Linear bilevel programming technology : models and algorithms
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NO FULL TEXT AVAILABLE. Access is restricted indefinitely. ----- Bilevel programming technology has mainly been developed for solving decision problems with decision makers in a hierarchical organization. This dissertation addresses both theory and technology of linear bilevel programming. It first proposes a new definition of solution and related theorems for linear bilevel programming problems to overcome a fundamental deficiency of existing linear bilevel programming theory. It then presents a comprehensive framework for linear bilevel multifollower programming (BLMFP) problems including ten linear BLMFP models. These models are classified into four types, namely linear BLP, linear BLMFP without shared variables among followers, linear BLMFP with shared variables among followers, and linear BLMFP with partial shared variables among followers. Further, this dissertation proposes related theory and a set of approaches including the Kuhn-Tucker approach, Kth-best approach and branch and bound algorithm for solving each type of the models. Finally, this research develops a prototype of web-based linear bilevel decision support systems by integrating the web, databases and decision support system technologies with the proposed solution approaches of linear BLMFP.
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