The Pasture, Rangeland, Forage (PRF) insurance program is aimed to assist producers to manage the risk of forage loss due to the lack of precipitation. However, limited attention has been given to understanding the implications of policyholders’ coverage selection decisions. In this study, three alternative risk-efficient portfolio selection strategies are assessed in to the context of the PRF program. Proposed methods consider all the decision parameters and program restrictions, and they highlighted the underlying relationships between expected revenue, risk, and choice of the coverage parameters. Selection strategies are illustrated by examining the optimal coverage for a grid in South Texas.

The multi-arm robotic systems consisting of redundant robots are able to conduct more complex and coordinated tasks, such as manipulating large or heavy objects. The challenges of the motion planning and control for such systems mainly arise from the closed-chain constraint and redundancy resolution problem. The closed-chain constraint reduces the configuration space to lower-dimensional subsets, making it difficult for sampling feasible configurations and planning path connecting them. A global motion planner is proposed in this paper for the closed-chain systems, and motions in different disconnected manifolds are efficiently bridged by two type regrasping moves. The regrasping moves are automatically chosen by the planner based on cost-saving principle, which greatly improve the success rate and efficiency. Furthermore, to obtain the optional inverse kinematic solutions satisfying joint physical limits (e.g., joint position, velocity, acceleration limits) in the planning, the redundancy resolution problem for dual redundant robots is converted into a unified quadratic programming problem based on the combination of two diff erent-level optimizing criteria, i.e. the minimization velocity norm (MVN) and infinity norm torque-minimization (INTM). The Dual-MVN-INTM scheme guarantees smooth velocity, acceleration profiles, and zero final velocity at the end of motion. Finally, the planning results of three complex closed-chain manipulation task using two Franka Emika Panda robots and two Kinova Jaco2 robots in both simulation and experiment demonstrate the effectiveness and efficiency of the proposed method.

The Inverse Kinematics (IK) problem of manipulators can be divided into two distinct steps: (1) Problem formulation, where the problem is developed into a form which can then be solved using various methods. (2) Problem solution, where the IK problem is actually solved by producing the values of different joint space variables (joint angles, joint velocities or joint accelerations). The main focus of this paper is concentrated on the discussion of the IK problem of redundant manipulators, formulated as a quadratic programming optimization problem solved by different kinds of recurrent neural networks.

An analytical model with coupled dynamics for a realistic six-legged robotic system locomoting on various terrains has been developed, and its effectiveness has been proven through computer simulations and validated using virtual prototyping tools and real experiment. The approach is new and has not been attempted before. This study investigated the optimal feet-forces’ distributions under body force and foot–ground interaction considering compliant contact and friction force models for the feet undergoing slip. The kinematic model with 114 implicit constraints in 3D Cartesian space has been transformed in terms of generalized coordinates with a reduced explicit set of 24 constrained equations using kinematic transformations. The nonlinear constrained inverse dynamics model of the system has been formulated as a coupled dynamical problem using Newton–Euler method with realistic environmental conditions (compliant foot–ground contact, impact, and friction) and computed using optimization techniques due to its indeterminate nature. One case study has been carried out to validate the analytical data with the simulated ones executed in MSC.ADAMS® (Automated Dynamic Analysis of Mechanical Systems), while the other case study has been conducted to validate the analytical and simulated data with the experimental ones. In both these cases, results are found to be in close agreement, which proves the efficacy of the model.

In order to solve joint-angle drift problem of dual redundant manipulators at acceleration-level, an acceleration-level tri-criteria optimization motion planning (ALTC-OMP) scheme is proposed, which combines the minimum acceleration norm, repetitive motion planning, and infinity-norm acceleration minimization solutions via weighting factor. This scheme can resolve the joint-angle drift problem of dual redundant manipulators which will arise in single criteria or bi-criteria scheme. In addition, the proposed scheme considers joint-velocity joint-acceleration physical limits. The proposed scheme can not only guarantee joint-velocity and joint-acceleration within their physical limits, but also ensure that final joint-velocity and joint-acceleration are near to zero. This scheme is realized by dual redundant manipulators which consist of left and right manipulators. In order to ensure the coordinated operation of manipulators, two motion planning problems are reformulated as two general quadratic program (QP) problems and further unified into one standard QP problem, which is solved by a simplified linear-variational-inequalities-based primal-dual neural network at the acceleration-level. Computer-simulation results based on dual PUMA560 redundant manipulators further demonstrate the effectiveness and feasibility of the proposed ALTC-OMP scheme to resolve joint-angle drift problem arising in the dual redundant manipulators.

Individuals may perceive personalised dietary advice as more relevant and motivational than national guidelines. Personal preference and food cost are factors that can affect consumer decisions. The objective of this study was to present a method for modelling and analysing the trade-off between deviation from preference and food cost for optimised personalised dietary recommendations. Quadratic programming was applied to minimise deviation from fish preference and cost simultaneously with different weights on the cost for 3016 Danish adults (whose dietary intake and body weight were recorded in a national dietary survey). Model constraints included recommendations for EPA, DHA and vitamin D and tolerable levels for methyl mercury, dioxins and dioxin-like polychlorinated biphenyls. When only minimising deviation from preference, 50 % of the study population should be recommended to increase fish intake, 48 % should be suggested to maintain current consumption and 2 % should be suggested to decrease fish consumption. When only minimising cost, the vast majority (99 %) should be recommended to only consume herring, which is the least-expensive fish species. By minimising deviation from preference and cost simultaneously with different weights on the cost, personalised optimal trade-off curves between deviation from fish intake preference and fish cost could be generated for each individual in our study population, except for twenty-two individuals (0·7 %) whose contaminant background exposure was too high. In the future, the method of this paper could be applied in the personal communication of healthy and safe food recommendations that fit the preferences of individual consumers.

National dietary guidelines are directed at the general population. However, these guidelines may be perceived as unrealistic by a substantial part of the population, as they differ considerably from individual consumption patterns and preferences. Personalised dietary recommendations will probably improve adherence, and it has been shown that these recommendations can be derived by mathematical optimisation methods. However, to better account for risks and benefits of specific foods, the background exposure to nutrients and contaminants needs to be considered as well. This background exposure may come from other foods and supplements, and also from environmental sources like the air and the sun. The objective of this study was therefore to analyse the effect of including individual variation in background exposure when modelling personalised dietary recommendations for fish. We used a quadratic programming model to generate recommended fish intake accounting for personal preference by deviating as little as possible from observed individual intake. Model constraints ensure that the modelled intake meets recommendations for EPA, DHA and vitamin D without violating tolerable exposure to methyl mercury, dioxins and dioxin-like polychlorinated biphenyls. Several background exposures were analysed for 3016 Danish adults, whose food intakes and body weights were reported in a national dietary survey. We found that the lower nutrient constraints were critical for the largest part of the study population, and that a total of 55% should be advised to increase their fish intake. The modelled fish intake recommendations were particularly sensitive to the vitamin D background exposure.

This study proposes and investigates a new type of bi-criteria minimization (BCM) for the motion planning and control of redundant robot manipulators to address the discontinuity problem in the infinity-norm acceleration minimization (INAM) scheme and to guarantee the final joint velocity of motion to be approximate to zero. This new type is based on the combination of minimum weighted velocity norm (MWVN) and INAM criteria, and thus is called the MWVN–INAM–BCM scheme. In formulating such a scheme, joint-angle, joint-velocity, and joint-acceleration limits are incorporated. The proposed MWVN–INAM–BCM scheme is reformulated as a quadratic programming problem solved at the joint-acceleration level. Simulation results based on the PUMA560 robot manipulator validate the efficacy and applicability of the proposed MWVN–INAM–BCM scheme in robotic redundancy resolution. In addition, the physical realizability of the proposed scheme is verified in practical application based on a six-link planar robot manipulator.

Economic feasibility of Texas Blacklands corn production in relation to sorghum, wheat, and cotton is studied. Biophysical simulation generated yield data are integrated with an economic decision model using quadratic programming. Given the various scenarios analyzed, corn is economically feasible for the Blacklands. A crop mix of half corn and half cotton production is selected under risk neutrality with wheat entering if risk aversion is present. Corn and grain sorghum production are highly substitutable. Profit effects attributed to changing corn planting dates are more pronounced than profit changes resulting from altering corn population or maturity class.

“Multifunctionality” emphasizes the benefit externality properties of nonfood products that coincide with agricultural commodity production, some of which also have public-good properties. However, determining the willingness to pay for local benefit externalities is seen as necessary but daunting. This paper pursues the idea that the valuation process might first start by estimating the incentives required to supply various levels of a benefit externality. With the use of carbon sequestration through the adoption of no-till cultivation as an example of a multifunctional benefit externality, mathematical programming is used to derive representative price schedules. The implication for incentive prices are examined in light of risk aversion.

The most useful and practical strategy available for reducing variability of net farm income is ascertained. Of the many risk management tools presently available, five of the most commonly used are simultaneously incorporated in an empirically tested model. Quadratic programming provides the basis for decisionmaking in risk management wherein expected utility is assumed to be a function of the mean and variance of net income. Results demonstrate that farmers can reduce production and price risks when a combination strategy including a diversified crop production plan and participation in the futures market and the Federal Crop Insurance Program (FCIP) is implemented.

Least cost feed mix by linear programming (LP) is a standard economic analysis in the poultry industry. A significant body of nutrition knowledge is now contained in the constraint set of industry LP models. This knowledge might be merged into an improved economic model that contains production response information. Analysis using a quadratic programming model indicated that a leading broiler firm could have improved economic efficiency by increasing protein density and reducing energy density of broiler finisher feed. If applicable industry wide, similar savings could be as high as $120 million per year.

The ornamental horticultural industry continues to be one of the most rapidly expanding sectors in agriculture. This study examined a decision model for landscape plant production based on portfolio analysis. A quadratic programming model was developed to generate an optimal crop portfolio for a selected southeastern nursery. Empirical results indicate opportunities exist for modest diversification to offset income variability in landscape plant production and marketing.

This study evaluates the economic impact of a ban on methyl bromide on the U.S. winter fresh vegetable market for six major crops: tomatoes, green peppers, cucumbers, squash, eggplant, and watermelons. Florida is the primary domestic supplier of these products. Mexico and Texas are the competing suppliers of the five vegetable crops and peppers, respectively. Leontief technologies represent both monocrop and double-crop production systems; linear inverse demand functions represent four demand regions in the U.S. and Canada. By increasing production costs and reducing yields, a ban on methyl bromide decreases Florida's FOB revenues by 54% and increases those of Mexico by 65%. Price increases to U.S. fresh vegetable consumers range from near zero to over 10%, depending upon the commodity and location.

An iterative algorithm to minimize energy loss in kinematic chains is proposed. This algorithm is designed to low level of control where variables such as terminal states, runtime, and physical and electrical parameters of the movement are given by higher levels of control. An original complex problem of optimization is transformed into a simple quadratic programming problem subject to linear constraints by discretizing all dynamic system variables. The whole system is then converted into a recursive matrix equation that is solved iteratively. A proof of convergence is suggested. The performance of the algorithm is illustrated by using it in the motion planning of a quadruped robot.

This paper presents a whole-body dynamics controller for robust push recovery on a force-controlled bipedal robot. Featherstone's spatial vector method is used to deduce dynamics formulas. We reveal a relationship between the accelerations of the floating base and the desired external forces needed for those accelerations. Introducing constraints on the desired external forces causes corresponding constraints on the accelerations. Quadratic programming is applied to find the extremal accelerations, which recover the robot from pushes as best as possible. A robustness criterion is proposed based on the linear inverted pendulum model to evaluate the performance of push recovery methods quantitatively. We evaluate four typical push recovery methods and the results show that our method is more robust than these. The effectiveness of the proposed method is demonstrated by push recovery in simulations.

In this paper, we show that the direct semidefinite programming (SDP) bound for thenonconvex quadratic optimization problem over ℓ1 unit ball(QPL1) is equivalent to the optimal d.c. (difference between convex) bound for thestandard quadratic programming reformulation of QPL1. Then we disprove a conjecture aboutthe tightness of the direct SDP bound. Finally, as an extension of QPL1, we study therelaxation problem of the sparse principal component analysis, denoted by QPL2L1. We showthat the existing direct SDP bound for QPL2L1 is equivalent to the doubly nonnegativerelaxation for variable-splitting reformulation of QPL2L1.

To achieve desired configuration, a scheme for state adjustment of a redundant robot manipulator with no end-effector task explicitly assigned and referred to as a state-adjustment scheme is proposed in this paper. Owing to the physical limits in an actual robot manipulator, both joint and joint-velocity limits are incorporated into the proposed scheme for practical purposes. In addition, the proposed state-adjustment scheme is formulated as a quadratic program and resolved at the joint-velocity level. A numerical computing algorithm based on the conversion technique of the quadratic program to linear variational inequalities is presented to address the robot state-adjustment scheme. By employing the state-adjustment scheme, the robot manipulator can automatically move to the desired configuration from any initial configuration with the movement kept within its physical limits. Computer simulation and experimental results using a practical six-link planar robot manipulator with variable joint-velocity limits further verify the realizability, effectiveness, accuracy, and flexibility of the proposed state-adjustment scheme.

In this paper, to diminish discontinuity points arising in the infinity-norm velocity minimization scheme, a bi-criteria velocity minimization scheme is presented based on a new neural network solver, i.e., an LVI-based primal-dual neural network. Such a kinematic planning scheme of redundant manipulators can incorporate joint physical limits, such as, joint limits and joint velocity limits simultaneously. Moreover, the presented kinematic planning scheme can be reformulated as a quadratic programming (QP) problem. As a real-time QP solver, the LVI-based primal-dual neural network is developed with a simple piecewise linear structure and high computational efficiency. Computer simulations performed based on a PUMA560 manipulator model are presented to illustrate the validity and advantages of such a bi-criteria velocity minimization neural planning scheme for redundant robot arms.

We consider the parallel approximability of two problems arising from high multiplicity scheduling, namely the unweighted model with variable processing requirements and the weighted model with identical processing requirements. These two problems are known to be modelled by a class of quadratic programs that are efficiently solvable in polynomial time. On the parallel setting, both problems are P-complete and hence cannot be efficiently solved in parallel unless P = NC. To deal with the parallel approximablity of these problems, we show first a parallel additive approximation procedure to a subclass of multi-valued quadratic programming, called smooth multi-valued QP, which is defined by imposing certain restrictions on the coefficients of the instance. We use this procedure to obtain parallel approximation to dense instances of the two problems by observing that dense instances of these problems are instances of smooth multi-valued QP. The dense instances of the problems considered here are defined similarly as for other combinatorial problems in the literature. For such instances we can find in parallel a near optimal schedule. The definition of smooth multi-valued QP as well as the procedure for approximating it in parallel are of interest independently of the application to the scheduling problems considered in this paper.