International Journal of Reliability, Quality and Safety Engineering

The multistate sliding window system (SWS) comprises $n$ multistate components arranged in a line; each group of $r$ consecutive multistate components is considered as a window. If the total performance rate in a window does not meet the predetermined demand $W$, then that window is regarded as a failure. The SWS fails if and only if there exists at least one failed window. Several researchers have considered the component assignment problem for the SWS with the aim of finding an appropriate component arrangement that maximizes system reliability. Such an arrangement is called the optimal arrangement. Although several metaheuristic and heuristic algorithms have been proposed, an exact algorithm for solving the component assignment problem of the SWS has not been developed thus far. Therefore, in this study, a branch-and-bound-based algorithm is developed to determine the optimal arrangement of the SWS efficiently. Furthermore, a recursive method is proposed to compute the system reliability. Combining the branch-and-bound-based algorithm with the recursive method enables reduction of the complexity of the reliability computations for determining the optimal arrangement. To investigate the efficiency of the branch-and-bound-based algorithm, numerical experiments were conducted; it was observed that the parameters $n$ and $r$ have the maximum effect on computation time, whereas parameter $W$ has minimal effect. The proposed algorithm is useful for improving the reliability of a practical system that can be expressed as an SWS. In addition, the optimal arrangements can be used to measure the heuristic and metaheuristic performances because they guarantee global optimality.

In this paper, we study the closure property of the Increasing Failure Rate (IFR) class under the formation of coherent systems. Sufficient conditions for the nonpreservation of the IFRattribute for reliability structures consisting of $n$ independent and identically distributed ($i.i.d.)$ components are provided. More precisely, we deal with the IFR preservation (or nonpreservation) under the formation of structures with two common failure criteria by the aid of their signature vectors.

Software Quality has many parameters that govern its value. Of them, usually, Reliability has gained much attention of researchers and practitioners. However, today’s ever-demanding environment poses severe challenges in front of software creators as to continue treating Reliability as one of the most important attributes for governing software quality when other important parameters like re-usability, security and resilience to name a few are also available. Evaluating, ranking and selecting the most approximate attribute to govern the software quality is a complex concern, which technically requires a multi-criteria decision-making environment. Through this paper, we have proposed an Intuitionistic Fuzzy Set-based TOPSIS approach to showcase why reliability is one of the most preferable parameters for governing software quality. In order to collate individual opinions of decision makers; software developers of various firms were administered for rating the importance of various criteria and alternatives.

Code error correction methods have been important techniques at a radio environment and video stream transmission. In general, when a server transmits some data packets to a client, the server resends the only loss packets. But in this method, a delay occurs in a transmission. In order to prevent the transmission delay, the loss packets are restored by the error correction packet on a client side. The code error correction method is called Hybrid Automatic Repeat reQuest (ARQ) and has been researched. On the other hand, congestion control schemes have been important techniques at a data communication. Some packet losses are generated by network congestion. In order to prevent some packet losses, the congestion control performs by prolonging packet transmission intervals, which is called High-performance and Flexible Protocol (HpFP). In this paper, we present a stochastic model of congestion control based on packet transmission interval with Hybrid ARQ for data transmission. That is, if the packet loss occurs, the data packet received in error is restored by the error correction packet. Moreover, if errors occur in data packets, the congestion control performs by prolonging packet transmission intervals. The mean time until packet transmissions succeed is derived analytically, and a window size which maximizes the quantity of packets per unit of time until the transmission succeeds is discussed.

Here, we appraise the reliability for numerous complex structures (series structure, parallel structure and bridge structure) using accuracy and score function under fuzzy environment. The main focus of this effort is to address an advanced technique for fuzzy reliability evaluation of various complex systems having different arrangements by treating reliability of the unit/component as an interval valued intuitionistic hesitant fuzzy element. This technique helps to handle uncertainty and hesitancy in multi-attribute group decision-making related issues, specially when information occurs in interval form in fuzzy set. A numerical illustration is also included to demonstrate the proposed technique.

Enterprise resource planning (ERP) projects are prone to risk from the strategic, operational, technical and organizational perspective. The assessment of project risks is a challenging task for project managers and is considered in a class of a multi-criteria decision making (MCDM) problem. In this work, an MCDM approach has been presented for risk assessment in ERP project by COPRAS under fuzzy environment where the vagueness and subjectivity are handled with linguistic terms parameterized by triangular fuzzy number. Fuzzy COPRAS (COPRAS-F) has been used to determine the weight of risk criteria and then prioritize the risk factor based on the calculated weight of criteria. The prioritized risk factors have been classified in scale from almost certain to rare risks. The proposed approach is illustrated with a real case studying fertilizer plant and the associated results have been compared with fuzzy TOPSIS technique. The result of this study demonstrates that excessive customization, ineffective consulting services experiences, complex architecture and high number of modules, poor project team skills, inadequate change management, inadequate ERP selection, ineffective strategic thinking and planning, poor leadership, lack of business process re-engineering and low-key user involvement are top ten risks that need to be mitigated to avoid failure of ERP project.

Software reliability evaluation of complex systems is always a challenging task with conventional methods comprising both functional as well as nonfunctional aspects of real-world applications. Prevailing model frameworks moreover apply a nonfunctional approach (black-box model) that is modeled on defect data or through a functional approach (white-box model) that uses component or state-based interactions. Also, other challenges involve integrating both approaches, and validating user profiles of software operation. Further, reliability assessment is one among the most important and desirable qualities of service requirements of software systems, particularly in monitoring critical business transactions. Here, we propose a model framework to evaluate the overall reliability estimation involving both functional and nonfunctional model analyses using: (a) white-box assessment based on intercomponent analysis via component-based Cheung’s model and user profile validations with one of the identified deep learning techniques and (b) black-box modeling evaluation via generalized stochastic Petri nets based on orthogonal defect classification. A newly introduced deep learning model using white-box analysis is validated with pertinent usage profiles to establish a new trend in artificial neural networks and as well with software reliability estimation. Additionally, we introduce and present a quantitative technique — analytical hierarchy process — to integrate reliability assessment and provide weights to the white-box and as well for black-box approaches to quantify overall reliability estimation. The proposed framework is illustrated with an application case study.