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.

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.

Interval Type 2 Fuzzy Fireworks Algorithm for Clustering

2020 ◽  
pp. 195-211
Author(s):  
Juan Barraza ◽  
Fevrier Valdez ◽  
Patricia Melin ◽  
Claudia I. Gonzalez
Keyword(s):  
Fuzzy Inference ◽  
Optimization Method ◽  
Optimal Number ◽  
Dynamic Adjustment ◽  
Number Of Clusters ◽  
Inference System ◽  
Fireworks Algorithm ◽  
Interval Type ◽  
Optimal Number Of Clusters

This chapter presents Interval Type 2 Fuzzy Fireworks Algorithm for clustering (IT2FWAC). It is an optimization method for finding the optimal number of clusters based on the centroid features which uses the Fireworks Algorithm (FWA), but with a dynamic adjustment of parameters using an Interval Type 2 Fuzzy Inference System (IT2FIS). Three variations of the IT2FWAC are proposed to find the optimal number of clusters for different datasets: IT2FWAC -I, IT2FWAC -II, and IT2FWAC –III. They are explained in detail.

scholarly journals Cognitive and Hierarchical Fuzzy Inference System for Generating Next Release Planning in SaaS Applications

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 102966-102974 ◽  
Author(s):  
Mubarak Alrashoud ◽  
Etessam Hazza ◽  
Fayez Alqahtani ◽  
Muneer Al-Hammadi ◽  
Abdolreza Abhari ◽  
...  
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Release Planning

Adaptive Neural Fuzzy Inference System Optimization in Converter

2017 ◽  
Vol 14 (1) ◽  
pp. 640-646
Author(s):  
R Premalatha ◽  
P Murugesan
Keyword(s):  
Fuzzy Inference ◽  
Good Control ◽  
Speed Control ◽  
System Optimization ◽  
Optimization Method ◽  
Pi Controller ◽  
Process Industries ◽  
Inference System ◽  
Inference Systems ◽  
Neural Fuzzy

A new technique of AFIS speed control of DC drives with Asymmetrical Half-Bridge converter is proposed. The PI controller is the most common feedback controller used in the process industries. PI is easily understood algorithm, which give good control action of varied dynamic characteristics. However, PI controller have drawback of not giving the optimum response for non-linear systems. By the introduction of novel intelligent techniques, the PI controller and FLC are optimized by Adaptive Neural Fuzzy Inference Systems. In this paper the ANFIS optimization method is applied for speed control of DC to DC converter fed drive. The main motive of this work is to attain minimum transient and switching losses to decrease the energy loss by which the efficiency gets increased.

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.

scholarly journals Model Fuzzy Mamdani Untuk Penilaian Tingkat Kepuasan Pelayanan Pengaduan Masyarakat

2018 ◽  
Vol 5 (2) ◽  
pp. 237-247 ◽  
Author(s):  
Martin Martin ◽  
Lala Nilawati
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Comfort Level ◽  
System Model ◽  
Service Satisfaction ◽  
Competitive Advantages ◽  
Inference System ◽  
Service Levels ◽  
Input Variables ◽  
The Government

AbstrakKualitas pelayanan adalah salah satu keunggulan kompetitif, karena pelayanan yang baik adalah salah satu faktor dasar yang mampu mempengaruhi tingkat kenyamanan penerima layanan. Pelayanan publik oleh aparatur pemerintah dewasa ini masih banyak dijumpai kelemahan, sehingga belum dapat memenuhi kualitas yang diharapkan masyarakat. Penelitian ini ditujukan untuk melihat seberapa besar kepuasan pelayanan, dan pengaruh tingkat pelayanan terhadap tingkat kepuasan berdasarkan Logika Fuzzy Inference System Model Mamdani. Ada empat variabel input yang digunakan yaitu kejelasan informasi, kejelasan persyaratan, kemampuan petugas dan ketersediaan sarana dan prasarana untuk menghasilkan output kepuasan pelayanan. Berdasarkan tahapan-tahapan menggunakan Logika Fuzzy Inference System Model Mamdani mulai dari pembentukan himpunan fuzzy, aplikasi fungsi impilkasi, komposisi aturan sampai proses penegasan (defuzzyfikasi), dapat dibuktikan adanya korelasi antara variabel-variabel input sehingga dapat menentukan output hasil kepuasan pelayanan. Hasil penelitian ini diharapkan dapat digunakan oleh pihak instansi, sebagai pendukung sistem keputusan terhadap hasil penilaian yang diberikan oleh masyarakat untuk pelayanan yang dirasakan. Pengembangan penelitian ini kedepan nya akan diuji coba kembali dengan menambahkan lebih banyak variabel dan akan dibuat sebuah interface untuk memudahkan pemprosesan hasil penilaian kualitas pelayanan pengaduan masyarakat.  Kata Kunci: Pelayanan, Fuzzy Mamdani, Logika Fuzzy.AbstractService quality is one of the competitive advantages, because good service is one of the basic factors that can affect the comfort level of service recipients. Public services by the government apparatus today are still often found to be weak, so that they cannot meet the quality expected by the community. This study is intended to see how much service satisfaction is, and the effect of service levels on satisfaction levels based on Mamdani Model Fuzzy Inference System Logic. There are four input variables used namely clarity of information, clarity of requirements, ability of officers and availability of facilities and infrastructure to produce service satisfaction output. Based on the stages using Mamdani Model Fuzzy Inference System Logic starting from the formation of fuzzy sets, application of the implementation function, composition of the rules until the confirmation process (defuzzyfication), it can be proved the correlation between input variables so that it can determine the output of service satisfaction. The results of this study are expected to be used by the agency, as a support system for the decision on the results of the assessment given by the community for perceived services. The future development of this research will be re-tested by adding more variables and an interface will be created to facilitate the processing of the results of the quality assessment of public complaints services. Keywords: Service, Fuzzy Mamdani, Fuzzy Logic.

ESTIMATION OF ANKLE JOINT ANGLE FROM PERONEAL AND TIBIAL ELECTRONEUROGRAMS — A MUSCLE SPINDLE MODEL APPROACH

2012 ◽  
Vol 12 (04) ◽  
pp. 1250080 ◽  
Author(s):  
CHING-CHAO CHAN ◽  
CHOU-CHING K. LIN ◽  
MING-SHAUNG JU
Keyword(s):  
Ankle Joint ◽  
Muscle Fiber ◽  
Muscle Spindle ◽  
Joint Angle ◽  
Fuzzy Inference ◽  
Optimization Method ◽  
Model Parameters ◽  
Inference System ◽  
Intrafusal Muscle ◽  
Intrafusal Muscle Fiber

This study develops a method for estimating the angle of a passively stretched ankle joint from electroneurograms (ENGs) based on structural muscle spindle models of the tibial and peroneal nerves. Passive ramp-and-hold and alternating stretches of the ankle joint are performed on an anesthetized rabbit. Two cuff electrodes are employed to measure the ENGs of peroneal and tibial nerves simultaneously. From the two ENG signals and the joint angle trajectory, two intrafusal muscle fiber models are constructed and their inverse models are derived. The results of the two models are combined to generate the final angle estimate. An optimization method, called sequential quadratic programming, is employed to find the model parameters that minimize the squared errors between the ankle angles predicted by the model and the measured ankle angles. The performance of the proposed approach is compared with those of an adaptive neuro-fuzzy inference system and an artificial neural network model. The results reveal that the proposed model has the best performance in estimating the ankle joint angle in large-range movements and the smallest tracing error. The proposed method effectively estimates the passive ankle joint angle using the inverse physiological model of an intrafusal muscle fiber.

Making decision toward overseas construction projects

2019 ◽  
Vol 26 (2) ◽  
pp. 285-302 ◽  
Author(s):  
Wahyudi P. Utama ◽  
Albert P.C. Chan ◽  
Hafiz Zahoor ◽  
Ran Gao ◽  
Dwifitra Y. Jumas
Keyword(s):  
Decision Support ◽  
Construction Projects ◽  
Fuzzy Inference ◽  
Optimization Method ◽  
Preliminary Assessment ◽  
Anfis Model ◽  
Data Set ◽  
Inference System ◽  
Content Type ◽  
Neuro Fuzzy

Purpose The purpose of this paper is to introduce a decision support aid for deciding an overseas construction project (OCP) using an adaptive neuro fuzzy inference system (ANFIS). Design/methodology/approach This study presents an ANFIS approach as a decision support aid for assessment of OCPs. The processing data were derived from 110 simulation cases of OCPs. In total, 21 international factors observed from a Delphi survey were determined as assessment variables to examine the cases. The experts were involved to evaluate and judge whether the company should Go or Not Go for an OCP, based on the different parameter scenarios given. To measure the performance of the ANFIS model, root mean square error (RMSE) and coefficient of correlation (R) were employed. Findings The result shows that optimum ANFIS model indicating RMSE and R scores adequately near between 0 and 1, respectively, was obtained from parameter set of network algorithm with two input membership functions, Gaussian type of membership function and hybrid optimization method. When the model tested to nine real OCPs data, the result indicates 88.89 percent accurate. Research limitations/implications The use of simulation cases as data set in development the model has several advantages. This technique can be replicated to generate other case scenarios which are not available publicly or limited in terms of quantity. Originality/value This study evidences that the developed ANFIS model can predict the decision satisfactorily. Therefore, it can help companies’ management to make preliminary assessment of an OCP.

scholarly journals Model Fuzzy Mamdani Untuk Penilaian Tingkat Kepuasan Pelayanan Pengaduan Masyarakat

2018 ◽  
Vol 5 (2) ◽  
pp. 237-247
Author(s):  
Martin Martin ◽  
Lala Nilawati
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Comfort Level ◽  
System Model ◽  
Service Satisfaction ◽  
Competitive Advantages ◽  
Inference System ◽  
Service Levels ◽  
Input Variables ◽  
The Government

AbstrakKualitas pelayanan adalah salah satu keunggulan kompetitif, karena pelayanan yang baik adalah salah satu faktor dasar yang mampu mempengaruhi tingkat kenyamanan penerima layanan. Pelayanan publik oleh aparatur pemerintah dewasa ini masih banyak dijumpai kelemahan, sehingga belum dapat memenuhi kualitas yang diharapkan masyarakat. Penelitian ini ditujukan untuk melihat seberapa besar kepuasan pelayanan, dan pengaruh tingkat pelayanan terhadap tingkat kepuasan berdasarkan Logika Fuzzy Inference System Model Mamdani. Ada empat variabel input yang digunakan yaitu kejelasan informasi, kejelasan persyaratan, kemampuan petugas dan ketersediaan sarana dan prasarana untuk menghasilkan output kepuasan pelayanan. Berdasarkan tahapan-tahapan menggunakan Logika Fuzzy Inference System Model Mamdani mulai dari pembentukan himpunan fuzzy, aplikasi fungsi impilkasi, komposisi aturan sampai proses penegasan (defuzzyfikasi), dapat dibuktikan adanya korelasi antara variabel-variabel input sehingga dapat menentukan output hasil kepuasan pelayanan. Hasil penelitian ini diharapkan dapat digunakan oleh pihak instansi, sebagai pendukung sistem keputusan terhadap hasil penilaian yang diberikan oleh masyarakat untuk pelayanan yang dirasakan. Pengembangan penelitian ini kedepan nya akan diuji coba kembali dengan menambahkan lebih banyak variabel dan akan dibuat sebuah interface untuk memudahkan pemprosesan hasil penilaian kualitas pelayanan pengaduan masyarakat.  Kata Kunci: Pelayanan, Fuzzy Mamdani, Logika Fuzzy.AbstractService quality is one of the competitive advantages, because good service is one of the basic factors that can affect the comfort level of service recipients. Public services by the government apparatus today are still often found to be weak, so that they cannot meet the quality expected by the community. This study is intended to see how much service satisfaction is, and the effect of service levels on satisfaction levels based on Mamdani Model Fuzzy Inference System Logic. There are four input variables used namely clarity of information, clarity of requirements, ability of officers and availability of facilities and infrastructure to produce service satisfaction output. Based on the stages using Mamdani Model Fuzzy Inference System Logic starting from the formation of fuzzy sets, application of the implementation function, composition of the rules until the confirmation process (defuzzyfication), it can be proved the correlation between input variables so that it can determine the output of service satisfaction. The results of this study are expected to be used by the agency, as a support system for the decision on the results of the assessment given by the community for perceived services. The future development of this research will be re-tested by adding more variables and an interface will be created to facilitate the processing of the results of the quality assessment of public complaints services. Keywords: Service, Fuzzy Mamdani, Fuzzy Logic.

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

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