A new multiple objective decision-making model for increasing participation to decision-making processes

This dissertation is on multiple objective decision-making (MODM) in the presence of infinite trade-offs between the objectives and reflecting hybrid importance information (cardinal and ordinal) to the decision process by employing fuzzy logic. Many proposed MODM methods in the related literature tend to lose their grip on reflecting the preference information to the trade-offs between the many objectives when the general infimum of all the satisfaction degrees is zero-valued. Also, current research on the MODM cannot process both the cardinal preference information and the ordinal preference information simultaneously. In the proposed new fuzzy goal programming (FGP) approach, ordinal importance information and cardinal importance information are processed by sorting achievement membership functions and scaling achievement membership functions using fuzzy logic. In addition, the proposed method provides means to fine-tune the trade-off between the overall satisfaction and the elimination of intransitivity & incommensurability issues. The FGP model consists of linear constraints and objective function and thus, adds a minimal extra complexity to the base problem with infinite trade-offs between objectives. It is observed that, for values of the coefficient of compensation set close to unity within the interactive iterations, the model can produce efficient compromise solutions. It is believed that this dissertation is the first to study the MODM in the presence of infinite trade-offs between the objectives as a research topic and introduces the first FGP that incorporates ordinal and cardinal preference information in a single model. Moreover, the proposed FGP is a robust model. If the base problem has a non-empty solution set, the FGP is always feasible. Lastly, the model introduced in the study is rare and significant for not employing crisp constraints that cut out a subset of the solution set that may contain reasonable solutions that the decision-makers can settle on.