(Download) "Cooperative and Noncooperative Multi-Level Programming" by Masatoshi Sakawa & Ichiro Nishizaki ~ Book PDF Kindle ePub Free
eBook details
- Title: Cooperative and Noncooperative Multi-Level Programming
- Author : Masatoshi Sakawa & Ichiro Nishizaki
- Release Date : January 18, 2009
- Genre: Economics,Books,Business & Personal Finance,Science & Nature,Mathematics,Computers & Internet,Computers,Management & Leadership,
- Pages : * pages
- Size : 13562 KB
Description
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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