Simulation-based Optimization Problems Research Articles

A Sampling Strategy for High-Dimensional, Simulation-Based Transportation Optimization Problems

When tackling high-dimensional, continuous simulation-based optimization (SO) problems, it is important to balance exploration and exploitation. Most past SO research focuses on the enhancement of exploitation techniques. The exploration technique of an SO algorithm is often defined as a general-purpose sampling distribution, such as the uniform distribution, which is inefficient at searching high-dimensional spaces. This work is motivated by the formulation of exploration techniques that are suitable for large-scale transportation network problems and high-dimensional optimization problems. We formulate a sampling mechanism that combines inverse cumulative distribution function sampling with problem-specific structural information of the underlying transportation problem. The proposed sampling distribution assigns greater sampling probability to points with better expected performance as defined by an analytical network model. Validation experiments on a toy network illustrate that the proposed sampling distribution has important commonalities with the underlying and typically unknown true sampling distribution of the simulator. We study a high-dimensional traffic signal control case study of Midtown Manhattan in New York City. The results show that the use of the proposed sampling mechanism as part of an SO framework can help to efficiently identify solutions with good performance. Using the analytical information for exploration, regardless of whether it is used for exploitation, outperforms benchmarks that do not use it, including standard Bayesian optimization. Using the analytical information for exploration only yields solutions with similar performance than when the information is used for exploitation only, reducing the total compute times by 65%. This paper sheds light on the importance of developing suitable exploration techniques to enhance both the scalability and the compute efficiency of general-purpose SO algorithms. Funding: T. Tay thanks the Agency for Science, Technology and Research (A*STAR) Singapore for funding his work. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0110 .

An Optimal Computing Allocation Strategy for Swarm-Based Algorithm in Simulation-Based Optimization Problem with Stochastic Constraint

For realizing smart manufacturing, recent studies have been focusing on the capabilities of digital twins (DTs) that offer a virtual portrayal of a process or system and thereby facilitate real-time monitoring, analysis, and optimization. DTs construct a “digital model” of factories by using advanced technologies such as smart sensors, Internet of Things (IoT), artificial intelligence (AI) techniques, and optimization processes for executing critical decisions for enhancing productivity and predicting future events. Therefore, simulation-based optimization techniques play key supporting roles in the decision-making process of the DT shop. Thus, to enhance competitiveness in industrial companies, discourse on properly designing simulation-based optimization techniques begets significant study. This study aims to address a simulation-based optimization problem with stochastic constraint (SO[Formula: see text] using a hybrid particle swarm optimization (PSO). Given the large solution space, the PSO supports design space exploration. However, since all solutions must be verified for feasibility, the optimal computing allocation strategy (OCAS) strategy for SO[Formula: see text] is proposed to allocate the computing budget efficiently. OCAS is an improved version of optimal computing budget allocation for constrained optimization (OCBA-CO), specifically designed for complex systems with an expansive design space that possesses multiple local optimal solutions. During a typical PSO algorithm searching process, OCBA-CO is applied separately for each generation, resulting in a significant number of replications merely distinguishing similar solutions. To overcome this problem, our study proposes the PSO[Formula: see text], which combines PSO with OCAS to avoid wasting simulation runs on similar solutions. Two quantitative experiments are performed to assess the efficacy of PSO[Formula: see text] compared to other competitors. The simulation results indicate that the PSO[Formula: see text] algorithm presents a competitive and superior solution in terms of both quality and searching efficiency.

Contextual Bayesian optimization of congestion pricing with day-to-day dynamics

Congestion pricing is a common approach to alleviate urban traffic congestion. The design of second-best congestion pricing schemes is typically formulated as non-linear programming and bi-level optimization problems, where the lower-level problem involves either a static or dynamic network equilibrium model. The complexity of these bi-level toll optimization problems increases considerably when incorporating day-to-day dynamic models of travel behavior and dynamic models of network congestion. These models are often operationalized using simulation, and consequently, the toll design problem is a computationally challenging simulation-based optimization problem where the evaluation of a single candidate pricing scheme involves simulating the day-to-day model until convergence. In order to circumvent this issue, we propose a contextual Bayesian optimization (BO) framework, where the BO scheme is embedded within the day-to-day dynamic model by using temporal contextual information. The framework implicitly incorporates the relationship between the objective function across days and uses past days’ observations (function evaluations) as weak priors when constructing the Gaussian process underlying the BO algorithm for the current day’s toll optimization problem, resulting in gains in computational efficiency.The contextual BO approach is applied to the design of distance-based pricing schemes for the morning commute problem. We demonstrate numerically that the scheme converges to the system optimum, and moreover, utilizes a significantly smaller number of simulation evaluations (ten-fold reduction) than the standard approach wherein each function evaluation involves simulating the day-to-day model until convergence. From a policy perspective, we find that the distance-based schemes yield significant welfare gains relative to area-based schemes and show that the design of the distance-based tariff scheme can significantly affect distributional impacts. A suitably designed two-part tariff structure can partially offset the relatively large welfare losses of travelers with longer commute distances while maintaining overall welfare. The proposed contextual BO scheme is also extended to incorporate context specific demand and supply information, which can be of value to policy-makers when evaluating optimal toll design schemes under a wide range of scenarios in a computational tractable manner.

Multi-fidelity data-driven design and analysis of reactor and tube simulations

Optimizing complex reactor geometries is vital to promote enhanced efficiency. We present a framework to solve this nonlinear, computationally expensive, and derivative-free problem. Gaussian processes are used to learn a multi-fidelity model of reactor simulations correlating multiple continuous mesh fidelities. The search space of reactor geometries is explored through lower fidelity simulations, evaluated based on a weighted acquisition function, trading off information gain with cost. Within our framework, DARTS, we derive a novel criteria for dictating optimization termination, ensuring a high fidelity solution is returned before budget is exhausted. We investigate the design of helical-tube reactors under pulsed-flow conditions, which have demonstrated outstanding mixing characteristics. To validate our results, we 3D print and experimentally validate the optimal reactor geometry, confirming mixing performance. Our approach is applicable to a broad variety of expensive simulation-based optimization problems, enabling the design of novel parameterized chemical reactors.

A novel high-dimension shale gas reservoir hydraulic fracture network parameters optimization framework

Optimization design of hydraulic fracture network parameters significantly reduces the high investment risk in the production of shale gas. This is also a numerical simulation-based optimization problem. Surrogate-assisted optimization method which adopts a simple-yet-accurate approximate model to facilitate the optimization workflow is one of the most promising methods to lessen the computational burden. However, due to multi-stage fracturing and “well factory” production model, the dimension of variables in hydraulic fracture network parameters optimization increases sharply, and a significant number of data are required to ensure the performance of the surrogate model. To address this problem, a novel surrogate-assisted multi-objective optimization method using the tri-training strategy and variable-length non-dominated sorting genetic algorithm-II (VL-NSGA-II) is proposed for high-dimension hydraulic fracture network parameters optimization in shale gas reservoir. The proposed method is called HDWHF. In this HDWHF method, the tri-training strategy is adopted to infill solutions with the highest-confidence fitness as new samples to retrain the surrogate model during each iteration while VL-NSGA-II is adopted as the optimizer. This strategy enables the increase of samples without numerical simulation runs, which realizes a decrease of the numerical simulation runs and enhance the performance of the surrogate model. The superior performance of the HDWHF against conventional surrogate-assisted methods is verified using three high-dimension examples with various numbers of wells and fracture types. This proposed HDWHF workflow provides an intelligent means for efficient optimization and decision-making of high-dimension hydraulic fracture network parameters in shale gas reservoirs.

Macroscopic parking dynamics and equitable pricing: Integrating trip-based modeling with simulation-based robust optimization

Characterizing the integrated traffic and parking dynamics in a coupled road-parking system is challenging due to the interactions between parking and non-parking vehicles and the presence of cruising-for-parking. In this paper, we propose a new trip-based macroscopic model of parking considering various sources of user heterogeneity that are key to modeling parking. The macroscopic or network fundamental diagram is used to describe the accumulation-based network speed at which vehicles are traveling. An efficient event-based simulation algorithm is proposed as the resolution scheme for the trip-based model. Numerical results reveal that after calibration, the proposed trip-based model yields similar results to those obtained using an accumulation-based approach and microscopic simulation. Taking the trip-based model as the simulation engine, we formulate a simulation-based robust optimization problem of equitable duration-based parking pricing. To solve this problem with simulation stochasticity, a new global optimizer termed NoisyDIRECT is proposed as an open-source solution algorithm. This algorithm automatically identifies the level of simulation stochasticity for each decision vector across the search space, based on which the variable number of simulation runs per point is determined. Thus, the computational resource can be better allocated so that the optimal solution can be found in a more computationally efficient and reliable manner. Numerical results demonstrate that NoisyDIRECT provides better solutions than those yielded by fixed-number sample-path optimization and non-robust optimization methods.

Simulation-based optimization considering energy consumption for assisted station locations to enhance flex-route transit

As an emerging mode of public transit, flex-route transit (FRT) is regarded as the key to reducing energy consumption and promoting sustainable development in low-density suburban cities. However, its performance in cases with unexpectedly high demand levels is usually unsatisfactory. To address this issue, a simulation-based optimization method is proposed for assisted station locations in FRT and to reduce the system costs, including that for energy consumption. First, considering the uncertain travel demands of passengers, this location problem is modelled as a simulation-based optimization (SO) problem based on Monte Carlo simulation (MCS). Then, a multiple metamodel-based efficient global optimization (MMEGO) algorithm is proposed to effectively solve the SO problem. The numerical results demonstrate that the proposed MMEGO algorithm can obtain good results with lower computing cost and stronger robustness than counterpart algorithms (EGO and MISK). Finally, simulation tests of FRT based on data from the operation of 320 buses in Shenzhen city and the related OD data are conducted to test the feasibility of the proposed method. The results show that the proposed optimization method can be used to identify suitable locations for assisted stations and reduce the total system cost by 26.6% compared with that for an FRT system with unoptimized assisted station locations.

Biobjective robust Network-wide traffic signal optimization against Cyber-attacks

In the cyber age, modern traffic signal control systems are inevitably exposed to a wide range of cyber-attacks, which would negatively impact traffic system performance. To defend against direct cyber-attacks that alter signal settings directly and maliciously by hacking into the wireless connected traffic control center or signal controllers, this study applies a precautionary and inexpensive measure of designing the robust signal control plan. This is realized by modelling the biobjective traffic signal control simulation-based optimization (SO) problem under cyber-attacks in a bi-level framework, with the aim of minimizing the negative impact of potential cyber-attacks on traffic efficiency and safety performance. Depending on the type of attacker with complete or incomplete knowledge on traffic signal control system, two typical bi-level models exist. Then, we propose an improved biobjective robust simulation-based optimization (IBORSO) algorithm to solve the two models, during which the sequential interaction between the attacker and the defender is seen as the Stackelberg game, and the resulting equilibrium solutions are the desired robust signal control plans to withstand cyber-attacks. An urban road network in Changsha, China, is modelled as the experimental site and numerical results show the effectiveness of IBORSO in solving the two bi-level models. Meanwhile, the solved robust signal control plans can defend against two typical attacks satisfactorily and achieve better or comparable biobjective performance compared with the field-implemented one and optimized ones by biobjective efficient global optimization (BOEGO).

A Kriging-based optimization method for meeting point locations to enhance flex-route transit services

As a promising on-demand transportation mode in low-demand areas, flex-route transit, has attracted much attention in the transportation research field. However, unexpectedly high demand levels caused by travel uncertainty impact the reliability and development of flex-route transit services. Although the meeting point strategy can deal with this problem effectively, selecting a location for the meeting points can substantially influence the performance of this strategy. In this study, meeting point location selection is modeled as a simulation-based optimization (SO) problem, and a Kriging-based global optimization method using a Pareto-based multipoint sampling strategy (KGO-PS) is proposed to solve this problem. Through comparison of several typical benchmark functions with other counterparts, the effectiveness of KGO-PS has been verified. Moreover, a real-life flex-route transit service is employed to construct the SO problem, and the optimization results show that the proposed algorithm can improve the performance of flex-route transit services under unexpectedly high demand levels.

Adaptive scenario subset selection for worst-case optimization and its application to well placement optimization

In this study, we consider simulation-based worst-case optimization problems with continuous design variables and a finite scenario set. To reduce the number of simulations required and increase the number of restarts for better local optimum solutions, we propose a new approach referred to as adaptive scenario subset selection (AS3). The proposed approach subsamples a scenario subset as a support to construct the worst-case objective function in a given neighborhood, and we introduce such a scenario subset. Moreover, we develop a new optimization algorithm by combining AS3 and the covariance matrix adaptation evolution strategy (CMA-ES), denoted AS3-CMA-ES. At each algorithmic iteration, a subset of support scenarios is selected, and CMA-ES attempts to optimize the worst-case objective computed only through a subset of the scenarios. The proposed algorithm reduces the number of simulations required by executing simulations on only a scenario subset, rather than on all scenarios. In numerical experiments, we verified that AS3-CMA-ES is more efficient in terms of the number of simulations than the brute-force approach and a surrogate-assisted approach lq-CMA-ES when the ratio of the number of support scenarios to the total number of scenarios is relatively small. In addition, the usefulness of AS3-CMA-ES was evaluated for well placement optimization for carbon dioxide capture and storage (CCS). In comparison with the brute-force approach and lq-CMA-ES, AS3-CMA-ES was able to find better solutions because of more frequent restarts.

Gaussian Process Regression for Transportation System Estimation and Prediction Problems: The Deformation and a Hat Kernel

Gaussian process regression (GPR) is an emerging machine learning model with potential in a wide range of transportation system estimation and prediction problems, especially those where the uncertainty of estimation needs to be measured, for instance, traffic flow analysis, the transportation infrastructure performance estimation problems and transportation simulation-based optimization problems. The kernel function is the core component of GPR, and the radial basis function (RBF) kernel is the most commonly used one, suitable for tasks without special knowledge about the patterns of data, like trend and periodicity. However, an inappropriate hyperparameter of the kernel function may lead to over-fitting or under-fitting of GPR. During hyperparameter optimization, the usage of the RBF kernel often suffers from the issue of failing to find the optimal hyperparameter. This paper aims to address this problem by promoting the use of the hat kernel, which can reduce the risk of under-fitting. Moreover, we propose the notion of deformation, corresponding to severe over-fitting of a GPR. To further address this issue, we investigate the connection between deformation and the Bayesian generalization error of GPR. Two lower bounds for the hyperparameter of the hat kernel are also proposed to avoid deformation of GPR.

Time-of-day pricing for toll roads under traffic demand uncertainties: A distributionally robust simulation-based optimization method

Road pricing has received increasing attention for traffic congestion mitigation, but traffic demand uncertainties cause a profound impact on its effectiveness. This study proposes a link-based time-of-day pricing distributionally robust simulation-based optimization (DRSO) model to minimize the worst-case expected total travel time (TTT). This model aims to answer where and how much to charge under origin–destination (OD) traffic demand uncertainties that are described by an ambiguity set of probability distributions, and is solved by a DRSO method with two stages. The first stage searches an infill sample after building two stochastic kriging models (i.e., SKG-I and SKG-II) to map the relation between the traffic demand uncertainty parameter and the average TTT for each decision sample, and that between the decision variable and the worst-case average TTT, respectively. The second stage introduces the optimal computational budget allocation (OCBA) method to distribute more simulation replications to more promising combination samples, which helps to improve the prediction accuracy of SKGs in promising regions. The DRSO method is first tested with an M/M/1 queuing problem, and numerical results show its outperformance compared with one counterpart algorithm. After that, it is applied to address the link-based time-of-day pricing DRSO problem on Anaheim network and two solution algorithms are also added for comparison. Numerical results show that the robust toll plan solved by the DRSO method outperforms no toll plan and two other toll plans solved by the comparison algorithms in handling traffic dynamics and different levels of traffic demand uncertainties. In conclusion, the DRSO method is promising to address the costly simulation-based optimization (SO) problems under uncertainties characterized by an ambiguity set of probability distributions.

Bayesian optimization techniques for high-dimensional simulation-based transportation problems

Bayesian optimization (BO) is an attractive method for tackling transportation optimization problems due to its ability to balance exploitation and exploration. However, scaling BO to solve high-dimensional problems is a major challenge which has remained unsolved. Since transportation problems can be high-dimensional, the use of BO to solve transportation problems has been limited. This paper explores the use of BO with Gaussian process (GP) models to tackle high-dimensional transportation problems. It proposes formulations of the prior mean function and covariance function of the GP that enable BO to incorporate problem-specific transportation information, while remaining computationally tractable. This is done through the use of an analytical surrogate model. We validate the method with the 1-D and 100-D Griewank functions. The impact of different forms of bias in the surrogate model was also evaluated using the 100-D Griewank function example. We then apply the method to a high-dimensional traffic signal control problem in New York City. The results indicate the added value of using the problem-specific information in the prior mean and/or the covariance function. Importantly, for surrogate models that are not accurate approximations of, but have significant (anti-)correlation with the true objective function, the better approach is to embed the information in the covariance function, rather than in the prior mean function. More generally, the use of problem-specific information in the covariance function is robust to the accuracy of the surrogate model. This opens the way for a variety of low-resolution analytical transportation models to be used to tackle high-dimensional simulation-based optimization problems.

Multiobjective ensemble surrogate-based optimization algorithm for groundwater optimization designs

• A novel surrogate-based algorithm is proposed for groundwater multiobjective designs. • The adaptive switching technique is proposed to dynamically build the best surrogate. • Three sampling criteria with a population filter improve the ability of the algorithm. • Benchmark and practical cases test the performance of the proposed algorithm. Simulation technique is an increasingly focused method for conveniently evaluating a solution or scenario in the field of groundwater. However, traditional evolutionary algorithms require at least thousands of simulation executions when addressing groundwater simulation-based optimization problems to find reasonable solutions. Intensive simulations usually yield a prohibitive computational burden if the simulation involved is time-consuming. To defeat the issue, this paper proposes a multiobjective ensemble surrogate-based optimization algorithm named MESOA for groundwater optimization designs. Surrogate models can reduce the continual usage of expensive-cost models in a way that approximates objective functions. Unlike existing surrogate-assisted evolutionary algorithms, MESOA employs surrogates with various basis functions (RBFs) and Kriging as available surrogates, and presents an adaptive switching technique to construct surrogate models in an online way. In addition, MESOA involves three sample infill criteria and a novel population filter. With the assistance of these techniques, MESOA can fully depict the outline of the true Pareto front, although the times of invoking simulation are limited. Some representative benchmark cases are provided to test the applicability and effectiveness of the proposed algorithm at first. Afterward, MESOA is applied to solve some practical groundwater multiobjective optimization designs, such as groundwater remediation and requirement optimization. All empirical results indicate that the proposed algorithm obtains more availability and effectiveness than other algorithms and has wide universality for groundwater optimization designs.

Improvement of Maneuverability Within a Multiagent Fuzzy Transportation System With the Use of Parallel Biobjective Real-Coded Genetic Algorithm

Over the past two decades, several simulation-based approaches have been developed to seek optimal solutions in complex multiagent systems (MASs). One example of these complex systems is the proposed multiagent fuzzy transportation system (FTS), in which agents, such as manned ground vehicles (MGVs) and unmanned ground vehicles (UGVs), use fuzzy logic for maneuvering while interacting with each other. The complexity of an FTS as an optimization problem is caused by two major factors, viz., inability to apply gradient-based optimization methods and uncertainty present in the FTS model. In these MASs, the objective functions are computed as an outcome of multiple agent-to-agent interactions. For the proposed FTS, two interconnected objectives are of prime importance while modeling the system: the number of potential traffic accidents, which should be minimized, and the output traffic that needs to be maximized. The resulting model is a simulation-based biobjective optimization problem. In an attempt to improve the maneuverability at various road network configurations (RNCs), in this work, a new parallel real-coded genetic algorithm based on fuzzy clustering (FCGA) is proposed that considers the specific requirements of the FTS model. Use of the FCGA aggregated with the FTS overcomes the difficulties associated with the optimization model and the limitations caused by simulation-based optimization (the large dimensionality of decision space, uncertainty present in the MAS environment, etc.). After showing superior performance in approximating the Pareto-optimal solutions for known test instances, the proposed FCGA is applied to the proposed multiagent FTS to approximate the optimal parameters. Because of the optimization of FTS parameters with the FCGA and the use of the proposed fuzzy clustering algorithm (FCA) for the traffic density estimate, the suggested approach substantially improves the maneuverability of UGVs and MGVs to change lanes while considering turning based on fuzzy rules and overtaking.

Global Optimization Algorithm Based on Kriging Using Multi-Point Infill Sampling Criterion and Its Application in Transportation System

Public traffic has a great influence, especially with the background of COVID-19. Solving simulation-based optimization (SO) problem is efficient to study how to improve the performance of public traffic. Global optimization based on Kriging (KGO) is an efficient method for SO; to this end, this paper proposes a Kriging-based global optimization using multi-point infill sampling criterion. This method uses an infill sampling criterion which obtains multiple new design points to update the Kriging model through solving the constructed multi-objective optimization problem in each iteration. Then, the typical low-dimensional and high-dimensional nonlinear functions, and a SO based on 445 bus line in Beijing city, are employed to test the performance of our algorithm. Moreover, compared with the KGO based on the famous single-point expected improvement (EI) criterion and the particle swarm algorithm (PSO), our method can obtain better solutions in the same amount or less time. Therefore, the proposed algorithm expresses better optimization performance, and may be more suitable for solving the tricky and expensive simulation problems in real-world traffic problems.

Biobjective robust simulation-based optimization for unconstrained problems

We propose a biobjective robust simulation-based optimization (BORSO) method to solve unconstrained problems involving implementation errors and parameter perturbations. We adopt the notion that a solution is robust efficient (RE) if the region that dominates its worst-case realizations of the biobjectives under uncertainty does not contain (all) the worst-case realizations of the biobjectives of any other solution under uncertainty. Our algorithm aims to efficiently find a set of RE solutions through a series of function evaluations or simulations. First, we design surrogate-model guided search mechanisms for the worst-case neighbors of the current iterate. Subsequently, we determine the iteration distance along an effective local move direction, which excludes the worst-case neighbors from the neighborhood of the new iterate. Depending on the practical need for solution diversity, multiple initial solutions can be specified in our algorithm, and the final iterates of these solutions generate a set of RE solutions. The test results of a synthetic biobjective nonconvex optimization problem show the effectiveness of the BORSO method and its superior performance against a sampling-based robust multiobjective optimization solver. Furthermore, when the proposed algorithm is applied to a real-world biobjective traffic signal timing problem, satisfactory solutions can be obtained under a limited computational budget. These results indicate that the proposed BORSO method can solve unconstrained biobjective simulation-based optimization problems under uncertainties.

An adaptive direct multisearch method for black-box multi-objective optimization

At present, black-box and simulation-based optimization problems with multiple objective functions are becoming increasingly common in the engineering context. In many cases, the functional relationships that define the objective and constraints are only known as black-boxes, cannot be differentiated accurately, and may be subject to unexpected failures. Directional direct search techniques, in particular the direct multisearch (DMS) methodology, may be applied to identify Pareto fronts for such problems. In this work, we propose a mechanism for adaptively selecting search directions in the DMS framework, with the goal of reducing the number of black-box evaluations required during the optimization. Our method relies on the concept of simplex derivatives in order to define search directions that are optimal for a local, linear model of the objective function. We provide a detailed description of the resulting algorithm and offer several practical recommendations for efficiently solving the associated subproblems. The overall performance in an academic context is assessed via a standard benchmark. Through a realistic case study, involving the bi-objective design optimization of a mechatronic quarter-car suspension, the performance of the novel method in a multidisciplinary engineering setting is tested. The results show that our method is competitive with standard implementations of DMS and other state-of-the-art multi-objective direct search methods.

Computationally Efficient Approximations Using Adaptive Weighting Coefficients for Solving Structural Optimization Problems

With rapid development of advanced manufacturing technologies and high demands for innovative lightweight constructions to mitigate the environmental and economic impacts, design optimization has attracted increasing attention in many engineering subjects, such as civil, structural, aerospace, automotive, and energy engineering. For nonconvex nonlinear constrained optimization problems with continuous variables, evaluations of the fitness and constraint functions by means of finite element simulations can be extremely expensive. To address this problem by algorithms with sufficient accuracy as well as less computational cost, an extended multipoint approximation method (EMAM) and an adaptive weighting-coefficient strategy are proposed to efficiently seek the optimum by the integration of metamodels with sequential quadratic programming (SQP). The developed EMAM stems from the principle of the polynomial approximation and assimilates the advantages of Taylor’s expansion for improving the suboptimal continuous solution. Results demonstrate the superiority of the proposed EMAM over other evolutionary algorithms (e.g., particle swarm optimization technique, firefly algorithm, genetic algorithm, metaheuristic methods, and other metamodeling techniques) in terms of the computational efficiency and accuracy by four well-established engineering problems. The developed EMAM reduces the number of simulations during the design phase and provides wealth of information for designers to effectively tailor the parameters for optimal solutions with computational efficiency in the simulation-based engineering optimization problems.

A Data-Driven Method for Reconstructing a Distribution from a Truncated Sample with an Application to Inferring Car-Sharing Demand

This paper proposes a method to recover an unknown probability distribution given a censored or truncated sample from that distribution. The proposed method is a novel and conceptually simple detruncation method based on sampling the observed data according to weights learned by solving a simulation-based optimization problem; this method is especially appropriate in cases where little analytic information is available but the truncation process can be simulated. The proposed method is compared with the ubiquitous maximum likelihood estimation (MLE) method in a variety of synthetic validation experiments, where it is found that the proposed method performs slightly worse than perfectly specified MLE and competitively with slightly misspecified MLE. The practical application of this method is then demonstrated via a pair of case studies in which the proposed detruncation method is used alongside a car-sharing service simulator to estimate demand for round-trip car-sharing services in the Boston and New York metropolitan areas.