Simplified approaches for portfolio decision analysis

Traditional choice decisions involve selecting a single, best alternative from a larger set of potential options. In contrast, portfolio decisions involve selecting the best subset of alternatives — alternatives that together maximize some measure of value to the decision maker and are within their available resources to implement. Examples include capital investment, R&D project selection, and maintenance planning. Portfolio decisions involve a combinatorial aspect that makes them more theoretically and computationally challenging than choice problems, particularly when there are interactions between alternatives. Several portfolio decision analysis methods have been developed over the years and an increasing interest has been noted in the field of portfolio decision analysis. These methods are typically called “exact” methods, but can also be called prescriptive methods. These are generally computationally-intensive algorithms that require substantial amounts of information from the decision maker, and in return yield portfolios that are provably optimal or optimal within certain bounds. These methods have proved popular for choice decisions — for example, those based on multiattribute value or utility theory. But whereas information and computational requirements for choice problems are probably manageable for the majority of diligent decision makers, it is much less clear that this is true of portfolio decisions. That is, for portfolio decisions it may be more common that decision makers do not have the time, expertise and ability to exert the effort to assess all the information required of an exact method. Heuristics are simple, psychologically plausible rules for decision making that limit the amount of information required and the computation effort needed to turn this information into decisions. Previous work has shown that people often use heuristics when confronted with traditional choice problems in unfacilitated contexts, and that these can often return good results, in the sense of selecting alternatives that are also ranked highly by exact methods. This suggests that heuristics may also be useful for portfolio decisions. Moreover, while the lower information demands made by choice problems mean that heuristics have not generally been seen as prescriptive options, the more substantial demands made by portfolio decisions make a priori case for considering their use not just descriptively, but as tools for decision aid. Very little work exists on the use of heuristics for portfolio decision making, the subject of this thesis. Durbach et al. (2020) proposed a family of portfolio selection heuristics known collectively as add-the-best. These construct portfolios by adding, at every step, the alternative that is best in a greedy sense, with different definitions of what “best” is. This thesis extends knowledge on portfolio heuristics in three main respects. Firstly, we show that people use certain of the add-the-best heuristics when selecting portfolios without facilitation, in a context where there are interactions between alternatives. We run an experiment involving actual portfolio decision making behaviour, administered to participants who had the opportunity to choose as many alternatives as they wanted, but under the constraint of a limited budget. This experiment, parts of which were reported in Durbach et al. (2020), provides the first demonstration of the use of heuristics in portfolio selections. Secondly, we use a simulation experiment to test the performance of the heuristics in two novel environments: those involving multiple criteria, and those in which interactions between projects may be positive (the value of selecting two alternatives is more than the sum of their individual values) or negative (the opposite). This extends the results in Durbach et al. (2020), who considered only environments involving a single criterion and positive interactions between alternatives. In doing so we differentiate between heuristics that guide the selection of alternatives, called selection heuristics, and heuristics for aggregating performance across criteria, which we call scoring heuristics. We combine various selection and scoring heuristics and test their performance on a range of simulated decision problems. We found that certain portfolio heuristics continued to perform well in the presence of negative interactions and multiple criteria, and that performance depended more on the approach used to build portfolios (selection heuristics) than on the method of aggregation across criteria (scoring heuristics). We also found that in these extended conditions heuristics continued to provide outcomes that were competitive with optimal models, but that heuristics that ignored interactions led to potentially poor results. Finally, we complement behavioral and simulation experimental studies with an application of both exact methods and portfolio heuristics in a real-world portfolio decision problem involving the selection of the best subset of research proposals out of a pool of proposals submitted by researchers applying for grants from a research institution. We provide a decision support system to this institution in the form of a web-based application to assist with portfolio decisions involving interactions. The decision support system implements exact methods, namely the linear-additive portfolio value model and the robust portfolio model, as well as two portfolio heuristics found to perform well in simulations

Solving multiple-criteria R&D project selection problems with a data-driven evidential reasoning rule

In this paper, a likelihood based evidence acquisition approach is proposed to acquire evidence from experts'assessments as recorded in historical datasets. Then a data-driven evidential reasoning rule based model is introduced to R&D project selection process by combining multiple pieces of evidence with different weights and reliabilities. As a result, the total belief degrees and the overall performance can be generated for ranking and selecting projects. Finally, a case study on the R&D project selection for the National Science Foundation of China is conducted to show the effectiveness of the proposed model. The data-driven evidential reasoning rule based model for project evaluation and selection (1) utilizes experimental data to represent experts' assessments by using belief distributions over the set of final funding outcomes, and through this historic statistics it helps experts and applicants to understand the funding probability to a given assessment grade, (2) implies the mapping relationships between the evaluation grades and the final funding outcomes by using historical data, and (3) provides a way to make fair decisions by taking experts' reliabilities into account. In the data-driven evidential reasoning rule based model, experts play different roles in accordance with their reliabilities which are determined by their previous review track records, and the selection process is made interpretable and fairer. The newly proposed model reduces the time-consuming panel review work for both managers and experts, and significantly improves the efficiency and quality of project selection process. Although the model is demonstrated for project selection in the NSFC, it can be generalized to other funding agencies or industries.Comment: 20 pages, forthcoming in International Journal of Project Management (2019

Multi crteria decision making and its applications : a literature review

This paper presents current techniques used in Multi Criteria Decision Making (MCDM) and their applications. Two basic approaches for MCDM, namely Artificial Intelligence MCDM (AIMCDM) and Classical MCDM (CMCDM) are discussed and investigated. Recent articles from international journals related to MCDM are collected and analyzed to find which approach is more common than the other in MCDM. Also, which area these techniques are applied to. Those articles are appearing in journals for the year 2008 only. This paper provides evidence that currently, both AIMCDM and CMCDM are equally common in MCDM

Optimization of the supplier selection process in prefabrication using BIM

Prefabrication offers substantial benefits including reduction in construction waste, material waste, energy use, labor demands, and delivery time, and an improvement in project constructability and cost certainty. As the material cost accounts for nearly 70% of the total cost of the prefabrication project, to select a suitable material supplier plays an important role in such a project. The purpose of this study is to present a method for supporting supplier selection of a prefabrication project. The proposed method consists of three parts. First, a list of assessment criteria was established to evaluate the suitability of supplier alternatives. Second, Building Information Modelling (BIM) was adopted to provide sufficient information about the project requirements and suppliers’ profiles, which facilitates the storage and sharing of information. Finally, the Analytic Hierarchy Process (AHP) was used to rank the importance of the assessment criteria and obtain the score of supplier alternatives. The suppliers were ranked based on the total scores. To illustrate how to use the proposed method, it was applied to a real prefabrication project. The proposed method facilitates the supplier selection process by providing sufficient information in an effective way and by improving the understanding of the project requirements

Enhancing geographical information systems capabilities with multi-criteria evaluation functions

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Using multiple criteria decision analysis to aid the selection of enterprise resource planning software : a case study

BHC Ltd is a family owned SME which specialises in steel fabrication for the construction industry. Due to rapid growth over the past decade the company’s current business software has evolved from a collection of semi-integrated individual packages and Excel spreadsheets. To help the company become more efficient during the current financial downturn and to ensure they are capable of future growth, BHC Ltd initiated a project with the University of Strathclyde to select and implement an Enterprise Resource Planning (ERP) solution. This paper will provide a case study of BHC’s ERP selection process. In particular it will discuss how steel specific business requirements and organisational culture led us to use multiple criteria decision analysis (MCDA) when making a final software selection. The MCDA process that was followed is further discussed and includes the success that was achieved by using this approach

A Decision Making System for Selecting Sustainable Technologies for Retail Buildings

CIB Publication 382: Selected papers presented at the CIB World Building Congres Construction and Society, Brisbane 5-9 May 2013 Papers from the Designated Session TG66 - Energy and the Built EnvironmentThe implementation of sustainable technologies can improve the energy and carbon efficiency of existing retail buildings. However, the selection of an appropriate sustainable technology is a complex task due to the large number of technological alternatives and decision criteria that need to be considered. Also, there exist series of uncertainties that are associated with the use of sustainable technologies, but have to be evaluated to achieve realistic and transparent results. The selection of sustainable technology is therefore most challenging. An earlier study was conducted with UK experienced practitioners including clients/developers, engineers, contractors and suppliers to identify the drivers and barriers for the use of sustainable technologies in UK retail construction. One major barrier identified from the study was the lack of a decision making tool, highlighted by both construction professionals and stakeholders in the retail industry. The large number of alternatives and potential solutions require a decision support method to be implemented. Information data on the economic variables, energy performance and impact on the environment of these systems is presently affected by vagueness and lack of knowledge. To deal with this high level of complexity and uncertainty an evaluation support approach is needed. This paper aims to develop a decision making framework to assist both retailers and construction professionals to define and evaluate the selection of sustainable technological options for delivering retail buildings. The research was carried out through a combination of a critical literature review and a survey-based study using expert opinions of retailers and contractors. The developed framework of decision criteria should provide a sustainable technology model to assist both construction professionals and stakeholders in the retail industry to systematically and effectively select the most appropriate technology. This approach should make the decision progression more transparent and facilitate sustainable development of retail buildings in achieving the carbon targets set by the UK and other governments