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This paper considers a series system consisting of n different components having unknown and variable failure rates, where the lifetime of components follow exponential distribution having non-constant failure rates. Moreover, the failure rates are bounded by above and are dependent on environmental factors such as temperature, pressure, through linear relationship. The purpose of this paper is to design a component reliability test plan for such a series system with an unknown variable failure rate.

The reliability of the system is estimated with the help of the unbiased estimator of failure rate. The testing procedure is stopped when a fixed number of failures occur for each component.

An optimal reliability test plan is designed and the resultant non-linear integer optimization problem is formulated satisfying the constraints of producer’s and consumer’s risks. The obtained results are compared with the results available in the literature. Some examples are considered to illustrate the approach.

It is observed that use of prior information in the form of an upper bound and incorporation of environmental factors have the advantage of savings in the total testing cost.

An application development framework for a software project based on fusion as an object‐oriented application development method is presented. An object‐oriented approach has been adopted for the design and implementation of the prototype interactive visual modelling system for building a visual presentation of a refinery process and creation of linear programming model for optimizing production decision variables. The main reason for this selection is the consideration of object‐oriented programming (OOP) as an obvious vehicle for the development of complex visual interactive modelling systems. The main dimensions of the framework are as follows: OO approach, fusion method, computer‐aided software engineering (CASE) tool, application development tool, GUI development tool, and C++ as an implementation language.

A variant of Kohonen's algorithm for the self‐organizing map (SOM) is used to solve a continuous location‐routing problem that can be applied to identify potential sites for subsequent selection by a discrete finite set model. The paper aims to show how the algorithm may be customized to fit the problem structure in a way that allows aspects of location and routing to be integrated into the solution procedure.

A set of test instances is used to compare the solutions of the neural network to those obtained by sequential approaches based on a savings procedure.

Compared to the results of the sequential approaches, the neural network yields good results.

Future work may cover the expansion of the neural approach to multi‐depot and multi‐stage problems. Additionally, application of procedures other than the savings procedure should be evaluated with respect to their potential for further enhancing the solution quality of the sequential approaches.

This paper shows that strategic location decisions in practical applications with long‐term customer relationships can be taken using simultaneously generated routing information on an operational level.

The paper provides a new variety of applications for SOM as well as high quality results for the specific type of problem considered.

In the field of inventory management, it is a well‐known fact that centralisation, by sharing the risk between several entities, helps reduce the inventory required to provide a certain level of service. In practice, centralisation can be difficult to accomplish, because improvements to the system’s general performance may be achieved at the expense of some of the entities involved. This paper describes a simulation‐based methodology used to study the impacts of equipment pooling on a group of local community service centres (CLSCs) in the Montreal (Canada) region. In addition to quantifying the benefits of the pooling process, the approach allowed the stakeholders to reach an agreement by appraising various pooling scenarios and identifying the conditions that would help ensure fairness.

The purpose of this paper is to propose a robust optimization approach for the short‐term scheduling of batch plants under demand uncertainty where the uncertain parameters can be described by a normal distribution function.

The robust optimization formulation introduces a small number of auxiliary variables and additional constraints into the original mixed integer linear programming problem, generating a deterministic robust counterpart problem which provides the optimal solution given the magnitude of the uncertain data, a feasibility tolerance, and a reliability level.

Developed robust optimization approaches produce robust solutions for uncertainties in both the coefficients and right‐hand‐side parameters of the linear inequality constraints and can be applied to address the problem of production scheduling with uncertain parameters.

The choice of the magnitude of the uncertain data, a feasibility tolerance, and a reliability level are the main limitation of the model.

Very useful advice for short‐term scheduling of batch plants under demand uncertainty.

The paper proposes a robust optimization approach for short‐term scheduling of batch plants under demand uncertainty. Computational results are presented to demonstrate the effectiveness of the proposed approach.

The failure of organizations to adopt CASE tools has been an area of interest to business researchers for over a decade. The purpose of this study is to test whether the previous research provides a basis for predicting the current adoption of CASE tools by organizations. This study uses a neural network methodology to predict CASE tool adoption using factors that were previously identified in the literature. The model consisted of six variables: IS department stability, need to improve IS department performance, use of external sources of knowledge, job rotation, pressure to reduce development time, and CASE champion. The study found that all the variables were relevant in the prediction of CASE tool adoption with an average accuracy of 71.43 percent.

The purpose of this paper is to develop energy optimizer (ENEO) – a model‐based decision support system (DSS) for an existing European chemical plant with a multi‐stage continuous production process. The system comprises two modules – energy cost minimization and joined energy cost minimization and output maximization. Following the description of the researched production, the paper presents a gist of the underlying formulations. Then, it tests the DSS on real data instances with a focus on its configuration, practical implications and implementation challenges.

The design of the planning tool is consistent with that of the model‐based DSS and based on the existing information systems. The defined research problems are explored with the use of quantitative methods – the operations research methodology.

The findings show that ENEO reflects the essence of the researched production process and can provide benefits in practical business operations.

Both the proposed system configuration and the formulated models lay a foundation to further research within the described industrial setting.

The system can be utilized in daily operations to provide substantial cost savings, improved capacity utilization and reactivity.

This paper contributes to research by bridging the gap between theory and practice. On the one hand, it describes an unexplored problem and its subsequent solution embodied in the DSS. On the other hand, it emphasizes the importance of applying the operations research methodology to the real‐world issues. Therefore, this work is valuable to both academics and practitioners.

This paper aims to study the impact of information and communication technologies in organizations to capture and manage intellectual capital. The paper focuses particularly on the use of Web-based geographical information systems (Web GIS) to increase relational capital.

This paper analyzes in detail the Web sites of 143 general merchandise retailers, which have been grouped according to their dominant operational format. Menus and search tools have been used to find out about the way in which these retailers provide information to the customers about their stores, with special attention to the use of Web GIS.

The results obtained show that most of the companies analyzed use Web GIS to provide information about the location and other characteristics of the stores. The findings in this paper also suggest that the quantity and quality of the information provided by is somewhat related to the company size.

The limitations of this study come from the difficulty of predicting whether small and medium enterprises (SMEs) will generalize the use of Web GIS in the future.

The findings of the paper suggest that large retail firms have adopted Web GIS to provide information to the customers and for other geomarketing purposes. Moreover, SMEs should use Web GIS to improve their relationship with customers.

To the authors’ knowledge, no paper has analyzed in detail the use of Web GIS by companies with the aim of enhancing relational capital.

XML has spread beyond the computer science fields and reached other areas such as, e‐commerce, identification, information storage, instant messaging and others. Data communicated over these domains are now mainly based on XML. Thus, allowing non‐expert programmers to manipulate and control their XML data is essential. The purpose of this paper is to present an XA2C framework intended for both non‐expert and expert programmers and provide them with means to write/draw their XML data manipulation operations.

In the literature, this issue has been dealt with from two perspectives: first, XML alteration/adaptation techniques requiring a certain level of expertise to be implemented and are not unified yet; and second, Mashups, which are not formally defined yet and are not specific to XML data, and XML‐oriented visual languages are based on structural transformations and data extraction mainly and do not allow XML textual data manipulations. The paper discusses existing approaches and the XA2C framework is presented.

The framework is defined based on the dataflow paradigm (visual diagram compositions) while taking advantage of both Mashups and XML‐oriented visual languages by defining a well‐founded modular architecture and an XML‐oriented visual functional composition language based on colored petri nets allowing functional compositions. The framework takes advantage of existing XML alteration/adaptation techniques by defining them as XML‐oriented manipulation functions. A prototype called XA2C is developed and presented here for testing and validating the authors' approach.

This paper presents a detailed description of an XML‐oriented manipulation framework implementing the XML‐oriented composition definition language.

Measuring the similarity between two resources is considered difficult due to a lack of reliable information and a wide variety of available information regarding the resources. Many approaches have been devised to tackle such difficulty. Although content-based approaches, which adopted resource-related data in comparing resources, played a major role in similarity measurement methodology, the lack of semantic insight on the data may leave these approaches imperfect. The purpose of this paper is to incorporate data semantics into the measuring process.

The emerged linked open data (LOD) provide a practical solution to tackle such difficulty. Common methodologies consuming LOD mainly focused on using link attributes that provide some sort of semantic relations between data. In this work, methods for measuring semantic distances between resources using information gathered from LOD were proposed. Such distances were then applied to music recommendation, focusing on the effect of various weight and level settings.

This work conducted experiments using the MusicBrainz dataset and evaluated the proposed schemes for the plausibility of LOD on music recommendation. The experimental result shows that the proposed methods electively improved classic approaches for both linked data semantic distance (LDSD) and PathSim methods by 47 and 9.7%, respectively.

The main contribution of this work is to develop novel schemes for incorporating knowledge from LOD. Two types of knowledge, namely attribute and path, were derived and incorporated into similarity measurements. Such knowledge may reflect the relationships between resources in a semantic manner since the links in LOD carry much semantic information regarding connecting resources.