Simulated Optimization Technique Research Articles

Multi-objective optimization design of thermal management system for lithium-ion battery pack based on Non-dominated Sorting Genetic Algorithm II

The thermal management of batteries was a significant issue considering the safety and efficiency. Optimal design of a novel liquid cooling system with symmetrical double-layer reverting bifurcation channel was performed by combining experimental, numerical simulation and multi-objective optimization techniques. The thermophysical parameters and heat production rate of the battery for numerical simulation were obtained by experiments. The convective heat transfer coefficient and the surface friction coefficient were chosen as objective functions to visually reflect the heat transfer process. Furthermore, batteries were confined to work at the optimal temperature (25–40 °C) and the optimal temperature difference between cells (less than 5 °C). The performance values of design points obtained by Latin hypercube sampling were calculated numerically. Response surface approximation was adopted to approximate the objective function and the constraint function to reduce computing time. The Pareto-optimal front between −h and f was obtained using Non-dominated Sorting Genetic Algorithm II. 17.19% change in heat transfer coefficient was accomplished by 85.53% change in skin friction coefficient. The results reported that the cooling system with optimized thermal performance can be obtained at low flow loss.

A Managerial Analysis of Urban Parcel Delivery: A Lean Business Approach

The improper integration of traditional transportation modes with low emissions vehicles can generate a price war that reduces the service quality, undermining the efficiency and the profitability of parcel delivery operators. This paper aims to provide managerial insights to design a win-win strategy for the co-existence of traditional and green business models. In doing so, we adopt a multi-disciplinary approach that integrates a qualitative analysis through a Lean Business methodology, named GUEST, with a quantitative analysis based on simulation-optimisation techniques. This kind of holistic vision has received little attention in the literature. The first analysis investigates the parcel delivery industry with an emphasis on the main business models involved, their costs and revenues structures, while the quantitative part aims to simulate the system and extract sustainable policies. In particular, results highlight that in deploying mixed-fleet policies, the decision-makers have to focus both on the environmental sustainability that benefits from the adoption of low-emission vehicles, and on the operational feasibility and economic sustainability of the two services. In this direction, the paper suggests some managerial insights concerning the split of the customer demand between traditional and green operators, according to the classes of parcels and geographical areas of the city.

The Analyses of Vehicle-to-Pedestrian Accidents by Integrating Rigid-Body Simulation and Robust Optimization Techniques

The objective of this study is to propose an approach for improving the efficiency of accident reconstruction and obtaining the probabilistic distribution of pre-impact parameters through integrating numerical simulation and robust optimization techniques. First, the ranges and probabilistic distribution of collision parameters are determined by investigators. Then, the optimal Latin hypercube design is used to generate sample points in predefined design space and corresponding responses are obtained by PC-Crash. The radial basis functions method is used to construct the surrogate models, accuracy evaluation is conducted using the cross-validation method. Finally, a robust optimization is completed to obtain the probabilistic distribution of collision parameters. Furthermore, a real-world vehicle-to-pedestrian accident is reconstructed to specifically explain and validate the method. The results show that the numerical simulation coupled with robust optimization technique is an effective means of obtaining the probabilistic distribution of pre-impact inputs. The proposed approach will help the traffic accident to scientifically conduct the traffic accident identification.

Application of Mixed-Integer Nonlinear Optimization Programming Based on Ensemble Surrogate Model for Dense Nonaqueous Phase Liquid Source Identification in Groundwater

Groundwater contamination source identification (GCSI) is critical for taking effective measures to protect groundwater resources, assess risks, mitigate disasters, and design remediation strategies. Simulation–optimization techniques have been effective tools for GCSI. However, previous studies have applied individual surrogate models when replacing simulation models, rather than making efforts to combine various methods to improve the approximation accuracy of the surrogate model over the simulation model. In this study, the kernel extreme learning machine (KELM) model was proposed to enhance the surrogate model, and to approach GCSI problems, especially those of dense nonaqueous phase liquid-contaminated aquifers, more effectively. In addition, a kriging model and a support vector regression (SVR) model were built and compared with the KELM model, and various ensemble surrogate (ES) modeling techniques were applied to establish four ES models. Results showed that the KELM model was more accurate than the kriging and SVR models; however, the ES models performed much better than the three individual surrogate models. The most precise ES model increased the certainty coefficient (R2) to 0.9837, whereas limiting the maximum relative error to 13.14%. Finally, a mixed-integer nonlinear programming optimization model was established to identify the groundwater contamination source in terms of location and release history, and simultaneously assess aquifer parameters. The ES model developed in this article could reasonably predict the system response under given operation conditions. Replacement of the simulation model by the ES model considerably reduced the computation burden of the simulation–optimization process and simultaneously achieved high computation accuracy.

TD-OCBA: Optimal computing budget allocation and time dilation for simulation optimization of manufacturing systems

Discrete event simulation has been widely applied to study the behavior of stochastic manufacturing systems. This is due to the fact that manufacturing systems are usually too complex to obtain a closed-form analytical model that can accurately predict their performance. This becomes particularly critical when the optimization of these systems is of concern. In fact, Simulation optimization techniques are employed to identify the manufacturing system configuration which can maximize the expected system performance when this can only be estimated by running a simulator. In this article, we look into simulation-based optimization when a finite number of solutions are available and we have to identify the best. In particular, we propose, for the first time, the integration of Optimal Computing Budget Allocation (OCBA), which is based on independent measures from each simulation experiment, and Time Dilation (TD), which is a single-run simulation optimization algorithm. As a result, the optimization problem is solved when only one experiment of the system is performed by changing the “speed” of the simulation at each configuration in order to control the computational effort. The challenge is how to iteratively select such a speed. We solve this problem by proposing TD-OCBA, which integrates TD and OCBA while relying on standardized time series variance estimators. Numerical experiments have been conducted to study the performance of the algorithm when the response is generated from a time series. This provides the possibility to test the robustness of TD-OCBA. Comparison between TD-OCBA and the original TD method was performed by simulating a job shop system reported in the literature. Finally, an application involving semiconductors remote diagnostics is used to compare the TD-OCBA method and what is known as the equal allocation method.

One vendor and multiple retailers system in vendor managed inventory problem with stochastic demand

In many supply networks, the retailers are reluctant to share information about demand and inventory level to the vendor. This might lead to many difficulties for the vendor in establishing his own order/production plan. Vendor managed inventory (VMI) policy can help to solve that problem. By applying VMI, information sharing is not really a problem for the vendor anymore and this policy have been proven to help reduce total inventory cost as well as improve customer service level in the supply network. In this research, a VMI model for the system with one vendor and multiple retailers will be developed. The main target of the model is to determine the retailer's lot size, the vendor's lot size, the retailer cycle time, and the number of replenishments in a vendor cycle so as to minimise the total system cost. For solution purpose, simulation-optimisation technique using genetic algorithm is employed to help find optimal solutions for the decision variables. Numerical experiments are conducted to show the applicability of the proposed model. Sensitivity analysis is also conducted to examine the effects of some input parameters on the optimal solution.

Stochastic Simulation Optimization for Route Selection Strategy Based on Flight Delay Cost

The continuous and strong growth of the civil aviation in the world combined with the severe adverse weather problem have made necessary the collaboration between the different civil aviation agents to improve the management of the capacity-demand imbalances in the airspace. In this paper, we consider a stochastic simulation optimization problem for air route selection strategy based on flight delay cost. The problem takes consideration of airspace capacity and demand uncertainty, three strategies, including collaborative reroute strategy (CRS), full information reroute strategy (FIRS) and hybrid stated route preference strategy (HSR), are employed to mitigate the flight delay cost. To find the best strategy, a discrete event simulation model is built by Arena Software, and the Monte Carlo method combined with the OCBA simulation optimization technique is employed for assessing a common severe convective weather scenario in the Central and Southern China airspace. Simulation results imply that HSR schemes show better system-wide performance than CRS and FIRS, these benefits are supposed to come from the batch allocations method. Although the airline can receive full information in advance, FIRS does not show obvious advantage in reducing the total airborne waiting time than CRS. For the system-wide performance FIRS is better than CRS, but not as good as HSR.

A New Optimisation-based Design Methodology for Energy-efficient Crude Oil Distillation Systems with Preflash Units

In a heat-integrated crude oil distillation system with an atmospheric distillation unit and heat recovery system, pre-separation units (a preflash or prefractionator) can help to reduce the furnace duty and improve energy efficiency of the system. The high operating and equipment cost and complexity of the system motivate the development of systematic approaches for optimal design. The approach needs to consider both design of the distillation unit and the optimisation of operating conditions in the system, including those related to the preflash unit. This work introduces an optimisation-based methodology for the design of crude oil distillation systems with preflash units. The objective is to minimise fired heat demand of the system while meeting product quality and yield specifications, where pinch analysis is applied to estimate minimum utility requirements. A stochastic optimisation algorithm (a genetic algorithm) is applied to identify the feed location of the flash vapour, the number of stages in each column section and the optimal operating conditions. Operational optimisation variables include pump-around duties and temperature drops, stripping steam flow rates, the column feed temperature, reflux ratio and preflash temperature. The approach is based on the simulation–optimisation technique of Caballero et al. (2005). In order to facilitate integration of modelling and optimisation, an interface between MatLab R2016a and Aspen HYSYS v8.8 is employed. A case study illustrates how the design methodology proposed in this work can reduce demand for fired heating.

REAL POWER LOSS REDUCTION & VOLTAGE STABILITY AMPLIFICATION BY HYBRIDIZATION OF RESTARTED SIMULATED ANNEALING WITH PARTICLE SWARM OPTIMIZATION ALGORITHM

This paper presents an algorithm for solving the multi-objective reactive power dispatch problem in a power system. Modal analysis of the system is used for static voltage stability assessment. Loss minimization and maximization of voltage stability margin are taken as the objectives. Generator terminal voltages, reactive power generation of the capacitor banks and tap changing transformer setting are taken as the optimization variables. Evolutionary algorithm and Swarm Intelligence algorithm (EA, SI), a part of Bio inspired optimization algorithm, have been widely used to solve numerous optimization problem in various science and engineering domains. Particle swarm optimization (PSO) is a population-based swarm intelligence algorithm that shares many similarities with evolutionary computation techniques. However, the PSO is driven by the simulation of a social psychological metaphor motivated by collective behaviors of bird and other social organisms instead of the survival of the fittest individual. The Simulated Annealing (SA) algorithm is a probabilistic hill-climbing technique that is based on the annealing/cooling process of metals. In total, most moves may be accepted at initial stages, but at the final stage only improving ones are likely to be allowed. This can help the procedure jump out of a local minimum. However, sometimes it is better to move back to a former solution that was significantly better rather than always moving from the current state. This process is called “restarting” of SA & called as Restarted Simulated Annealing (RSA). In this paper we proposed a hybridized restarted simulated annealing particle swarm optimization (RSAPSO) technique to find global minima more efficiently and robustly. The proposed RSAPSO combines the global search ability of PSO and the local search ability of RSA, and offsets the weaknesses of each other. In order to evaluate the proposed algorithm, it has been tested on IEEE 30 bus system and compared to other reported algorithms.

Addressing over-correction in adaptive card-based pull control systems

ABSTRACTAdaptive card control strategy-based heuristics are used to change the number of cards dynamically in response to stochastic and fluctuated customers’ demands. However, when the decisions are taken in real-time and without the use of forecasts, existing heuristics may yield to change too often the number of cards. This over-correction, related to system nervousness, can induce undesirable effects for the workshop. Our literature analysis underlines that, despite nervousness has been discussed in other industrial areas, it seems not to have been studied in the context of adaptive pull control systems. We first introduce and discuss nervousness in the context of adaptive pull control systems. We identify the main factor that induces nervousness and its consequences. To reduce nervousness, we propose a new approach, which relies both on an adaptive freezing interval and a multi-objective simulation optimisation technique. We first show the relevance of the proposed approach through a comparison with an adaptive Kanban system taken from the literature. This comparison shows that our approach yields better results. In addition, the resulting Pareto front offers flexibility to the decision-maker. The suggested approach can be useful: for managers to better implement their adaptive pull systems, and for decision-makers to define operational procedures taking nervousness into account.

Saltwater scavenging optimization under surrogate uncertainty for a multi-aquifer system

Surrogate-assisted simulation-optimization technique is an effective approach for identifying aquifer remediation strategies. Using surrogate models to replace high-fidelity groundwater simulation models is key to reduce computing time. Inevitably, uncertainties persist in surrogate model structure and parameters and may lead to poor accuracy in prediction. However, past studies often adopt one surrogate to capture the original model response and rarely investigate uncertainties and approximation accuracy of surrogate models. In this study, we present an ensemble surrogate modeling approach to address uncertainty in surrogate model predictions for saltwater intrusion mitigation designs using a hypothetical horizontal scavenger well. Firstly, polynomial response surface surrogate models are constructed to capture the relationship between chloride concentrations and saltwater extraction rates of a horizontal well. Secondly, a stochastic optimization model that includes multiple ensemble surrogates is developed to determine the optimal saltwater extraction schedule. Chance-constrained (CC) programming is used to account for model selection uncertainty in probabilistic nonlinear concentration constraints. Akaike information criterion correction (AICc) based Bayesian model averaging (BMA) method is adopted to determine required statistics of concentrations in the chance constraints. Finally, a set of saltwater intrusion mitigation strategies are obtained through an interior point optimization algorithm. The proposed methodology is applied to the saltwater intrusion problem in Baton Rouge, Louisiana. A high-fidelity flow and solute transport model is developed, but requires 16 h for one model run. The study shows that the developed surrogate models perform well and have strong predictive capabilities with respect to the solute transport model response, while considerably reducing computation time. Including model selection uncertainty through multimodel inference and model averaging provides more reliable remediation strategies compared with the single-surrogate assisted approach.

A Simulation–Optimization Technique to Estimate Discharge in Open Channels Based on Water Level Data Alone: Gradually Varied Flow Condition

Discharge estimation in open-channel hydraulics is required for a variety of purposes including design of hydraulic structures, reservoir routing, water pricing and payment. This estimation procedure lasts for almost two centuries. Nowadays, research interest in discharge estimation methods based on the water level data alone has grown due to the drawbacks of direct velocity measurement. Recent trend in this field is switched from physical measurement to numerical by linking a gradually varied flow equation solver with either structured or random search optimization algorithm to obtain channel roughness and associated discharge. In this paper, in reference to the existing research gaps, the authors intend to evaluate the performance and accuracy of the above proposal for both M1 and M2 water surface profiles in both hypothetical and real-world settings. In this regard, a number of water surface profiles with various discharges, slopes and bed roughness were synthetically generated and/or measured on a very fine stream-wise grid in a laboratory flume for both M1 and M2 profiles. This enabled us to assess and investigate the impact of hydraulic conditions, simulation and optimization parameters on discharge estimation. The results show that, while synthetic water depth data on M1 and M2 can be effectively utilized to compute channel discharge, the discharge estimation based on laboratory data has a high accuracy for M2 profiles compared with M1 profiles. As the roughness coefficient and consequently the error in water level data measurements increases, the accuracy of the proposed method decreases. In addition, the aforementioned method has better results in steep waterways where the subcritical flow regime acquires high Froude number. Moreover, the results show that the values of initial guess and upper bounds of n and Q are not important in M2 water surface profiles. However, for M1, using initial guesses and upper bounds approximately near to the actual known’s n and Q gives better results. In addition, by increasing the distance between nodes and consequently decreasing the number of required water level data, the accuracy of discharge estimation decreases. In conclusion, both private and government sectors in water industry can benefit from water level monitoring to estimate discharge for various applications in real-world settings.

US Army performance appraisal policy analysis: a simulation optimization approach

An effective performance appraisal system is critical in identifying officers with the knowledge, skills, and abilities to lead the future military force. The US Army uses a forced distribution performance appraisal system that limits the number of above average evaluations raters can award to their subordinates. Aside from job performance, multiple factors contribute to the rating an individual receives in such systems. These factors include a rater’s span of control (the number of subordinates being rated), the frequency at which individuals change raters, regulatory constraints pertaining to the number of top evaluations a rater can award, and the rater behavior. Using performance appraisal data provided by the US Army Human Resources Command, we develop a discrete-event simulation model that represents Army officers in the current forced distribution performance appraisal system. We then apply ranking and selection simulation optimization techniques to evaluate and optimize controllable input parameters in the simulated system. Our results show the potential of reducing the number of officers not receiving the number of above average evaluations commensurate with their performance level by as much as 24%. The results also further indicate the general applicability of simulation optimization in the fields of manpower modeling and policy analysis.

Simulation-optimization techniques for closed-loop supply chain design with multiple objectives

El objetivo de este trabajo es determinar el diseño óptimo para una cadena de suministro de tres eslabones de acuerdo a consideraciones económicas, ambientales y de gestión de riesgo. Se plantea un modelo de programación entera mixta que busca simultáneamente: Primero, minimizar el costo total del transporte y el costo asociado al uso de nodos intermedios; Segundo, minimizar las pérdidas de producto en el transporte como factor de riesgo; Tercero, minimizar el impacto ambiental por emisiones de CO2 en cada una de las conexiones y nodos. El modelo se resuelve utilizando un método exacto y métodos de optimización vía simulación que permiten obtener distintas soluciones de tal manera que el usuario podrá escoger de acuerdo a sus prioridades. Experimentos en instancias aleatorias demuestran la capacidad de los modelos y métodos propuestos.

Wastewater treatment systems selection inside watersheds by using multiobjective analysis

ABSTRACT In order to determine required wastewater treatment plants (WWTP’s) efficiencies, several methodologies have been proposed to assist the sewage treatment systems process selection, usually aiming to achieve economic objectives. However, in water resources problems solving, search involves multiple and conflicting objectives. This work used, for Pardo’s river basin (Espírito Santo State, Brazil), water quality simulation model, optimization technique and multiobjective analysis for selecting sewage treatment systems, taking into account Brazilian environmental effluents Biochemical Oxygen Demand standards, imposed by CONAMA Resolution 430/2011. After proposing an optimization model that allowed the estimation of minimum organic matter removal efficiencies, a multiobjective analysis was applied by using the ELECTRE III method, which selected sewage treatment systems for localities responsible for domestic wastewater disposal in Pardo’s river basin. The results indicated that keeping the effluents standards can produce treatment plants overestimation and inadequate allocated financial resources distribution for sewage treatment plants implementation. Treatment systems such as Submerged Aerated Biofilters (with Nitrification), Wetlands and Facultative Ponds were the most selected, considering different contexts appreciated in this work.

LIQUID HOLDUP MANAGEMENT BY PREDICTING STEADY STATE TURNDOWN RATE IN WET GAS PIPELINE NETWORK

Now days, one of the greatest challenges in gas development is transport the fluid especially multiphase fluid to long distances and multiphase pipeline to sell point. Yet, a challenge to transport multiphase fluid is how to operate the systemsin operating a long distance, large diameter, and multiphase pipeline.The operating system include how to manage high liquid holdup, mainly built during low production rate (turn down rate) periods especially during transient operations such as restart and ramp-up, so that liquid surge arriving onshore will not exceed the liquid handling capacity of the slug catcher. The objective of this research is to predict liquid trapped in pipeline network by analysis turn down rate in order to determine minimal gas production rate for stable operation. This research was carried out by two steps: Simulation Approach and Optimization Techniques. Simulation approach include define fluid composition and built pipeline network configuration while optimization technique include conduct scenario for turn down rate. The fluid composition from wellhead to manifold is wet gas. First scenario and Second scenario of turndown rate yield minimum gas rate for stable operation. The pipeline has to be operated above 600 MMSCFD from peak gas production rate is 1200 MMSCFD (A-Manifold Mainline) and 60 MMSCFD from peak gas production rate is 150 MMSCFD for D-Manifold Mainline.

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

This paper proposes a shipbuilding schedule planning system based on the constant work-in-process (CONWIP) as a new way to deepen the schedule planning management and implement the lean shipbuilding. It fully discusses the forms, key elements, the ways and processes of implementation, etc. It demonstrates the advantages of the CONWIP system over the KANBAN system and the feasibility of applying the CONWIP system in shipbuilding by taking the flat section manufacturing of the shipbuilding as an example and applying the plant-simulation software to simulation analysis. We also provide the corresponding solutions to solve the problems, found in the application of the CONWIP system, and discuss the process and result of a simulation of the solutions. 1. Background The level and the structure of the shipbuilding production plan are relatively complex, and the derivative of the plan has many kinds of the species. After many years of researches and practices of shipbuilding enterprises in domestic and foreign, the types and classifications of the shipbuilding production plan have become consistent, which means that the shipbuilding production plan is composed of the three types of plans: basic plan, load plan, schedule plan. Basic plan is a programmatic plan in system, load plan is a system of protection plan, and schedule plan is implementation plan. Shipbuilding production planning system is shown in Table 1. Schedule plan usually decomposes the plan into each workstation and process, and then comes into the schedule plan of workstations and processes according to the general production task, the basic plan and building procedure. The plan mainly contains large schedule plan, medium schedule plan, and detailed schedule plan. Large schedule plan mainly refers to the construction of the main process planning, main material demand date, and comprehensive plan of each professional drawing; the medium schedule plan includes preinstallment medium schedule plan, the succeeding medium schedule plan, and installment medium schedule plan, which are based on the erection network scheduling; detailed schedule plan mainly refers to the monthly, weekly, and daily production plan which are made on the standard of the medium schedule plan by the production department. The traditional shipbuilding planning is top-down push plan system. In this system, it requires a complete production management data for production department from the design and planning departments. Load plan and schedule plan are strictly made and carried out on the basis of production load balance which conducted by working hours, capacity, and the data of energy consumption. However, because of the planning system in which monthly plan is made to meet the line table as a starting point, so it lacks of the balance of the actual state. Eventually, this plan system leads to a poor balance and operability, random plan execution, and a great deviation in the actual production situation of the system, because of these the scheduling production relies heavily on spot control. Therefore, during the current time when the lean shipbuilding idea is a consensus of the industry development, how to enhance the schedule planning management has become a key issue for the realization of the lean shipbuilding.

Comparative study of surrogate models for groundwater contamination source identification at DNAPL-contaminated sites

Knowledge of groundwater contamination sources is critical for effectively protecting groundwater resources, estimating risks, mitigating disaster, and designing remediation strategies. Many methods for groundwater contamination source identification (GCSI) have been developed in recent years, including the simulation–optimization technique. This study proposes utilizing a support vector regression (SVR) model and a kernel extreme learning machine (KELM) model to enrich the content of the surrogate model. The surrogate model was itself key in replacing the simulation model, reducing the huge computational burden of iterations in the simulation–optimization technique to solve GCSI problems, especially in GCSI problems of aquifers contaminated by dense nonaqueous phase liquids (DNAPLs). A comparative study between the Kriging, SVR, and KELM models is reported. Additionally, there is analysis of the influence of parameter optimization and the structure of the training sample dataset on the approximation accuracy of the surrogate model. It was found that the KELM model was the most accurate surrogate model, and its performance was significantly improved after parameter optimization. The approximation accuracy of the surrogate model to the simulation model did not always improve with increasing numbers of training samples. Using the appropriate number of training samples was critical for improving the performance of the surrogate model and avoiding unnecessary computational workload. It was concluded that the KELM model developed in this work could reasonably predict system responses in given operation conditions. Replacing the simulation model with a KELM model considerably reduced the computational burden of the simulation–optimization process and also maintained high computation accuracy.

Stochastic Nonlinear Programming Based on Uncertainty Analysis for DNAPL-Contaminated Aquifer Remediation Strategy Optimization

AbstractSurrogate-based simulation-optimization techniques are widely applied in developing optimal remediation strategies that increase the efficiency and reduce the cost of surfactant-enhanced aq...

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.