This talk presents an efficient decomposition approach to surgical planning. Given a set of surgi... more This talk presents an efficient decomposition approach to surgical planning. Given a set of surgical waiting lists (one for each discipline) and an operating theater, the problem is to decide the room-to-discipline assignment for the next planning period (Master Surgical Schedule), and the surgical cases to be performed (Surgical Case Assignment), with the objective of optimizing a score related to priority and current waiting time of the cases.
Abstract This paper addresses Operating Room (OR) planning policies in elective surgery. In parti... more Abstract This paper addresses Operating Room (OR) planning policies in elective surgery. In particular, we investigate long-term policies for determining the Master Surgical Schedule (MSS) throughout the year, analyzing the tradeoff between organizational simplicity, favored by an MSS that does not change completely every week, and quality of the service offered to the patients, favored by an MSS that dynamically adapts to the current state of waiting lists, the latter objective being related to a lean approach to hospital management.
Abstract Optimization of simulated systems is the goal of many methods, but most methods assume k... more Abstract Optimization of simulated systems is the goal of many methods, but most methods assume known environments. In this paper we present a methodology that does account for uncertain environments. Our methodology uses Taguchi's view of the uncertain world, but replaces his statistical techniques by either Response Surface Methodology or Kriging metamodeling. We illustrate the resulting methodology through the well-known Economic Order Quantity (EOQ) model.
Abstract This paper concerns the problem of optimally scheduling a set of appliances at the end-u... more Abstract This paper concerns the problem of optimally scheduling a set of appliances at the end-user premises. The user's energy fee varies over time, and moreover, in the context of smart grids, the user may receive a reward from an energy aggregator if he/she reduces consumption during certain time intervals. In a household, the problem is to decide when to schedule the operation of the appliances, in order to meet a number of goals, namely overall costs, climatic comfort level and timeliness.
Abstract In this work, the ability of the Dynamic Objectives Aggregation Methods to solve the por... more Abstract In this work, the ability of the Dynamic Objectives Aggregation Methods to solve the portfolio rebalancing problem is investigated conducting a computational study on a set of instances based on real data. The portfolio model considers a set of realistic constraints and entails the simultaneously optimization of the risk on portfolio, the expected return and the transaction cost.
Abstract Research on metamodel-based optimization has received considerably increasing interest i... more Abstract Research on metamodel-based optimization has received considerably increasing interest in recent years, and has found successful applications in solving computationally expensive problems. The joint use of computer simulation experiments and metamodels introduces a source of uncertainty that we refer to as metamodel variability. To analyze and quantify this variability, we apply bootstrapping to residuals derived as prediction errors computed from cross-validation.
Metamodels are often used in simulation-optimization for the design and management of complex sys... more Metamodels are often used in simulation-optimization for the design and management of complex systems. These metamodels yield insight into the relationship between responses and decision variables, providing fast analysis tools instead of the more expensive computer simulations. Moreover, these metamodels enable the integration of discipline-dependent analysis into the overall decision process.
Abstract Most methods in simulation-optimization assume known environments, whereas this research... more Abstract Most methods in simulation-optimization assume known environments, whereas this research accounts for uncertain environments combining Taguchi's world view with either regression or Kriging (also called Gaussian Process) metamodels (emulators, response surfaces, surrogates). These metamodels are combined with Non-Linear Mathematical Programming (NLMP) to find robust solutions. Varying the constraint values in this NLMP gives an estimated Pareto frontier.
The increasing pressure on the development time of new materials and devices has changed the mode... more The increasing pressure on the development time of new materials and devices has changed the modelling and design process over the years. In the past, they mainly consisted of experimentation and physical prototyping. Clearly, it is hard to incorporate changes in finished prototypes, while producing a variety of different prototypes at once may be very expensive. To this aim, computer simulation models such as circuit design models and continuous system simulation models are widely used in engineering modelling, design and analysis. The studies towards a better understanding of complex systems require quantitative model development, making optimisation and experimental data fitting tools indispensable. In this framework, the modelling of ionic polymer-metal composites (IPMCs) is studied. In particular, this paper deals with simulation-optimisation issues arising in the model calibration of a particular IPMC-based actuator in air. We consider a non-linear dynamical model of the device, with lumped parameters, able to estimate the IPMC actuator absorbed current, together with the mechanical quantities of interest, which, in the case under study, are the free deflection and/or the blocked force. Two optimisation problems have been formulated, focusing on different stages of the model parameters identification. The strategies adopted to solve the problems allow to achieve some promising - although preliminary - results.
International Journal of Production …, Jan 1, 2010
Optimization of simulated systems is tackled by many methods, but most methods assume known envir... more Optimization of simulated systems is tackled by many methods, but most methods assume known environments. This article, however, develops a ‘robust’ methodology for uncertain environments. This methodology uses Taguchi's view of the uncertain world, but replaces his statistical techniques by Response Surface Methodology (RSM). George Box originated RSM, and Douglas Montgomery recently extended RSM to robust optimization of real (non-simulated) systems. We combine Taguchi's view with RSM for simulated systems. We illustrate the resulting methodology through classic Economic Order Quantity (EOQ) inventory models, which demonstrate that robust optimization may require order quantities that differ from the classic EOQ.
Optimization of simulated systems is the goal of many methods, but most methods assume known envi... more Optimization of simulated systems is the goal of many methods, but most methods assume known environments. We, however, develop a “robust” methodology that accounts for uncertain environments. Our methodology uses Taguchi's view of the uncertain world but replaces his statistical techniques by design and analysis of simulation experiments based on Kriging (Gaussian process model); moreover, we use bootstrapping to quantify the variability in the estimated Kriging metamodels. In addition, we combine Kriging with nonlinear programming, and we estimate the Pareto frontier. We illustrate the resulting methodology through economic order quantity (EOQ) inventory models. Our results suggest that robust optimization requires order quantities that differ from the classic EOQ. We also compare our results with results we previously obtained using response surface methodology instead of Kriging.
This paper deals with the use of Kriging metamodels in multi-objective engineering design optimiz... more This paper deals with the use of Kriging metamodels in multi-objective engineering design optimization. The metamodel management issue to find the tradeoff between accuracy and efficiency is addressed. A comparative analysis of different strategies is conducted for a case study devoted to the design of a component of the injection system for Compressed Natural Gas (CNG) engines. The computational results are reported and analyzed for a performance assessment conducted with a data envelopment analysis approach.
Several approaches for solving multi-objective optimization problems entail a form of scalarizati... more Several approaches for solving multi-objective optimization problems entail a form of scalarization of the objectives. This chapter proposes a study of different dynamic objectives aggregation methods in the context of evolutionary algorithms. These methods are mainly based on both weighted sum aggregations and curvature variations. Since the incorporation of chaotic rules or behaviour in population-based optimization algorithms has been shown to possibly enhance their searching ability, this study proposes to introduce and evaluate also some chaotic rules in the dynamic weights generation process. A comparison analysis is presented on the basis of a campaign of computational experiments on a set of benchmark problems from the literature.
Automated negotiation for transshipment coordination at a maritime terminal: protocol design and ... more Automated negotiation for transshipment coordination at a maritime terminal: protocol design and simulation analysis Alessandro Agnetis1, Gabriella Dellino1, Gianluca De Pascale1, Marco Pranzo1 1Universita di Siena, Dipartimento di Ingegneria dell'Informazione, Siena, Italy In ...
Most methods in simulation-optimization assume known environments, whereas this research accounts... more Most methods in simulation-optimization assume known environments, whereas this research accounts for uncertain environments combining Taguchi's world view with either regression or Kriging (Gaussian Process) metamodels (response surfaces). These metamodels are combined with Non-Linear Mathematical Programming (NLMP) to find a robust optimal solution. Varying the constraint values in the NLMP model gives an estimated Pareto frontier. To account for the variability of the estimated Pareto frontier, this research uses bootstrapping which gives confidence regions for the robust optimal solution. This methodology is illustrated through the Economic Order Quantity (EOQ) inventory-management model, accounting for the uncertainties in the demand rate and the cost coefficients.
This paper proposes a study of different dynamic objectives aggregation methods (DOAMs) in the co... more This paper proposes a study of different dynamic objectives aggregation methods (DOAMs) in the context of a multi-objective evolutionary approach to portfolio optimisation. Since the incorporation of chaotic rules or behaviour in population-based optimisation algorithms has been shown to possibly enhance their searching ability, this study considers and evaluates also some chaotic rules in the dynamic weights generation process. The ability of the DOAMs to solve the portfolio rebalancing problem is investigated conducting a computational study on a set of instances based on real data. The portfolio model considers a set of realistic constraints and entails the simultaneous optimisation of the risk on portfolio, the expected return and the transaction cost.
This talk presents an efficient decomposition approach to surgical planning. Given a set of surgi... more This talk presents an efficient decomposition approach to surgical planning. Given a set of surgical waiting lists (one for each discipline) and an operating theater, the problem is to decide the room-to-discipline assignment for the next planning period (Master Surgical Schedule), and the surgical cases to be performed (Surgical Case Assignment), with the objective of optimizing a score related to priority and current waiting time of the cases.
Abstract This paper addresses Operating Room (OR) planning policies in elective surgery. In parti... more Abstract This paper addresses Operating Room (OR) planning policies in elective surgery. In particular, we investigate long-term policies for determining the Master Surgical Schedule (MSS) throughout the year, analyzing the tradeoff between organizational simplicity, favored by an MSS that does not change completely every week, and quality of the service offered to the patients, favored by an MSS that dynamically adapts to the current state of waiting lists, the latter objective being related to a lean approach to hospital management.
Abstract Optimization of simulated systems is the goal of many methods, but most methods assume k... more Abstract Optimization of simulated systems is the goal of many methods, but most methods assume known environments. In this paper we present a methodology that does account for uncertain environments. Our methodology uses Taguchi's view of the uncertain world, but replaces his statistical techniques by either Response Surface Methodology or Kriging metamodeling. We illustrate the resulting methodology through the well-known Economic Order Quantity (EOQ) model.
Abstract This paper concerns the problem of optimally scheduling a set of appliances at the end-u... more Abstract This paper concerns the problem of optimally scheduling a set of appliances at the end-user premises. The user's energy fee varies over time, and moreover, in the context of smart grids, the user may receive a reward from an energy aggregator if he/she reduces consumption during certain time intervals. In a household, the problem is to decide when to schedule the operation of the appliances, in order to meet a number of goals, namely overall costs, climatic comfort level and timeliness.
Abstract In this work, the ability of the Dynamic Objectives Aggregation Methods to solve the por... more Abstract In this work, the ability of the Dynamic Objectives Aggregation Methods to solve the portfolio rebalancing problem is investigated conducting a computational study on a set of instances based on real data. The portfolio model considers a set of realistic constraints and entails the simultaneously optimization of the risk on portfolio, the expected return and the transaction cost.
Abstract Research on metamodel-based optimization has received considerably increasing interest i... more Abstract Research on metamodel-based optimization has received considerably increasing interest in recent years, and has found successful applications in solving computationally expensive problems. The joint use of computer simulation experiments and metamodels introduces a source of uncertainty that we refer to as metamodel variability. To analyze and quantify this variability, we apply bootstrapping to residuals derived as prediction errors computed from cross-validation.
Metamodels are often used in simulation-optimization for the design and management of complex sys... more Metamodels are often used in simulation-optimization for the design and management of complex systems. These metamodels yield insight into the relationship between responses and decision variables, providing fast analysis tools instead of the more expensive computer simulations. Moreover, these metamodels enable the integration of discipline-dependent analysis into the overall decision process.
Abstract Most methods in simulation-optimization assume known environments, whereas this research... more Abstract Most methods in simulation-optimization assume known environments, whereas this research accounts for uncertain environments combining Taguchi's world view with either regression or Kriging (also called Gaussian Process) metamodels (emulators, response surfaces, surrogates). These metamodels are combined with Non-Linear Mathematical Programming (NLMP) to find robust solutions. Varying the constraint values in this NLMP gives an estimated Pareto frontier.
The increasing pressure on the development time of new materials and devices has changed the mode... more The increasing pressure on the development time of new materials and devices has changed the modelling and design process over the years. In the past, they mainly consisted of experimentation and physical prototyping. Clearly, it is hard to incorporate changes in finished prototypes, while producing a variety of different prototypes at once may be very expensive. To this aim, computer simulation models such as circuit design models and continuous system simulation models are widely used in engineering modelling, design and analysis. The studies towards a better understanding of complex systems require quantitative model development, making optimisation and experimental data fitting tools indispensable. In this framework, the modelling of ionic polymer-metal composites (IPMCs) is studied. In particular, this paper deals with simulation-optimisation issues arising in the model calibration of a particular IPMC-based actuator in air. We consider a non-linear dynamical model of the device, with lumped parameters, able to estimate the IPMC actuator absorbed current, together with the mechanical quantities of interest, which, in the case under study, are the free deflection and/or the blocked force. Two optimisation problems have been formulated, focusing on different stages of the model parameters identification. The strategies adopted to solve the problems allow to achieve some promising - although preliminary - results.
International Journal of Production …, Jan 1, 2010
Optimization of simulated systems is tackled by many methods, but most methods assume known envir... more Optimization of simulated systems is tackled by many methods, but most methods assume known environments. This article, however, develops a ‘robust’ methodology for uncertain environments. This methodology uses Taguchi's view of the uncertain world, but replaces his statistical techniques by Response Surface Methodology (RSM). George Box originated RSM, and Douglas Montgomery recently extended RSM to robust optimization of real (non-simulated) systems. We combine Taguchi's view with RSM for simulated systems. We illustrate the resulting methodology through classic Economic Order Quantity (EOQ) inventory models, which demonstrate that robust optimization may require order quantities that differ from the classic EOQ.
Optimization of simulated systems is the goal of many methods, but most methods assume known envi... more Optimization of simulated systems is the goal of many methods, but most methods assume known environments. We, however, develop a “robust” methodology that accounts for uncertain environments. Our methodology uses Taguchi's view of the uncertain world but replaces his statistical techniques by design and analysis of simulation experiments based on Kriging (Gaussian process model); moreover, we use bootstrapping to quantify the variability in the estimated Kriging metamodels. In addition, we combine Kriging with nonlinear programming, and we estimate the Pareto frontier. We illustrate the resulting methodology through economic order quantity (EOQ) inventory models. Our results suggest that robust optimization requires order quantities that differ from the classic EOQ. We also compare our results with results we previously obtained using response surface methodology instead of Kriging.
This paper deals with the use of Kriging metamodels in multi-objective engineering design optimiz... more This paper deals with the use of Kriging metamodels in multi-objective engineering design optimization. The metamodel management issue to find the tradeoff between accuracy and efficiency is addressed. A comparative analysis of different strategies is conducted for a case study devoted to the design of a component of the injection system for Compressed Natural Gas (CNG) engines. The computational results are reported and analyzed for a performance assessment conducted with a data envelopment analysis approach.
Several approaches for solving multi-objective optimization problems entail a form of scalarizati... more Several approaches for solving multi-objective optimization problems entail a form of scalarization of the objectives. This chapter proposes a study of different dynamic objectives aggregation methods in the context of evolutionary algorithms. These methods are mainly based on both weighted sum aggregations and curvature variations. Since the incorporation of chaotic rules or behaviour in population-based optimization algorithms has been shown to possibly enhance their searching ability, this study proposes to introduce and evaluate also some chaotic rules in the dynamic weights generation process. A comparison analysis is presented on the basis of a campaign of computational experiments on a set of benchmark problems from the literature.
Automated negotiation for transshipment coordination at a maritime terminal: protocol design and ... more Automated negotiation for transshipment coordination at a maritime terminal: protocol design and simulation analysis Alessandro Agnetis1, Gabriella Dellino1, Gianluca De Pascale1, Marco Pranzo1 1Universita di Siena, Dipartimento di Ingegneria dell'Informazione, Siena, Italy In ...
Most methods in simulation-optimization assume known environments, whereas this research accounts... more Most methods in simulation-optimization assume known environments, whereas this research accounts for uncertain environments combining Taguchi's world view with either regression or Kriging (Gaussian Process) metamodels (response surfaces). These metamodels are combined with Non-Linear Mathematical Programming (NLMP) to find a robust optimal solution. Varying the constraint values in the NLMP model gives an estimated Pareto frontier. To account for the variability of the estimated Pareto frontier, this research uses bootstrapping which gives confidence regions for the robust optimal solution. This methodology is illustrated through the Economic Order Quantity (EOQ) inventory-management model, accounting for the uncertainties in the demand rate and the cost coefficients.
This paper proposes a study of different dynamic objectives aggregation methods (DOAMs) in the co... more This paper proposes a study of different dynamic objectives aggregation methods (DOAMs) in the context of a multi-objective evolutionary approach to portfolio optimisation. Since the incorporation of chaotic rules or behaviour in population-based optimisation algorithms has been shown to possibly enhance their searching ability, this study considers and evaluates also some chaotic rules in the dynamic weights generation process. The ability of the DOAMs to solve the portfolio rebalancing problem is investigated conducting a computational study on a set of instances based on real data. The portfolio model considers a set of realistic constraints and entails the simultaneous optimisation of the risk on portfolio, the expected return and the transaction cost.
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Papers by Gabriella Dellino