Operations Research Management Science at Work

As optimization researchers tackle larger and larger problems, scale interactions play an increasingly important role. One general strategy for dealing with a large or difficult problem is to partition it into smaller ones, which are hopefully much easier to solve, and then work backwards towards the solution of original problem, using a solution from a previous level as a starting guess at the next level. This volume contains 22 chapters highlighting some recent research.

This work prepares current and future managers and consultants focused on health care delivery systems to improve the efficiency of processes that deliver care. This material will help you manage capacity, improve patient flow, estimate process costs, and conduct experiments that lead to process improvement. Essential tools covered include process mapping and measurement, data collection and analysis, and the use of discrete event simulation as a tool for virtual experimentation and improvement. Tools are introduced with no assumption of prior training. Many examples of settings, problems, and solutions are presented, along with a generalized approach to process improvement that is specifically tailored to health care settings. Readings, exercises, and cases suitable for discussion or end-course projects are also provided. Writing is based on a decade of experience teaching at the MBA and MS levels, managing dozens of improvement processes, and a host of our prior scholarly publications, book chapters, and case studies.

Introduction to Global Optimization Exploiting Space-Filling Curves provides an overview of classical and new results pertaining to the usage of space-filling curves in global optimization. The authors look at a family of derivative-free numerical algorithms applying space-filling curves to reduce the dimensionality of the global optimization problem; along with a number of unconventional ideas, such as adaptive strategies for estimating Lipschitz constant, balancing global and local information to accelerate the search. Convergence conditions of the described algorithms are studied in depth and theoretical considerations are illustrated through numerical examples.

Introduction to Global Optimization Exploiting Space-Filling Curves provides an overview of classical and new results pertaining to the usage of space-filling curves in global optimization. The authors look at a family of derivative-free numerical algorithms applying space-filling curves to reduce the dimensionality of the global optimization problem; along with a number of unconventional ideas, such as adaptive strategies for estimating Lipschitz constant, balancing global and local information to accelerate the search. Convergence conditions of the described algorithms are studied in depth and theoretical considerations are illustrated through numerical examples.

by Trish Reay (Editor), Tammar B. Zilber (Editor), Ann Langley (Editor), Haridimos Tsoukas (Editor)
Institutions - the structures, practices, and meanings that define what people and organizations think, do, and aspire to - are created through process