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NOTION OF SOFT COMPUTING
Published in Kumar S. Ray, Soft Computing and Its Applications, Volume One, 2014
The applications of SC range from the purely theoretical ones, those which develop new lines in abstract mathematics or logic, passing across the areas of multimedia, preference modeling, information retrieval, hybrid intelligent systems, image processing, and so on to practical applications domains such as robotics and manufacturing, actuarial science, nuclear, or medical engineering.
Saving Money with Optimization
Published in Kim H. Pries, Jon M. Quigley, Reducing Process Costs with Lean, Six Sigma, and Value Engineering Techniques, 2012
There is some, albeit limited, evidence that a number of these influences were at work in the United States after 1970. For example, Geoffrion noted in the early 1990s that the obvious term to describe the process of operations research in industry was “dispersed” in the sense that the “long-term net decentralization and disbanding of operations research groups” from the late 1960s onwards meant that operations research in practice was conducted mainly “by individuals in myriad types of staff groups and functional areas.” In explaining the demise of internal operations research groups, Geoffrion identified the following interrelated factors: Operations research groups are susceptible to cost cutting in response to “lack of functional responsibility,” especially if senior management champions move on.The move towards “leaner staffs in headquarters, flatter organizational structures and greater decentralization” had resulted in the dispersion of central or/ms groups.Standard operations research techniques had diffused into other disciplines and professions, notably “actuarial science, applied mathematics, computer science, finance, industrial and other kinds of engineering, logistics, marketing and operations management.” This had undermined the uniqueness of central groups, thereby encouraging the use of operations research ideas by other professionals.The diffusion of operations research computer software had augmented the above process.In its early days, operations research had enjoyed a “bandwagon effect,” which had dissipated. This meant that it was increasingly unlikely that operations research was going to attract the attention of senior managers.Although R/MS groups had enjoyed “strategic impact,” too much of their work was conducted at the tactical level, leading to a decline in managerial interest.Some operations research practitioners had harmed their cause as a result of arrogance, poor communication skills, and ineffective marketing of their skills.
Mathematical understanding and ownership in learning: affordances of and student views on templates for proof-writing
Published in International Journal of Mathematical Education in Science and Technology, 2022
Sarah Klanderman, V. Rani Satyam
Our participants were N = 10 undergraduate students enrolled in the transition-to-proof course at one of two large public research universities: Institution A was located in the midwestern United States and Institution B was in the southeastern United States. Table 1 lists the participants; the first four students were from Institution A and the remaining six students were enrolled at Institution B (all student identities have been replaced by pseudonyms). The interviewed students are indicative of the demographics of the overall course enrolment at both Institution A and B, as male-dominated and a roughly even split between white and minority students. The transition-to-proof courses at both institutions were designed for mathematics majors and minors, however, students were typically science or mathematics-related majors or minors (e.g. actuarial science, computer science, statistics). Students were generally in their second year of university or beyond.
Bayesian capacity model for hurricane vulnerability estimation
Published in Structure and Infrastructure Engineering, 2021
Grzegorz Kakareko, Sungmoon Jung, Spandan Mishra, O. Arda Vanli
Because of this, researchers developed an engineering approach, which follows three major components: (1) estimation of the wind-load effects on buildings; (2) assessment of the damage components; (3) the use of probabilistic methods i.e., Monte Carlo simulations, to assess the component failures and interactions among them. The Florida Public Hurricane Loss Projection Model developed by Gurley et al. (2005) answered many questions about vulnerability estimations. In their analysis, a multi-disciplinary team of experts from different fields (structural engineering, meteorology, computer science, statistics, finance, and actuarial science) developed a hurricane catastrophe model. Vickery et al. (2006) used different damage stages in the vulnerability predictions. Pinelli et al. (2011) conducted a vulnerability estimations of masonry and timber buildings. Pita, Pinelli, Gurley, and Hamid (2013) described future trends in this research area, such as including geographic information systems (GIS) into the loss estimation.
A Unified Approach to Sparse Tweedie Modeling of Multisource Insurance Claim Data
Published in Technometrics, 2020
Simon Fontaine, Yi Yang, Wei Qian, Yuwen Gu, Bo Fan
Traditionally, actuarial practitioners adopt a single-target approach that, for a given insurance product, assumes one population to be homogeneously characterized by some covariates and aims to build a single Tweedie model solely from the product’s sample data. Despite the wide use and simple nature of this approach, practitioners now have access to multiple sources of insurance data. For instance, many insurers have multiple business lines such as the auto insurance and the property insurance; in umbrella coverage, claim amounts are available for multiple types of coverage and for different hazard causes of the same coverage; multiple datasets can be accumulated for a long period of time, during which business environment may have changed significantly so that earlier-year and later-year data sources may not be treated as one homogeneous population. As a result, the modern multisource insurance data may not be characterized well by a homogeneous model. With these emerging multisource insurance data problems, much attention has been drawn to addressing their modeling issues in statistics and actuarial science. Both the frequency-severity and Tweedie model approaches have been investigated in the context of multivariate regressions to model the multiple responses simultaneously (see Frees, Lee, and Yang 2016; Shi 2016 and references therein).