A presentation of general results for discussing local optimality and computation of the expansion of value function and approximate solution of optimization problems, followed by their application to various fields, from physics to economics. The book is thus an opportunity for popularizing these techniques among researchers involved in other sciences, including users of optimization in a wide sense, in mechanics, physics, statistics, finance and economics. Of use to research professionals, including graduate students at an advanced level.
This book shows how the Bayesian Approach (BA) improves wellÂ known heuristics by randomizing and optimizing their parameters. That is the Bayesian Heuristic Approach (BHA). The ten in-depth examples are designed to teach Operations Research using Internet. Each example is a simple representation of some imporÂ tant family of real-life problems. The accompanying software can be run by remote Internet users. The supporting web-sites include software for Java, C++, and other lanÂ guages. A theoretical setting is described in which one can discuss a Bayesian adaptive choice of heuristics for discrete and global optimization probÂ lems. The techniques are evaluated in the spirit of the average rather than the worst case analysis. In this context, "heuristics" are understood to be an expert opinion defining how to solve a family of problems of disÂ crete or global optimization. The term "Bayesian Heuristic Approach" means that one defines a set of heuristics and fixes some prior distribuÂ tion on the results obtained. By applying BHA one is looking for the heuristic that reduces the average deviation from the global optimum. The theoretical discussions serve as an introduction to examples that are the main part of the book. All the examples are interconnected. DifÂ ferent examples illustrate different points of the general subject. HowÂ ever, one can consider each example separately, too.
This book presents a radically new approach to problems of evaluating and optimizing the performance of continuous-time stochastic systems. This approach is based on the use of a family of Markov processes called Piecewise-Deterministic Processes (PDPs) as a general class of stochastic system models. A PDP is a Markov process that follows deterministic trajectories between random jumps, the latter occurring either spontaneously, in a Poisson-like fashion, or when the process hits the boundary of its state space. This formulation includes an enormous variety of applied problems in engineering, operations research, management science and economics as special cases; examples include queueing systems, stochastic scheduling, inventory control, resource allocation problems, optimal planning of production or exploitation of renewable or non-renewable resources, insurance analysis, fault detection in process systems, and tracking of maneuvering targets, among many others.
This tribute to Ortrun Zuber-Skerritt is a celebratory Festschrift of her learning/research action-packed life. Colleagues around the world reflect on their own learning, research and professional development, with and through Ortrun, in action learning and action research (ALAR). Four Parts identify focus areas in Ortrun's work and interests over the last 40 years. Higher Education is the site for most of Ortrun's work experience since 1974 when she joined Griffith University in Australia. Organisations is a context where Ortrun has actively explored processes of learning, leadership and development in management education. Communities of Practice characterise Ortrun's work throughout her career, particularly through participatory action learning and action research (PALAR) in communities. Futures focusses Ortrun's recent writing advocating for PALAR as a flexible and effective methodology for responding to rapid change. Here we see why Ortrun is a quintessential international scholar. And an ALAR practitioner/advocate. Her world view, understandings of knowledge and personal qualities naturally orient her along this path of inclusive, purposeful action. This is why Ortrun is a vital energy in shaping the evolution of the 'Action' family of scholarship, now including PALAR and LAL (Lifelong Action Learning). No wonder her life and pioneering work are an adventure story - not just of learning and research, but also of passion and action. This tribute opens windows onto that story.
Arguably, many industrial optimization problems are of the multiobjective type. The present work, after providing a survey of the state of the art in multiobjective optimization, gives new insight into this important mathematical field by consequently taking up the viewpoint of differential geometry. This approach, unprecedented in the literature, very naturally results in a generalized homotopy method for multiobjective optimization which is theoretically well-founded and numerically efficient. The power of the new method is demonstrated by solving two real-life problems of industrial optimization.
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