Abel Fernandez

Understanding Simulation Solutions to Resource Constrained Project Scheduling Problems with Stochastic Task Durations

AbstractThe project scheduling problem domain is an important research and applications area of engineering management. Recently introduced project scheduling software such as Risk+, @Risk for Project, SCRAM and Risk Master have facilitated the use of simulation to solve project scheduling problems with stochastic task durations. Practitioners, however, should be made aware that the solution algorithm used in these software systems is based on the implicit assumption of perfect information, an assumption that jeopardizes the feasibility of solution results. This paper discusses the impact of assuming perfect information, introduces a multi-period stochastic programming based model of the project scheduling problem with stochastic task durations, and presents an alternative simulation algorithm that does not assume the availability of perfect information. A simple case study is used to illustrate the practical implications of applying simulation to address project scheduling problems with stochastic task d...

The role of the nonanticipativity constraint in commercial software for stochastic project scheduling

Recently announced commercial project scheduling products address the stochastic project scheduling problem. Their solution methods do not consider the nonanticipativity constraint and thus provide potentially unattainable solutions to stochastic resource constrained project scheduling problems. The nonanticipativity constraint of multiperiod stochastic problems imposes the requirement that solutions be based only on information known at the time of decisions. Users should use the results from these programs with extreme care.

A methodology for analysis of complex sociotechnical processes

This article presents the design and application of a Modified Sociotechnical Systems (MoSTS) methodology for holistic analysis of complex technical processes. Successes and failures of process redesign initiatives have demonstrated the strong influence of human elements on outcomes. (Sociotechnical Systems) STS provides a foundation for structured analysis and redesign of complex processes which emphasizes human aspects in process redesign. The MoSTS methodology is developed from STS research and practice and applied to analyze a complex technical process in the research and development sector. MoSTS is shown to be an effective methodology to facilitate analysis for process redesign, particularly where human influences may have a significant impact on success. The article concludes with limitations and implications for process analysis based on the MoSTS methodology application.

A Model for Allocating Resources to Research Programs by Evaluating Technical Importance and Research Productivity

Abstract Research management requires practical and effective decision tools to support selection of investment alternatives. In recent years, many research organizations have changed from a discipline orientation to a focus on integrated programs and related outcomes. For managers of these high-profile research programs, it is critical to understand which activities are most important, considering both technical impact and cost-effectiveness. This article proposes a model that integrates quality function deployment and data envelopment analysis to perform this essential task. Based on information from these two decision science tools, the model develops a two-axis evaluation space for research alternatives. By locating particular activities in this decision space, a program manager can compare and prioritize alternative research investments.

Application of a Method for Selecting and Evaluating Environmental Technologies with Commercial Potential

This paper focuses on a method to select and evaluate environmental technologies with commercial potential. The process helps guide decision making within government and university laboratories, private research institutions, and companies as they pursue technologies that are most likely to be successfully transferred from the laboratory to the marketplace. The strategic technology evaluation method (STEM), which was designed by Chifos and Jain to evaluate environmental technologies originating in federal laboratories, has been adapted to widen its applicability to include the industrial interests responsible for commercialising technology. The modified STEM incorporates insight gained from new product development processes designed for companies and from the use of technology clusters in regional economic development. The improved method is described, and its application in a recent study of technological innovation in the State of West Virginia is briefly summarised. Application of the method to environmental technologies relevant to Hawaii is then illustrated.(Publication abstract)

A market attraction model for predicting the US market share of large civil aircraft

With global annual sales exceeding

Team-Based Methods To Analyze High-Technology Production Systems

65 billion, the large civil aircraft industry is an important economic and strategic element of the European Union and US economies. Here we define large civil aircraft (LCA) as those aircraft with capacities exceeding 121 passengers and dedicated to the air passenger market served by commercial airlines. This paper employs a transformed log-centered market attraction model to forecast the US market share of LCA. This model specification ensures that the predicted market share is in the range [0,1] and that the sum of all predicted market shares is equal to 1.0, both logical process requirements. In this special case, where there are two producers, the market attraction model becomes a logit regression model. Here we specify the logit regression model as an autoregressive distributed lag model in which US market share is predicted by quantitative and qualitative predictor variables, an autoregressive lag operator and a linear trend component.

Assessing Community Fire Risk: A Decision Analysis Based Approach

Abstract Sociotechnical systems (STS) has previously been successfully deployed to redesign technical and social aspects of work systems. This article discusses the application of a team-based approach to a complex manufacturing process—cryomodule production—in a high-technology production facility. Cryomodules are complex and highly critical components that are the heart of a linear accelerator, which supports state-of-the-art physics experiments. The objectives of this study, which involved a participatory team of Old Dominion University researchers and cryomodule production experts, were to: (1) investigate both technical and organizational aspects of the production operation using an STS team-based approach, (2) identify areas of improvement with the potential to increase production effectiveness and reduce future production costs, and (3) provide recommendations to improve production process effectiveness. We report on the team-based STS methodology developed and the results of deploying the methodology to a complex technical process. Implications for theory and practice are discussed.

Using Quality Function Deployment to Select the Courses and Topics that Enhance Program Effectiveness

A prototype community fire risk assessment study was performed for accreditation under the National Fire Service Accreditation Program, developed by the International Association of Fire Chiefs (IAFC). The fire risk model used for the study quantifies risk as a function of perceived consequence from a fire and probability of fire at a structure. The consequence from a fire is determined through expert judgment using the Analytic Hierarchy Process (AHP). The probability of fire at a structure is derived from empirical evidence. A simple additive weighting scheme is employed to combine the consequence and probability factors into a single valued measure of perceived fire risk potential for a structure. A classification scheme is then used to group structures according to risk level. The results of this study provide a model and risk assessment tool for fire science practitioners. Language: en