Simulation-optimization Model Research Articles

Machine learning-based rainfall forecasting in real-time optimal operation of urban drainage systems

This paper presents a rainfall forecast-based real-time optimal operation model of urban drainage systems (UDSs) investigating the role of rainfall forecasts in predictive real-time operation of UDSs. Two rainfall forecast models, namely long short-term memory (LSTM) and optimized-by-PSO (particle swarm optimization) support vector regression, are linked to an integrated simulation–optimization model. For any given operation policies, the rainfall-runoff stormwater management model (SWMM) simulates the urban system’s operation under flooding conditions in which the operation policies are optimized by the metaheuristic harmony search (HS) optimizer. The objective function of the HS algorithm is minimizing flood volumes at control points. The presented approach is therefore a dynamic predictive real-time operation (PRTOP) model that continuously updates rainfall forecasts and operational decisions during flood events. The performance of the developed forecast-based integrated SWMM-HS simulation–optimization modeling framework is tested through its application in a real urban system case study located in Tehran, Iran. Results demonstrate that a notable reduction in flood volume (11.8%) is achieved by using the LSTM-driven rainfall forecasts in the proposed PRTOP model compared to a reactive real-time operation (RTOP) model not utilizing rainfall forecasts.

Evaluation of intensive remediation using simulation-optimization modeling based on long-term monitoring at a DNAPL contaminated site

Simulation-optimization modeling is extensively used to identify optimal remediation designs. However, verifying these optimal solutions often remains unclear. In this study, we determine optimal groundwater remediation strategies using simulation-optimization modeling and assess the effectiveness of previous remediation efforts by validating optimized results through 14 years of long-term monitoring of trichloroethylene (TCE) contamination. The study site is the Road Administrative Office (RAO) in Wonju, Korea, where significant TCE contamination has occurred, and long-term in-situ remediation and monitoring have been conducted. We employ MODFLOW for simulating groundwater flow and MT3D for modeling dissolved TCE concentration distribution. The Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is applied to derive optimal groundwater remediation designs. Initial simulation results effectively predicted long-term TCE contamination trends and the impact of short-term in-situ remediation. Our evaluation involved comparing these optimal designs with field test outcomes, leading to the integration of continuous intensive pump-and-treat with in-situ remediation strategies. By comparing various modeling scenarios against long-term TCE contamination trends, we confirmed the effectiveness of previous remediation efforts and demonstrated that the optimal remediation design substantially minimized TCE concentrations at the main source zone. This study highlights successful strategies in historical contamination and remediation trend assessments, proposing an optimal design for pump-and-treat with reduced pumping stress to manage remaining TCE contamination at the site effectively.

An integrated simulation-optimization modeling approach for coupled risk management of blue water and green water under changing environmental conditions

Due to the impact of global climate change and land use changes, the spatiotemporal distribution and water cycle processes of watershed water resources are affected. In order to sustainably manage watershed water resources, it is necessary to accurately assess the available water volume to meet the coordinated management of the watershed's socio-economic subsystem and eco-environment subsystem. The amount of Bluewater resources (BW) is closely related to the amount of Greenwater resources (GW). BW is directly related to human consumption in the socio-economic subsystem, while GW is used to maintain the health of the ecosystem. A method called the integrated simulation-optimization modeling system (ISOMS) was developed to evaluate adaptive strategies for dealing with the combined effects of climate change and land-use changes. ISOMS not only predicts future hydrological trends under varying environmental conditions but also generates comprehensive risk management plans that incorporate different types of uncertainties, including random and fuzzy factors. The Copula function is introduced to handle the interaction between available BW and available GW. The results showed that: (i) uncertainties in the hydrologic system could result in alterations to the distribution of water resources; (ii) system benefits are, to some extent, affected by land use change and climate change; (iii) the shortage of BW is affected by the level of risk, and the joint risk increases, resulting in an increase in water scarcity; (iv) the planned annual agricultural water consumption is the highest, followed by domestic water consumption, and industrial water consumption is the lowest. Through the results of the model operation, the joint risk assessment and adaptive management of BW and GW in the East River Basin (ERB) under changing environments, as well as the BW allocation plan, are obtained to provide support for scientific and reasonable resource collaborative decision-making and promote the synchronized advancement of the socio-economic subsystem and eco-environment subsystem in the ERB.

Adaptive Operating Rules for Flood Control of a Multi-Purpose Reservoir

Almost all multipurpose reservoirs in Romania were put into operation 30–50 years ago or even earlier. Meanwhile, a large volume of hydrologic data has been collected, and the initial design flood should be reconsidered. Consequently, the operating rules of flow control structures (bottom gates and weir gates) should be re-examined, mainly for medium and low-frequency floods. The design flood is not unique, being characterized by different shapes and time to peak, which has consequences for flood mitigation rules. Identifying the critical design flood is an important preliminary step, although it is usually neglected in flood management. Simulating the operation of the Stânca–Costești reservoir on the Prut River, it was found that the design flood corresponding to the maximum value of the compactness coefficient is the most difficult to mitigate considering the specific conditions of the dam and the reservoir: the prescribed conservation level in the reservoir, and the design flood volume of medium and rare floods that far exceeds the flood control volume. These conditions can jeopardize both dam safety and downstream flood protection. The main steps of the proposed approach are as follows: (1) developing the hydraulic model; (2) statistical processing of the registered floods and defining critical design floods for different AEPs (Annual Exceedance Probabilities); (3) deriving optimal operation rules based on a simulation-optimization model; (4) implementing real-time adaptive operation of the mechanical outlets; and (5) critically assessing the operating rules after the event. Based on the hydrological forecast, if necessary, new outlets are put into operation while keeping the ones already activated. Based on the hydrological forecast and properly operated, the safety of the Stânca–Costești dam is guaranteed even in the event of a 0.1% CC (Climate Change) flood. However, for floods greater than 1% magnitude, the carrying capacity of the downstream riverbed is exceeded. The main gaps addressed in this paper are the following: (1) the establishment of critical design floods, and (2) the adaptive operating rules of outlet devices aimed at optimizing flood control results, using short-term flood forecasts.

A Review on the Optimization of Irrigation Schedules for Farmlands Based on a Simulation–Optimization Model

Agriculture is the most important sector that is consuming water resources. In the context of global water scarcity, how to use limited water resources to improve water use efficiency in agriculture or achieve maximum crop yield and fruit quality is of great significance for ensuring food and water security. Optimizing irrigation schedules is an effective measure to improve water use efficiency, where crop models also play an important role. However, there is little research summarizing the optimization of irrigation schedules based on crop models. This study provides a systematic review on how to optimize irrigation schedules based on crop models and simulation–optimization models. When optimizing irrigation schedules based on crop models, the selected models are usually mechanistic agro-hydrological models. Irrigation scenarios and optimization objectives are mainly focused on both crop and water aspects, such as maximizing crop yield, fruit quality, water productivity, and irrigation water productivity. Minimizing crop water consumption and total irrigation amounts serve as optimization objectives, and irrigation quantity, irrigation frequency, and irrigation interval serve as decision variables. In saline areas or low fertilizer utilization areas, the optimization objectives and decision variables also involve some indicators related to salt and nitrogen, such as the maximum desalination rate, minimum salt content, fertilizer utilization efficiency, nitrogen fertilizer productivity, nitrogen fertilizer utilization efficiency, nitrogen leaching rate, which serve as the optimization objectives, and the irrigation water salinity, or fertilization schedules serve as the decision variables. When optimizing irrigation schedules based on simulation–optimization models, the models have mainly been upgraded from water-production function to crop mechanism models. In addition, optimization algorithms have been upgraded from traditional optimization techniques to intelligent optimization algorithms. Decision-making techniques are used to make decisions on optimization results. In addition, the spatial scale for the optimization problem of irrigation schedules was developed from fields to regions, and the time scale was developed from the growth stage, beginning with months, and shortening to ten days, then to a day, and then to an hour. This study also provides a detailed introduction to widely used optimization algorithms, such as genetic algorithms, as well as decision techniques. At the same time, it is proposed that the future should focus on improving crop models and analyzing uncertainty in research on irrigation schedule optimization, which is of great significance for the precise regulation of irrigation schedules.

A prescriptive analytics approach for evaluating two production systems: Simulation optimization algorithm

Production systems influence cost performance and carbon emissions. Environmental concerns compel companies to optimize energy efficiency in their production processes. This study explores the dilemma associated with fixed-time and fixed-lot systems during random disruptions and how these systems can improve performance. We employ simulation optimization models in business analytics using the discrete event simulation provided by the SimPy library within a Python environment. The study is based on the statistical analysis of data collected from 624,000 simulated hours. Our analysis reveals that a higher service level tilts the balance, favoring adopting a fixed-time production system in scenarios characterized by significant disruptions. A system with higher demand variability and lower standalone workstation availability (indicative of more variable production) tends to favor a fixed-time batch production approach. When workstations operate at low-capacity utilization combined with high standalone availability, the fixed-lot batch production system becomes more cost-effective. Overall, the fixed-time system demonstrates a superior capacity to accommodate higher production variability levels than the fixed-lot system. This paper contributes to the existing literature by providing simulation-optimization evidence to assess the relative efficiencies of fixed-size and fixed-time lot batch production systems. This paper considers the impact of random disruptions on operational efficiency within fixed-size lot batch production systems, highlighting the consequences of variability in lot completion times. The study also contributes to strategically selecting production systems to optimize energy usage in manufacturing processes.

Investigations of the Laser Ablation Mechanism of PMMA Microchannels Using Single-Pass and Multi-Pass Laser Scans.

CO2 laser machining is a cost effective and time saving solution for fabricating microchannels on polymethylmethacrylate (PMMA). Due to the lack of research on the incubation effect and ablation behavior of PMMA under high-power laser irradiation, predictions of the microchannel profile are limited. In this study, the ablation process and mechanism of a continuous CO2 laser machining process on microchannel production in PMMA in single-pass and multi-pass laser scan modes are investigated. It is found that a higher laser energy density of a single pass causes a lower ablation threshold. The ablated surface can be divided into three regions: the ablation zone, the incubation zone, and the virgin zone. The PMMA ablation process is mainly attributed to the thermal decomposition reactions and the splashing of molten polymer. The depth, width, aspect ratio, volume ablation rate, and mass ablation rate of the channel increase as the laser scanning speed decreases and the number of laser scans increases. The differences in ablation results obtained under the same total laser energy density using different scan modes are attributed to the incubation effect, which is caused by the thermal deposition of laser energy in the polymer. Finally, an optimized simulation model that is used to solve the problem of a channel width greater than spot diameter is proposed. The error percentage between the experimental and simulation results varies from 0.44% to 5.9%.

A circular economy integration approach into vertical farming with computer-based simulation model for resource optimization and waste reduction

Integrating circular economy into vertical farming holds promises to transform the current economic landscape and bolster environmental sustainability. However, knowledge gaps persist regarding the efficiency of vertical farming methods within the circular economy context, particularly in the development of a simulation model aimed at resource optimization and waste reduction. This study aimed to analyze the vertical farming methods in the circular economy framework and introduce a zero-waste circular economy model both experimentally and through simulation. In the study, a circular economy model was proposed and implemented, utilizing waste generated from hydroponics and aquaponics, alongside vermicompost tea and aerobic compost tea/quick compost, as nutrients for six distinct halophyte plants in the secondary farming setup. The model serves as a valuable tool for identifying and assessing critical factors influencing the circular economy principle, as well as for optimizing resource allocation and management. Over six weeks, plant growth was monitored and compared across the vertical farming methods. Results show that both hydroponic and aquaponic waste proved superior growth compared to the alternatives, highlighting vertical farming's potential for the circular economy. Using the results from the experimental observation, a simulation model was developed. The simulation model's accuracy was validated by comparing outcomes with actual data. Furthermore, the model was used to optimize for balancing resources and capacities in each stage of vertical farming. The vertical farming circular economy model proposed in this study can serve as a blueprint for agriculturalists and decision-makers to adopt more sustainable and efficient farming practices that not only reduce waste but also generate profits.

An optimization-simulation modeling framework for exploring the dynamic correlation between the coordinated development of the human-nature nexus and land use under uncertainty

Sustainable land use optimization (LUO) requires simultaneously reconciling human development and nature conservation, and effectively adapting to changes in socioeconomic development. We proposed a modeling framework that includes stochastic simulation of population and GDP uncertainty, multidimensional LUO, and spatial land use simulation combining random forests and cellular automata. The framework can (1) balance the multidimensional contradictory conflicts between human-nature nexus (HNN); (2) obtain the dynamic correlation and probability distribution of land use with the dimensional objectives and the HNN; and (3) obtain the dynamic response of land use to changes in socioeconomic development and the elasticity space of land use. The framework was applied to Hubei Province in the middle reaches of the Yangtze River. The results indicated that by 2030, the land use intervals that can cope with uncertainty and achieve high levels of HNN coordination of 0.66–0.69 at 51,500–55,500 (km2) for cultivated land, 47,750–49,750 (km2) for forestland, 1,950–2,350 (km2) for grassland, 9,300–10,100 (km2) for construction land, 11,249–11,418 (km2) for water area, and 285–307 (km2) for unused land. This study elucidates the complex dynamic correlation between HNN and land use, and provides regions with LUO decisions that can respond to changing conditions and promote a harmonious HNN development.

A novel integration of regret-based methodology and bankruptcy theory for waste load allocation.

Developing a suitable index for Waste Load Allocation (WLA) is essential for both industrial polluters and environmental organizations. Identifying the index that best describes the quality conditions of the river is the main concern of this study. To achieve this purpose, a novel framework incorporating a regret-based index and a bankruptcy-based approach to address the impacts of low water quality and pollutant locations within the WLA are introduced. The framework includes a simulation-optimization model to minimize river quality regret for environmental organizations and total treatment cost for industrial polluters, employing Nash bargaining theory for conflict resolution. Additionally, a new bankruptcy approach, the Namin's rule, is proposed for redistributing the River Quality Regret Index among industrial polluters. Applying this methodology to data from the KhoramAbad River, a sensitivity analysis reveals that while there is no significant difference between the methodology and fuzzy risk when polluters are close, the methodology provides more accurate results as the distance between polluters increases. When the distance between two pollutants was 20km, the sum of WLA was evaluated to be 300kg per day higher than that in the compared method, potentially enhancing environmental justice.

Determining the factor levels for a green supply chain using response surface methodology based discrete event simulation

PurposeThis study aims to optimize the levels of factors for a green supply chain (GSC) while concurrently gaining valuable insights into the dynamic interrelationships among several factors, leading to reductions in CO2 emissions and the maximization of the average service level, thereby enhancing overall supply chain performance.Design/methodology/approachResponse surface methodology (RSM) is employed as a technique for multiple response optimization. This study uses a supply chain simulation model that includes decision variables related to the level of inventory control parameters and vehicle capacity. The desirability approach is adopted to achieve optimization objectives by focusing on minimizing CO2 emissions and maximizing service levels while simultaneously determining the optimum levels of considered decision variables.FindingsThe high R2 values of 97.38% for CO2 and 97.28% for service level, along with adjusted R2 values reasonably close to predicted values, affirm the models' capability to predict responses accurately. Key significant model terms for CO2 encompassed reorder point, order up to quantity, vehicle capacity, and their interaction effects, while service level is notably influenced by reorder point, order up to quantity, and their interaction effects. The study successfully achieved a high level of desirability value of %99.1 and the validated performance levels confirmed that the results fall within the prediction interval.Originality/valueThis study introduces a metamodel framework designed to optimize various design parameters for a GSC combining discrete event simulation (DES) and RSM in the form of a simulation optimization model. In contrast to the literature, the current study offers an exhaustive and in-depth analysis of the structural elements of the supply chain, particularly the inventory control parameters and vehicle capacity, which are crucial for comprehending its performance and environmental impact.

A Simulation–Optimization Model for Optimal Aquifer Remediation, Using Genetic Algorithms and MODFLOW

This paper investigates the optimal remediation process in an aquifer using Modflow 6 software and genetic algorithms. A theoretical confined aquifer has been polluted over a long period of time by unnoticed leakage in a pipeline conveying leachate from an adjacent landfill to a wastewater treatment plant. When the extended leakage and groundwater pollution are discovered, the optimal planning of the remediation strategy is investigated using the pump-and-treat method or/and hydrodynamic control of the pollution. The practical goal is to find the optimal locations and flow rates of two additional pumping wells, which will pump the polluted water or/and control pollution, protecting an existing drinking water pumping well, securing its fully operational mode even during the remediation process with the minimum possible cost, simply represented by the pumped water volume of the additional wells. The remediation process is considered complete when the maximum concentration in the aquifer drops below a certain limit. The Modflow software (handled by the Flopy Python package) simulates the flow field and advective–dispersive mass transport, and a genetic algorithm is used as the optimization tool. The coupled simulation–optimization model, Modflow-GA, complemented by a sophisticated post-processing results analysis, provides optimal and alternate sub-optimal remediation strategies for the decision makers to select from.

An imbalance multi-faults data transfer learning diagnosis method based on finite element simulation optimization model of rolling bearing

In practical engineering applications, rotating machinery is often in a healthy state, with more healthy samples and fewer multi-faults samples, which leads to be misdiagnosed. A finite element simulation model (FEM) is proposed to expand imbalanced multi-faults sample data. However, there is a significant difference in vibration response between the FEM simulation data and the measured data collected on the rotating machinery. Therefore, a method based on vibration response eigenvalue model correction has been adopted, aiming to narrow the gap between the simulation data and the measured data. In order to solve the problem of low accuracy of sample imbalance fault diagnosis under different working conditions, a transfer learning (TL) fault diagnosis method based on finite element simulation optimization model of rolling bearings to supplement imbalanced multi-fault data is proposed. Firstly, the FEM of healthy bearings is established, and the optimal radial clearance of bearings is obtained by modifying the vibration response eigenvalue model. Then, corresponding simulated fault vibration responses were obtained by setting composite faults on the optimal model. Finally, supplementing the unbalanced fault data with simulation data to create a mixed balanced dataset as samples, the TL model is trained to accomplish intelligent fault diagnosis for multi-faults. The experimental results show that using mixed data as training samples to train neural network models can improve the accuracy of transfer learning fault diagnosis by over 10%. Expanding fault samples by modifying the bearing clearance of the FEM is an effective method to address sample imbalance.

Conjunctive management of groundwater and surface water resources using a hybrid simulation–optimization method

ABSTRACT This study aimed to determine the optimal conjunctive utilization of groundwater and surface water resources in the Halil River basin, one of the most significant study regions in Kerman Province (Iran). Multi-verse optimizer (MVO) and the ANFIS (adaptive neuro-fuzzy inference systems) simulation model, known as the MVO–ANFIS simulation–optimization model, were used for this purpose. Moreover, the optimal exploitation policy for the studied basin was presented. The ANFIS model yielded a coefficient of determination greater than 0.99 in Baft, Rabor, and Jiroft. This model had a high capability to simulate groundwater levels in these three regions. Therefore, the ANFIS model was adopted as the simulation model to predict the aquifer water table in these regions. Regarding the conjunctive utilization of groundwater and surface water resources, the exploitation policy resulting from the MVO–ANFIS simulation–optimization model had a desirable performance by supplying 91.70, 87.75, and 97.58% of the total demands of Baft, Rabor, and Jiroft, respectively. Moreover, results of water system performance indicators, including reliability (82.96, 72.65, 95.07), resiliency (70, 53.47, 80), vulnerability (29.54, 25.64, 17.02), and sustainability (74.24%, 66.10%, 85.78%) in the mentioned regions, respectively, showed the appropriate performance of the proposed model for the simulation–optimization problem.

Simulation–optimization model for the structural design of cantilever retaining walls

Simulation–optimization model for the structural design of cantilever retaining walls

Dynamic selection of risk response strategies with resource allocation for construction project portfolios

To promote the dynamic risk management of construction project portfolios (CPPs) and improve the effect of risk response, a simulation–optimization model is proposed. The model integrates System Dynamics and optimization to dynamically select risk response strategies (RRSs) and facilitate more refined resource allocation. Specifically, a System Dynamics sub-model simulates the risk level by capturing one-stage and cross-stage risk interactions. Second, develop an optimization sub-model to select RRSs with resource allocation, which allows for partially allocated resources for RRSs. Afterward, integrate them into a simulation–optimization model. The closed-loop information flow between the two sub-models enables the assessment of the initial risk at a specific stage, the selection of optimal RRSs, and the updating of the dynamic impact of the risks of that stage on later stages. Finally, the proposed model is validated using a numerical example. The findings accentuate the significance of considering cross-stage risk interactions and optimizing resource allocation in the risk response of CPPs, and validate the feasibility and superiority of the model for solving the dynamic selection of RRSs.

Liquid metal-enhanced thermoelectric generator for high-temperature thermal harvesting

It is a challenge to achieve high output power of the thermoelectric generator (TEG) for high-temperature waste heat recovery due to the working fluids with low thermal conductivity (such as air) or low tolerance of temperature (water or oil). In this paper, the gallium-based liquid metal (LM) with a high boiling point (>1000 ℃) and thermal conductivity (>20 W/mK) are introduced to significantly enhance heat charging and discharging at the hot/cold sides of TEG. We also developed an electromagnetic induction pumping method for the effective LM-driven flow at high temperatures. A three-dimensional coupling multi-parameter optimization simulation model of LM-based TEG was developed to reveal the internal heat transfer and power generation mechanism for clarifying the impact factors of the performance. The results have shown that the power generation capacity can be effectively improved by increasing the inlet temperature (Thot-in). It is worth noting that the temperature difference (ΔT) of the TEG module is severely limited by the thermal resistance (Rcapacity) of the LM heat capacity, such as, when the flow rate of 2 L/min, the Rcapacity accounts for 90.6 % (0.020 ℃/W). It is easier to achieve a larger open circuit voltage by increasing the height of the thermoelectric leg, but the Pmax is constantly weakened. Compared with Xceltherm 600 and Solar salt, LM heat transfer fluid has more obvious advantages in enhancing the power generation performance of the system. The LM-based TEG experiment platform was established to study heat transfer and power generation characteristics. The results have demonstrated that good consistency was proved by comparing experimental and numerical data. And the Pmax of the LM-based TEG system is 0.16 W/cm2 at ΔT of 136 ℃. LM-based TEG can open new perspectives for broad applications in high-temperature waste heat recovery.

Data-driven modeling for designing a sustainable and efficient vaccine supply chain: A COVID-19 case study

We present an agent-based simulation-optimization modeling framework to determine the optimal location of warehouses for the distribution of vaccines. We first extend an agent-based epidemiological simulation model of COVID-19 to capture disease transmission and forecast the number of susceptible individuals and infections. We then develop a sustainable VSC considering the impact of greenhouse gases and integrate the simulation model into the VSC model to minimize total costs and environmental impacts. We validate our proposed model using a real-world COVID-19 VSC in the US. Our findings underscore the importance of strategically managing vaccine supplies to control COVID-19 and other infectious outbreaks.

Coupled simulation-optimization model for pedestrian evacuation guidance planning

Effective evacuation guidance can guarantee people's safety by facilitating their swiftly exit hazardous areas during an emergency. However, pre-determined guidance plans based solely on distance comparisons to exits may not always be the most effective due to unstable accessibility conditions and uneven crowd distribution. Therefore, it is imperative to incorporate real-time optimal guidance information in the plan. Coupling simplified CTM (Cell Transmission Model)-based simulation, this study proposed a computationally efficient DRF (Directed Rooted Forest)-encoded planning for developing evacuation guidance plan. Taking them as a holistic model, the simulator predicts evacuation dynamics at a constant computational cost regardless of crowd size, while the planning module optimizes the guidance plan directionally by leveraging the simulation's intermediate and final outputs. Numerical tests have demonstrated that the tight coupling between optimization and simulation module has substantially enhanced the model's capacity to fine-tune the guidance plan and optimization efficiency. The proposed model may serve as the foundation for developing real-time evacuation guidance plans for large-scale crowded buildings, either on the premise of accelerated simulation or as an efficient generator of training data for machine learning models.

Simulation–optimization configurations for real-time decision-making in fugitive interception

Simulation–optimization models are well-suited for real-time decision-support to the control room for search and interception of fugitives by Police on a road network, due to their ability to encode complex behavior while still optimizing the interception.The typical simulation–optimization configuration is simulation model optimization, where the simulation model describes the system to be optimized, and the optimizer attempts to find the combination of decision variables that maximizes the interception probability. However, the repeated evaluation of the simulation model leads to high computation time, thus rendering it inadequate for time-constrained decision contexts. To support police interception operations in real-time, timely calculation of the solution is essential. Sequential simulation–optimization, where the simulation model, with its rich behavior, constructs (part of) the constraints of an optimization problem, could decrease the computation time.We compare the computation time for two configurations of simulation–optimization (typical simulation model optimization and sequential simulation–optimization) for various problem instances of the fugitive interception problem. We show that sequential simulation–optimization reduces the computation time of large instances of the fugitive interception case study ten-fold. This result illustrates the potential of sequential simulation–optimization to mitigate the expensive optimization of simulation models.