International Journal of Reliability, Quality and Safety Engineering

This paper introduces the rationale and the fundamental elements and algorithms of a reliability engineering methodology combining analytical and simulation tools, and discusses its application to the design of a large, multi-cell and heterogeneous production system with just-in-time (JIT) deliveries. In order to cope with the inherent complexity of such analysis, a two level hierarchical modeling and evaluation framework was developed. Local models are first obtained from the failure and repair processes of the manufacturing equipment. Then, these models are combined with the failure propagation delays introduced by the work-in-process buffers in order to obtain the system level model. The evaluation algorithm is able to deal with reliability models containing stochastic processes with generalized distributions. This fundamental requirement comes from the fact that repair and failure propagation processes typically present hyper-exponential distributions, e.g., lognormal distributions, that cannot be assessed using the conventional reliability techniques. The second part of the paper addresses several design issues of the production system that directly impact the reliability of the deliveries, and explains how the behavioral and structural characteristics of JIT production systems were explored in order to implement effective evaluation algorithms.

The today's fast-paced, competitive environment in the field of Science and Technology, demands highly reliable hardware and software in order to achieve new breakthroughs in quality and productivity. In this scenario, first release of software products includes enough features and functionality to make it useful for the customers. Later, software companies have to come up with upgradation or add-ons in their software to survive in the market through a series of releases. Each succeeding upgradation offers some innovative performance or new functionality, distinguishing itself from the past releases. In one-dimensional Software Reliability Growth Models (SRGM) researcher used one factor such as Testing-Time, Testing-Effort or Coverage, etc. but within a two-dimensional SRGM environment, the process depends on two-types of reliability growth factors like Testing-time and Testing-effort. In addition, we also consider the combined effect of bugs encountered during testing of present release and user reported bugs from the operational phase. The model developed in the paper takes into consideration the testing and the operational phase where fault removal phenomenon follows, logistic and Weibull model, respectively. The paper also comprises of formulating an optimal release problem based on Multi-Attribute Utility Theory (MAUT). Lastly, the model validation is done on real dataset of software already released in the market with successive generations.

Probability distributions play an important role in the reliability studies and have many applications in engineering. Estimation of the distribution parameters is also significant for making a decision. It is difficult to trap the global solution for the estimation of parameters by the traditional methods such as maximum likelihood estimator (MLE), as these methods trap a local solution. To get the global values of these parameters is complicated. In this paper, we have developed a two-phase approach by taking the advantages of one of the evolutionary algorithms, namely the particle swarm optimization (PSO) for getting the global values of the distribution parameters. PSO exploits the concept of the moment method to estimate the distribution parameters by using mean, standard deviation, coefficient of variation (CV) and Anderson–Darling (AD) statistics for the best fitting of the distribution to failure data. In comparison with the widely used algorithms, a meta-heuristic algorithm (PSO) offers much more choices for system designers.

Ships have a wide variety of machinery available onboard that is crucial for her sustenance at sea for prolonged durations. The machinery can be grouped into various plants, such as propulsion plant, air conditioning plants, power generation plants, etc., each having its own specific function. The plants in turn are composed of various systems which further comprise various types of machinery. There are redundancies built in at the plant level, as well as at the system and at machinery level, so as to improve the reliability of the ship as a whole. Planning of maintenance schedule, specifically for tasks which can only be undertaken in an ashore repair yard is a daunting task for the maintenance managers. The paper presents a NSGA-II (nondominated sorting genetic algorithm) based multi-objective optimization approach to arrive at an optimum maintenance plan for the vast variety of machinery in order to improve the average reliability of ship's operations at sea at minimum cost. The paper presents the advantages that can accrue from introducing short maintenance periods for a select group of machinery, within the constraints of mandatory operational time, over the method of following a common maintenance interval for all the machinery. The problem function in hand is nonlinear, multi-modal and multi-objective in nature. The search spaces for the problem is noncontinuous and the (multiple) variables, such as time interval for maintenance, serial number of equipment, number of minor maintenance actions, etc., are uncoded real parameters.

The globalization of the economy and the competition between emerging countries has compelled machinery manufacturers in developed countries to have new production methods in an environment of "consensual" standardization and extensive regulation. However, very little research has focused on the use of these standards and regulations. This empirical exploratory study aims to develop a profile of the adoption and use of normative and regulatory documents by Québec machinery manufacturers. It investigated the data from 46 manufacturing companies in this Canadian province. The statistical analyses led to many relevant findings. They revealed that some factual characteristics positively affect these companies' use of standards and regulations. However, other apparently important factors, but having no significant impact, were also identified. The results will serve as tools for the actors (machine designers, quality and safety and health managers, government agencies, and universities) in improving machinery production and utilization conditions in Québec, Canada and elsewhere.