This research intends to advance knowledge in the technology management field, most importantly in the study of organizations that develop technologies in-house and wish to enhance their technology transfer performance while maintaining adherence between R&D activities and overall business strategies. The objective was to build a multi-criteria decision-making model capable of producing a technology transfer score, which can be used by practitioners in order to assess and later improve their organizations' technology transfer capabilities -- ultimately aiming to improve technology development as a whole. The model was applied to a major power utility organization in the Pacific Northwest of the United States. The introduction brings initial and basic information on the topic, along with the problem statement -- this chapter is aimed at situating the reader on the boundaries of the topic while highlighting its importance within the technology management field of study. The second chapter is the literature review. It brings general and specific information on technology transfer, as well as its complexities, gaps, relationship with other fields and the characteristics of this topic within the energy realm. It also tries to shed a light on how the alignment between R&D and business strategy is perceived by the literature, discussing some of the methods used and its shortcomings. Additionally, the literature review brings an analysis that builds the argument in favor of a continuous technology transfer process, and tries to show how it would be helpful in aligning R&D and business strategy. The third chapter presents the methodological approach -- hierarchical decision modeling (HDM) aided by action research -- which constitutes a methodological novelty piloted and validated throughout the development of the study. The fourth chapter details the model development process step-by-step, and the fifth chapter details the model application process with the analysis of the aforementioned organization. Additionally, results are interpreted and analyzed, and insights for the specific case and for technology managers in general are discussed. Lastly, the contributions of the study towards the advancement of the body of knowledge are discussed, as well as the study limitations and future research opportunities.

While design theories provide a foundation for representing and reasoning about design methods, existing design theories do not explicitly include uncertainty considerations or recognize tradeoffs between the design artifact and the design process. These limitations prevent the existing theories from adequately describing and explaining observed or proposed design methods. In this thesis, Rational Design Theory is introduced as a normative theoretical framework for evaluating prescriptive design methods. This new theory is based on a two-level perspective of design decisions in which the interactions between the artifact and the design process decisions are considered. Rational Design Theory consists of normative decision theory applied to design process decisions, and is complemented by a decision-theory-inspired conceptual model of design. The application of decision analysis to design process decisions provides a structured framework for the qualitative and quantitative evaluation of design methods. The qualitative evaluation capabilities are demonstrated in a review of the systematic design method of Pahl and Beitz. The quantitative evaluation capabilities are demonstrated in two example problems. In these two quantitative examples, Value of Information analysis is investigated as a strategy for deciding when to perform an analysis to gather additional information in support of a choice between two design concepts. Both quantitative examples demonstrate that Value of Information achieves very good results when compared to a more comprehensive decision analysis that allows for a sequence of analyses to be performed.

The research objective of this thesis is to formulate and demonstrate a computational framework for modeling the design decisions of engineers. This framework is intended to be descriptive in nature as opposed to prescriptive or normative; the output of the model represents a plausible result of a designer's decision making process. The framework decomposes the decision into three elements: the problem statement, the designer's beliefs about the alternatives, and the designer's preferences. Multi-attribute utility theory is used to capture designer preferences for multiple objectives under uncertainty. Machine-learning techniques are used to store the designer's knowledge and to make Bayesian inferences regarding the attributes of alternatives. These models are integrated into the framework of a Markov decision process to simulate multiple sequential decisions. The overall framework enables the designer's decision problem to be transformed into an optimization problem statement; the simulated designer selects the alternative with the maximum expected utility. Although utility theory is typically viewed as a normative decision framework, the perspective in this research is that the approach can be used in a descriptive context for modeling rational and non-time critical decisions by engineering designers. This approach is intended to enable the formalisms of utility theory to be used to design human subjects experiments involving engineers in design organizations based on pairwise lotteries and other methods for preference elicitation. The results of these experiments would substantiate the selection of parameters in the model to enable it to be used to diagnose potential problems in engineering design projects. The purpose of the decision-making framework is to enable the development of a design process simulation of an organization involved in the development of a large-scale complex engineered system such as an aircraft or spacecraft. The decision model will allow researchers to determine the broader effects of individual engineering decisions on the aggregate dynamics of the design process and the resulting performance of the designed artifact itself. To illustrate the model's applicability in this context, the framework is demonstrated on three example problems: a one-dimensional decision problem, a multidimensional turbojet design problem, and a variable fidelity analysis problem. Individual utility functions are developed for designers in a requirements-driven design problem and then combined into a multi-attribute utility function. Gaussian process models are used to represent the designer's beliefs about the alternatives, and a custom covariance function is formulated to more accurately represent a designer's uncertainty in beliefs about the design attributes.

Alexander H. Levis [et al.]. / Prepared for Air Force [Office] of Scientific Research, Bolling Air Force Base, Washington, D.C. Contract AFOSR - 80-0229. / Description based on: July 1981/June 1982.

South Africa has been experiencing several challenges regarding access to higher education, quality of higher education, effectiveness of higher education course delivery, and funding for higher education. In the higher education sector, the bulk of the burden is placed on traditional higher education institutions, most notably universities, in providing higher education to a growing youth base in dire need of education that supports their individual learning needs. With these challenges facing traditional universities, online higher education provided by both public sector higher education institutions and private sector education providers can act as a valuable alternative and solution to access for some of the population. Online education and face-to-face education differ considerably in how they deliver courses to students. Many have argued that these differences are in some cases attributable to strengths in face-to-face education and drawbacks or limitations in online education, large enough that they should serve as the criteria for selecting the former over the latter as the better mode of delivery. While there have been examples of online programmes that have failed to deliver courses successfully by underutilising or misusing the tools and techniques available, there are positive examples where these programmes perform equally as well as face-to-face courses. The defining difference is ultimately and often the management of these courses’ resources, activities, people, processes, and practices. Considering the above, and with examination of the available literature, a conceptual and theoretical framework was constructed and a quantitative research study was undertaken to prove the significant correlational relationships between elements of course delivery and a management framework to govern those elements. The sample consisted of 115 students from a postgraduate degree programme presented in two formats, online and on-campus. The findings provide evidence of significant relationships between the core functions of management as well as between aspects of course delivery, such as opportunities for interaction, opportunities for feedback, and course content in achieving learning outcomes for students and contributing to engagement. The findings also indicate positive perceptions from students in relation to the delivery of the courses.

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We study dynamic decision-making problems in networks and markets under uncertainty about future payoffs. This problem is difficult in general since 1) Although the current decision (potentially) affects future decisions, the decision-maker does not have exact information on the future payoffs when he/she commits to the current decision; 2) The decision made at one part of the network usually interacts with the decisions made at the other parts of the network, which makes the computation scales very fast with the network size and brings computational challenges in practice. In this thesis, we propose computationally efficient methods to solve dynamic optimization problems on markets and networks, specify a general set of conditions under which the proposed methods give theoretical guarantees on global near-optimality, and further provide numerical studies to verify the performance empirically. The proposed methods/algorithms have a general theme as “local algorithms”, meaning that the decision at each node/agent on the network uses only partial information on the network. In the first part of this thesis, we consider a network model with stochastic uncertainty about future payoffs. The network has a bounded degree, and each node takes a discrete decision at each period, leading to a per-period payoff which is a sum of three parts: node rewards for individual node decisions, temporal interactions between individual node decisions from the current and previous periods, and spatial interactions between decisions from pairs of neighboring nodes. The objective is to maximize the expected total payoffs over a finite horizon. We study a natural decentralized algorithm (whose computational requirement is linear in the network size and planning horizon) and prove that our decentralized algorithm achieves global near-optimality when temporal and spatial interactions are not dominant compared to the randomness in node rewards. Decentralized algorithms are parameterized by the locality parameter L: An L-local algorithm makes its decision at each node v based on current and (simulated) future payoffs only up to L periods ahead, and only in an L-radius neighborhood around v. Given any permitted error ε > 0, we show that our proposed L-local algorithm with L = O(log(1/ε)) has an average per-node-per- period optimality gap bounded above by ε, in networks where temporal and spatial interactions are not dominant. This constitutes the first theoretical result establishing the global near-optimality of a local algorithm for network dynamic optimization. In the second part of this thesis, we consider the previous three types of payoff functions under adversarial uncertainty about the future. In general, there are no performance guarantees for arbitrary payoff functions. We consider an additional convexity structure in the individual node payoffs and interaction functions, which helps us leverage the tools in the broad Online Convex Optimization literature. In this work, we study the setting where there is a trade-off between developing future predictions for a longer lookahead horizon, denoted as k versus increasing spatial radius for decentralized computation, denoted as r. When deciding individual node decisions at each time, each node has access to predictions of local cost functions for the next k time steps in an r-hop neighborhood. Our work proposes a novel online algorithm, Localized Predictive Control (LPC), which generalizes predictive control to multi-agent systems. We show that LPC achieves a competitive ratio approaching to 1 exponentially fast in ρT and ρS in an adversarial setting, where ρT and ρS are constants in (0, 1) that increase with the relative strength of temporal and spatial interaction costs, respectively. This is the first competitive ratio bound on decentralized predictive control for networked online convex optimization. Further, we show that the dependence on k and r in our results is near-optimal by lower bounding the competitive ratio of any decentralized online algorithm. In the third part of this work, we consider a general dynamic matching model for online competitive gaming platforms. Players arrive stochastically with a skill attribute, the Elo rating. The distribution of Elo is known and i.i.d across players. However, the individual’s rating is only observed upon arrival. Matching two players with different skills incurs a match cost. The goal is tominimize a weighted combination of waiting costs and matching costs in the system. We investigate a popular heuristic used in industry to trade-off between these two costs, the Bubble algorithm. The algorithm places arriving players on the Elo line with a growing bubble around them. When two bubbles touch, the two players get matched. We show that, with the optimal bubble expansion rate, the Bubble algorithm achieves a constant factor ratio against the offline optimal cost when the match cost (resp. waiting cost) is a power of Elo difference (resp. waiting time). We use players’ activity logs data from a gaming start-up to validate our approach and further provide guidance on how to tune the Bubble expansion rate in practice.

Tuition and fees are a major source of income for colleges and universities and a major portion of the cost of a student's education. The university administration's task of making sound and effective tuition and fee policy decisions is becoming both more critical and more complex. This is a result of the increased reliance on student-generated tuition-and-fee income, the declining college-age student population, reductions in state and Federal funds, and escalating costs of operation. The comprehensive computerized decision support system (DSS) developed in this research enhances the administration's planning, decision-making, and policy-setting processes. It integrates data and reports with modeling and analysis in order to provide a systematic means for analyzing tuition and fee problems, at a detailed and sophisticated level, without the user having to be an expert in management science techniques or computers. The DSS with its imbedded multi-year goal programming (GP) model allocates the university's revenue requirements to charges for individual student categories based on a set of user-defined objectives, constraints, and priorities. The system translates the mathematical programming model into a valuable decision-making aid by making it directly and readily accessible to the administration. The arduous tasks of model formulation and solution, the calculation of the model's parameter values, and the generation of a series of reports to document the results are performed by the system; whereas, the user is responsible for defining the problem framework, selecting the goals, setting the targets, establishing the priority structure, and assessing the solution. The DSS architecture is defined in terms of three highly integrated subsystems - dialog, data, and models - that provide the following functions: user/system interface, program integration, process control, data storage and handling, mathematical, statistical, and financial computations, as well as display, memory aid, and report generation. The software was developed using four programming languages/systems: EXEC 2, FORTRAN, IFPS, and LINDO. While the system was developed, tested, and implemented at Virginia Polytechnic Institute and State University, the concepts developed in this research are general enough to be applied to any public institution of higher education. / Ph. D.

Thesis (MBA)--Stellenbosch University, 2002. / ENGLISH ABSTRACT: Changes in the global salt market have presented Walvis Bay Salt Refiners with an opportunity to increase its current sales by approximately 40%. Following several pre-feasibility studies, the expansion project plan was created. The construction of new ponds, canals and sluices were to be performed by a subcontractor as selected through a tender process. The scope of the work comprised approximately 70% of the total project cost and it also represented the most critical part of the expansion project. Thomas Saaty’s Analytic Hierarchy Process, was used as a group decision support system for the selection of the most suitable subcontractor. The weighted average mean method was used to aggregate individual scores. A sensitivity analysis was performed following the final outcome to gain a deeper understanding of the problem, obtain a measure of margin between subcontractor scores and to check for the correctness of numbers. / AFRIKAANSE OPSOMMING: Veranderinge in die wêreld soutmark het vir Walvis Bay Salt Refiners 'n geleentheid gebied om sy verkope met ʼn beraamde 40% te verhoog. Na verskeie voorondersoeke is 'n volledige projekplan opgestel. Die vervaardiging van damwalle, kanale en sluise sou deur 'n kontrakteur gedoen word wat deur ʼn tenderprosedure gekeur sou word. Die omvang van hierdie gedeelte van die uitbreidingsprojek verteenwoordig ongeveer 70% van die totale projekkostes en is terselfdelyk die mees sensitiewe gedeelte van die projek. Thomas Saaty se Analytic Hierarchy Process is gebruik as die groepbesluitnemingsondersteuningstelsel om die mees geskikte kontrakteur te kies. Die geweegde gemiddelde is gebruik om die individuele oordele saam te voeg. Sensitiwiteits analise is uitgevoer nadat die finale uitslag bepaal is om sodoende beter insig in die probleem te ontwikkel, om ʼn beter onderskeiding tussen die kontrakteur puntetellings te kry en om die juistheid van die syfers na te gaan.

This research assisted the Yuma, Arizona citrus orchard manager in his strategic planning for achieving a low-cost position in a focused segment of the citrus industry. Citrus growers in the Yuma district are faced with major changes in their competitive environment and must adopt new strategic plans in order to continue to compete effectively in what has recently become a global industry. Since the planning horizon for new citrus orchards is in excess of 20 years, a long range planning model was developed to aid in evaluating alternative operating strategies. This research established the interrelatedness of water, nitrogen, and phosphorous relative to the yields of Valenica Oranges, Lisbon Lemons, and Redblush Grapefruit on Rough Lemon, Sour Orange, and Troyer rootstocks. A computer simulation model was used to evaluate optimal operating policies for a variety of resource prices and market conditions. The methodology utilized in development of the simulation model was unique in that it emulates individual tree performance from the time of planting until maturation. Four operating strategies were investigated and the profit maximizing and cost minimizing strategies were found to be significant. Evaluation of market selling prices indicated that the profit maximizing strategy was optimal except at very low market prices where the cost minimization strategy was optimal. Price sensitivity for water and fertilizer resources was investigated. Operating strategies were not affected by water price increases over the foreseeable future, however, price changes in nitrogen and phosphorous were found to affect the optimal operating strategy primarily through the substitution of manure in the system. Existing horticultural practices in the Yuma growing area were confirmed by the research. Additional optimal operating strategies were suggested relative to market prices. The long run policy decision making process for orchard managers was enhanced.