriya oil field had been selected based on the availability of the data. Dynamic elastic properties of Mishrif formation in the selected wells were determined by using Interactive Petrophysics (IP V3.5) software and based on the las files and log records provided. The average rate of penetration of the studied wells was determined and listed against depth with the average dynamic elastic properties and fed into the fuzzy system. The average values of bulk modulus for the ten wells ranged between (20.57) and (27.57) . For shear modulus, the range was from (8.63) to (12.95) GPa. Also, the Poisson’s ratio values varied from (0.297) to (0.307). For the first group of wells (NS-1, NS-3, NS-4, NS-5, and NS-18), the ROP values were taken from the drilling reports and the lowest ROP was at the bottom of the formation with a value of (3.965) m/hrs while the highest ROP at the top of the formation with a value (4.073) m/hrs. The ROP values predicted by the ANFIS for this group were (3.181) m/hrs and (4.865) m/hrs for the lowest and highest values respectively. For the second group of wells (NS-9, NS-15, NS-16, NS-19, and NS-21), the highest ROP obtained from drilling reports was (4.032) m/hrs while the lowest value was (3.96) m/hrs. For the predicted values by ANFIS model were (2.35) m/hrs and (4.3) m/hrs for the lowest and highest ROP values respectively.

User-centric framework to facilitate trust worthy cloud service provider selection based on fuzzy inference system

2021 ◽  
pp. 1-9
Author(s):  
M. Sujatha ◽  
K. Geetha ◽  
P. Balakrishnan
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Service Providers ◽  
Selection Process ◽  
Cost Model ◽  
Service Selection ◽  
Cloud Service ◽  
Second Phase ◽  
Inference System ◽  
The Cost

The widespread adoption of cloud computing by several companies across diverse verticals of different sizes has led to an exponential growth of Cloud Service Providers (CSP). Multiple CSPs offer homogeneous services with a vast array of options and different pricing policies, making the suitable service selection process complex. Our proposed model simplifies the IaaS selection process that can be used by all users including clients from the non-IT background. In the first phase, requirements are gathered using a simple questionnaire and are mapped with the compute services among different alternatives.In the second phase, we have implemented the Sugeno Fuzzy inference system to rank the service providers based on the QoS attributes to ascertain the appropriate selection. In the third phase, we have applied the cost model to identify the optimal CSP. This framework is validated by applying it for a gaming application use case and it has outperformed the online tools thus making it an exemplary model.

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