The Experts below are selected from a list of 16632 Experts worldwide ranked by ideXlab platform
Daniel E Rivera - One of the best experts on this subject based on the ideXlab platform.
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a control engineering framework for managing whole hospital occupancy
Mathematical and Computer Modelling, 2012Co-Authors: Kevin T Roche, Daniel E Rivera, Jeffery K CochranAbstract:Abstract As factors such as population growth, nation-wide closure of hospitals, and an aging population combine to strain the healthcare system of the United States (US), the demand for better resource and capacity planning increases. This paper proposes a five-step methodology to model and control whole hospital occupancy. The hospital system is viewed using a continuous-time, fluid tank analogy. The system is subsequently discretized and a framework using control theory and Model Predictive Control (MPC) is developed to assist in Tactical Decision making, while maintaining occupancy targets. The result is a customizable modeling approach that represents interactions between different hospital areas, and interactions between the hospital and the outside world, or the population seeking hospital services.
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Model predictive control for Tactical Decision-making in semiconductor manufacturing supply chain management
IEEE Transactions on Control Systems Technology, 2008Co-Authors: Wenlin Wang, Daniel E RiveraAbstract:Supply chain management (SCM) in semiconductor manufacturing poses significant challenges that arise from the presence of long throughput times, unique constraints, and stochasticity in throughput time, yield, and customer demand. To address these concerns, a model predictive control (MPC) algorithm is developed which relies on a control-oriented formulation to generate daily Decisions on starts of factories. A multiple-degree-of-freedom observer formulated for ease of tuning is implemented to achieve robustness and performance in the presence of nonlinearity and stochasticity in both supply and demand. The control algorithm is configured to meet the requirements of meeting customer demand (both forecasted and unforecasted), and track inventory and starts targets provided by higher level Decision policies. Unique features of semiconductor manufacturing, such as capacity limits, packaging, and product reconfiguration, are formally addressed by imposing different constraints related to starts and inventories. This functionality contrasts that of standard approaches to MPC and makes this controller suitable as a Tactical Decision tool for semiconductor manufacturing and similar forms of high-volume discrete-parts manufacturing problems. Two representative case studies are examined under diverse realistic conditions with this flexible formulation of MPC. It is demonstrated that system robustness, performance, and high levels of customer service are achieved with proper tuning of the filter gains and weights, as well as the presence of adequate capacity in the supply chain.
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model predictive control strategies for supply chain management in semiconductor manufacturing
International Journal of Production Economics, 2007Co-Authors: Wenlin Wang, Daniel E Rivera, Karl G. KempfAbstract:Abstract This paper examines the application of model predictive control (MPC), an advanced control technique originating from the process industries, to supply chain management (SCM) problems arising in semiconductor manufacturing. The main goal of this work is to demonstrate the usefulness of MPC as a Tactical Decision policy that is an integral part of a comprehensive hierarchical Decision framework aimed at achieving operational excellence. A fluid analogy is used to describe the dynamics of the supply chain. Compared to traditional flow control problems, challenges of SCM in semiconductor manufacturing result from high stochasticity and nonlinearity in throughput times, yields and customer demands. The advantages of the control-oriented receding horizon formulation behind MPC are presented for three benchmark problems which highlight distinguishing features of semiconductor manufacturing. The effects of tuning, model parameters, and capacity are shown by comparing system robustness and multiple performance metrics in each case study.
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a model predictive control approach for managing semiconductor manufacturing supply chains under uncertainty
2003Co-Authors: Wenlin Wang, Kirk D. Smith, Karl G. Kempf, Daniel E Rivera, Chandler W. BlvdAbstract:AbstractA two level architecture using Model Predictive Control as a Tactical Decision module ispresented for supply chain management in the semiconductor manufacturing industries. Thestrategic and inventory planning steps in the outer loop provide the inventory targets and ca-pacity limits by solving an optimization problem that maximizes pro ts. These Decisions areusually weekly or monthly. The MPC-based Tactical Decision module takes advantage of thesetargets, capacity limits and demand forecasts to make daily Decisions on starts at the variousmanufacturing nodes. Fluid analogies are used to model the supply chain dynamics in semi-conductor manufacturing which facilitates the application of Model Predictive Control. Severalbenchmark problems which contain distinguishing features of semiconductor manufacturing,such as nonlinear and stochastic throughput times and customer demands, are examined. All ofthese problems involve two types of manufacturing nodes, Fab/Test1 and Assembly/Test2, andthree types of inventories, Assembly-Die Inventory, Semi-Finished Goods Inventory and Fin-ished Components Inventory. Both supply side uncertainty, including varying throughput timesand yields, and demand side uncertainty are addressed. The nonlinear relationship between thethroughput time and load is considered in each case. The e ects of judiciously picking tuningand model parameters to achieve performance, robustness and improved customer satisfactionare studied by comparing the variance in starts, inventories, and load as well as the percentageof un lled orders. Increasing move suppression and choosing the nominal throughput times ataverage values usually gives better performance with lower variance and less backlog. The exi-bility provided by the choice of tuning and model parameters in MPC to achieve more e ectivesupply chain management in semiconductor manufacturing is demonstrated in each case study.
Wenlin Wang - One of the best experts on this subject based on the ideXlab platform.
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Model predictive control for Tactical Decision-making in semiconductor manufacturing supply chain management
IEEE Transactions on Control Systems Technology, 2008Co-Authors: Wenlin Wang, Daniel E RiveraAbstract:Supply chain management (SCM) in semiconductor manufacturing poses significant challenges that arise from the presence of long throughput times, unique constraints, and stochasticity in throughput time, yield, and customer demand. To address these concerns, a model predictive control (MPC) algorithm is developed which relies on a control-oriented formulation to generate daily Decisions on starts of factories. A multiple-degree-of-freedom observer formulated for ease of tuning is implemented to achieve robustness and performance in the presence of nonlinearity and stochasticity in both supply and demand. The control algorithm is configured to meet the requirements of meeting customer demand (both forecasted and unforecasted), and track inventory and starts targets provided by higher level Decision policies. Unique features of semiconductor manufacturing, such as capacity limits, packaging, and product reconfiguration, are formally addressed by imposing different constraints related to starts and inventories. This functionality contrasts that of standard approaches to MPC and makes this controller suitable as a Tactical Decision tool for semiconductor manufacturing and similar forms of high-volume discrete-parts manufacturing problems. Two representative case studies are examined under diverse realistic conditions with this flexible formulation of MPC. It is demonstrated that system robustness, performance, and high levels of customer service are achieved with proper tuning of the filter gains and weights, as well as the presence of adequate capacity in the supply chain.
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model predictive control strategies for supply chain management in semiconductor manufacturing
International Journal of Production Economics, 2007Co-Authors: Wenlin Wang, Daniel E Rivera, Karl G. KempfAbstract:Abstract This paper examines the application of model predictive control (MPC), an advanced control technique originating from the process industries, to supply chain management (SCM) problems arising in semiconductor manufacturing. The main goal of this work is to demonstrate the usefulness of MPC as a Tactical Decision policy that is an integral part of a comprehensive hierarchical Decision framework aimed at achieving operational excellence. A fluid analogy is used to describe the dynamics of the supply chain. Compared to traditional flow control problems, challenges of SCM in semiconductor manufacturing result from high stochasticity and nonlinearity in throughput times, yields and customer demands. The advantages of the control-oriented receding horizon formulation behind MPC are presented for three benchmark problems which highlight distinguishing features of semiconductor manufacturing. The effects of tuning, model parameters, and capacity are shown by comparing system robustness and multiple performance metrics in each case study.
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a model predictive control approach for managing semiconductor manufacturing supply chains under uncertainty
2003Co-Authors: Wenlin Wang, Kirk D. Smith, Karl G. Kempf, Daniel E Rivera, Chandler W. BlvdAbstract:AbstractA two level architecture using Model Predictive Control as a Tactical Decision module ispresented for supply chain management in the semiconductor manufacturing industries. Thestrategic and inventory planning steps in the outer loop provide the inventory targets and ca-pacity limits by solving an optimization problem that maximizes pro ts. These Decisions areusually weekly or monthly. The MPC-based Tactical Decision module takes advantage of thesetargets, capacity limits and demand forecasts to make daily Decisions on starts at the variousmanufacturing nodes. Fluid analogies are used to model the supply chain dynamics in semi-conductor manufacturing which facilitates the application of Model Predictive Control. Severalbenchmark problems which contain distinguishing features of semiconductor manufacturing,such as nonlinear and stochastic throughput times and customer demands, are examined. All ofthese problems involve two types of manufacturing nodes, Fab/Test1 and Assembly/Test2, andthree types of inventories, Assembly-Die Inventory, Semi-Finished Goods Inventory and Fin-ished Components Inventory. Both supply side uncertainty, including varying throughput timesand yields, and demand side uncertainty are addressed. The nonlinear relationship between thethroughput time and load is considered in each case. The e ects of judiciously picking tuningand model parameters to achieve performance, robustness and improved customer satisfactionare studied by comparing the variance in starts, inventories, and load as well as the percentageof un lled orders. Increasing move suppression and choosing the nominal throughput times ataverage values usually gives better performance with lower variance and less backlog. The exi-bility provided by the choice of tuning and model parameters in MPC to achieve more e ectivesupply chain management in semiconductor manufacturing is demonstrated in each case study.
Franklin Dexter - One of the best experts on this subject based on the ideXlab platform.
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Tactical increases in operating room block time for capacity planning should not be based on utilization
Anesthesia & Analgesia, 2008Co-Authors: Ruth E Wachtel, Franklin DexterAbstract:When a Decision has been made to expand operating room (OR) capacity, the choice of surgical subspecialties to receive additional block time and fill the additional OR capacity is a Tactical Decision. Such Decisions are made approximately once a year. Afterwards, typically a few months before the day of surgery, a second stage occurs in which operational Decisions allocate OR time and determine the hours of staffing for each specialty based on its expected workload. In practice, cases are not scheduled into block time that has been planned Tactically, but instead are scheduled during the second stage into the staffed time that is allocated operationally. This article reviews the literature on Tactical Decision-making for expansion of OR capacity. When additional OR capacity is available, it should be planned for those subspecialties that have the greatest contribution margin per OR hour, that have the potential for growth, and that have minimal need for limited resources such as intensive care unit beds. Numerous reasons are presented to explain why Tactical planning of additional block time should not be based on current or past utilization of block time.
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Tactical increases in operating room block time for capacity planning should not be based on utilization
Anesthesia & Analgesia, 2008Co-Authors: Ruth E Wachtel, Franklin DexterAbstract:When a Decision has been made to expand operating room (OR) capacity, the choice of surgical subspecialties to receive additional block time and fill the additional OR capacity is a Tactical Decision. Such Decisions are made approximately once a year. Afterwards, typically a few months before the da
Karl G. Kempf - One of the best experts on this subject based on the ideXlab platform.
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model predictive control strategies for supply chain management in semiconductor manufacturing
International Journal of Production Economics, 2007Co-Authors: Wenlin Wang, Daniel E Rivera, Karl G. KempfAbstract:Abstract This paper examines the application of model predictive control (MPC), an advanced control technique originating from the process industries, to supply chain management (SCM) problems arising in semiconductor manufacturing. The main goal of this work is to demonstrate the usefulness of MPC as a Tactical Decision policy that is an integral part of a comprehensive hierarchical Decision framework aimed at achieving operational excellence. A fluid analogy is used to describe the dynamics of the supply chain. Compared to traditional flow control problems, challenges of SCM in semiconductor manufacturing result from high stochasticity and nonlinearity in throughput times, yields and customer demands. The advantages of the control-oriented receding horizon formulation behind MPC are presented for three benchmark problems which highlight distinguishing features of semiconductor manufacturing. The effects of tuning, model parameters, and capacity are shown by comparing system robustness and multiple performance metrics in each case study.
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a model predictive control approach for managing semiconductor manufacturing supply chains under uncertainty
2003Co-Authors: Wenlin Wang, Kirk D. Smith, Karl G. Kempf, Daniel E Rivera, Chandler W. BlvdAbstract:AbstractA two level architecture using Model Predictive Control as a Tactical Decision module ispresented for supply chain management in the semiconductor manufacturing industries. Thestrategic and inventory planning steps in the outer loop provide the inventory targets and ca-pacity limits by solving an optimization problem that maximizes pro ts. These Decisions areusually weekly or monthly. The MPC-based Tactical Decision module takes advantage of thesetargets, capacity limits and demand forecasts to make daily Decisions on starts at the variousmanufacturing nodes. Fluid analogies are used to model the supply chain dynamics in semi-conductor manufacturing which facilitates the application of Model Predictive Control. Severalbenchmark problems which contain distinguishing features of semiconductor manufacturing,such as nonlinear and stochastic throughput times and customer demands, are examined. All ofthese problems involve two types of manufacturing nodes, Fab/Test1 and Assembly/Test2, andthree types of inventories, Assembly-Die Inventory, Semi-Finished Goods Inventory and Fin-ished Components Inventory. Both supply side uncertainty, including varying throughput timesand yields, and demand side uncertainty are addressed. The nonlinear relationship between thethroughput time and load is considered in each case. The e ects of judiciously picking tuningand model parameters to achieve performance, robustness and improved customer satisfactionare studied by comparing the variance in starts, inventories, and load as well as the percentageof un lled orders. Increasing move suppression and choosing the nominal throughput times ataverage values usually gives better performance with lower variance and less backlog. The exi-bility provided by the choice of tuning and model parameters in MPC to achieve more e ectivesupply chain management in semiconductor manufacturing is demonstrated in each case study.
Ruth E Wachtel - One of the best experts on this subject based on the ideXlab platform.
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Tactical increases in operating room block time for capacity planning should not be based on utilization
Anesthesia & Analgesia, 2008Co-Authors: Ruth E Wachtel, Franklin DexterAbstract:When a Decision has been made to expand operating room (OR) capacity, the choice of surgical subspecialties to receive additional block time and fill the additional OR capacity is a Tactical Decision. Such Decisions are made approximately once a year. Afterwards, typically a few months before the day of surgery, a second stage occurs in which operational Decisions allocate OR time and determine the hours of staffing for each specialty based on its expected workload. In practice, cases are not scheduled into block time that has been planned Tactically, but instead are scheduled during the second stage into the staffed time that is allocated operationally. This article reviews the literature on Tactical Decision-making for expansion of OR capacity. When additional OR capacity is available, it should be planned for those subspecialties that have the greatest contribution margin per OR hour, that have the potential for growth, and that have minimal need for limited resources such as intensive care unit beds. Numerous reasons are presented to explain why Tactical planning of additional block time should not be based on current or past utilization of block time.
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Tactical increases in operating room block time for capacity planning should not be based on utilization
Anesthesia & Analgesia, 2008Co-Authors: Ruth E Wachtel, Franklin DexterAbstract:When a Decision has been made to expand operating room (OR) capacity, the choice of surgical subspecialties to receive additional block time and fill the additional OR capacity is a Tactical Decision. Such Decisions are made approximately once a year. Afterwards, typically a few months before the da
