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The purpose of this paper is to present the technique for evaluating the performance of a condensate system of a coal-based thermal power plant situated in the northern part of India. The data which used for system availability evaluation are not precise and are uncertain and, further, collected from concerned power plant history sheets and from discussion through plant personnel.

In the proposed model, traditional Markov birth-death process using a probabilistic approach is used to analyze the performance of a complex repairable condensate system of power plant up to a desired degree of accuracy. This approach has been demonstrated by breaking the condensate system into six subsystems arranged in series with two feasible states, namely, working and failed, labeled in a transition diagram and modeled as a Markov process, using Chapman–Kolmogorov equations, which are used for development of a probabilistic stochastic model for availability analysis in a more effecting manner, considering some suitable assumptions.

This study of analysis of reliability and availability can help in increasing the plant production and performance. The analysis is done with the help of availability matrices, which are developed using different combinations of failures and repair rates of all subsystems. To achieve the goal of maximum power generation, it is required to run the various subsystem of the concerned system of plant, failure free for a long duration. Therefore, the present approach may be a more powerful analysis tool to access the performance of all subsystems of a condensate system in terms of availability level achieved in availability matrices. The results of present study are found to be highly beneficial to the plant management for making maintenance decisions.

The present paper suggests a suitable technique for stochastic modeling and availability evaluation of an industrial system using Markovian approach and drawing a transition diagram to represent the operational behavior of the system. The present methodology includes the advantage of the ability to model and develop a more complex industrial system and helps in improving the performance and handling the uncertainties and possibilities of an industrial system.

The paper discusses the problems encountered in the management and quantitative evaluation of fire risk and safety in a building. Rational methods for obtaining solutions to these problems are provided by non‐deterministic mathematical models rather than deterministic models. This is due to the fact that the occurrence and spread of an accidental (not arson) fire are random phenomena affected by uncertainties caused by several factors. Non‐deterministic models discussed briefly in the paper include simple statistical and probabilistic models, regression methods, probability distributions, fault and event trees and stochastic models. The paper only provides a framework for applying these models to any type of facility. For any type, it may be necessary to modify these techniques, collect all the relevant data and perform the analyses to derive results and conclusions applicable to that type.

The interaction of two species can be considered to be either mutually competitive, the “co‐predator” case, or dominated by one species, the “predator‐prey” model. The study of stochastic versions of these models has been impeded by the occurrence of nonlinear expressions in the characterization of the resulting probability structure. Generating function methods are used to study the bivariate and conditional probability distributions and their moments. The results are illustrated using a Laplace transform technique for obtaining numerical values. These methods can be used in the development of ecological prediction techniques and can be extended easily to the bivariate logistic model.

Airport capacity constraints lead to operational congestion and delays, which have become major threats to the aviation industry. They impose large costs on airlines and their passengers. Uncertainty in demand or unexpected events can cause a mismatch between capacity and demand, resulting in either capacity oversupply, with a decrease in efficiency, or airport congestion over an extended period. Moreover, airport capacity is rather difficult to define due to its multifaceted and dynamic nature, and it depends both on the available infrastructure and on operating procedures. Additionally, traditional capacity management methods do not consider relevant behavioral economic challenges to conventional analysis, particularly failure of the expected utility hypotheses and dependence of valuations on reference points. This study aims to develop a preliminary framework to include economic concepts when evaluating expansions of airport capacity.

This paper reviews major opportunities in airport demand and capacity management from an economic perspective while appraising the challenges involved in airport capacity expansion processes that have not been fully completely in past studies. Although welfare economics provides the conceptual foundations for demand/capacity analyses, the authors integrate the findings regarding capacity definition, uncertainty management and behavioral economics into standard economics to guide the measurement of the airport capacity expansion problem.

The authors obtain several insights regarding airport capacity and demand management. First, airport capacity is a complex metric when evaluating airport expansion, and it depends both on the available infrastructure and on operating procedures. Furthermore, airport throughput is highly conditioned by factors that shape capacity and delay and shows significant variability when these factors are modified. Second, a marginal change in capacity at congested airports may have a great impact on demand distribution, airline competition, aircraft types, fares, operating revenues, route map and other characteristics of a given airport. Behavior after capacity expansion is highly reliant on the slot allocation models. Additionally, overall social welfare is usually affected after changes in infrastructure in terms of increased connectivity, economic benefits and negative externalities, including noise and local pollution. Third, on-time performance is clearly nonlinear, and thus sensitive to variations in demand and capacity. Finally, airport capacity and demand management involve a trade-off between mitigating congestion and maximizing capacity utilization, so decision-making tools are required to support and enhance policy and managerial choices. Three main challenges arise when developing new methods for evaluating airport expansions: the definition of capacity, the management of uncertainty in demand and the need to consider economic concepts.

This paper explores and produces an in-depth understanding of the problem of airport capacity and demand balance. The authors propose a preliminary framework that considers the challenges that have been previously identified and that, particularly, provides an economic perspective for airport capacity expansion processes. This framework is completed with a theoretical model to help policymakers and airport operators when faced with a capacity development decision.

The purpose of this paper is to identify the criticality of various sub-systems through the behavioral study of a multi-state repairable system with hot redundancy. The availability of the system is optimized to evaluate the optimum combinations of failure and repair rate parameters for various sub-systems.

The behavioral study of the system is conducted through the stochastic model under probabilistic approach, i.e., Markov process. The first-order differential equations associated with the stochastic model are derived with the use of mnemonic rule assuming that the failure and repair rate parameters of all the sub-systems are constant and exponentially distributed. These differential equations are further solved recursively using the normalizing condition to obtain the long-run availability of the system. A particle swarm optimization (PSO) algorithm for evaluating the optimum availability of the system and supporting computational results are presented.

The maintenance priorities for various sub-systems can easily be set up, as it is clearly identified in the behavioral analysis that the sub-system (A) is the most critical component which highly influences the system availability as compared to other sub-systems. The PSO technique modifies input failure and repair rate parameters for each sub-system and evaluates the optimum availability of the system.

A bottom case manufacturing system is under the evaluation, which is the main component of front shock absorber in two-wheelers. The input failure and repair rate parameters were parameterized from the information provided by the plant personnel. The finding of the paper provides the various availability measures and shows the grate congruence with the system behavior.

Various explanations are possible for the observation of similar outcomes from repeated choices, for example, both cognitive psychologists and behaviourists find support for their viewpoints in such patterns. Pattern recognition and interpretation are necessarily model‐bound activities as, indeed, are attempts to analyse such data in terms of probabilities. In the face of many alternative procedures with their associated rationales relative simplicity of screening is important in assessing new data in terms of their structure and conformity to a model. This article outlines in a non‐technical way one such procedure which is appropriate to analyse data, particularly brand choice observations, expressed in dichotomous fashion. The recognition of such patterns and a formalisation of an underlying rationale are necessary steps in the improvement of both data‐handling technique and the furtherance of our understanding of consumer behaviour.

This paper aims to propose a framework for building a flexible supply chain business model using an object‐oriented approach.

The proposed modeling framework has used the de facto object‐oriented modeling standard, Unified Modeling Language (UML), for building a supply chain model. To compensate for the potential weakness of the pure object‐oriented model and UML, the framework integrates business rules with its model.

This paper provides information about several sources, which are the bases of modeling a supply chain. The paper regards a supply chain as five view models with four business domains and each domain consists of functions, resources, processes, interactions and business rules.

A typical manufacturing supply chain is considered as a target system to apply this design methodology. The paper does not handle a real case study as an application example.

This paper newly added the category of “Business Process Reengineering (BPR)‐driven” models to the traditional taxonomy of supply chain models and proposed an object‐oriented business model (OOBM) for a supply chain as one of the BPR‐driven models. It is hoped that this modeling approach is highly usable and adds value to an analyst, practitioner, and designer in the field of supply chain.

To propose a categorization of the different conflation procedures at the two basic approaches, non‐linguistic and linguistic techniques, and to justify the application of normalization methods within the framework of linguistic techniques.

Presents a range of term conflation methods, that can be used in information retrieval. The uniterm and multiterm variants can be considered equivalent units for the purposes of automatic indexing. Stemming algorithms, segmentation rules, association measures and clustering techniques are well evaluated non‐linguistic methods, and experiments with these techniques show a wide variety of results. Alternatively, the lemmatisation and the use of syntactic pattern‐matching, through equivalence relations represented in finite‐state transducers (FST), are emerging methods for the recognition and standardization of terms.

The survey attempts to point out the positive and negative effects of the linguistic approach and its potential as a term conflation method.

Outlines the importance of FSTs for the normalization of term variants.

This study aims to analyze the epidemic modeling applications and policy‐making strategies for six different infectious diseases in a number of countries, thus comparing and contrasting research in underdeveloped, developing, and developed countries.

A systematic review has been conducted by identifying relevant studies for six diseases from different sources and selecting 74 publications for inclusion. These selected publications are classified and analyzed based on infectious disease, control policies, theme and objective, methodology, origin of population data, publication year and results.

Review results indicate that disaster preparedness and surveillance plans for epidemics are available mostly for developed countries. There is a need for further research in both developing and developed countries because of the ease of dispersion, which constitutes a universal threat. Analysis of the publications suggests that epidemic disasters are mostly studied by researchers in the field of medicine or biology with the aim of assessing the potential impact of an epidemic. The authors highlight the need for further research in operations research and disaster management fields and propose further research directions in the area of disaster management.

This review emphasizes the importance of epidemic disaster modeling for the preparedness stage of disaster management and policy making. Disease and population‐specific intervention policies (e.g. vaccination) reported in this review should set an example and help policy makers during their decision making.

Potential use of the epidemiological modeling on further planning and decision‐making issues in the context of disaster management is studied for the first time.