Simulated Optimization Technique Research Articles

Coupled Monte Carlo simulation and Copula theory for uncertainty analysis of multiphase flow simulation models.

Simulation-optimization techniques are effective in identifying an optimal remediation strategy. Simulation models with uncertainty, primarily in the form of parameter uncertainty with different degrees of correlation, influence the reliability of the optimal remediation strategy. In this study, a coupled Monte Carlo simulation and Copula theory is proposed for uncertainty analysis of a simulation model when parameters are correlated. Using the self-adaptive weight particle swarm optimization Kriging method, a surrogate model was constructed to replace the simulation model and reduce the computational burden and time consumption resulting from repeated and multiple Monte Carlo simulations. The Akaike information criterion (AIC) and the Bayesian information criterion (BIC) were employed to identify whether the t Copula function or the Gaussian Copula is the optimal Copula function to match the relevant structure of the parameters. The results show that both the AIC and BIC values of the t Copula function are less than those of the Gaussian Copula function. This indicates that the t Copula function is the optimal function for matching the relevant structure of the parameters. The outputs of the simulation model when parameter correlation was considered and when it was ignored were compared. The results show that the amplitude of the fluctuation interval when parameter correlation was considered is less than the corresponding amplitude when parameter estimation was ignored. Moreover, it was demonstrated that considering the correlation among parameters is essential for uncertainty analysis of a simulation model, and the results of uncertainty analysis should be incorporated into the remediation strategy optimization process.

Estimation of Water Demand in Water Distribution Systems Using Particle Swarm Optimization

Demand estimation in a water distribution network provides crucial data for monitoring and controlling systems. Because of budgetary and physical constraints, there is a need to estimate water demand from a limited number of sensor measurements. The demand estimation problem is underdetermined because of the limited sensor data and the implicit relationships between nodal demands and pressure heads. A simulation optimization technique using the water distribution network hydraulic model and an evolutionary algorithm is a potential solution to the demand estimation problem. This paper presents a detailed process simulation model for water demand estimation using the particle swarm optimization (PSO) algorithm. Nodal water demands and pipe flows are estimated when the number of estimated parameters is more than the number of measured values. The water demand at each node is determined by using the PSO algorithm to identify a corresponding demand multiplier. The demand multipliers are encoded with varying step sizes and the optimization algorithm particles are also discretized in order to improve the computation time. The sensitivity of the estimated water demand to uncertainty in demand multiplier discrete values and uncertainty in measured parameters is investigated. The sensor placement locations are selected using an analysis of the sensitivity of measured nodal heads and pipe flows to the change in the water demand. The results show that nodal demands and pipe flows can be accurately determined from a limited number of sensors.

A dynamic simulation–optimization approach for managing mass casualty incidents

To handle mass casualty incidents (MCIs), emergency medical services in central European countries such as Austria or Germany are used to build up small field hospitals called advanced medical posts (AMPs). The main policy behind is to categorize, treat, and stabilize emergency patients before they are transported to hospitals. To investigate the organization of the AMP type operated in Austria such as by Austrian Samaritan Organization, we developed a discrete event simulation (DES) policy model for MCIs regarding advanced process management including staff allocation (physicians, medics) for emergency patient treatment and transport at the incident site. As health care policy makers aim at improving the training of emergency staff, we developed a management policy game as a training tool adapted from the DES policy model. In this paper, we address the research question whether or not the policy decisions made by simple heuristics or disaster management players could be further improved by using advanced simulation–optimization techniques. We present a generic method consisting of (i) an automated policy for dynamic staff re-allocation at an AMP with arbitrary structure, and (ii) a simulation–optimization approach for optimally parametrizing this automated policy. Three simulation–optimization techniques with two complexity levels are investigated in detail for the purpose of incorporation in our system applied to the Austrian AMP case study: the method by Kiefer–Wolfowitz, the metaheuristic OptQuest approach, and the Response Surface Methodology. Our results show that the optimized automated policies can improve the performance of the AMP compared to the management by simple heuristics or by human decision makers. We discuss policy implications for improving strategic decision making and process management for incident commanders at the Austrian AMP based on the results of the dynamic simulation–optimization techniques.

A comparative research of different ensemble surrogate models based on set pair analysis for the DNAPL-contaminated aquifer remediation strategy optimization

Surrogate-based simulation-optimization technique is an effective approach for optimizing the surfactant enhanced aquifer remediation (SEAR) strategy for clearing DNAPLs. The performance of the surrogate model, which is used to replace the simulation model for the aim of reducing computation burden, is the key of corresponding researches. However, previous researches are generally based on a stand-alone surrogate model, and rarely make efforts to improve the approximation accuracy of the surrogate model to the simulation model sufficiently by combining various methods. In this regard, we present set pair analysis (SPA) as a new method to build ensemble surrogate (ES) model, and conducted a comparative research to select a better ES modeling pattern for the SEAR strategy optimization problems. Surrogate models were developed using radial basis function artificial neural network (RBFANN), support vector regression (SVR), and Kriging. One ES model is assembling RBFANN model, SVR model, and Kriging model using set pair weights according their performance, and the other is assembling several Kriging (the best surrogate modeling method of three) models built with different training sample datasets. Finally, an optimization model, in which the ES model was embedded, was established to obtain the optimal remediation strategy. The results showed the residuals of the outputs between the best ES model and simulation model for 100 testing samples were lower than 1.5%. Using an ES model instead of the simulation model was critical for considerably reducing the computation time of simulation-optimization process and maintaining high computation accuracy simultaneously.

Voltage Collapse Optimization for the Iraqi Extra High Voltage 400 kV Grid based on Particle Swarm Optimization

The continuously ever-growing demand for the electrical power causing the continuous expansion and complexity of power systems, environmental and economic factors forcing the system to work near the critical limits of stability, so research's stability have become research areas worthy of attention in the resent day. The present work includes two phases: The first one is to determine the Voltage Stability Index for the more insensitive load bus to the voltage collapse in an interconnected power system using fast analyzed method based on separate voltage and current for PQ buses from these of PV buses, while the second phase is to suggested a simulated optimization technique for optimal voltage stability profile all around the power system. The optimization technique is used to adjust the control variables elements: Generator voltage magnitude, active power of PV buses, VAR of shunt capacitor banks and the position of transformers tap with satisfied the limit of the state variables (load voltages, generator reactive power and the active power of the slack bus). These control variables are main effect on the voltage stability profile to reach the peak prospect voltage stable loading with acceptable voltage profile. An optimized voltage collapse based on Particle Swarm Optimization has been tested on both of the IEEE 6 bus system and the Iraqi Extra High Voltage 400 kV Grid 28 bus. To ensure the effectiveness of the optimization technique a comparison between the stability indexes for load buses before and after technical application are presented. Simulation results have been executed using Matlab software.

Conservative strategy-based ensemble surrogate model for optimal groundwater remediation design at DNAPLs-contaminated sites

The surrogate-based simulation-optimization techniques are frequently used for optimal groundwater remediation design. When this technique is used, surrogate errors caused by surrogate-modeling uncertainty may lead to generation of infeasible designs. In this paper, a conservative strategy that pushes the optimal design into the feasible region was used to address surrogate-modeling uncertainty. In addition, chance-constrained programming (CCP) was adopted to compare with the conservative strategy in addressing this uncertainty. Three methods, multi-gene genetic programming (MGGP), Kriging (KRG) and support vector regression (SVR), were used to construct surrogate models for a time-consuming multi-phase flow model. To improve the performance of the surrogate model, ensemble surrogates were constructed based on combinations of different stand-alone surrogate models. The results show that: (1) the surrogate-modeling uncertainty was successfully addressed by the conservative strategy, which means that this method is promising for addressing surrogate-modeling uncertainty. (2) The ensemble surrogate model that combines MGGP with KRG showed the most favorable performance, which indicates that this ensemble surrogate can utilize both stand-alone surrogate models to improve the performance of the surrogate model.

Notes on Using Simulation-Optimization Techniques in Traffic Simulation

Abstract Mathematical and simulation models of systems lay at the core of many decision support systems, and their role becomes more critical when the system is more complex. The decision process usually involves optimizing some utility function that evaluates the performance indicators measuring the impacts of the decisions. The complexity of the system directly increases the difficulty when the associated function to be optimized is a non-analytical, non-differentiable, non-linear function that can only be evaluated by simulation. Simulation-optimization techniques are especially suited to these cases, and its use is becoming increasingly used with traffic models, which represent an archetypal case of complex, dynamic systems that exhibit highly stochastic characteristics. In this approach, simulation is used to evaluate the objective function, and it is combined with a non-differentiable optimization technique for solving the associated optimization problem. Of these techniques, one of the most commonly used is Stochastic Perturbation Stochastic Approximation (SPSA). This paper analyses, discusses and presents the computational results from applying this technique in the calibration of traffic simulation models. This study uses variants of the SPSA by replacing the usual gradient approach with a combination of projected gradient and trust region methods. A special approach has also been analyzed for parameter calibration cases in which each variable has a different magnitude.

The Research of Shipbuilding Schedule Planning and Simulation Optimization Technique Based on Constant Work-In-Process System

The Research of Shipbuilding Schedule Planning and Simulation Optimization Technique Based on Constant Work-In-Process System

Work Study and Simulation Optimization of Supply-demand Balancing in the Moth Orchid Plant Factory

This study develops a two-stage framework. An empirical study of a moth orchid plant factory is conducted. The first part includes developing the standard operating procedures (SOP) by eliminating seven muda (e.g., transportation waste, etc.) based on the lean production management for the plant factory. The second part is that we build up a simulation model of the production process via the SOP. By collecting global trade data, we estimate the supply distribution of the case factory as our output distribution and obtain the input portfolio with minimal cost by simulation optimization techniques to address the supply-demand mismatching problem.

Optimal design of multi-conditions for axial flow pump

Passage components of the pump device will have a negative flow state when axial pump run off the design condition. Combined with model tests of axial flow pump, this paper use numerical simulation and numerical optimization techniques, and change geometric design parameters of the impeller to optimal design of multi conditions for Axial Flow Pump, in order to improve the efficiency of non-design conditions, broad the high efficient district and reduce operating cost. The results show that, efficiency curve of optimized significantly wider than the initial one without optimization. The efficiency of low flow working point increased by about 2.6%, the designed working point increased by about 0.5%, and the high flow working point increased the most, about 7.4%. The change range of head is small, so all working point can meet the operational requirements. That will greatly reduce operating costs and shorten the period of optimal design. This paper adopted the CFD simulation as the subject analysis, combined with experiment study, instead of artificial way of optimization design with experience, which proves the reliability and efficiency of the optimization design of multi-operation conditions of axial-flow pump device.

Comparison of alternative route selection strategies based on simulation optimization

Air traffic flow management (ATFM) is a collaborative process between the airspace provider and the airspace users. The result of the collaboration should be an outcome that maximizes the utility of the system without excessively penalizing any of the agents. This paper develops a discrete-event simulation model which consists of aggregate departure/arrival airports, flight routes, and sectors for evaluating the alternative collaborative route selection strategy. Given the different perspectives from air traffic control center (ACC) and airlines, eight performance-metrics and five alternative route selection strategies represent the past, current and proposed air traffic flow management operations that were evaluated. The Monte Carlo method combined with the Optimal Computing Budget Allocation (OCBA) simulation optimization technique is employed to assess the performance of different strategies. A case study of the upper air routes in central and southern China shows that the proposed model can be readily implemented to simulate different kinds of air traffic flow management strategies and predict the effect of changes on the airspace system. It also shows that the proposed alternative collaborative route selection strategy is an effective way in alleviating the en-route traffic congestion.

Fuzzy C-Mean Clustering Based Inference System for Saltwater Intrusion Processes Prediction in Coastal Aquifers

Flow as well as salt transport processes of coastal aquifers are dependent on the density variation of water, and are complicated and extremely non-linear in nature. Simulation of these complicated systems to an acceptable degree of accuracy using a suitable surrogate model can be useful to achieve computational efficiency in modelling. In this study, a Sugeno type Fuzzy Inference System (FIS) is developed to predict salinity concentrations at specified monitoring locations as a result of groundwater pumping. Fuzzy c-mean clustering (FCM) algorithm is used to develop the FIS. Heterogeneity of aquifer is incorporated by using different hydraulic conductivities at different layers of the aquifer. A numerical simulation model, FEMWATER is adopted to generate the required patterns of inputs and outputs for initial training of the developed FIS. The FIS analyzed different combinations of antecedent transient pumping values, and returns concentration values at different monitoring locations. The performance of the FIS for training and validation datasets is evaluated by comparing the outputs obtained from the numerical simulation model. The trained and validated FIS is utilized as an approximate simulator of the coupled flow and salt transport processes. It is then used to forecast salt concentrations at different monitoring locations. Performance evaluation results indicate that the developed FIS can be applied to forecast salt concentrations at specified monitoring locations. The FIS is able to simulate the complex physical processes of heterogeneous coastal aquifers, and can be suitable for incorporation in a coupled simulation-optimization technique to develop optimum pumping strategy.

Surrogate-Based Sensitivity Analysis and Uncertainty Analysis for DNAPL-Contaminated Aquifer Remediation

AbstractTo limit the high cost of surfactant-enhanced aquifer remediation (SEAR) for clearing dense nonaqueous phase liquids (DNAPLs), the simulation-optimization technique is generally adopted for determining the optimal remediation strategy in advance. The simulation model requires an uncertainty analysis, and incorporating the results into the SEAR strategy optimization process is critical. However, previous studies have rarely involved corresponding problems. In the present study, an uncertainty analysis is performed by combining a Monte Carlo random simulation with the Sobol’ global sensitivity analysis to assess the contribution of different parameters to the remediation efficiency and distribution characteristics of the simulation model outputs. The surrogate model technique based on Kriging was used to reduce the high computational load of the sensitivity and uncertainty analyses. The results of the sensitivity analysis showed that the porosity is the most important parameter with the largest influ...

Optimum design of a hybrid PV–CSP–LPG microgrid with Particle Swarm Optimization technique

Designing an energy system using multiple energy sources including renewables and providing multiple energy services (e.g. electricity, heating) can enhance the reliability and efficiency of the system while mitigating the environmental footprint. However, interaction among various components, variation of the energy demand profile, and local ambient conditions make design optimization a complex task, and suggesting that efficient simulation tools and optimization techniques can help designers to determine the best solutions within a reasonable timeframe and budget.Previous work on a dynamic microgrid simulation tool called “u-Grid” used an exhaustive search technique to find optimum configurations. However, the high computational cost of the exhaustive search was a motivation to explore alternative optimization methods to improve the optimization process and also to enhance search speed. In this paper Particle Swarm Optimization (PSO) has been presented as a global optimizer and incorporated within the problem context. Results from the exhaustive search have been used as a benchmark for testing and validation of the newly introduced optimization technique. The result shows that the PSO method is an efficient technique which has the ability to determine a high quality design solution for an optimized microgrid with a relatively low computational cost. Applying this PSO-based algorithm to the case study has reduced the total computation time a factor of about 6 in a significantly smaller computational platform.

Adaptation of pressurized irrigation networks to new strategies of irrigation management: Energy implications of low discharge and pulsed irrigation

This paper analyzes the consequences of adopting new on-farm irrigation management strategies (low discharge rates, long irrigation times and high frequencies) in an existing on-demand and sectorized pressurized irrigation system in eastern Spain. The sectorized behavior of the network was analyzed using two criteria: (i) the operating sectors obtained in a first stage by arranging the hydrants depending on their altitude respecting the pumping station and (ii) the operating sectors obtained by means of an optimization process. The Simulated Annealing combinatorial metaheuristic optimization technique was employed to find the best solution. Random on-demand patterns were generated using a Montecarlo simulation. The hydraulic requirements of the network were analyzed in every scenario by the Epanet 2.0 engine. The effect on energy consumption, power requirements and energy costs was assessed taking into account the electricity tariff billing structure. It was found that reductions in emitter discharge (qe) and Energy consumption (E)-Energy Cost (EC) savings are not inherently related to each other. Certain amounts of E and EC could be saved when the number of sectors and operating time parameters were properly selected. Pulsed irrigation in the current scenario showed an energy saving potential of 10.67, 6.43 and 6.99% for power capacity, E and EC, respectively.

The Development of Patient Scheduling Groups for an Effective Appointment System.

Patient access to care and long wait times has been identified as major problems in outpatient delivery systems. These aspects impact medical staff productivity, service quality, clinic efficiency, and health-care cost. This study proposed to redesign existing patient types into scheduling groups so that the total cost of clinic flow and scheduling flexibility was minimized. The optimal scheduling group aimed to improve clinic efficiency and accessibility. The proposed approach used the simulation optimization technique and was demonstrated in a Primary Care physician clinic. Patient type included, emergency/urgent care (ER/UC), follow-up (FU), new patient (NP), office visit (OV), physical exam (PE), and well child care (WCC). One scheduling group was designed for this physician. The approach steps were to collect physician treatment time data for each patient type, form the possible scheduling groups, simulate daily clinic flow and patient appointment requests, calculate costs of clinic flow as well as appointment flexibility, and find the scheduling group that minimized the total cost. The cost of clinic flow was minimized at the scheduling group of four, an 8.3% reduction from the group of one. The four groups were: 1. WCC, 2. OV, 3. FU and ER/UC, and 4. PE and NP. The cost of flexibility was always minimized at the group of one. The total cost was minimized at the group of two. WCC was considered separate and the others were grouped together. The total cost reduction was 1.3% from the group of one. This study provided an alternative method of redesigning patient scheduling groups to address the impact on both clinic flow and appointment accessibility. Balance between them ensured the feasibility to the recognized issues of patient service and access to care. The robustness of the proposed method on the changes of clinic conditions was also discussed.

최적화 기법을 이용한 홍수 시 한강유역 댐군 모의 기법 연계 운영의 효과 개선

A reservoir simulation operation method is used more frequently than an optimal operation method because the simulation method is more flexible enough to reflect sudden changes of practical flood conditions or the intuitions and experience of dam operators. However, the results from a reservoir simulation operation model vary according to input control data. Thus, it is required to choose appropriate input control data of the reservoir simulation operation model through repetitive preliminary simulations. The purpose of the study is to derive optimal input control data of the reservoir simulation operation model by an optimization technique, and to improve effect of flood control. The shuffled complex evolution algorithm was used to optimize input control data of the model, which are initiation time of spillway release and release quantity. The reservoir simulation operation model and optimization technique was applied to a rainfall event in September 1990 when it rained a lot in the Han River Basin and showed the better flood control effects.

Selecting Parameter-Optimized Surrogate Models in DNAPL-Contaminated Aquifer Remediation Strategies

Abstract Surfactant-enhanced aquifer remediation (SEAR) is an efficient way for clearing dense nonaqueous phase liquids (DNAPLs) which may result in serious environment and health threats. To limit the high cost of SEAR, simulation optimization techniques are generally applied to ensure that an optimal remediation strategy is achieved. Furthermore, surrogate model techniques have been widely used to reduce the high computational burden associated with these processes. However, previous research rarely involved comparison of different surrogate models for multiphase flow numerical simulation models. In this regard, we conducted a comparative analysis to select the optimal modeling technique and parameter optimization algorithm for surrogate models in DNAPL-contaminated aquifer remediation strategy optimization problems. Latin hypercube sampling method was used to collect data in the feasible region of input variables. Surrogate models were developed using radial basis function artificial neural network, Kr...

The Optimization of Solar Drying of Grain by Using a Genetic Algorithm

Particularly in the low drying temperature drying is an energy intensive process and requires a suitable optimization technique. This work represents the kinetic simulation and energy efficient optimization technique for drying long grain, i.e. rice. At 30–60°C temperature the grains have been dried in a solar powered air dryer. After certain time interval the enzymatic activity and the moisture content have been measured. Genetic Algorithm (GA) has been used for the simulation and the optimization process while the experimental data have been used to fit the thin layer drying model. The results indicate that between 40 and 50°C temperature and the 1.3–1.5 m/s air flow rate the drying time for the rice is around 120–180 min. The kinetic simulation and the quality analysis confirm that the dried products have a better enzymatic activity which resembles the quality standard and the required energy for the optimized drying process is lower than the others.

Simulation Optimization: A Review and Exploration in the New Era of Cloud Computing and Big Data

Recent advances in simulation optimization research and explosive growth in computing power have made it possible to optimize complex stochastic systems that are otherwise intractable. In the first part of this paper, we classify simulation optimization techniques into four categories based on how the search is conducted. We provide tutorial expositions on representative methods from each category, with a focus in recent developments, and compare the strengths and limitations of each category. In the second part of this paper, we review applications of simulation optimization in various contexts, with detailed discussions on health care, logistics, and manufacturing systems. Finally, we explore the potential of simulation optimization in the new era. Specifically, we discuss how simulation optimization can benefit from cloud computing and high-performance computing, its integration with big data analytics, and the value of simulation optimization to help address challenges in engineering design of complex systems.