Entropic risk analysis by a high level decision support system (e-FDSS) for construction SMEs

The method of entropy has been useful in evaluating inconsistency on human judgments. This paper illustrates an entropy-based decision support system called e-FDSS to the solution of multicriterion risk and decision analysis in projects of construction small and medium enterprises (SMEs). It is optimized and solved by fuzzy logic, entropy, and genetic algorithms. A case study demonstrated the use of entropy in e-FDSS on analyzing multiple risk criteria in the predevelopment stage of SME projects. Survey data studying the degree of impact of selected project risk criteria on different projects were input into the system in order to evaluate the preidentified project risks in an impartial environment. Without taking into account the amount of uncertainty embedded in the evaluation process; the results showed that all decision vectors are indeed full of bias and the deviations of decisions are finally quantified providing a more objective decision and risk assessment profile to the stakeholders of projects in order to search and screen the most profitable projects.

Future contributions of crop modelling - from heuristics and supporting decision making to understanding genetic regulation and aiding crop improvement

Crop modelling has evolved over the last 30 or so years in concert with advances in crop physiology, crop ecology and computing technology. Having reached a respectable degree of acceptance, it is appropriate to review briefly the course of developments in crop modelling and to project what might be major contributions of crop modelling in the future. Two major opportunities are envisioned for increased modelling activity in the future. One opportunity is in a continuing central, heuristic role to support scientific investigation, to facilitate decision making by crop managers, and to aid in education. Heuristic activities will also extend to the broader system-level issues of environmental and ecological aspects of crop production. The second opportunity is projected as a prime contributor in understanding and advancing the genetic regulation of plant performance and plant improvement. Physiological dissection and modelling of traits provides an avenue by which crop modelling could contribute to enhancing integration of molecular genetic technologies in crop improvement. Crown Copyright (C) 2002 Published by Elsevier Science B.V. All rights reserved.

Multicriteria decision making: a case study in the automobile industry

Multi-criteria decision analysis(MCDA) has been one of the fastest-growing areas of operations research during the last decades. The academic attention devoted to MCDA motivated the development of a great variety of approaches and methods within the field. These methods distinguish themselves in terms of procedures, theoretical assumptions and type of decision addressed. This diversity poses challenges to the process of selecting the most suited method for a specific real-world decision problem. In this paper we present a case study in a real-world decision problem arising in the painting sector of an automobile plant. We tackle the problem by resorting to the well-known AHP method and to the MCDA method proposed by Pereira and Fontes (2012) (MMASSI). By relying on two, rather than one, MCDA methods we expect to improve the confidence and robustness of the obtained results. The contributions of this paper are twofold: first, we intend to investigate the contrasts and similarities of the results obtained by distinct MCDA approaches (AHP and MMASSI); secondly, we expect to enrich the literature of the field with a real-world MCDA case study on a complex decision making problem since there is a paucity of applied research work addressing real decision problems faced by organizations.

Multicriteria decision making: a case study in the automobile industry

Multi-criteria decision analysis (MCDA) has been one of the fastest-growing areas of operations research during the last decades. The academic attention devoted to MCDA motivated the development of a great variety of approaches and methods within the field. These methods distinguish themselves in terms of procedures, theoretical assumptions and type of decision addressed. This diversity poses challenges to the process of selecting the most suited method for a specific real-world decision problem. In this paper we present a case study in a real-world decision problem arising in the painting sector of an automobile plant. We tackle the problem by resorting to the well-known AHP method and to the MCDA method proposed by Pereira and Fontes (2012) (MMASSI). By relying on two, rather than one, MCDA methods we expect to improve the confidence and robustness of the obtained results. The contributions of this paper are twofold: first, we intend to investigate the contrasts and similarities of the results obtained by distinct MCDA approaches (AHP and MMASSI); secondly, we expect to enrich the literature of the field with a real-world MCDA case study on a complex decision making problem since there is a paucity of applied research work addressing real decision problems faced by organizations.

The use of analytical hierarchy process in spatial decision support system for land use management

Geographic information systems give us the possibility to analyze, produce, and edit geographic information. Furthermore, these systems fall short on the analysis and support of complex spatial problems. Therefore, when a spatial problem, like land use management, requires a multi-criteria perspective, multi-criteria decision analysis is placed into spatial decision support systems. The analytic hierarchy process is one of many multi-criteria decision analysis methods that can be used to support these complex problems. Using its capabilities we try to develop a spatial decision support system, to help land use management. Land use management can undertake a broad spectrum of spatial decision problems. The developed decision support system had to accept as input, various formats and types of data, raster or vector format, and the vector could be polygon line or point type. The support system was designed to perform its analysis for the Zambezi river Valley in Mozambique, the study area. The possible solutions for the emerging problems had to cover the entire region. This required the system to process large sets of data, and constantly adjust to new problems’ needs. The developed decision support system, is able to process thousands of alternatives using the analytical hierarchy process, and produce an output suitability map for the problems faced.

A multi-criteria decision support system for a routing problem in waste collection

Autor proof

Cost-effectiveness analysis of universal childhood hepatitis A vaccination in Brazil: Regional analyses according to the endemic context

Objective: To To conduct a cost-effectiveness analysis of a universal childhood hepatitis A vaccination program in Brazil. Methods: An age and time-dependent dynamic model was developed to estimate the incidence of hepatitis A for 24 years. The analysis was run separately according to the pattern of regional endemicity, one for South + Southeast (low endemicity) and one for the North + Northeast + Midwest (intermediate endemicity). The decision analysis model compared universal childhood vaccination with current program of vaccinating high risk individuals. Epidemiologic and cost estimates were based on data from a nationwide seroprevalence survey of viral hepatitis, primary data collection, National Health Information Systems and literature. The analysis was conducted from both the health system and societal perspectives. Costs are expressed in 2008 Brazilian currency (Real). Results: A universal immunization program would have a significant impact on disease epidemiology in all regions, resulting in 64% reduction in the number of cases of icteric hepatitis, 59% reduction in deaths for the disease and a 62% decrease of life years lost, in a national perspective. With a vaccine price of R$16.89 (US$7.23) per dose, vaccination against hepatitis A was a cost-saving strategy in the low and intermediate endemicity regions and in Brazil as a whole from both health system and society perspective. Results were most sensitive to the frequency of icteric hepatitis, ambulatory care and vaccine costs. Conclusions: Universal childhood vaccination program against hepatitis A could be a cost-saving strategy in all regions of Brazil. These results are useful for the Brazilian government for vaccine related decisions and for monitoring population impact if the vaccine is included in the National Immunization Program. (C) 2012 Elsevier Ltd. All rights reserved.

A study of nurses' judgement of and participation in decision-making for nursing home placement for the elderly

This study investigated the effects of patient variables (physical and cognitive disability, significant others' preference and social support) on nurses' nursing home placement decision-making and explored nurses' participation in the decision-making process.^ The study was conducted in a hospital in Texas. A sample of registered nurses on units that refer patients for nursing home placement were asked to review a series of vignettes describing elderly patients that differed in terms of the study variables and indicate the extent to which they agreed with nursing home placement on a five-point Likert scale. The vignettes were judged to have good content validity by a group of five colleagues (expert consultants) and test-retest reliability based on the Pearson correlation coefficient was satisfactory (average of.75) across all vignettes.^ The study tested the following hypotheses: Nurses have more of a propensity to recommend placement when (1) patients have severe physical disabilities; (2) patients have severe cognitive disabilities; (3) it is the significant others' preference; and (4) patients have no social support nor alternative services. Other hypotheses were that (5) a nurse's characteristics and extent of participation will not have a significant effect on their placement decision; and (6) a patient's social support is the most important, single factor, and the combination of factors of severe physical and cognitive disability, significant others' preference, and no social support nor alternative services will be the most important set of predictors of a nurse's placement decision.^ Analysis of Variance (ANOVA) was used to analyze the relationships implied in the hypothesis. A series of one-way ANOVA (bivariate analyses) of the main effects supported hypotheses one-five.^ Overall, the n-way ANOVA (multivariate analyses) of the main effects confirmed that social support was the most important single factor controlling for other variables. The 4-way interaction model confirmed that the most predictive combination of patient characteristics were severe physical and cognitive disability, no social support and the significant others did not desire placement. These analyses provided an understanding of the importance of the influence of specific patient variables on nurses' recommendations regarding placement. ^

Design and implementation of a multicriteria medical decision support system for diagnosis and treatment

Health care providers face the problem of trying to make decisions with inadequate information and also with an overload of (often contradictory) information. Physicians often choose treatment long before they know which disease is present. Indeed, uncertainty is intrinsic to the practice of medicine. Decision analysis can help physicians structure and work through a medical decision problem, and can provide reassurance that decisions are rational and consistent with the beliefs and preferences of other physicians and patients. ^ The primary purpose of this research project is to develop the theory, methods, techniques and tools necessary for designing and implementing a system to support solving medical decision problems. A case study involving “abdominal pain” serves as a prototype for implementing the system. The research, however, focuses on a generic class of problems and aims at covering theoretical as well as practical aspects of the system developed. ^ The main contributions of this research are: (1) bridging the gap between the statistical approach and the knowledge-based (expert) approach to medical decision making; (2) linking a collection of methods, techniques and tools together to allow for the design of a medical decision support system, based on a framework that involves the Analytic Network Process (ANP), the generalization of the Analytic Hierarchy Process (AHP) to dependence and feedback, for problems involving diagnosis and treatment; (3) enhancing the representation and manipulation of uncertainty in the ANP framework by incorporating group consensus weights; and (4) developing a computer program to assist in the implementation of the system. ^

Decision evaluation in complex risk network systems (DecernsSDSS) demo and Black River case study

The selection of metrics for ecosystem restoration programs is critical for improving the quality of monitoring programs and characterizing project success. Moreover it is oftentimes very difficult to balance the importance of multiple ecological, social, and economical metrics. Metric selection process is a complex and must simultaneously take into account monitoring data, environmental models, socio-economic considerations, and stakeholder interests. We propose multicriteria decision analysis (MCDA) methods, broadly defined, for the selection of optimal sets of metrics to enhance evaluation of ecosystem restoration alternatives. Two MCDA methods, a multiattribute utility analysis (MAUT), and a probabilistic multicriteria acceptability analysis (ProMAA), are applied and compared for a hypothetical case study of a river restoration involving multiple stakeholders. Overall, the MCDA results in a systematic, unbiased, and transparent solution, informing restoration alternatives evaluation. The two methods provide comparable results in terms of selected metrics. However, because ProMAA can consider probability distributions for weights and utility values of metrics for each criteria, it is suggested as the best option if data uncertainty is high. Despite the increase in complexity in the metric selection process, MCDA improves upon the current ad-hoc decision practice based on the consultations with stakeholders and experts, and encourages transparent and quantitative aggregation of data and judgement, increasing the transparency of decision making in restoration projects. We believe that MCDA can enhance the overall sustainability of ecosystem by enhancing both ecological and societal needs.

MOIRA-PLUS use in decision making on the long-term management of contaminated freshwater bodies and catchments

In the mid-long-term after a nuclear accident, the contamination of drinking water sources, fish and other aquatic foodstuffs, irrigation supplies and people?s exposure during recreational activities may create considerable public concern, even though dose assessment may in certain situations indicate lesser importance than for other sources, as clearly experienced in the aftermath of past accidents. In such circumstances there are a number of available countermeasure options, ranging from specific chemical treatment of lakes to bans on fish ingestion or on the use of water for crop irrigation. The potential actions can be broadly grouped into four main categories, chemical, biological, physical and social. In some cases a combination of actions may be the optimal strategy and a decision support system (DSS) like MOIRA-PLUS can be of great help to optimise a decision. A further option is of course not to take any remedial actions, although this may also have significant socio-economic repercussions which should be adequately evaluated. MOIRA-PLUS is designed to allow for a reliable assessment of the long-term evolution of the radiological situation and of feasible alternative rehabilitation strategies, including an objective evaluation of their social, economic and ecological impacts in a rational and comprehensive manner. MOIRA-PLUS also features a decision analysis methodology, making use of multi-attribute analysis, which can take into account the preferences and needs of different types of stakeholders. The main functions and elements of the system are described summarily. Also the conclusions from end-user?s experiences with the system are discussed, including exercises involving the organizations responsible for emergency management and the affected services, as well as different local and regional stakeholders. MOIRAPLUS has proven to be a mature system, user friendly and relatively easy to set up. It can help to better decisionmaking by enabling a realistic evaluation of the complete impacts of possible recovery strategies. Also, the interaction with stakeholders has allowed identifying improvements of the system that have been recently implemented.

Systematic Approach for the selection of monitoring technologies in CO2 geological storage projects. Application of multicriteria decision making

Geologic storage of carbon dioxide (CO2) has been proposed as a viable means for reducing anthropogenic CO2 emissions. Once injection begins, a program for measurement, monitoring, and verification (MMV) of CO2 distribution is required in order to: a) research key features, effects and processes needed for risk assessment; b) manage the injection process; c) delineate and identify leakage risk and surface escape; d) provide early warnings of failure near the reservoir; and f) verify storage for accounting and crediting. The selection of the methodology of monitoring (characterization of site and control and verification in the post-injection phase) is influenced by economic and technological variables. Multiple Criteria Decision Making (MCDM) refers to a methodology developed for making decisions in the presence of multiple criteria. MCDM as a discipline has only a relatively short history of 40 years, and it has been closely related to advancements on computer technology. Evaluation methods and multicriteria decisions include the selection of a set of feasible alternatives, the simultaneous optimization of several objective functions, and a decision-making process and evaluation procedures that must be rational and consistent. The application of a mathematical model of decision-making will help to find the best solution, establishing the mechanisms to facilitate the management of information generated by number of disciplines of knowledge. Those problems in which decision alternatives are finite are called Discrete Multicriteria Decision problems. Such problems are most common in reality and this case scenario will be applied in solving the problem of site selection for storing CO2. Discrete MCDM is used to assess and decide on issues that by nature or design support a finite number of alternative solutions. Recently, Multicriteria Decision Analysis has been applied to hierarchy policy incentives for CCS, to assess the role of CCS, and to select potential areas which could be suitable to store. For those reasons, MCDM have been considered in the monitoring phase of CO2 storage, in order to select suitable technologies which could be techno-economical viable. In this paper, we identify techniques of gas measurements in subsurface which are currently applying in the phase of characterization (pre-injection); MCDM will help decision-makers to hierarchy the most suitable technique which fit the purpose to monitor the specific physic-chemical parameter.

A decision model for it supplier selection

En los últimos años la externalización de TI ha ganado mucha importancia en el mercado y, por ejemplo, el mercado externalización de servicios de TI sigue creciendo cada año. Ahora más que nunca, las organizaciones son cada vez más los compradores de las capacidades necesarias mediante la obtención de productos y servicios de los proveedores, desarrollando cada vez menos estas capacidades dentro de la empresa. La selección de proveedores de TI es un problema de decisión complejo. Los gerentes que enfrentan una decisión sobre la selección de proveedores de TI tienen dificultades en la elaboración de lo que hay que pensar, además en sus discursos. También de acuerdo con un estudio del SEI (Software Engineering Institute) [40], del 20 al 25 por ciento de los grandes proyectos de adquisición de TI fracasan en dos años y el 50 por ciento fracasan dentro de cinco años. La mala gestión, la mala definición de requisitos, la falta de evaluaciones exhaustivas, que pueden ser utilizadas para llegar a los mejores candidatos para la contratación externa, la selección de proveedores y los procesos de contratación inadecuados, la insuficiencia de procedimientos de selección tecnológicos, y los cambios de requisitos no controlados son factores que contribuyen al fracaso del proyecto. La mayoría de los fracasos podrían evitarse si el cliente aprendiese a comprender los problemas de decisión, hacer un mejor análisis de decisiones, y el buen juicio. El objetivo principal de este trabajo es el desarrollo de un modelo de decisión para la selección de proveedores de TI que tratará de reducir la cantidad de fracasos observados en las relaciones entre el cliente y el proveedor. La mayor parte de estos fracasos son causados por una mala selección, por parte del cliente, del proveedor. Además de estos problemas mostrados anteriormente, la motivación para crear este trabajo es la inexistencia de cualquier modelo de decisión basado en un multi modelo (mezcla de modelos adquisición y métodos de decisión) para el problema de la selección de proveedores de TI. En el caso de estudio, nueve empresas españolas fueron analizadas de acuerdo con el modelo de decisión para la selección de proveedores de TI desarrollado en este trabajo. Dos softwares se utilizaron en este estudio de caso: Expert Choice, y D-Sight. ABSTRACT In the past few years IT outsourcing has gained a lot of importance in the market and, for example, the IT services outsourcing market is still growing every year. Now more than ever, organizations are increasingly becoming acquirers of needed capabilities by obtaining products and services from suppliers and developing less and less of these capabilities in-house. IT supplier selection is a complex and opaque decision problem. Managers facing a decision about IT supplier selection have difficulty in framing what needs to be thought about further in their discourses. Also according to a study from SEI (Software Engineering Institute) [40], 20 to 25 percent of large information technology (IT) acquisition projects fail within two years and 50 percent fail within five years. Mismanagement, poor requirements definition, lack of comprehensive evaluations, which can be used to come up with the best candidates for outsourcing, inadequate supplier selection and contracting processes, insufficient technology selection procedures, and uncontrolled requirements changes are factors that contribute to project failure. The majority of project failures could be avoided if the acquirer learns how to understand the decision problems, make better decision analysis, and good judgment. The main objective of this work is the development of a decision model for IT supplier selection that will try to decrease the amount of failures seen in the relationships between the client-supplier. Most of these failures are caused by a not well selection of the supplier. Besides these problems showed above, the motivation to create this work is the inexistence of any decision model based on multi model (mixture of acquisition models and decision methods) for the problem of IT supplier selection. In the case study, nine different Spanish companies were analyzed based on the IT supplier selection decision model developed in this work. Two software products were used in this case study, Expert Choice and D-Sight.

Planning and decision-making for energy recovery from waste at the sub-regional level

This research project has developed a novel decision support system using Geographical Information Systems and Multi Criteria Decision Analysis and used it to develop and evaluate energy-from-waste policy options. The system was validated by applying it to the UK administrative areas of Cornwall and Warwickshire. Different strategies have been defined by the size and number of the facilities, as well as the technology chosen. Using sensitivity on the results from the decision support system, it was found that key decision criteria included those affected by cost, energy efficiency, transport impacts and air/dioxin emissions. The conclusions of this work are that distributed small-scale energy-from-waste facilities score most highly overall and that scale is more important than technology design in determining overall policy impact. This project makes its primary contribution to energy-from-waste planning by its development of a Decision Support System that can be used to assist waste disposal authorities to identify preferred energy-from-waste options that have been tailored specifically to the socio-geographic characteristics of their jurisdictional areas. The project also highlights the potential of energy-from-waste policies that are seldom given enough attention to in the UK, namely those of a smaller-scale and distributed nature that often have technology designed specifically to cater for this market.

Design of a novel solar thermal collector using a multi-criteria decision-making methodology

Three novel solar thermal collector concepts derived from the Linear Fresnel Reflector (LFR) are developed and evaluated through a multi-criteria decision-making methodology, comprising the following techniques: Quality Function Deployment (QFD), the Analytical Hierarchy Process (AHP) and the Pugh selection matrix. Criteria are specified by technical and customer requirements gathered from Gujarat, India. The concepts are compared to a standard LFR for reference, and as a result, a novel 'Elevation Linear Fresnel Reflector' (ELFR) concept using elevating mirrors is selected. A detailed version of this concept is proposed and compared against two standard LFR configurations, one using constant and the other using variable horizontal mirror spacing. Annual performance is analysed for a typical meteorological year. Financial assessment is made through the construction of a prototype. The novel LFR has an annual optical efficiency of 49% and increases exergy by 13-23%. Operational hours above a target temperature of 300 C are increased by 9-24%. A 17% reduction in land usage is also achievable. However, the ELFR suffers from additional complexity and a 16-28% increase in capital cost. It is concluded that this novel design is particularly promising for industrial applications and locations with restricted land availability or high land costs. The decision analysis methodology adopted is considered to have a wider potential for applications in the fields of renewable energy and sustainable design. © 2013 Elsevier Ltd. All rights reserved.