OVID-BV : optimising value in decision making for best value in the UK social housing sector

The Governments’ promotion and support of Best Value within the Social Housing Sector has been a prime catalyst in the move by Registered Social Landlord’s [RSL’s] away from the traditional culture of acceptance of the lowest bid towards consideration of both price and quality criteria as a basis for contractor selection. Manifestly this radical change in the way the sector procures its construction services has forced many of its stakeholders to undergo significant cultural and organisational changes within a relatively short period of time, and problems have developed during this transitional period that have affected the efficiency of the best value process. This research traced the root causes of these problems and its overarching aim was to develop an approach which will enable RSL’s and their stakeholders to streamline the best value tender analysis procedure thereby allowing tenders to be dealt with effectively and efficiently whilst also creating a transparent and auditable decision making process. The approach has been established using a mixed methods research methodology utilising; case studies, surveys, rational decision analysis and system evaluation. The main output of the research is the development of a support tool known by the acronym OVID-BV which aids the multi objective decision making process. The underlying rationale for the support tool is based on the innovative use of uncertainty in decision making and the functionality of the tool uses a combination of the analytical hierarchy process (AHP), multi attribute utility theory (MAUT) and whole life costing (WLC)

An architecture based on computing with words to support runtime reconfiguration decisions of service-based systems

Service-based systems (SBSs) need to be reconfigured when there is evidence that the selected Web services configurations no further satisfy the specifications models and, thus the decision-related models will need to be updated accordingly. However, such updates need to be performed at the right pace. On the one hand, if the updates are not quickly enough, the reconfigurations that are required may not be detected due to the obsolescence of the specification models used at runtime, which were specified at design-time. On the other hand, the other extreme is to promote premature reconfiguration decisions that are based on models that may be highly sensitive to environmental fluctuations and which may affect the stability of these systems. To deal with the required trade-offs of this situation, this paper proposes the use of linguistic decision-making (LDM) models to represent specification models of SBSs and a dynamic computing-with-words (CWW) architecture to dynamically assess the models by using a multi-period multi-attribute decision making (MP-MADM) approach. The proposed solution allows systems under dynamic environments to offer improved system stability by better managing the trade-off between the potential obsolescence of the specification models, and the required dynamic sensitivity and update of these model

A multi-criteria decision-making model for evaluating priorities for foreign direct investment

The objective of this study is to evaluate the relative priority of nine developed countries as a home country for foreign direct investment (FDI) from the vantage point of the United States during three time periods: pre-crisis (2004-2006), crisis (2007-2009), and post-crisis (2010-2012). Our study suggests a methodology based on a combination of the analytic hierarchy process (AHP), the technique for order preference by similarity to ideal solution (TOPSIS), and the multi-period multi-attribute decision-making (MP-MADM) technique. To investigate our research question, we selected fifteen robust FDI determinants from recent studies. The results for all three time periods show that productivity, market potential, market size, GDP growth and development have the highest priority in the decision-making process. On the other hand, we found that the 2007 global financial crisis significantly affected each variable in the decision-making process. During the crisis, two variables in particular - corruption and GDP growth - significantly increased in importance. These findings have far-reaching policy implications and can assist policymakers and investors in their strategic decision-making process

Operational decision making for medical clinics through the use of simulation and multi-attribute utility theory.

Currently, health care is a large industry that concerns everyone. Outpatient health care is an important part of the American health care system and is one of the strongest growth areas in the health care system. Many people pay attention to how to keep basic health care available to as many people as possible. A large health care system is usually evaluated by many performance measures. For example, the managers of a medical clinic are concerned about increasing staff utilization; both managers and patients are concerned about patient waiting time. In this dissertation, we study decision making for clinics in determining operational policies to achieve multiple goals (e.g. increasing staff utilization，reducing patient waiting time, reducing overtime). Multi-attribute utility function and discrete even simulation are used for the study. The proposed decision making framework using simulation is applied to two case studies, i.e., two clinics associated with University of Louisville in Louisville, Kentucky. In the first case, we constructed of a long period simulation model for a multi-resource medical clinic. We investigated changes to the interarrival times for each type of patient, assigned patients to see different staff in different visits (e.g., visit #2, visit #5) and assigned medical resources accordingly. Two performance measures were considered: waiting time for patients, and utilization of clinic staff. The second case involved the construction of a one-morning simulation model for an ambulatory internal medicine clinic. Although all the resident doctors perform the same task, their service times are different due to their varying levels of experience. We investigated the assignment of examination rooms based on residents’ varying service times. For this model, we also investigated the effect of changing the interarrival times for patients. Four performance measures were considered: waiting time for patients, overtime for the clinic staff, utilization of examination rooms and utilization of clinic staff. We developed a ranking and selection procedure to compare the various policies, each based on a multiple attribute performance. This procedure combines the use of multi-attribute utility functions with statistical ranking and selection in order to choose the best results from a set of possible outputs using an indifferent-zone approach. We applied this procedure to the outputs from “Healthy for Life” clinic and “AIM” clinic simulation models in selecting alternative operational policies. Lastly, we performed sensitivity analyses with respect to the weights of the attributes in the multi-attribute utility function. The results will help decision makers to understand the effects of various factors in the system. The clinic managers can choose a best scheduling method based on the highest expected utility value with different levels of weight on each attribute. The contribution of this dissertation is two-fold. First, we developed a long term simulation model for a multi-resource clinic consisting of providers with diverse areas of expertise and thus vastly different no-show rate and service times. Particularly, we modeled the details on assigning patients to providers when they come to the clinic in their different visits. The other contribution was the development of a special ranking and selection procedure for comparing performances on multiple attributes for alternative policies in the outpatient healthcare modeling area. This procedure combined a multiple attribute utility function with statistical ranking and selection in determining the best result from a set of possible outputs using the indifferent-zone approach

Innovation Performance Analysis of G20 Countries: A Novel Integrated LOPCOW-MAIRCA MCDM Approach Including the COVID-19 Period

Purpose: This study aims to examine the innovation performance of G20 countries in 2018-2022 with multi criteria decision making methods. When the 5-year performance was analyzed, it was also revealed whether the COVID-19 outbreak has an impact on the innovation performance of the countries. Methodology: An integrated LOPCOW (Logarithmic Percentage Change-driven Objective Weighting) - MAIRCA (Multi Attribute Ideal-Real Comparative Analysis) method was applied in the study. First, the indicators representing innovation performance (institutions, human capital, and research, infrastructure, market sophistication, business sophistication, knowledge and technology outputs, creative outputs) was objectively weighted by the LOPCOW method. Then, the innovation performance of G20 countries was calculated with the MAIRCA method. Finally, a comparative analysis was also presented to support the findings. Findings: As a result of the innovation performance analysis using multi criteria decision making methods, human capital, and research were found to be the most important indicators, and the United States was found to be the country with the best innovation performance. In the sensitivity and comparative analysis, it was concluded that the integrated LOPCOW-MAIRCA method provides robust outputs. Originality: This study makes original contributions by analyzing the impact of the COVID-19 pandemic on the innovation performance of countries considering the 2018-2022 period and the integrated multi criteria decision making methods it uses that have not yet been applied in the literature

CREATION OF OPTIMAL PERFORMANCE OF AN INVESTMENT PROJECT

The selection of an investment project is formulated as a multi-criteria decision-making problem. This paper presents a case in which the decision-maker uses nine criteria or rather attributes (Net Present Value, Internal Rate of Return, Payback Period, Accounting Rate of Return, Cumulative Cash Flows, Return on Investment, Net Profit Margin, Interest Coverage Ratio and Current Ratio). Individual utility functions are constructed for each attribute separately, as well as a global utility function representing a weighted sum of individual utility functions. For every attribute a finite set of ordered pairs or utility points is determined, taking into account the decision-maker’s assessment. The given points are then approximated by the utility function. Finally, according to the decision-maker’s assessment the optimization problem is solved with the purpose of achieving an optimal performance for each project. By way of negotiation the performances on offer approach the optimal performance of the project with the purpose of realising an agreement between the decision-maker and the investor

An application of incomplete pairwise comparison matrices for ranking top tennis players

Pairwise comparison is an important tool in multi-attribute decision making. Pairwise comparison matrices (PCM) have been applied for ranking criteria and for scoring alternatives according to a given criterion. Our paper presents a special application of incomplete PCMs: ranking of professional tennis players based on their results against each other. The selected 25 players have been on the top of the ATP rankings for a shorter or longer period in the last 40 years. Some of them have never met on the court. One of the aims of the paper is to provide ranking of the selected players, however, the analysis of incomplete pairwise comparison matrices is also in the focus. The eigenvector method and the logarithmic least squares method were used to calculate weights from incomplete PCMs. In our results the top three players of four decades were Nadal, Federer and Sampras. Some questions have been raised on the properties of incomplete PCMs and remains open for further investigation.Comment: 14 pages, 2 figure

Multicriteria generation and transmission expansion planning in Paraguay

The purpose of this research is to develop a methodology using welfare economics expanded with multi attribute decision making for portfolio selection of renewable sources in Paraguay’s regulated hydroelectric market. This approach considers expansion planning of the Paraguayan Interconnected System (SIN), including generation, transmission, and 2016 Paraguay’s Energy Policy. This optimization of the generation expansion problem involves a study period from 2017-2040 assuming a rate of 9.84% per year increasing demand. This study models the SIN using Stochastic Dynamic Dual Programming (SDDP), a probabilistic hydrothermal operation cost optimizer. Based on this Base Case, generation expansion is needed after 2026. Using the Optimization Expansion-Operation Module (OPTGEN-SDDP) software to solve the Expansion Case Optimization, after 2029 the transmission system cannot sustain the demand increase. By 2040, the new installed capacity needed is 26,900 MW. By 2040, there is a need for 13,571 MVA transmission expansion to connect mostly South systems to the Metro system. At 2014 prices, the generation expansion would be at a cost of $7.1 million per MVA of new generation capacity. A mixed-integer linear optimization formulation is implemented outside the OPTGEN-SDDP Module. The multicriteria expansion problem is analyzed using a utility function to consider socio-economical, technology and environmental criteria of energy policy interest. The result is then incorporated back to the OPTGEN-SDDP Module. Analyzing a sustainability index for each case, in all cases the Net Import index has a decreasing trend in the period of study. Furthermore, due to transmission constraints, the Reliability index cannot be improved without transmission expansion in any study case. The resultant generation portfolio in both expansion problems includes 26% solar generation, a scale in line with Paraguay’s Energy Policy of diversification of the energy matrix. Small hydro and solar generation sources are a viable alternative to build an electricity generation portfolio mix for the Paraguayan electricity market, by using both a welfare economics optimization and an extended welfare economics optimization with a multi attribute decision making approach. But transmission constraints are still a major issue for the full exploitation of these resources.Energy and Earth Resource

Intertemporal Choice of Fuzzy Soft Sets

This paper first merges two noteworthy aspects of choice. On the one hand, soft sets and fuzzy soft sets are popular models that have been largely applied to decision making problems, such as real estate valuation, medical diagnosis (glaucoma, prostate cancer, etc.), data mining, or international trade. They provide crisp or fuzzy parameterized descriptions of the universe of alternatives. On the other hand, in many decisions, costs and benefits occur at different points in time. This brings about intertemporal choices, which may involve an indefinitely large number of periods. However, the literature does not provide a model, let alone a solution, to the intertemporal problem when the alternatives are described by (fuzzy) parameterizations. In this paper, we propose a novel soft set inspired model that applies to the intertemporal framework, hence it fills an important gap in the development of fuzzy soft set theory. An algorithm allows the selection of the optimal option in intertemporal choice problems with an infinite time horizon. We illustrate its application with a numerical example involving alternative portfolios of projects that a public administration may undertake. This allows us to establish a pioneering intertemporal model of choice in the framework of extended fuzzy set theorie

Supplier evaluation and selection in fuzzy environments: a review of MADM approaches

In past years, the multi-attribute decision-making (MADM) approaches have been extensively applied by researchers to the supplier evaluation and selection problem. Many of these studies were performed in an uncertain environment described by fuzzy sets. This study provides a review of applications of MADM approaches for evaluation and selection of suppliers in a fuzzy environment. To this aim, a total of 339 publications were examined, including papers in peer-reviewed journals and reputable conferences and also some book chapters over the period of 2001 to 2016. These publications were extracted from many online databases and classified in some categories and subcategories according to the MADM approaches, and then they were analysed based on the frequency of approaches, number of citations, year of publication, country of origin and publishing journals. The results of this study show that the AHP and TOPSIS methods are the most popular approaches. Moreover, China and Taiwan are the top countries in terms of number of publications and number of citations, respectively. The top three journals with highest number of publications were: Expert Systems with Applications, International Journal of Production Research and The International Journal of Advanced Manufacturing Technology