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Craig B.N.,Lamar University | Congleton J.J.,Texas A&M University | Kerk C.J.,South Dakota School of Mines and Technology | Amendola A.A.,Texas A&M University | And 2 more authors.
IIE Annual Conference and Expo 2010 Proceedings | Year: 2010

Moving materials provides an essential function however these activities are also riddled with occupational injuries and illnesses (OIIs). Better understanding the risk factors associated with OIIs may assist in the reduction of the number and severity of these OIIs. Potential psychosocial risk factors were measured and evaluated for association with OIIs in 442 manual material handlers, across three companies, nine US locations, and 15 different job descriptions. OIIs were tracked within this population for one year after the testing was completed. Higher occurrences of OIIS were significantly associated with the same five risk factors in the univariate and multivariate models, with odds ratios ranging from 2.13 - 7.29. Source


Jin S.,Iowa State University | Jin S.,Liberty Mutual Insurance Group | Botterud A.,Argonne National Laboratory | Ryan S.M.,Iowa State University
IEEE Transactions on Power Systems | Year: 2014

We propose a stochastic generation expansion model, where we represent the long-term uncertainty in the availability and variability in the weekly wind pattern with multiple scenarios. Scenario reduction is conducted to select a representative set of scenarios for the long-term wind power uncertainty. We assume that the short-term wind forecast error induces an additional amount of operating reserves as a predefined fraction of the wind power forecast level. Unit commitment (UC) decisions and constraints for thermal units are incorporated into the expansion model to better capture the impact of wind variability on the operation of the system. To reduce computational complexity, we also consider a simplified economic dispatch (ED) based model with ramping constraints as an alternative to the UC formulation. We find that the differences in optimal expansion decisions between the UC and ED formulations are relatively small. We also conclude that the reduced set of scenarios can adequately represent the long-term wind power uncertainty in the expansion problem. The case studies are based on load and wind power data from the state of Illinois. © 2014 IEEE. Source


Jin S.,Iowa State University | Jin S.,Liberty Mutual Insurance Group | Ryan S.M.,Iowa State University
IEEE Transactions on Power Systems | Year: 2014

We study a tri-level integrated transmission and generation expansion planning problem in a deregulated power market environment. The collection of bi-level sub-problems in the lower two levels is an equilibrium problem with equilibrium constraints (EPEC) that can be approached by either the diagonalization method (DM) or a complementarity problem (CP) reformulation. This paper is a continuation of its Part I, in which a hybrid iterative algorithm is proposed to solve the tri-level problem by iteratively applying the CP reformulation of the tri-level problem to propose solutions and evaluating them in the EPEC sub-problem by DM. It focuses on the numerical results obtained by the hybrid algorithm for a 6-bus system, a modified IEEE 30-bus system, and an IEEE 118-bus system. In the numerical instances, the (approximate) Nash equilibrium point for the sub-problem can be verified by examining local concavity. © 2013 IEEE. Source


Craig B.N.,Lamar University | Congleton J.J.,Texas A&M University | Beier E.,Lamar University | Kerk C.J.,South Dakota School of Mines and Technology | And 2 more authors.
International Journal of Occupational Safety and Ergonomics | Year: 2013

Twenty-one risk factors affecting laborers in manual materials handling tasks were analyzed to determine what, if any, statistically significant relationships existed between the factors and the emergence of occupational back injury. The statistically significant risk factors (p ≤ .05) in the univariate analysis were determined to be weight lifted per hour (work intensity), trunk twists per hour, weight lifted per day, frequency of lift, trunk motions per hour, and trunk flexions per hour, with odds ratios (ORs) of 1.28-2.88. In addition, self-reported discomfort in the neck, middle back, knees, and lower back was associated with the outcome of back injury (p ≤ .05, OR 1.75-2.66). In the multivariate analysis, the statistically significant risk factors (p ≤ .05) were weight lifted per hour (work intensity), average weight of lift, and number of trunk twists per hour, with ORs of 1.74-4.98. Source


Jin S.,Iowa State University | Jin S.,Liberty Mutual Insurance Group | Botterud A.,Argonne National Laboratory | Ryan S.M.,Iowa State University
IEEE Transactions on Smart Grid | Year: 2013

We present a stochastic programming model for investments in thermal generation capacity to study the impact of demand response (DR) at high wind penetration levels. The investment model combines continuous operational constraints and wind scenarios to represent the implications of wind variability and uncertainty at the operational level. DR is represented in terms of linear price-responsive demand functions. A numerical case study based on load and wind profiles of Illinois is constructed with 20 candidate generating units of various types. Numerical results show the impact of DR on both investment and operational decisions. We also propose a model in which DR provides operating reserves and discuss its impact on lowering the total capacity needed in the system. We observe that a relatively small amount of DR capacity is sufficient to enhance the system reliability. When compared to the case with no DR, a modest level of DR results in less wind curtailment and better satisfaction of reserve requirements, as well as improvements in both the social surplus and generator utilization, as measured by capacity factors. © 2010-2012 IEEE. Source

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