We analyze the two recent Medicare alternative payment models, the comprehensive primary care plu... more We analyze the two recent Medicare alternative payment models, the comprehensive primary care plus (CPC+) and the primary care first (PCF). Both models comprise fee-for-service, traditional capitation, and pay-for-performance (P4P) components. The main objective of these reimbursement models is to advance toward value-based care. However, the models confer some hesitations since the P4P component is based on factors not entirely controlled by the practice, increasing the potential admission of healthier patients and affecting the profit of small primary care practices. We have modified the P4P component in both models to include a non-controllable agent (the hierarchical condition category score) and a controllable factor (the Bice–Boxerman continuity of care index) through a probabilistic classification model to predict hospital admissions. This study aims to determine the impact of adjusting the P4P component, in the CPC+ and PCF reimbursement models, on the profit per team, reven...
The aim of this paper is to analyze the results of the implementation of several parallelization ... more The aim of this paper is to analyze the results of the implementation of several parallelization techniques for the metaheuristics applied to the p-median problem. Further analysis showed: constraints on the number of processors used because of the network technology, advantages in the use of multiprocessors and the penalty in performance when using intensive communications strategies. Moreover, within the optimization using heuristics there is always an implicit balance between speed in obtaining a solution and the quality of it, which suggests the use of hybrid strategies that exploit the best of each.
A novel problem for the collection of raw milk from a network of farms supplying a dairy is speci... more A novel problem for the collection of raw milk from a network of farms supplying a dairy is specified and solved. The proposed approach incorporates milk blending and the delivery of production to collection points by small, distant farms. The milk is collected by, and blended in, a homogeneous fleet of trucks and classified according to the lowest quality product included in the blend. Optimization criteria are used to determine where the collection points should be located and which producers are allocated to them for delivery, with all other production picked up directly at the farms. The approach is built around an integer programming model and two implementation strategies, one using a branch-and-cut algorithm for small instances and the other a heuristic procedure combining both exact and approximated methods to handle large instances within a reasonable computation time. A real case study involving 500 farms and 112 possible collection points is solved and the results compared. The impact on the solutions of dividing the real instance into zones is also explored.
International Journal of Mining, Reclamation and Environment, 2015
Equipment selection is a key strategic decision in the design of a material handling system, beca... more Equipment selection is a key strategic decision in the design of a material handling system, because an improper one will lead to operational problems and unnecessary investment costs. It involves determining the number and combination of loaders and trucks which will move the material, fulfilling a specified production schedule. Previous works have addressed this problem with deterministic approaches, without considering the inter-dependent availability of trucks and loaders. In order to fill this gap, we developed a stochastic model that combines the selection and equipment replacement problems, subject to a stochastic production rate constraint. This is a new idea that will help decision-makers to decide faster and more reliable. The proposed model optimises the fleet by minimising the total life cycle costs. To solve it, we used a linearisation approach that reduces the computational effort. We tested our approach with a benchmark model, using a mining case study. Results indicate that the solutions ensure with a high probability a determined production target, producing good robust solutions compared to the deterministic counterpart.
A milk collection problem with blending is introduced. A firm collects milk from farms, and each ... more A milk collection problem with blending is introduced. A firm collects milk from farms, and each farm produces one out of three possible qualities of milk. The revenue increases with quality, and there is a minimum requirement at the plant for each quality. Different qual- ities of milk can be blended in the trucks, reducing revenues, but also transportation costs, resulting in higher profit. A mixed integer-programming model, a new cut, and a branch- and-cut algorithm are proposed to solve medium-sized instances. A three-stage heuristic is designed for large instances. Computational experience for test instances and a large-sized real case is presented.
We analyze the two recent Medicare alternative payment models, the comprehensive primary care plu... more We analyze the two recent Medicare alternative payment models, the comprehensive primary care plus (CPC+) and the primary care first (PCF). Both models comprise fee-for-service, traditional capitation, and pay-for-performance (P4P) components. The main objective of these reimbursement models is to advance toward value-based care. However, the models confer some hesitations since the P4P component is based on factors not entirely controlled by the practice, increasing the potential admission of healthier patients and affecting the profit of small primary care practices. We have modified the P4P component in both models to include a non-controllable agent (the hierarchical condition category score) and a controllable factor (the Bice–Boxerman continuity of care index) through a probabilistic classification model to predict hospital admissions. This study aims to determine the impact of adjusting the P4P component, in the CPC+ and PCF reimbursement models, on the profit per team, reven...
The aim of this paper is to analyze the results of the implementation of several parallelization ... more The aim of this paper is to analyze the results of the implementation of several parallelization techniques for the metaheuristics applied to the p-median problem. Further analysis showed: constraints on the number of processors used because of the network technology, advantages in the use of multiprocessors and the penalty in performance when using intensive communications strategies. Moreover, within the optimization using heuristics there is always an implicit balance between speed in obtaining a solution and the quality of it, which suggests the use of hybrid strategies that exploit the best of each.
A novel problem for the collection of raw milk from a network of farms supplying a dairy is speci... more A novel problem for the collection of raw milk from a network of farms supplying a dairy is specified and solved. The proposed approach incorporates milk blending and the delivery of production to collection points by small, distant farms. The milk is collected by, and blended in, a homogeneous fleet of trucks and classified according to the lowest quality product included in the blend. Optimization criteria are used to determine where the collection points should be located and which producers are allocated to them for delivery, with all other production picked up directly at the farms. The approach is built around an integer programming model and two implementation strategies, one using a branch-and-cut algorithm for small instances and the other a heuristic procedure combining both exact and approximated methods to handle large instances within a reasonable computation time. A real case study involving 500 farms and 112 possible collection points is solved and the results compared. The impact on the solutions of dividing the real instance into zones is also explored.
International Journal of Mining, Reclamation and Environment, 2015
Equipment selection is a key strategic decision in the design of a material handling system, beca... more Equipment selection is a key strategic decision in the design of a material handling system, because an improper one will lead to operational problems and unnecessary investment costs. It involves determining the number and combination of loaders and trucks which will move the material, fulfilling a specified production schedule. Previous works have addressed this problem with deterministic approaches, without considering the inter-dependent availability of trucks and loaders. In order to fill this gap, we developed a stochastic model that combines the selection and equipment replacement problems, subject to a stochastic production rate constraint. This is a new idea that will help decision-makers to decide faster and more reliable. The proposed model optimises the fleet by minimising the total life cycle costs. To solve it, we used a linearisation approach that reduces the computational effort. We tested our approach with a benchmark model, using a mining case study. Results indicate that the solutions ensure with a high probability a determined production target, producing good robust solutions compared to the deterministic counterpart.
A milk collection problem with blending is introduced. A firm collects milk from farms, and each ... more A milk collection problem with blending is introduced. A firm collects milk from farms, and each farm produces one out of three possible qualities of milk. The revenue increases with quality, and there is a minimum requirement at the plant for each quality. Different qual- ities of milk can be blended in the trucks, reducing revenues, but also transportation costs, resulting in higher profit. A mixed integer-programming model, a new cut, and a branch- and-cut algorithm are proposed to solve medium-sized instances. A three-stage heuristic is designed for large instances. Computational experience for test instances and a large-sized real case is presented.
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