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Cooperative and Noncooperative Multi-Level Programming

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This addition to the OPERATIONS RESEARCH/COMPUTER SCIENCE INTERFACES Series represents a sorely-needed advance in
decision science and game theory literature. Drs. Sakawa and Nishizaki present their combined work in applying both
cooperative and noncooperative game theory in the solving of real-world problems in fuzzy, multiobjective, and uncertain
environments, and the potential applications of their approaches range from corporate environments to economics, applied
mathematics, and policy decision making. Sakawa has gained recognition for his work on genetic algorithms, and shows in
this book how they can be used when linear programming doesn’t suffice. Nishizaki has worked extensively in
systems engineering, especially in game theory, multiobjective decision making and fuzzy mathematical programming, and
is doing much to advance theory and practice in real-world decision science.



The monograph first provides a review of the optimization concepts that underlie the rest of the book: fuzzy
programming; multiobjective programming; stochastic programming; and genetic algorithms. The authors then apply these
concepts to noncooperative decision making in hierarchical organizations, using multiobjective and two-level linear
programming, and then consider cooperative decision making in hierarchical organizations. They then present applications
in a work force assignment problem; a transportation problem; and an inventory and production problem in supply chain
management. After examining possible future directions in two-level programming, including use of metaheuristics and
genetic algorithms to help manage large numbers of integer decision variables, they present conclusions.

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