On Solving Bi-level Multi-Objective Fully Quadratic Fractional Optimization Model with Fuzzy Demands

This paper has been designed to introduce the method for solving the Bi-level Multi-objective (BL-MO) Fully Quadratic Fractional Optimization Model through Fuzzy Goal Programming (FGP) approach by utilising non-linear programming. In Fully Quadratic Fractional Optimization Model, the objective functions are in fractional form, having quadratic functions in both numerator and denominator subject to quadratic constraints set. The motive behind this paper is to provide a solution to solve the BL-MO optimization model in which number of decision-makers (DM) exists at two levels in the hierarchy. First, the fractional functions with fuzzy demand, which are in the form of fuzzy numbers, are converted into crisp models by applying the concept of α-cuts. After that, membership functions are developed which are corresponding to each decision-maker’s objective and converted into simpler form to avoid complications due to calculations. Finally, the model is simplified by applying FGP approach, and a compromised solution to the initial model is obtained. An algorithm, flowchart and example are also given at the end to explain the study of the proposed model.

Determination Transition Resistance of Cathode-Polarized Main Pipeline on Magnetometery Data

The article deals with the inverse problem of determining the transient resistance of the main pipeline insulating coating. For this, UAV measurements of the magnetic induction vector modulus of the magnetic field excited by the system of electrochemical cathodic protection of pipelines are used. The solution method is based on Tikhonov's method for finding the extremal of the regularizing functional. The developed algorithm is implemented in software. The results of computational experiments are presented.

Optimal Policies of Social Infrastructures Maintenance using Shock and Damage Model

Social infrastructures such as roads and bridges are indispensable for our lives. They have to be maintained continuously and such maintenance has become a big issue in Japan. Social infrastructures are maintained under strict restrictions such as decreasing in local finance revenue and scarcity of skilful engineers. Various kinds of factors such as inspection periods, maintenance costs, and degradation levels, are necessary to consider in establishing efficient maintenance plans of social infrastructures. Furthermore, the special circumstances of social infrastructures such as the delay of constructions which is caused by the scarcity of budget, must be discussed for the efficient maintenance plan. For such discussion, the stochastic cost model which contains preventive and corrective maintenances is useful. Although these models have been studied in mechanical and electronic systems, unique characteristics of social infrastructures such as their enormous scale and delays due to maintenance budget restrictions must be considered when such social infrastructure models are discussed. In this paper, we establish maintenance models of infrastructures which some of preventive maintenance must be prolonged. The expected maintenance cost rate is established using the cumulative damage model and optimal policies which minimizes them are considered. Three basic models and their extended models which consider natural disasters are discussed.

Forward Kinematics of Delta Manipulator by Novel Hybrid Neural Network

For the parallel configuration of the robot manipulator, the solution of Forward Kinematics (FK) is tough as compared to Inverse Kinematics (IK). This work presents a novel hybrid method of optimizing an Artificial Neural Network (ANN) specifically Multilayer Perceptron (MLP) with Genetic Algorithm (GA) and Step-wise Linear Regression (SWLR) to solve the complex FK of Delta Parallel Manipulator (DPM). The joint space angular positional data has been iterated using IK to generate point cloud of Cartesian space positional data. This data set is highly random and broad which leads to higher-order nonlinearity. Hence, normalization of the dataset has been done to avoid outliers from the dataset and to achieve better performance. The developed ANN based MLP gave a mean square error of 0.0000762 and an overall R2 value of 0.99918. Finally, the proposed network has been simulated to solve FK of the parallel manipulator and to check its efficacy. For given joint angles, the proposed network predicted positional values which are in good approximation with known trajectory solved by standard analytical method.

Ergonomic Risk Reduction Method in Aerospace Manufacturing

In the aerospace manufacturing, lots of processes cannot be automated and are performed manually by skilled workers. Because there exist some human error mistakes in such manual working processes, root cause investigations of these mistakes are indispensable and measures are implemented in working processes for preventing repetition of the same mistakes. Although skilled workers have strong confidence that they can complete their work with no mistake, there exist some cases that they cannot recognize their mistakes in practice. In such cases, root cause investigations cannot be performed and no measure is implemented. Such situation may become a serious risk in aerospace manufacturing because a tiny mistake can cause the serious mission failure of aviation system. To reduce such situation, the ergonomic risk reduction method is proposed. Skilled workers try to avoid frustration in performing their tasks and make mistakes through careless behavior. The cause of the frustration is discovered by ergonomic risk reduction method. Work risks can be removed by the progress of the working environment. Such risk reduction method contributes manufacturing organization resiliency. In this paper, we propose an ergonomic human error risk reduction method for skilled workers in Japanese domestic liquid rocket engine manufacturing.

An Integrated Picture Fuzzy Set with TOPSIS-AHP Approach to Group Decision-Making in Policymaking under Uncertainty

Group Decision-Making techniques have been applied to combine a group of decision maker’s preferences to deal with an evaluation of alternatives in a static environment. However, these conventional techniques are only concerned with an evaluation in a static environment. They cannot solve the policy evaluation problems in a dynamic environment or under uncertainty. This paper has presented a novel proposed model to handle the policy evaluation problems under uncertainty by integrating the Picture fuzzy set with the traditional TOPSIS-AHP model. The qualitative and quantitative factors are been quantified by using Picture fuzzy set to evaluate alternatives in order to make complex decisions in a dynamic environment. To validate the proposed model, a numerical example was illustrated meticulously. The experimental results also proved that the proposed method based on the indicator groups in the final urban development project in Vietnam combined with the expert's expertise and the decision-maker's preference gives the more confident evaluation result compared to the state-of-the-art works by applying the fuzzy decision point of the policy.

Optimization of Fuzzy Species Pythagorean Transportation Problem under Preserved Uncertainties

The transportation of big species is essential to rescue or relocate them and it requires the optimized cost of transportation. The present study brings out an optimized way to handle a special class of transportation problem called the Pythagorean fuzzy species transportation problem. To deal effectively with uncertain parameters, a new method for finding the initial fuzzy basic feasible solution (IFBFS) has been developed and applied. To test the optimality of the solutions obtained, a new approach named the Pythagorean fuzzy modified distribution method is developed. After reviewing the literature, it has been observed that till now the work done on Pythagorean fuzzy transportation problems is solely based on defuzzification techniques and so the optimal solutions obtained are in crisp form only. However, the proposed study is focused to get the optimal solution in its fuzzy form only. Getting results in the fuzzy form will lead to avoid any kind of loss of information during the defuzzification process. A comparative study with other defuzzification-based methods has been done to validate the proposed approach and it confirms the utility of the proposed methodology.

Power Usage Efficiency (PUE) Optimization with Counterpointing Machine Learning Techniques for Data Center Temperatures

The rapid increase in the IT infrastructure has led to demands in more Data Center Space & Power to fulfil the Information and Communication Technology (ICT) services hosting requirements. Due to this, more electrical power is being consumed in Data Centers therefore Data Center power & cooling management has become quite an important and challenging task. Direct impacting aspects affecting the power energy of data centers are power and commensurate cooling losses. It is difficult to optimise the Power Usage Efficiency (PUE) of the Data Center using conventional methods which essentially need knowledge of each Data Center facility and specific equipment and its working. Hence, a novel optimization approach is necessary to optimise the power and cooling in the data center. This research work is performed by varying the temperature in the data center through a machine learning-based linear regression optimization technique. From the research, the ideal temperature is identified with high accuracy based on the prediction technique evolved out of the available data. With the proposed model, the PUE of the data center can be easily analysed and predicted based on temperature changes maintained in the Data Center. As the temperature is raised from 19.73 oC to 21.17 oC, then the cooling load is decreased in the range 607 KW to 414 KW. From the result, maintaining the temperature at the optimum value significantly improves the Data Center PUE and same time saves power within the permissible limits.

Influence of Load on Reliability of Storage Area Networks

During the coronavirus pandemic, telecommuting is widely required, making remote data access grow significantly. This requires highly reliable data storage solutions. Storage area networks (SANs) are one of such solutions. To guarantee that SANs can deliver the desired quality of service, cascading failures must be prevented, which occur when a single initial incident triggers a cascade of unexpected failures of other devices. One such incident is the data loading/overloading, causing the malfunction of one device and further cascading failures. Thus, it is crucial to address influence of data loading on the SAN reliability modeling and analysis. In this work, we make contributions by modeling the effects of data loading on the reliability of an individual switch device in SANs though the proportional-hazards model and accelerated failure-time model. Effects of loading on the reliability of the entire SAN are further investigated through dynamic fault trees and binary decision diagrams-based analysis of a mesh SAN system.

A Six-Sigma DMAIC Approach to Improve the Sales Process of a Technology Start-Up

Despite the adoption of Six-Sigma in different service sectors, its application in the Sales function of a Technology-based Start-up has not been explored. This paper deploys an action research-based study methodology and conducts a thorough analysis of a Technology Start-up company in Indonesia, using Six-Sigma principles and the Define-Measure-Analyse-Improve-Control (DMAIC) approach. Statistical validation of the causes of problems helped to formulate a strategy that may have otherwise not been possible. The results of the study and proposed solutions confirm the potential benefits of adopting Six-Sigma in the Sales function of technology start-ups to reduce, particularly, customer waiting time. The novelty of this research lies in the fact that it applies Six-Sigma in a transactional process such as sales, which earlier studies have not explored in depth. This paper can be employed as a reference for organisations to undertake and guide specific process improvement projects similar to the one presented.