Decision Support Tools (DSTs) are commonly utilised within the Asset Management (AM) operations of infrastructure organisations. These manual or computerised tools are used to support decisions about what assets to acquire and how to operate them. Their performance can therefore have significant financial and non-financial implications for a business. Despite their importance, managing the performance of DSTs after implementation has received only limited attention within the literature. The output of this research is a conceptual approach for managing the performance of decision support tools used within an Asset Management context. It encompasses a risk-based DST Performance Management Process and DST Performance Assessment Techniques (the methods for applying the process in an industry setting).The novelty of the approach: (1) Alignment with the fundamental principles of the International Standard for Asset Management, ISO 5500x:2014. Thus, consistency of the management of DSTs with other assets types. (2) A generic process that is tailored to the context of the specific organisation. (3) Consistency with the risk management process (ISO 31000:2009) and meeting the requirements for a quality process defined within the Quality Management Standard (ISO 9000: 2015). (4) A cyclical process design ensuring that the approach, and how the approach is applied within an industry setting, will evolve to reflect the changing environment. A case study and the input of subject matter experts from within National Grid Electricity Transmission was used to both inform and evaluate the conceptual approach design. A semi-structured interview, with a water sector subject matter expert, assesses the transferability of the approach to a wider Asset Management population. The results of the evaluation demonstrate the conceptual approach to be both logical and useable in each context. The future research pathway looks to progress the conceptual approach through to industry adoption.

An Engineering Decision Support System, EDSS, was developed using Bayesian mathematics which incorporates knowledge and confidence components observed in alternative-criterion pair decision making. The separation of knowledge and confidence has been previously unaccounted for in decision-making methods. EDSS provides decision support to individuals or teams which must make choices between alternatives using alternative-criterion pair evaluations. Further, EDSS was instanciated into computer software. The EDSS decision support system was statistically tested using two variables, mechanical experience of the participants and the use of a decision method, at two different levels and in a replicated factorial experiment. The experiment consisted of teams of subjects solving a simple mechanical design problem. Data from the experiment was collected for eighteen different metrics in four categories. This research reports on each of eighteen metrics using the hypothesis that the use of EDSS will show improvements in, or positive impact on, the following four categories: the decision making productivity of idea processing, the decision-making process, the perception of the decisions made by the decision makers, and the ease of use of a computer decision support tool. Statistical results of the experiment showed that EDSS successfully matched ad-hoc and Pugh's decision matrix performance for sixteen of the eighteen metrics and statistically exceeded the remaining two. These two metrics are, the conduction of more evaluations of alternative-criterion pairs ,and increased problem understanding. This research also shows that a new alternative-criterion pair evaluation method has been successfully created that provides for: - A separation of knowledge and confidence in the Belief Model of decision making. - Decision results without complete evaluation of all alternative-criterion pairs. - Aggregation of preferences from team members. - A convenient means for investigating decision improvements. / Graduation date: 1997

Thesis (Ph.D.)--University of Delaware, 2006. / Principal faculty advisors: Sandra M. Carberry and Keith S. Decker, Dept. of Computer & Information Sciences. Includes bibliographical references.

Thesis (Ph.D.)--Kent State University, 2005. / Title from PDF t.p. (viewed Feb. 23, 2005). Advisor: Acar, William. Available online via OhioLINK's ETD Center. Keywords: strategy making; causal mapping; multi-agent systems; knowledge management; dialectical inquiry Includes bibliographical references (p. 111-116).

Introduction: The term Business Intelligence arose in the mid-1990s and is a growing share of the IT market. The need of Business Intelligence emerges from an increasing competition and a constantly changing and more complex business climate. Problem discussion: There are only few examples of research dealing with data-driven decision processes. How data are incorporated in decision making processes is crucial for the future use of decision support systems. The literature stress that managers must use more analytics and rationality to make better and more appropriate decisions. However, previously studies have indicated that intuition still plays a major role in decision making, even in organizations using Business Intelligence. With this background the following research question is presented: To what extent are Business Intelligence systems used to support decisions in organizations? Purpose: The purpose of this study is to describe and observe Business Intelligence from a decision making perspective. Method: The primary source of data is personal interviews and one observation study, which implies a qualitative method. The respondents are an organization in the start-up phase, IT-consultants and suppliers and current Business Intelligence users. An abductive approach is applied, and the analyses of data is done simultaneously as the examination of literature and previously made studies. Findings: The system is mainly used for producing reports and as a provider of information. More information and more detailed information are accessible due to the Business Intelligence system. The information itself is valued highly, it is assumed that if the decision maker has enough of information, an appropriate decision will be made. Intuition is still frequently used among the users, yet the Business Intelligence system can to some extent neutralize the user. This is due to that the system is used to confirm and follow up the intuition.

Shipping industry belongs to an international business. It plays an extremely important role in the economic and trade development of a country, so it can be regarded as a symbol of extension of the national strength of a country. Besides, it earns a lot of foreign capital for the country, but never creates any negative effects to the society domestically, such as environmental protection, noise, congestion, and so on. This is something that other manufacturing industries are incomparable with. Especially that Taiwan is an island, Taiwan should be engaged in the studies of shipping business through the rich marine resources. Then, unlimited business opportunities and vitality must be brought to Taiwan, achieving the aspiration of and confidence in standing in Taiwan but looking far to the world. International marine transportation business can be divided into irregular bulk shipping and regular container shipping. Between them, irregular bulk shipping is mostly the loading of the bulk cargoes by one or two consignors in one whole vessel. It is almost a completely competitive market. In order to obtain the greatest profit through the unlimited shipping lines and cargo loading, the best business operation ways and the best vessel allocation should be selected. For regular container shipping, services are provided to a great number of consignors with fixed shipping lines, fixed anchoring harbors and fixed rate. The cost of refueling of shipping industry occupies an extremely high proportion of the variable cost of marine transportation. When the price of the international fuel rises continuously, the cost of fuel increases accordingly. Taking the international vessel-use fuel MF-180 for example, the market quotation of oil in Taiwan on Jan. 6, 1999 was US$74/MT. After that, the price rose continuously. Down to Oct. 2007, there appeared the market quotation of oil at above US$500/MT. According to the ¡§Medium Term Oil Market Report of International Energy Agency (IEA)¡¨ announced on Jul. 9, 2007, the whole world would face the shortage of oil supply in five years, and the oil price was estimated to be rising continuously. Bulk marine transportation is irregular shipping without the fixing of any regular shipping lines and cargo loading. If refueling is to be made to vessels, there should be more efficient estimation and decision on the refueling harbor, refueling volume and refueling cost so as to achieve the minimum variable cost and acquire the greatest profit. Through review of the literature about the refueling of voyages as well as the interviews with experts, the paper investigates the literature of the decision support system, and uses the prototype development method to complete the model construction and the analytical design of system, practically establish a prototype of refuel decision support system for bulk shipping, and provide the solutions of average sailing speed of voyages, refueling volume and refueling harbor. After the results are compared with those of the actual cases, the paper evaluates the effects of the decision support system. Through the users¡¦ feedback, the prototype is revised to increase the completeness of the system, which provides a reference for the bulk shipping proprietors in constructing a refueling decision support system in future.

Thesis Erasmus University Rotterdam. / ook verschenen in gedrukte versie. With bibliogr., with a summary in Dutch.

Thesis (M. Phil.)--University of Hong Kong, 2001. / Includes bibliographical references (leaves 163-177).

Thesis (M.S.)--Florida State University, 2004. / Advisor: Dr. Lisa K. Spainhour, Florida State University, College of Engineering, Dept. of Civil Engineering. Title and description from dissertation home page (viewed 6/16/04). Includes bibliographical references.

Infrastructure networks that provide basic services such as transportation, telecommunications, electricity distribution, and water supply and drainage are critical for the smooth functioning of a nation’s economy and its society. To provide efficient and uninterrupted services, these infrastructure networks need to be periodically inspected, upgraded, and maintained. However, infrastructure networks are expensive to operate and maintain; many infrastructure service providers allocate more than half of their total capital investments to network maintenance and improvement. With increasing customer expectations, intensifying global competition, and challenging financial environments, the infrastructure service providers need to develop models that can optimize all of the different factors that must be taken into consideration when making important decisions related to infrastructure network inspection and maintenance. This dissertation, which consists of three essays, focuses on some of the key decision issues associated with inspection and maintenance of these large infrastructure networks. Specifically, the first two essays, respectively, address a project management problem to maintain and expand a large-scale network and a periodic network inspection problem. The third essay, motivated by the computational challenges of the first two problems, addresses the network reduction and approximation problem within the same context. These problems are deterministic optimization problems over large-scale networks, which are very difficult to solve, and have not been extensively studied in the literature. In this dissertation, we introduce new optimization models for each problem, develop theoretical and algorithmic strategies that exploit problem structures to effectively solve the problems, and implement and test these methods on actual problems using data provided by an infrastructure service provider. / text