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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.

Lot sizing models involve operational and tactical decisions. These decisions may entail multi-level production processes such as assembly operations with multiple plants and limited capacities. Lot sizing problems are widely recognized as NP-hard problems therefore difficult to solve, especially the ones with multiple plants and capacity constraints. The level of complexity rises to an even higher level when there is an interplant transfer between the plants. This paper presents a Genetic Algorithm (GA) based solution methodology applied to large scale multi-plant capacitated lot sizing problem with interplant transfer (MPCLSP-IT). Although the GA has been a very effective and widely accepted meta-heuristic approach used to solve large scale complex problems, it has not been employed for MPCLSP-IT problem. This paper solves the MPCLSP-IT problem in large scale instances by using a genetic algorithm, and in doing so successfully obtains a better solution in terms of computation time when compared to the results obtained by the other methods such as Lagrangian relaxation, greedy randomized adaptive search procedure (GRASP) heuristics, and GRASP-path relinking techniques used in extant literature.

This paper exploits the Markov reward method to availability and importance evaluation for CNC machine tools. The presented paper regards CNC machine tools as a multi-state system that state transitions process caused by elements and subsystem’s failures and corresponding maintenance activities during the lifetime. Simultaneously, considering the time-varying characteristics of the failure rates of subsystems, the non-homogeneous Markov reward model is introduced for evaluation of availability and subsystem importance identification for aging multi-state CNC machine tools under minimal repair. Corresponding procedures for the failure rates, state transition matrix, and reward matrix definition are suggested for the availability and importance measures. A numerical example is presented in order to illustrate the approach.

This paper proposes a semi-automatic methodology to assist the user in creating surveys about FMEA and Risk Analysis, based on a customized use of the tools for semantic analysis and in particular a home-developed syntactic parser called Kompat Cognitive. The core of this work has been the analysis of the specific FMEA-related jargon and its common modalities of description within scientific papers and patents in order to systematize the linguistic analysis of the reference documents within the proposed step-divided procedure. The main goals of the methodology are to assist not skilled in the art users about FMEA during the analysis of generic and specific features, by considering large moles of contributions in restricted amounts of time. The methodology has then been tested on the same pool of 286 documents, divided between 177 and 109 patents, manually analyzed in our previous survey, in order to replicate part of its classifications through the proposed new modality. In this way we evaluated the abilities of the methodology both to automatically suggesting the main features of interest and to classify the documents according to them.

Travel time estimation & reliability evaluation of any means of transportation in every type of travel mode- land, rail, sea and air has been of immense interest of the researchers; primarily due to growing economic concern in the field of logistics & passenger movement. In situations like quantitative data inaccessibility or data imprecision, fuzzy set based possibilistic approach is recognized as a practical choice in obtaining the reliability estimates. This paper proposes and advocates possibilistic approach for travel time reliability computation of any type transportation vehicle under fuzzy type of data. The proposed approach is a novel way of computing the travel time & obtaining the related reliability value. Initially, the paper proposes the general methodology for travel time reliability evaluation. Individual travel time components of a transportation vehicle are considered as fuzzy; as a result, travel time is modelled as a fuzzy variable. Travel time reliability of a transportation vehicle has been defined with the help of possibilistic measures. The proposed procedure is then demonstrated with an application to marine vessel carrying the bulk. After illustration of the proposed methodology, sensitivity analysis is carried out. The paper ends with the comments on comparative features of the three cases.

As the crime is increasing day by day around the world. To stop the crime many security gadgets and machines have been developed by various security agencies. The tripod turnstile machine also helps in stopping the crime and restricting the entry of unauthorized person on the premises. In this paper, the aim is to analyze the reliability measures of the two tripod turnstile machines which work in a parallel configuration. In the case of non-functionality of the machine, unauthorized persons may enter into the premises. Due to security and safety reason, organizations install these machines. Hence these machines must be highly reliable in the operation to avoid any unwanted event. In this paper, for the considered system, the transition state diagram has been drawn with the help of the Markov model. The Chapman-Kolmogorov differential equations are developed from the transition state diagram and solved using Laplace transformation. The failure and repair rates are assumed to be constant and follow negative exponential distribution. The system upstate and downstate probabilities are determined. The explicit expression of the system Availability, Reliability and MTTF are also obtained. The sensitivity analysis is also performed to determine which machine affects the system reliability the most.

When it comes across problems in creating Internet of Things (IOT) architecture, the major problem that arises is an automatic stipulation of resources. At the same time in today’s era, it is very important to integrate this problem with better Quality of Services (QoS) because of which the cloud computing is taking a shift. As being well acquainted that in fog computing, network’s bandwidth is limited, therefore it becomes quite important to build a joint architecture with resource allocation problem giving it a better quality of services with enhanced efficiency and low latency communication. Priority of QoS is determined by Systematic Ladder Process (SLP) and decision parameter evaluation by RECK algorithm. In this paper, there will be a design of a better framework for IOT resource allocation scheme with better efficiency and better QoS. The paper too highlights the comparison of the previous works of the resource allocation algorithms and schemes with RECK algorithm.

The goal of this paper is to explore the interrelated relationships between the quality of corporate governance, Stakeholder’s pressure, and Sustainability Reporting. We claim that sustainable development strategies are the result of the interaction of two variables: the standard of corporate governance and the pressure of stakeholders rather than the outcome of a single variable. The results indicate that the company size and corporate governance rating are positively correlated with sustainability reporting Scores disclosed by the Turkish companies. The analysis of 125 firm-year data suggest that the company provides more SRS if it has a better corporate governance rating; or larger total assets. The paper also reveals that social, environmental and economic indicators, as pillars of sustainable development, are not the result of corporate governance practices or of stakeholder pressure alone, but represent a holistic reflection of the accumulated effects of various factors related to both corporate governance practices and stakeholder theory. This paper extends the work of Taşkirmaz, and Gamze (2017) which examined the relationship between corporate governance and corporate social responsibility. Our results increase understanding of sustainable practices of the circular economy in Turkish industrial companies and indicate future trends for these applications. This research will help managers of manufacturing companies in formulating their plans to maximize the use of available resources and improve efficiency in the context of the circular economy.

A normalized version of the ubiquitous two-by-two contingency matrix is associated with a variety of marginal, conjunctive, and conditional probabilities that serve as appropriate indicators in diagnostic testing. If this matrix is enhanced by being interpreted as a probabilistic Universe of Discourse, it still suffers from two inter-related shortcomings, arising from lack of length/area proportionality and a potential misconception concerning a false assumption of independence between the two underlying events. This paper remedies these two shortcomings by modifying this matrix into a new Karnaugh-map-like diagram that resembles an eikosogram. Furthermore, the paper suggests the use of a pair of functionally complementary versions of this diagram to handle any ternary problem of conditional probability. The two diagrams split the unknowns and equations between themselves in a fashion that allows the use of a divide-and-conquer strategy to handle such a problem. The method of solution is demonstrated via four examples, in which the solution might be arithmetic or algebraic, and independently might be numerical or symbolic. In particular, we provide a symbolic arithmetic derivation of the well-known formulas that express the predictive values in terms of prevalence, sensitivity and specificity. Moreover, we prove a virtually unknown interdependence among the two predictive values, sensitivity, and specificity. In fact, we employ a method of symbolic algebraic derivation to express any one of these four indicators in terms of the other three. The contribution of this paper to the diagnostic testing aspects of mathematical epidemiology culminates in a timely application to the estimation of the true prevalence of the contemporary world-wide COVID-19 pandemic. It turns out that this estimation is hindered more by the lack of global testing world-wide rather than by the unavoidable imperfection of the available testing methods.

Environmental worries in production and inventory models have received large attention in inventory management literature. In this paper, an economic production model is proposed with two-echelon supply chain when trade credit is offered by the supplier. This paper proposes human errors of Type I and Type II due to fatigue and inexperience of the inspector during screening. It considers the use of energy for production along with greenhouse gases (GHG) emission from production and transportation operations. The developed model optimizes the environmental and economic performances of the supply chain. Our aim is to explore the effects of human errors during inspection on the emission cost, transportation cost and delay in payment on the replenishment of order sizes and the expected total profit of the retailer. A mathematical model is developed and numerical examples are provided to illustrate the solution procedure.

This paper proposes an optimal coordination scheduling methodology for a wind-thermal power system by considering the intermittent behavior of wind power and load demands. The optimum amount of spinning reserves (SRs) needed to handle the uncertain nature of hybrid power system with wind generators is determined by using the probability analysis. The proposed model demonstrates the error in wind power and load forecasts by using the probability distribution functions for over and under estimations of load and wind power. In this work, two objectives, i.e., total operating cost and risk level are considered, which handles both economical and reliability issues, simultaneously. This problem is solved by using the meta-heuristic based Cuckoo search algorithm. The reactive power capability limits of synchronous generators and variable speed doubly fed induction generators are considered in this paper. Simulations have been carried out on IEEE 300 bus system.

System safety is a discipline of applying engineering and management principles, criteria, and techniques to achieve acceptable or tolerable risk within the constraints of operational effectiveness, suitability, time, and cost throughout all phases of the system life. System safety engineering is the program to identify hazards, and to eliminate hazards or reduce the associated risks when the hazards cannot be eliminated. System safety management involves plans and activities taken to identify hazards; assess and mitigate associated risks; track, control, close, and document risks encountered in the design, development, test, manufacturing, installation, operation and maintenance, and the disposal of systems, subsystems, and equipment. In this paper, the concept and principle of system safety in the transit system is discussed. The paper also introduces the safety standards, safety life-cycle, Safety Integrity Levels (SILs), safety analysis techniques and safety cases etc.

In this paper, we consider the improved estimation of average production of peppermint at block level of Barabanki district of Uttar Pradesh State (India). We suggest certain estimators for population-mean. Here, population refers to production population as study variable and auxiliary-variable refers to Area of field. We study the sampling properties naming bias and MSE of estimators, which are presently proposed by us in the paper. We compare our proposed estimators with other ones existing in literature. For the support of the theoretical findings, we carry out a numerical study for the natural population on primary data collected from Banikodar Block of Barabanki District situated in Uttar Pradesh State.

This paper deals with a special type of voting systems, called two-stage intervened decision-making systems, in which the decision time of each decision unit will be a random variable and some supervising mechanism is included. A new decision rule is applied to such kind of systems, which makes the decision units become interdependent from each other. The reliability and cost-benefit models are developed. The optimization for the models is discussed and the optimal solution for a special case is also derived. A numerical example for model optimization is presented as well as some model comparison. Even though a specific application is used for model formulation and derivation throughout this paper, the modeling results can be easily modified and applied to many other systems.

In this paper, a new approach is introduced to solve transportation problem with type-2-fuzzy variables. In most of the real-life situations, the available data do not happen to be crisp in nature. It gives rise to the fuzzy transportation problem (FTP). This proposed approach concentrates on the problem when the vertical slices of type-2-fuzzy sets (T2FSs) are trapezoidal fuzzy numbers (TFNs). The original problem reduces to three different linear programming problems (LPPs) which are solved using the simplex algorithm. Then the effectiveness of this paper is discussed with numerical example. In conclusion, the significance of the paper and the scope of future study are discussed.

Multipole Field Electromagnetic Launchers (MFEML) is one of the radial electromagnetic launching mechanisms, which can propel a payload for long distances. This paper proposes formulae for inductance based on the magnetic flux distribution in the coils of MFEML. Flux lines flow is sectionalized, for each section reluctance formulae is derived based on the position of the projectile. Based on the principles of the Lorentz equation, magnetic forces are calculated. This paper presents an electrical and magnetic equivalent circuit of MFEML. The velocity and force characteristics are obtained for the proposed formulae. The results are validated using Finite element analysis.

Nowadays the mobile data usage has been significantly increased by an unprecedented amount with the wide spread of smart devices, which is known as the explosion of data traffic. The rapid growth in mobile data traffic leads to a deficiency of cellular network capacity. To solve this problem, readily available Wi-Fi networks are used to offload the data traffic from cellular networks. The Wi-Fi offloading must ensure guaranteed throughput and delay performance for the users. However, if the user doesn’t meet any Wi-Fi network during the download period, the quality of experience gets degraded. Quality of experience can be improved with the help of various techniques such as resource allocation, scheduling, and handoff schemes. To know the effect of the offloading process, some key parameters are identified in this paper and the effect of offloading on these parameters is studied. Here, in this paper a study of various parameters like download time, number of users, data size on the throughput, delay and packet loss is done in the cellular network -WiFi offloading scenarios. This study highlights the need for an efficient QoS mechanism in future heterogeneous networks. It can be considered as a research aspect in upcoming integrated networks.

Symmetric switching functions (SSFs) play a prominent role in the reliability analysis of a binary k-out-of-n: G system, which is a dichotomous system that is successful if and only if at least k out of its n components are successful. The aim of this paper is to extend the utility of SSFs to the reliability analysis of a multi-state k-out-of-n: G system, which is a multi-state system whose multi-valued success is greater than or equal to a certain value j (lying between 1 (the lowest output level) and M (the highest output level)) whenever at least km components are in state m or above for all m such that 1 ≤ m ≤ j. This paper is devoted to the analysis of non-repairable multi-state k-out-of-n: G systems with independent non-identical components. The paper utilizes algebraic techniques of multiple-valued logic (together with known properties of SSFs) to evaluate each of the multiple levels of the system output as an individual binary or propositional function of the system multi-valued inputs. The formula of each of these levels is then written as a probability–ready expression, thereby allowing its immediate conversion, on a one-to-one basis, into a probability or expected value. The symbolic reliability analysis of a commodity-supply system (which serves as a standard gold example of a multi-state k-out-of-n: G system) is completed successfully herein, yielding results that have been checked symbolically, and also were shown to agree numerically with those obtained earlier.

The growth of any developing economy depends largely on its transport sector. Growing economy leads to more job opportunities and movement of people from rural to urban areas. Public road transport hence plays a significant role as a support system in carrying passengers. This paper discusses the efficiency of State Transport Undertakings of India, in particular, Delhi Transport Corporation (DTC) using the technique of Data Envelopment Analysis (DEA) and regression analysis. A data set of 46 State Transport Undertakings of India have been considered for the study. DEA was applied to compute the efficiencies of units under study. Potential improvements in the input and output variables were computed for the inefficient units. Regression analysis was then performed to identify the explanatory variables that significantly affect the input and output variables. It was observed that DTC is one amongst the worst performers. It showed a technical inefficiency of 50.94% and was operating on decreasing returns to scale. Further, DTC needs to increase its output substantially in order to attain the level of efficiency. Also it is not utilizing its resources optimally as it needs to reduce all its inputs. In other words, DTC is not utilizing its resources as optimally as its efficient peers. This paper is an attempt to apply regression technique along with the non parametric technique of Data Envelopment Analysis so that the decision makers of DTC can identify the areas where improvement is required and plan a strategy to improve their performance. This would enable DTC to move from a loss incurring to a profit making unit.

Component importance analysis is to measure the effect on system reliability of component reliabilities, and is used to the system design from the reliability point of view. On the other hand, to guarantee high reliability of real-time computing systems, redundancy has been widely applied, which plays an important role in enhancing system reliability. One of commonly used type of redundancy is the standby redundancy. However, redundancy increases not only the complexity of a system but also the complexity of associated problems such as common-mode error. In this paper, we consider the component importance analysis of a real-time computing system with warm standby redundancy in the presence of Common-Cause Failures (CCFs). Although the CCFs are known as a risk factor of degradation of system reliability, it is difficult to evaluate the component importance measures in the presence of CCFs analytically. This paper introduces a Continuous-Time Markov Chain (CTMC) model for real-time computing system, and applies the CTMC-based component-wise sensitivity analysis which can evaluate the component importance measures without any structure function of system. In numerical experiments, we evaluate the effect of CCFs by the comparison of system performance measure and component importance in the case of system without CCF with those in the case of system with CCFs. Also, we compare the effect of CCFs on the system in warm and hot standby configurations.

This paper presents the use of genetic algorithm to solve reliability redundancy allocation problem of complicated system in fuzzy environment. Generally, this problem has been formulated as single objective integer non-linear programming problem with several resource constraints. In this paper, the reliability of each component as well as other parameters related to the problem is considered to be fuzzy valued. In this work, the corresponding constrained optimization problem has been transformed to crisp constrained optimization problem using defuzzification of fuzzy number. Here, widely known Yager ranking Index has been used for defuzzification of fuzzy number. The Big-M penalty function technique has been used to transform the constrained optimization problem into an unconstrained optimization problem. The converted problem has been solved with the help of real coded genetic algorithm. To illustrate the proposed methodology, a numerical example has been considered and solved. To study the performance of the proposed genetic algorithm, sensitivity analyses have been done graphically.

Sneak Circuit Analysis is intended for critical applications which are essential to mission success and safety. A sneak condition will occur when a designed circuit inhibits a wanted function or results in an unwanted function. Sneak conditions originate from one of the four following scenarios: a sneak path resulting in a flow of electrical current along an unexpected route; a sneak timing that may cause the activation of some desired/designed functionality at an unexpected time; a sneak indication in monitoring functions that may result in an ambiguous or false display of system operating conditions; and lastly, a sneak label which may induce operator error due to inappropriate instruction. This paper introduces a near miss event that occurred in the Sao Paulo monorail which was caused by a sneak time condition. Root cause analysis and design modifications are also discussed in the paper.

The paper utilizes the Karnaugh map in a pedagogical calculation of the Shapley-Shubik index of voting power, which is perhaps the most prominent such index for managerial, political, and engineering voting systems. The Karnaugh map has features that allow it to readily implement this calculation, namely: (a) it can conveniently represent pseudo-switching functions of the form g= B_2^n →R, (b) it can immediately detect the number of orderings in going from one of its configurations (cells) to another, and (c) it visually identifies a pair of configurations that is critical w. r. t. a certain voter, which are a primitive winning coalition of which this voter is a member and a corresponding primitive losing coalition that results when this voter defects. The paper offers a detailed description of these three features and of the associated calculations using the celebrated system of the European Economic Community as a running example.

Various climate impact studies need to generate estimates of climate variables at a given location based on values from other locations. It is well established fact that there are strong sensible physical linkages between global climate and local scale weather phenomenon. Therefore, empirical interpolation or downscaling has emerged as a prospective tool to relate atmospheric circulation patterns to surface variables for forecasting regional climate from GCM and RCM output dataset. In this paper, application of Artificial Neural Networks (ANNs) based soft computing model for empirical interpolation of precipitation in Himalayan region is attempted. This method uses ANNs to generate precipitation estimates for 11 districts of Uttarakhand state (India) given information from a lattice of surrounding locations. In the present paper, we have used Feed Forward Back Propagation (FFBP) algorithm to develop a Multilayer Perceptron ANN model for empirical downscaling of precipitation in Himalayan region. The model is developed using climate data of Climate Research Unit (CRU) and observed data for past 110 years (1901-2010). The robustness and suitability of the developed ANN model is verified by testing its applicability for 11 districts of Uttarakhand state. 80% of the data are used for training of the model and remain 20% are used for testing of the model. The performance evaluation of the model is tested by RMSE value. The study show that the model works quite well for climatic records of most of the district after bias correction.

In this paper, a dormant k-out-of-n systems redundancy calculation will be introduced. Dormant failure is a failure that cannot be detected when it occurs because of the nature of the failure characteristic. Therefore, a dormant failure becomes the blind point to the design for reliability and maintainability because of its inability to be detected. The most popular approach in detecting a dormant failure is to carry out a scheduled periodic inspection, test or maintenance activity. The scheduled periodic maintenance is applied to prevent and reduce the unexpected dormant failures that could lead to safety consequences, or costly corrective maintenance. This paper will introduce a methodology on how to calculate the reliability parameter such as Mean Time Between Failure (MTBF) for the dormant k-out-of-n redundant systems. The mathematical relationship between the effective MTBF and the scheduled periodic inspection/maintenance interval is also elaborated. Case studies are adopted to illustrate how to apply the developed reliability calculation methodology in the mass transit train reliability and safety design.

This paper develops a mathematical model for condition-based maintenance optimization of multi-state systems. The majority of the existing literature on maintenance optimization assume that there is no additional cost incurred because of side effects of equipment degradation. Nevertheless, as the operating cost increases with equipment age and degradation, it is important to consider the degradation side effects in the maintenance decision-making process. An important feature of the proposed model lies in the fact that it incorporates side effect of degradation process into condition-based preventive maintenance optimization. We develop a continuous-time discrete-state Markov chain model describing the deterioration stochastic process of a single component. The component is modeled as a multi-state system, where each discrete state is characterized by a degradation level. Numerical examples show the importance of considering such side effect costs when optimizing the choice of maintenance policy. The proposed model is extended to deal with multi-state series systems. Using an example of a series system with two components, it is shown that preventive maintenance and side effect costs should not be optimized for each component individually, but from the perspective of the series system as a whole.

Because a multifaceted aspect of research allows us to answer critical queries and address broad issues; studying associative and collaborative fields is the need of hour. Therefore, the theme of this series of Special Issue was kept as “Recent Trends of Modelling in Reliability, Quality and Technology Management”. After circulating the “Call for Papers” worldwide, the papers in the series have been selected based on the technical quality of the work and their innovative approach. The series is collection of invited technical communications from all the constituent areas namely; Reliability, Quality and Technology Management. In all 16 papers have been categorically clubbed in three issues (Vol. 2, No. 2, 2017; Vol. 2, No. 3, 2017; Vol. 2, No. 4, 2017) related to the appropriate theme and domain. The presentation, it is expected would create interest in the recent trends of the aforesaid theme and would bring in the intended interaction between various players of Reliability, Quality and Technology Management. Last but not the least we would like to thank Prof. Mangey Ram (Editor-in-Chief) for bringing out this series of special issues and all the researchers across the globe who have contributed for these issues.

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.

The minimization of automaton is important to reduce space and computational time. Reduction in number of states and transitions leads to equivalent automaton with less number of states and transitions. In this paper, state minimization of General Finite Fuzzy Automata (GFFA) is discussed. To obtain minimal equivalent GFFA we have removed redundant states and transitions using substitution property (SP) partition and quotient machine. The algorithm to find membership values of states of the GFFA is described and algorithm to associate states with quotient machine to obtain minimal machine with less number of states is discussed.

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.

Hydraulic surges are transient events frequently observed in various industrial and laboratory flow situations. Understanding surge physics and its accurate numerical prediction is crucial to the safety of flow systems. The maximum accuracy achievable for transient surge simulations is limited by the inefficiencies in the mathematical models used. In this work, we propose a mathematical model that incorporates an adaptive damping technique for the accurate prediction of hydraulic surges. This model also takes the compressibility effects in the liquid during the surge process into account. The novel approach of using the local pressure fluctuation data from the flow to adjust the unsteady friction for controlling the dissipation is introduced in this paper. The adaptive-dissipation is actualized through a unique 'variable pressure wave damping coefficient' function definition. Numerical simulation of three different valve-induced surge experiments demonstrates the reliability and robustness of the mathematical model. Numerical results from the proposed model show an excellent match with the experimental data by closely reproducing both the frequency and the amplitude of transient pressure oscillations. A comparative study explains the improvement in the simulation accuracy achieved by replacing the constant damping coefficient with the proposed variable coefficient. The superiority of the new model with the adaptive damping capability over the similar models in literature and those used in commercial software packages is also well established through this study.

Covid-19, a disease that originated in the Chinese city of Wuhan, has spread across almost the entire globe. Pneumonia, which infects the lungs, is one of the symptoms of this disease. In the past X-ray images were used to segment various diseases such as pneumonia, tuberculosis, or lung cancer. Recent studies showed that Covid-19 affects the lungs. As a result, an X-ray imaging could help to detect and diagnose Covid-19 infection. This study presents a novel hybrid algorithm (CHDPSOK) for segmenting a Covid-19 infected X-ray image. To find Covid-19 contamination in the lungs, we use a segmentation-based approach using K-means and Dynamic PSO algorithm. In the present paper, segmentation of infected regions in the X-ray image uses a cumulative histogram to initialize the population of the PSO algorithm. In a dynamic PSO algorithm, the velocity of the particle changes dynamically which is useful to avoid the local minima. K-means is used to change the position of the particle dynamically for better convergence. To validate the segmentation performance of our algorithm, we used the Kaggle dataset in our experiments. The performance of the proposed method is analyzed both qualitatively and quantitatively. The results explicitly demonstrate the outperformance of the proposed algorithm.

The distribution of electricity has become a challenge as there are losses associated with its distribution and transmission. In reducing such losses employment of Distributed Generation units in the transmission network can benefit greatly. Thus, the concern is on the optimal placement of Distributed Generation units that can provide maximum benefits and optimize several conflicting attributes. In this paper, the emphasis is laid on determining an optimal location for the placement of a Distributed Generation unit under conflicting attributes such as losses, real and reactive power, and voltages at different buses. For this purpose, the Technique for Order of Preference by Similarity to best Solution a Multi-Criteria Decision-Making technique, and Fuzzy TOPSIS technique have been employed for determining the optimal placement of 10 MW Distributed Generation unit at the IEEE 20 Bus System. The results obtained can significantly benefit in reducing losses and greatly help in economical perspective as well.

Integrated process of human error management in human factors engineering (HFE) process provides a systematic direction for the design countermeasures development to prevent potential human errors. The process analyzes performance influence factors (PIFs) for crew failure modes (CFMs) and human failure events (HFEvs) in human reliability analysis (HRA). This paper provides applications of the process to the event evaluation for nuclear power plant design, especially PWR. In this application, the HRA/HFE integrated process had specified further detail for PIF attributes which had not been obtained in HRA, and showed further investigations to treat how operators induced their human errors through their cognitive task process in their work environment. This application showed effectiveness of the process in order to provide design countermeasures for preventing potential human errors occurrence based on the extensive PIFs and their error forcing context in HRA.

In this paper, a multi-objective faculty course allocation problem with result analysis and feedback analysis based on uncertain preferences mathematical model is presented. To deal with an uncertain model, three different ranking criteria are being used to develop: a) Expected value, b) Optimistic value, c) Dependent optimistic value criterion. These mathematical models are transformed into their corresponding deterministic forms using the basic concepts of uncertainty theory. The deterministic model of DOCM consists of fractional objectives which are converted into their linear form using Charnes and Cooper’s transformation. These deterministic formulations MOFCAP are converted into a single objective problem by using the fuzzy programming technique with linear and exponential membership functions. Further, the single objective problem for all the defined models is solved in the Lingo 18.0 software to derive the Pareto-optimal solution. The sensitivity of the models is also performed to examine the variation in the objective function due to the variation in parameters. Finally, a numerical example is given to exhibit the application and algorithm of the models.

In the present paper we provide an up-to-date overview of nonparametric Cumulative Sum (CUSUM) monitoring schemes. Due to their nonparametric nature, such memory-type schemes are proved to be very useful for monitoring industrial processes, where the output does not match to a particular distributional model. Several fundamental contributions on the topic are mentioned, while recent advances are also presented in some detail. In addition, some practical applications of the nonparametric CUSUM-type control charts are highlighted, in order to emphasize their crucial role in the contemporary online Statistical Process Control.

This paper deals with the presentation and study of alternative coupling techniques for maximum and minimum values between data sets, namely the problem which is examined in this work is the possible appearance of maximum or minimum values between data sets in the same or neighboring time points. The data can be time-dependent (time series) or non-time-dependent. In this work, the analysis is focused on time series and novel indices are defined in order to measure whether the values of N sets of data display in terms of time, the maximum or minimum values at the same instances or at very close instances. For this purpose, two methods will be compared, one direct method and one indirect method. The indirect method is based on Matrices of dimensionless indicators which are denoted by [μ][MKN], and the direct method is based on a variance-type measure which is denoted by [V][MKN].

Since every mistake made in the healthcare industry can cause permanent harm or even death, some fundamental requirements should be considered when planning and designing operating rooms. Lighting is one of the most important ergonomic factors, especially for operating rooms. This paper investigates the integration of the lean approach and six sigma in measuring the efficiency of LED technology, which can also be regarded as a factor affecting the operating room efficiency. Measurement System Analysis (MSA) and Gage Control methods were applied to determine measurement variability in operating room illumination measurement process. Repeatability and reproducibility (%R&R) was found 12.89%. After calibration, %R&R value was found 8.21%, which implies that calibration helped reduce variability.

In the present paper we provide an up-to-date overview of nonparametric Exponentially Weighted Moving Average (EWMA) control charts. Due to their nonparametric nature, such memory-type schemes are proved to be very useful for monitoring industrial processes, where the output cannot match to a particular probability distribution. Several fundamental contributions on the topic are mentioned, while recent advances are also presented in some detail. In addition, some practical applications of the nonparametric EWMA-type control charts are highlighted, in order to emphasize their crucial role in the contemporary online statistical process control.

The block chain technology has immense potential in many different applications, including but not limited to cryptocurrencies, financial services, smart contracts, supply chains, healthcare services, and energy trading. Due to the critical nature of these applications, it is pivotal to model and evaluate dependability of the block chain-based systems, contributing to their reliable and robust operation. This paper models and analyzes the dependability of Bitcoin nodes subject to Eclipse attacks and state-dependent mitigation activities. Built upon the block chain technology, the Bitcoin is a peer-to-peer cryptocurrency system enabling an individual user to trade freely without the involvement of banks or any other types of intermediate agents. However, a node in the Bitcoin is vulnerable to the Eclipse attack, which aims to monopolize the information flow of the victim node. A semi-Markov process (SMP) based approach is proposed to model the Eclipse attack behavior and possible mitigation activities that may prevent the attack from being successful during the attack process. The SMP model is then evaluated to determine the steady-state dependability of the Bitcoin node. Numerical examples are provided to demonstrate the influence of the time to restart the Bitcoin software and time to detect and delete the malicious message on the Bitcoin node dependability.

The synchronous Boolean network (SBN) is a simple and powerful model for describing, analyzing, and simulating cellular biological networks. This paper seeks a complete understanding of the dynamics of such a model by employing a matrix method that relies on relating the network transition matrix to its function matrix via a self-inverse state matrix. A recursive ordering of the underlying basis vector leads to a simple recursive expression of this state matrix. Hence, the transition matrix is computed via multiplication of binary matrices over the simplest finite (Galois) field, namely the binary field GF(2), i.e., conventional matrix multiplication involving modulo-2 addition, or XOR addition. We demonstrate the conceptual simplicity and practical utility of our approach via an illustrative example, in which the transition matrix is readily obtained, and subsequently utilized (via its powers, characteristic equation, minimal equation, 1-eigenvectors, and 0-eigenvectors) to correctly predict both the transient behavior and the cyclic behavior of the network. Our matrix approach for computing the transition matrix is superior to the approach of scalar equations, which demands cumbersome manipulations and might fail to predict the exact network behavior. Our approach produces result that exactly replicate those obtained by methods employing the semi-tensor product (STP) of matrices, but achieves that without sophisticated ambiguity or unwarranted redundancy.

In this paper we model and analyze survivability and vulnerability of a cloud RAID (Redundant Array of Independent Disks) storage system subject to disk faults and cyber-attacks. The cloud RAID survivability is concerned with the system’s ability to function correctly even under the circumstance of hazardous behaviors including disk failures and malicious attacks. The cloud RAID invulnerability is concerned with the system’s ability to function correctly while occupying some state immune to malicious attacks. A continuous-time Markov chains-based method is suggested to perform the disk level survivability and invulnerability analysis. Combinatorial methods are then presented for the cloud RAID system level analysis, which can accommodate both homogeneous (based on binomial coefficients) and heterogeneous (based on multi-valued decision diagrams) disks. A detailed case study on a cloud RAID 5 system is conducted to illustrate the application of the proposed methods. Impacts of different parameters on the disk and system survivability and invulnerability are also investigated through numerical analysis.

This paper addresses machine repair problem (MRP) with M identical operating machines and control arrival policy. The server is unreliable, and can break down during service which is further repaired soon so as to avoid interruption with the service process. The server may go for working vacation in case all the customers are served. The transient analysis of machine repair problem has been done using numerical technique. Various performance measures have been derived. With the help of tables and graphs the numerical results have been shown. The present investigation find applications in various industrial and workshop situations like Automobile repair shop (ARS).

Emergency management in industrial plants is a fundamental issue to ensure the safety of operators. The emergency management analyses two fundamental aspects: the system reliability and the human reliability. System reliability is the capability of ensuring the functional properties within a variability of work conditions, considering the possible deviations due to unexpected events. However, system reliability is strongly related to the reliability of its weakest component. The complexity of the processes could generate incidental situations and the worker appears (human reliability) to be the weakest part of the whole system. The complexity of systems influences operator’s ability to take decisions during emergencies. The aim of the present research is to develop a new approach to evaluate human error probability (HEP), called Systematic Human Reliability Analysis (SHRA). The proposed approach considers internal and external factors that affect operator’s ability. The new approach is based on Nuclear Action Reliability Assessment (NARA), Simplified Plant Analysis Risk Human Reliability (SPAR-H) and on the Performance Shaping Factors (PSFs) relationship. The present paper analysed some shortcomings related to literature approaches, especially the limitations of the working time. We estimated HEP, after 8 hours (work standard) during emergency conditions. The correlations between the advantages of these three methodologies allows proposing a HEP analysis during accident scenarios emergencies. SHRA can be used to estimate human reliability during emergencies. SHRA has been applied in a nuclear accident scenario, considering 24 hours of working time. The SHRA results highlight the most important internal and external factors that affect operator’s ability.

In the reliability literature, maintenance efficiency is usually dealt with as a fixed value. Since repairable systems are subject to different degrees and types of repair, it is more convenient to regard a random variable for maintenance efficiency. This paper is devoted to the statistical study of a general hybrid model for repairable systems working under imperfect maintenance. For both failure improvement and virtual age reduction of the system, maintenance efficiency is assumed to be random, with an exponential distribution as a probability density function. The likelihood function of this model is provided, and the estimation of the model parameters is computed by considering the maximization likelihood procedure. Obtained results were tested and applied to simulated and real data sets. To construct confidence intervals, the bias-corrected accelerated bootstrap method has been used.

At the level of design of thermal power plants (TPP), when making decisions related to the choice of its macro location and micro location, disposition solution and equipment structure, the choice of the right decision method is of particular importance. Multi criteria analysis involves solving inadequately structured problems, while in practical terms it is a necessary tool in solving the day-to-day tasks of decision-making, management actions, both at the design level and the exploitation of complex energy and process systems. The development of new methods and the improvement of existing multi criteria analysis methods have influenced their increasing application in the field of energy, from both theoretical and practical aspects. A particular segment of the application of multi criteria analysis methods is to make strategic or operational decisions when solving multidisciplinary problems with predominantly technical or predominantly economic content, whether looking at a part or the energy system as a whole. The application of these methods in choosing the right solutions in the decision-making tasks of management in the design, maintenance and operation of thermal power plants is based on the development of information technology and computer technology. There are several methods in use today, of which the following should be emphasized: PROMETHEE (Preference Ranking Organization METHod of Enrichment Evaluation), AHP (Analytic Hierarchy Process), IKOR, ELECTRE (ELimination Et Choice Translating REality), MAX-MIN, MAX-MAX, Hurwicz (combination of max-max and max-min methods), SAW (Simple Additive Weighting Method), TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), conjunctive method, disjunctive method, etc. Depending on the method used, the ranking of alternatives or criteria, the best alternative or criterion or set of alternatives or criteria that meet certain conditions is obtained as a solution. Problems that can be considered using multi-criteria decision-making have certain common characteristics: a large number of criteria that must be created by the decision maker, a high likelihood of conflict between the criteria, inseparable (different) units of measure (as a rule, each criterion has different units of measure), as well as design or the choice of optimal concepts in a predetermined space. Solutions are either designing the best alternative or choosing the best action from a set of predefined final actions. This paper provides a brief description of the more important methods, along with an analysis of their advantages and disadvantages, as well as the possibility of applying them to solve the specific problem of choosing a micro location for TPP. Starting from the application of the modified multi criteria method for determining the priorities between individual alternatives for ranking and selecting the optimal micro location within a certain macro location within a predetermined framework, in a decision situation involving a number of decision makers of different specialty, examples of micro location selection for new thermal power facilities of TPP Stanari are given, TPP Gacko 2 and TPP Ugljevik 3, with preliminary definition of elimination criteria and criteria for comparison of locations of given TPPs.

Recycled aggregates (RA) are obtained from construction and demolished waste, laboratory crushed concrete and concrete waste at RMC plants. The concrete made from recycled aggregate is known as recycled aggregate concrete. The use of recycled aggregate is very beneficial to the environment in civil works. Its usage also helps in financial saving as the cost of transportation and production energy cost of natural coarse aggregate (NCA) is reduced. In India, the recycled aggregate application in lower grade concrete work is observed. However, the effect of recycled aggregate on the strength and durability of concrete restricts its use in higher-grade work. This paper presents a series of tests carried out on recycled coarse aggregate (RCA) and recycled coarse aggregate concrete (RCAC) and test results are compared with the NCA and parent concrete made from NCA. Tests were carried out as per IS code and concrete was prepared using a two-stage mixing approach in the concrete mix design. M30 concrete mix of four RCAC samples was tested at 28 days of curing and in comparison with parent concrete, it is found that on an average compressive strength is decreased by 12.89% at 28 days curing. Adhered mortar increases the porosity of the recycled aggregate and forms a weak zone between aggregate surface and mortar. In addition, test results showed the defects in recycled aggregate and helped to identify the area where concentration is necessary to improve the quality of recycled aggregate using six sigma DMAIC methodology. Total of 12 defects were found in the process and raw material. Statistical analysis was used to evaluate the performance of all the mix made with RCA.

COVID-19 is caused by a coronavirus called SARS-CoV-2. Many countries around the world implemented their own policies and restrictions designed to limit the spread of Covid-19 in recent months. Businesses and schools transitioned into working and learning remotely. In the United States, many states were under strict orders to stay home at least in the month of April. In recent weeks, there are some significant changes related restrictions include social-distancing, reopening states, and staying-at-home orders. The United States surpassed 2 million coronavirus cases on Monday, June 15, 2020 less than five months after the first case was confirmed in the country. The virus has killed at least 115,000 people in the United States as of Monday, June 15, 2020, according to data from Johns Hopkins University.With the recent easing of coronavirus-related restrictions and changes on business and social activity such as stay-at-home, social distancing since late May 2020 hoping to restore economic and business activities, new Covid-19 outbreaks are on the rise in many states across the country. Some researchers expressed concern that the process of easing restrictions and relaxing stay-at-home orders too soon could quickly surge the number of infected Covid-19 cases as well as the death toll in the United States. Some of these increases, however, could be due to more testing sites in the communities while others may be are the results of easing restrictions due to recent reopening and changed policies, though the number of daily death toll does not appear to be going down in recent days due to Covid-19 in the U.S. This raises the challenging question: How can policy decision-makers and community leaders make the decision to implement public policies and restrictions and keep or lift staying-at-home orders of ongoing Covid-19 pandemic for their communities in a scientific way?In this study, we aim to develop models addressing the effects of recent Covid-19 related changes in the communities such as reopening states, practicing social-distancing, and staying-at-home orders. Our models account for the fact that changes to these policies which can lead to a surge of coronavius cases and deaths, especially in the United States. Specifically, in this paper we develop a novel generalized mathematical model and several explicit models considering the effects of recent reopening states, staying-at-home orders and social-distancing practice of different communities along with a set of selected indicators such as the total number of coronavirus recovered and new cases that can estimate the daily death toll and total number of deaths in the United States related to Covid-19 virus. We compare the modeling results among the developed models based on several existing criteria. The model also can be used to predict the number of death toll in Italy and the United Kingdom (UK). The results show very encouraging predictability for the proposed models in this study.The model predicts that 128,500 to 140,100 people in the United States will have died of Covid-19 by July 4, 2020. The model also predicts that between 137,900 and 154,000 people will have died of Covid-19 by July 31, and 148,500 to 169,700 will have died by the end of August 2020, as a result of the SARS-CoV-2 coronavirus that causes COVID-19 based on the Covid-19 death data available on June 13, 2020.The model also predicts that 34,900 to 37,200 people in Italy will have died of Covid-19 by July 4, and 36,900 to 40,400 people will have died by the end of August based on the data available on June 13, 2020. The model also predicts that between 43,500 and 46,700 people in the United Kingdom will have died of Covid-19 by July 4, and 48,700 to 51,900 people will have died by the end of August, as a result of the SARS-CoV-2 coronavirus that causes COVID-19 based on the data available on June 13, 2020.The model can serve as a framework to help policy makers a scientific approach in quantifying decision-makings related to Covid-19 affairs.

Generally, the majority of the inventory models work on the concept that overall units produced must be perfect in terms of quality and that the storage capacity of the warehouse is unlimited. In fact, under realistic conditions, it is not possible to manufacture products with complete perfection. Furthermore, there are always some limits associated with storage capacity of the warehouse. This paper formulates an inventory model that considers the impact of imperfect quality items and shortages. The cost of storage in rented warehouse (RW) is greater than own warehouse (OW) due to fact there are better preservation facilities in RW. This work considers that defective items are completely withdrawn after the inspection process. The purpose of this inventory model is to establish the optimal order quantity and backorder size that maximize the total profit. Some numerical examples are solved, and a sensitivity analysis is included.

Due the increasing globalization of market and diverse consumer groups, market segmentation becomes an ever more important concept in current market scenario. After market segmentation, firms use the available promotional strategies to target market. In this paper, we present a model of determining the optimal dynamic promotional policies for a product in segmented market incorporating dynamic market size, where sales is assumed to be evolved through mass and differentiated promotion. Mass promotional effort is allowed in whole market with a fixed spectrum for each segment while differentiated promotion is targeted to each segment independently. The optimal promotional effort policy for each segment is obtained by applying maximum principle. Numerical illustrations are provided to show the effectiveness of the proposed method and solution procedure by discretizing the optimal control model. Furthermore, sensitivity analysis of the discount rate parameter is carried out and presented.