Search results

Software testing is needed to produce extremely reliable software products. A crucial decision problem that the software developer encounters is to ascertain when to terminate the testing process and when to release the software system in the market. With the growing need to deliver quality software, the critical assessment of reliability, cost of testing and release time strategy is requisite for project managers. This study seeks to examine the reliability of the software system by proposing a generalized testing coverage-based software reliability growth model (SRGM) that incorporates the effect of testing efforts and change point. Moreover, the strategic software time-to-market policy based on costreliability criteria is suggested.

The fault detection process is modeled as a composite function of testing coverage, testing efforts and the continuation time of the testing process. Also, to assimilate factual scenarios, the current research exhibits the influence of software users refer as reporters in the fault detection process. Thus, this study models the reliability growth phenomenon by integrating the number of reporters and the number of instructions executed in the field environment. Besides, it is presumed that the managers release the software early to capture maximum market share and continue the testing process for an added period in the user environment. The multiattribute utility theory (MAUT) is applied to solve the optimization model with release time and testing termination time as two decision variables.

The practical applicability and performance of the proposed methodology are demonstrated through real-life software failure data. The findings of the empirical analysis have shown the superiority of the present study as compared to conventional approaches.

This study is the first attempt to assimilate testing coverage phenomenon in joint optimization of software time to market and testing duration.

The purpose of this paper (Part 1 of 2) is to develop a systematic performance assessment model (PAM) for measuring facilities condition in terms of physical, environmental and operational impacts on performance.

The methodology entailed a review of literature to identify performance impacting factors (PIFs) of a facility. PIFs were categorized as physical, environmental and operational. Subsequently, 71 experts were interviewed to prioritize the identified PIFs in terms of their impact on facility performance via analytical hierarchy process (AHP) technique. A second round of interviews with 11 experts was conducted to set a utility scoring mechanism for the PIFs via multiattribute utility theory (MAUT) technique, the utility score in correlation to a set scale would describe the level of service(LoS). Finally, AHP and MAUT outputs were mathematically integrated to determine the final condition rating index of all PIFs and the whole facility as a result.

First, PIFs of a case study mosque facility under three scenarios with different groupings of functional spaces were defined. Functional spaces’ prioritization was as follows: prayer hall (57%–65%), wet areas (25%–26%), minaret (9%–10%), and yard/site (7%–8%). Subsequently, each space’s PIFs and its subcategories were assigned weights as well, the first level of PIFs’ relative weights was as follows: physical (68%–73%), operational (19%–20%) and environmental (7%–13%). Physical PIFs weights differed per functional space but structural factor dominated with (38%–71%) relative weight except for the yard/site. Finally, a detailed condition evaluation mechanism for each PIF was defined.

This study contributes to facilities management industry and offers a systematic approach encompassing multiple PIFs that will regulate inspection then maintenance through desired corrective actions by facility managers to achieve desired LoS.

Executives are searching for ways to deliver consistent improvements in productivity and profitability while addressing economic realities. One initiative that has been discovered by many organizations is the integration a quality process into their or ganization that is based on the Malcolm Baldrige National Quality Award (MBNQA). Many studies have been done showing that award winning companies tend to out perform peers and competitors, yet many managers are reluctant to under take the large initiative required to work toward the award. This reluctance may stem from the belief that the reported benefits are not those that are important for managers to justify the effort. The purpose of this research is to begin an exploratory study that examines the expectations of company managers, executives, and other professionals regarding the types of firm performance and returns that would be needed to justify undertaking the MBNQA process. The results showed that while financial performance of the firm is the strongest justification managers consider, and that while their expectations for improved financial performance are some what high, the financial returns are certainly not out of the realm of normal expectations for returns from other projects.

Behavioral economics poses a challenge for the welfare evaluation of choices, particularly those that involve risk. It demands that we recognize that the descriptive account of behavior toward those choices might not be the ones we were all taught, and still teach, and that subjective risk perceptions might not accord with expert assessments of probabilities. In addition to these challenges, we are faced with the need to jettison naive notions of revealed preferences, according to which every choice by a subject expresses her objective function, as behavioral evidence forces us to confront pervasive inconsistencies and noise in a typical individual’s choice data. A principled account of errant choice must be built into models used for identification and estimation. These challenges demand close attention to the methodological claims often used to justify policy interventions. They also require, we argue, closer attention by economists to relevant contributions from cognitive science. We propose that a quantitative application of the “intentional stance” of Dennett provides a coherent, attractive and general approach to behavioral welfare economics.

A repair contract problem has been analysed. Each contract alternative implies in a specific cost of the contract and a commitment of repair time. The decision maker has to choose the best alternative, taking into account the consequences modelled through an additive utility function. These consequences consist of system performance and the cost of the contract. The system performance is represented by system interruption time. The solution corresponds to a contract which optimises the additive utility function. The paper presents formulation and derivations in order to find the solution. A numerical application of the decision model proposed is presented including a sensitivity analysis step.

The purpose of this study is to develop an integrated model for the stochastic multiproject scheduling and material ordering problems, where some of the prominent features of offshore projects and their environmental-degrading effects have been embraced as well. The durations of activities are uncertain in this model. The developed formulation is tri-objective that seeks to minimize the expected time, total cost and CO₂ emission of all projects.

A new version of the multiobjective multiagent optimization (MOMAO) algorithm has been proposed to solve the amalgamated model. To empower the MOMAO, various procedures of this algorithm have been modified based on the multiattribute utility theory (MAUT) technique. Along with the MOMAO, this study has employed four other meta-heuristic methodologies to solve the model as well.

The outputs of the MOMAO have been put to test against four other optimizers in terms of convergence, diversity, uniformity and computation times. The results of the Mean Ideal Distance (MID) metric have revealed that the MOMAO has strongly prevailed its rival optimizers. In terms of diversity of the acquired solutions, the MOMAO has ranked the first among all employed optimizers since this algorithm has offered the best solutions in 56.66 and 63.33% of the test problems regarding the diversification metric and hyper-volume metrics. Regarding the uniformity of results, which is measured through the spacing metric (SP), the MOMAO has presented the best SP values in more than 96% of the test problems. The MOMAO has needed more computation times in comparison to its rivals.

A real case study comprising two concurrent offshore projects has been offered. The proposed formulation and the MOMAO have been implemented for this case study, and their effectiveness has been appraised.

Very few studies have focused on presenting an integrated formulation for the stochastic multiproject scheduling and material ordering problems. The model embraces some of the characteristics of the offshore projects which have not been adequately studied in the literature. Limited capacities of the offshore platforms and cargo vessels have been embedded in the proposed model. The offshore platforms have spatial limitations in storing the required materials. The vessels are also capacitated and they also have limited shipment capacities. Some of the required materials need to be transported from the base to the offshore platform via a fleet of cargo vessels. The workforces and equipment can become idle on the offshore platform due to material shortage. Various offshore-related costs have been integrated as a minimization objective function in the model. The cargo vessels release CO₂ detrimental emissions to the environment which are sought to be minimized in the developed formulation. To the best of the authors' knowledge, the MOMAO has not been sufficiently employed as a solution methodology for the stochastic multiproject scheduling and material ordering problems.

Industrial companies are continuously looking to improve their business turn-over and choose improvement actions to achieve this objective. Legitimating this choice by only a strategic financial point of view currently has some limits, because additional ones such as quality, delay, resources needs have also to be taken into account. The multicriteria decision aid (MCDA) approaches have been developed to deal with problems such as ranking, sorting or quantification. The purpose of this paper is to describe the operational aspects in MCDA deployment through a case study submitted by the SME Fournier Company.

This paper concerns analytical overall performance models to allow the decision-makers (DMs) to select potential actions to launch regarding given decision criteria. The principles of the main MCDA approaches, outranking and aggregation, are presented. Then representative MCDA methods, ELECTREIII and MACBETH, are successively deployed to a tactical decision problem of business turn-over increase. From the building of their respective preference modelling, decision aid is provided in the form of preference relation or performance function.

A formal framework supplying information to the industrial DMs in order to legitimate the action selection is described. In addition, the DMs feedback highlights the required expertise for the preference modelling.

The article gives a guideline for industrial practitioners to carry out ELECTREIII and MACBETH for their improvement action selection.

SMEs can find an adapted framework for their improvement actions choice dealing with financial and non-financial criteria. It is also enriched by practical considerations concerning the methods deployment for tactical decision problems.

This chapter examines why U.S. offshore wind farms do not exist and identifies sites most suitable for development based on European offshore wind farms. A survey of current literature indicates that U.S. development is stalled due to a lack of government and financial support. The literature identifies common attributes associated with the successful deployment of European offshore farms and provides a basis for a multi-criteria decision analysis of potential U.S offshore wind farm sites. A review of European wind farms indicates that a small, 10–50 MW farm located in shallow waters of less than 20 m might be more successful than previous U.S. development efforts. The review also identifies common European attributes deemed critical for success. These attributes are modified, taking into account unique U.S. factors, and a set of nine critical attributes are derived for use in a multi-criteria decision analysis model of suitable U.S. locations. The nine critical attributes (wind quality, water depth, shore distance, state support, public support, industrial support, population density, weather, and energy costs), along with associated utility function values, are applied to 23 past and current proposed U.S. sites. The model identified three sites, in Galveston Island, TX, Port Isabel, TX, and Block Island, RI, as being most favorable for a small wind farm.

Three distinct trends are evolving that characterize the challenges facing corporate planners in managing language as a corporate asset. These are the evolution of the knowledge economy, the globalization of business and economy, and the increasing diversity of the workforce. These trends call for attention to the role language plays in the creation of intellectual and organizational capital. Linguists have been hampered by their inability to model how linguistic conditions affect economic processes. They have long attempted to assess the economic value of language as a commodity, but with little success. The purpose of this article is to describe language as an asset, and argue that recent advances in decision sciences have sufficiently removed the deficit in theoretical and empirical research that challenged the linguists.

Drawing an analogy between language and currency, this article identifies the functions that language must perform in terms of exchange, accounting and storage of information, knowledge and know‐how.

Factors that contribute to the value of a language are discussed through comparative analysis of these functions from the perspectives of linguists, economists and decision analysts. Based on the Social Judgment Theory, the article develops a judgment‐analytic framework for the assessment of the value of a language.

Concluding that the corporate and community planning perspectives are divergent, the paper identifies the direction for future research to inform formulation of language policies. The value of a language may be affected by the degree to which the language is used in the demographic community defining an organization's strategic environment, the investment in the language relative to other available languages, demand for the language as a commodity within the organization's strategic community, and so on.

With such conception of language, corporations can begin to think in terms of a portfolio of language assets much in the same way as it thinks of portfolio of financial currency assets.