This book serves as an introductory text to optimization theory in normed spaces. Topics of this book are existence results, various differentiability notions together with optimality conditions, the ...contingent cone, a generalization of the Lagrange multiplier rule, duality theory, extended semidefinite optimization, and the investigation of linear quadratic and time minimal control problems. This textbook presents fundamentals with particular emphasis on the application to problems in the calculus of variations, approximation and optimal control theory. The reader is expected to have a basic knowledge of linear functional analysis.
Pragmatism and its consequences are central issues in American politics today, yet scholars rarely examine in detail the relationship between pragmatism and politics. In The Priority of Democracy, ...Jack Knight and James Johnson systematically explore the subject and make a strong case for adopting a pragmatist approach to democratic politics--and for giving priority to democracy in the process of selecting and reforming political institutions.
This book presents adaptive solution methods for multiobjective optimization problems based on parameter dependent scalarization approaches. With the help of sensitivity results an adaptive parameter ...control is developed such that high-quality approximations of the efficient set are generated. These examinations are based on a special scalarization approach, but the application of these results to many other well-known scalarization methods is also presented. Thereby very general multiobjective optimization problems are considered with an arbitrary partial ordering defined by a closed pointed convex cone in the objective space. The effectiveness of these new methods is demonstrated with several test problems as well as with a recent problem in intensity-modulated radiotherapy. The book concludes with a further application: a procedure for solving multiobjective bilevel optimization problems is given and is applied to a bicriteria bilevel problem in medical engineering.
This brilliant new book focuses on various techniques of computational intelligence, both single ones and those which form hybrid methods. These techniques are applied to issues of artificial ...intelligence, for example in the processing of speech and natural language, and in building expert systems and robots.
•A systematic approach is proposed to build causal decision-making models.•The proposed approach combined multi-criteria decision making tools and a Bayesian Network.•The resulting models are based ...on knowledge elicited from multiple experts.•The proposed approach is applied to a safety management system case study.•The model's advantages is evaluated with different available approaches.
Decision-making is a critical step in safety and risk analysis. Decision-making is conducted according to the different sources of information often elicited from filed matter and subject matter experts. Many team-based decision-making methods are developed to identify hazards, determine intervention actions, and to prioritize the efforts to reduce the risk in given conditions. However, the majority of decision-making methods are based on idealistic assumptions such as risk contributing factor in a complex system and independency between the factors. In reality, there is strong interaction among the risk factors and also among the sources of information used in decision-making procedure. There is a requirement to use a decision-making framework that captures the dependency of the risk factors and the source of information. This is achieved by integrating DEMATEL (decision-making trial and evaluation laboratory) methodology with Best-Worst method (BWM) and Bayesian network (BN). The integration is considered at two different stages in the DEMATEL methodology. Application of the integrated DEMATEL is illustrated by adopting a case study of safety management in the high-tech industry.
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•Modelling exit choice changing (adaptation) in crowd evacuation simulating.•Implement an econometric choice model for to simulate dynamic adaptive decisions.•Using experimental data ...to quantify how important the module is for simulation accuracy.•Numerical analyses to quantify how decision changing affects evacuation efficiency.•Intermediate degrees of decision adaptation and follow-the-peer are beneficial to the system.
A crucial aspect of simulating crowds’ evacuation processes is that humans can dynamically revisit and change their decisions. While a relatively great deal of attention has been paid by recent studies to modelling directional decision making, the ‘exit decision changing (or decision adaptation)’ phenomenon has been largely overlooked. Here, we quantitatively investigate (I) how important is to include a decision changing module in evacuation simulation models, and (II) whether decision changing is beneficial to evacuation processes. We propose and implement a parsimonious discrete-choice model of decision changing. The model embodies the most influential factors that make an evacuee revise and adapt their choice of exit. This includes the effects of ‘relative queue-size imbalance at exits’, ‘visibility of exits’, ‘social influence’ and ‘inertia (for maintaining initial decisions)’. Results showed that, the inclusion of the decision changing module made a very substantial difference in enhancing the accuracy of the simulation outputs. Simulating exit choices as one-off decisions strictly limited the degree of match that could be achieved between the simulated and experimental outputs (in terms of replicating the observed exit shares and evacuation times) (question I). Further analyses also revealed that an intermediate degree of decision changing is a strategy that most benefits the system. By contrast, the extreme decision-changing strategies (i.e. “no change” and “too many changes”) were found to be suboptimal. Also, while we have observed, in our other studies, that imitative (or the so-called herd-type) behaviour in ‘exit choices’ is invariably detrimental to evacuation systems, here, we observed that when it comes to ‘adapting exit choices’, a moderate degree of imitation (or follow-the-peer) tendency makes the system more efficient (question II).
Recently, the advancement of cognitive computing and three-way decisions has enabled in-depth sequential pattern understanding through temporal association analysis. The main challenge is to obtain ...concise patterns that express richer semantics for multivariate time series (MTS) analysis. In this paper, we propose a tri-partition state alphabet-based sequential pattern (Tri-SASP) for MTSs. First, a tri-wildcard gap inserted between each pair of adjacent states enhances the flexibility of the method. Second, a given set of states is partitioned into positive (POS), negative (NEG) and boundary (BND) regions. The states in POS can only be used to construct a Tri-SASP, the states in NEG can only be matched by a tri-wildcard gap, and the states in BND can be used in both ways. Finally, horizontal and vertical algorithms are proposed to obtain frequent Tri-SASPs in a breadth-first manner. The experimental results on four real-world datasets show that (1) the discovered Tri-SASPs and temporal rules can enrich human cognition; (2) the two tri-partition strategies can bring us very meaningful and varied Tri-SASPs; and (3) the two algorithms are effective and scalable.
Currently the methods of Soft Computing are successfully used for risk analysis in: budgeting, e-commerce development, portfolio selection, Black-Scholes option pricing models, corporate acquisition ...systems, evaluating investments in advanced manufacturing technology, interactive fuzzy interval reasoning for smart web shopping, fuzzy scheduling and logistic. An essential feature of economic and financial problems it that there are always at least two criteria to be taken into account: profit maximization and risk minimization. Therefore, the economic and financial problems are multiple criteria ones. In this book, a new systematization of the problems of multiple criteria decision making is proposed which allows the author to reveal unsolved problems. The solutions of them are presented as well and implemented to deal with some important real-world problems such as investment project's evaluation, tool steel material selection problem, stock screening and fuzzy logistic. It is well known that the best results in real -world applications can be obtained using the synthesis of modern methods of soft computing. Therefore, the developed by the author new approach to building effective stock trading systems, based on the synthesis of fuzzy logic and the Dempster-Shafer theory, seems to be a considerable contribution to the application of soft computing method in economics and finance. An important problem of capital budgeting is the fuzzy evaluation of the Internal Rate of Return. In this book, this problem is solved using a new method which makes it possible to solve linear and nonlinear interval and fuzzy equations and systems of them. The developed new method allows the author to obtain an effective solution of the Leontjev's input-output problem in the interval setting.
This book explores dimensions of project management and scheduling: the construction of a baseline schedule and the analysis of a project schedule's strengths and weaknesses as preparation of the ...project control phase during project progress.
A shared decision-making model is preferred for engaging prostate cancer patients in treatment decisions. However, the process of assessing an individual's preferences and values is challenging and ...not formalized. The purpose of this study is to develop an automated decision aid for patient-centric treatment decision-making using decision analysis, preference thresholds and value elicitations to maximize the compatibility between a patient's treatment expectations and outcome.
A template for patient-centric medical decision-making was constructed. The inputs included prostate cancer risk group, pre-treatment health state, treatment alternatives (primarily focused on radiation in this model), side effects (erectile dysfunction, urinary incontinence, nocturia and bowel incontinence), and treatment success (5-year freedom from biochemical failure). A linear additive value function was used to combine the values for each attribute (side effects, success and the alternatives) into a value for all prospects. The patient-reported toxicity probabilities were derived from phase II and III trials. The probabilities are conditioned on the starting state for each of the side effects. Toxicity matrices for erectile dysfunction, urinary incontinence, nocturia and bowel incontinence were created for the treatment alternatives. Toxicity probability thresholds were obtained by identifying the patient's maximum acceptable threshold for each of the side effects. Results are represented as a visual. R and Rstudio were used to perform analyses, and R Shiny for application creation.
We developed a web-based decision aid. Based on preliminary use of the application, every treatment alternative could be the best choice for a decision maker with a particular set of preferences. This result implies that no treatment has determinist dominance over the remaining treatments and that a preference-based approach can help patients through their decision-making process, potentially affecting compliance with treatment, tolerance of side effects and satisfaction with the decision.
We present a unique patient-centric prostate cancer treatment decision aid that systematically assesses and incorporates a patient's preferences and values to rank treatment options by likelihood of achieving the preferred outcome. This application enables the practice and study of personalized medicine. This model can be expanded to include additional inputs, such as genomics, as well as competing, concurrent or sequential therapies.
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