This is particularly important in a book that purports to promote the science of decision making.
As such, advanced undergraduate and graduate students, as weil as practitioners, will find this volume beneficial. While different authors prefer different degrees of mathematical sophistication, we have made every possible attempt to unify the approaches, provide clear explanations, and make this volume accessible to as many readers as possible.
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Write a Review. In a papersReese  summarized the literature on solutionsme Church and ReVelle proposed the linearsprogramming relaxation of the integersprogramming formulation of the problem andsgreedy-interchange heuristics to solve the MCLP.
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Decision Analysis, Location Models, and Scheduling Problems by Horst A. Eiselt
Networks and Discrete Location. Heuristic methods for estimating the generalized vertex median of a weighted graph - Teitz, Bart - Show Context Citation Context An efficient Tabu search procedure for the p-median problem - Rolland, Schilling, et al. Capacitated clustering problems by hybrid simulated annealing and tabu search - Osman, Creistofides Show Context Citation Context Layout Models.
Unconstrained Time Project Scheduling. Project Scheduling with Resource Constraints.
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Fundamentals of Machine Scheduling. Single Machine Scheduling. Parallel Machine Models. Dedicated Machine and Resource-Constrained Models.
Back Matter Pages About this book Introduction The purpose of this book is to provide readers with an introduction to the fields of decision making, location analysis, and project and machine scheduling. The combination of these topics is not an accident: decision analysis can be used to investigate decision seenarios in general, location analysis is one of the prime examples of decision making on the strategic Ievel, project scheduling is typically concemed with decision making on the tactical Ievel, and machine scheduling deals with decision making on the operational Ievel.