Bi-level programming techniques are developed for decentralized decision problems with decision makers located in a two-level decision making system; the upper decision maker is termed the leader while the lower is the follower. Both the leader and the follower try to optimise their own objective functions and the corresponding decisions do not control but do affect those of the other level. This research aims at solving bi-level decision problems with five extensions, i.e. multiple leaders/followers/objectives, fuzzy coefficients and goals. By using particle swarm optimisation and/or cut set and/or goal programming and/or Nash equilibrium concept, related mathematical models and corresponding algorithms are developed to solve fuzzy linear bi-level decision problems, fuzzy linear multi-objective bi-level decision problems, fuzzy linear multi-follower multi-objective bi-level decision problems, fuzzy linear goal bi-level decision problems, multi-leader one-follower bi-level decision problems, one-leader multi-follower bi-level decision problems, and multileader multi-follower bi-level decision problems. A fuzzy bi-level decision support system is then developed which implements all the algorithms to support bi-level decision making with different features. Finally, by using these bi-level models and algorithms, we explore possible applications in the fields of railway train set organisation, railway wagon flow management, strategic bidding in the electricity market, and supply chains to solve real world bi-level decision problems. The results of experiments show that the models and algorithms are effective for solving real world bi-level decision problems.

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In modern organizations where managers must constantly be dealing with an overload of information, it is often observed that participants in group decision processes either are not clearly aware of their specific preferences or that they are not capable of properly formulating those preferences. When this happens, inconsistent or incomplete expression of personal preferences and their use in decision making may lead to an unjustifiable outcome for the group. Due to this problem, the strengths and effectiveness of GDSS-supported group meetings may, in some situations, not be apparent. This dissertation develops a new approach to supporting group decision making, focusing on preference knowledge of individual participants in a group. A system architecture for the design of an MCDM (Multiple Criteria Decision Making) GDSS which facilitates the process of eliciting, formulating, utilizing, aggregating, and analyzing preferences for individuals within groups is presented. The architecture integrates multi-criteria decision making paradigms with a group decision support environment. A prototype has been developed in order to demonstrate the design feasibility of an architecture that centers around four phases of choice making: alternative generation, preference specification, alternative evaluation, and preference aggregation. The prototype is designed to support managerial choice and judgment processes in collaborative meetings. The intended problem domain of the model is semi-structured managerial decisions for which decision variables (attributes) can be represented in quantitative terms to some extent, yet for which evaluation of alternatives requires a high degree of intuition and personal analysis. The process of prototyping the proposed architecture and the results from a qualitative study have provided some instructive conclusions relating to MCDM GDSS design: (1) support for human choice strategies can be integrated into a GDSS, (2) appropriate management of preferences of group participants will facilitate collaborative decision processes, (3) hierarchical decomposition of a decision problem can provide structure to a problem and thereby reduce problem complexity, and (4) managerial decisions are appropriate problems to which the current approach can be applied.

Thesis (Ph. D.)--Ohio State University, 1974. / Includes vita. Includes bibliographical references (leaves 363-372). Available online via OhioLINK's ETD Center

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Thesis (M.S. in food science)--Washington State University, December 2009. / Title from PDF title page (viewed on Jan. 19, 2010). "School of Food Science." Includes bibliographical references (p. 94-99).

Thesis ( Ph.D. ) -- University of Texas at Arlington, 2008.

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This research project specifically investigates the use of information processing strategies by organizational buyers in the first stage of the supplier selection process, the selection of an evoked set of qualified suppliers. In this selection process it is hypothesized that the buyer's use of evaluation functions or information processing strategies is influenced by the task faced by the buyer. The varying levels of risk, familiarity and informational requirements of the buying situation should impact the use of the information processing strategies. Structured protocols--written descriptions of compensatory and noncompensatory information processing strategies--were used to determine the evaluation function which organizational buyers use to qualify suppliers into an evoked set. The data was collected in a field study of 135 organizational buyers from 76 different organizations. The subjects were interviewed about purchases they were presently working on in which suppliers had been selected but the final purchase decision was still pending. Identification of the buying task, new task, modified rebuy, and straight rebuy also utilized the structured protocol technique. Written descriptions, based on the constitutive definitions of Robinson and Farris (1967), were used to address the following research question: Is there a difference in the decision rules or information processing strategies utilized by organizational buyers in the development of an evoked set of qualified suppliers when the buyer is qualifying suppliers for a new task, modified rebuy, or straight rebuy buying task? In addition to the information gathered via the structured protocols, information was gathered about the level of risk, familiarity and information requirements of the purchasing task. This was done to gain a better understanding of the use of information-processing strategies by organizational buyers. Analysis of the data indicates that the buying task is related to the choice of an information-processing strategy. The data also support the contention that the organizational buyer will utilize any of the information-processing strategies in the selection of an evoked set of suppliers. Although the buying task was found to significantly influence the use of the information-processing strategies, the study shows that all of the strategies were reported as being used for each of the buying tasks. In addition to finding that the buying task influences the choice of an information-processing strategy, the data support the model of information processing presented. The model addressed the impact that risk, familiarity, and information load had on the use of the evaluation functions. The risk node of the model was supported by two of the five risk variables included in the study: product homogeneity and supplier homogeneity. At the familiarity nodes of the model, the subjective measures of familiarity which support the model are supplier familiarity and frequency of product purchase. Supplier familiarity was found to be significantly different between the weighted and unweighted compensatory strategies. The significant difference in the level of familiarity found in the use of the conjunctive and disjunctive information processing strategies is associated with the frequency of product purchase. The final nodal section of the information-processing model which was supported is the comparison of the conjunctive and lexicographic strategies. The lexicographic strategy was found to be used when there was a higher perceived number of suppliers capable of supplying the needed product. In general this study has shown that the situation in which suppliers are selected impacts the use of an information-processing strategy. The findings are consistent with the research and hypothesizing associated with the use of information-processing strategies by consumers.

Mothers with cancer are required to make medical and social decisions while attempting to balance their own physical, psychological and social needs with the needs of their children. To explore the decision-making process, in-depth interviews were conducted with 7 mothers with a cancer diagnosis and children aged birth to 6 years. They were asked to describe: 1) types of decisions; 2) process they used to make decisions; 3) conditions of their lives; 4) meanings assigned to their decisions. The grounded theory method was used. The driving force behind decision-making was the mothers’ desire to maintain the mother-child bond, influenced by the context of their lives. Making decisions to maintain the mother-child bond involved managing: 1) distance; 2) physical changes; 3) the information shared; and 4) the ongoing chain of decisions. The findings have implications for improving the quality and usefulness of psychosocial supports for mothers with cancer and their families.