Simulation-optimization Model Research Articles

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.

Enhancing CO2 Water-to-Water Heat Pump Performance Through the Application of a Multi-Objective Evolutionary Algorithm

Abstract Due to growing concerns about the environmental impact of refrigerants, carbon dioxide (CO2) heat pumps have been increasingly evaluated as efficient alternatives for conventional heat pumps. Performance analyses of CO2 heat pump water heaters (HPWHs) have been the subject of many studies, but these are typically limited to parametric analyses of air-source HPWHs. The interrelated behavior of the supercritical and subcritical thermodynamic properties, component operation, and efficiency means that a parametric study cannot adequately capture the inherent nonlinearity. Therefore, this paper, for the first time, aims to perform a multi-objective optimization on CO2 water-sourced HPWH performance in order to minimize the total component costs, maximize gas cooler (GC) heating capacity, and maximize the coefficient of performance (COP) using two different optimization scenarios. The decision variables are defined as GC pressure (75–140 bar), evaporator temperature (−19.5–0.2 °C), and GC outlet temperature for CO2 (16–36 °C). The model performance is constrained by the practical ranges of the GC and evaporator inlet and outlet temperatures for water. A coupled simulation-optimization model through python is developed using Engineering Equation Solver (EES) software and the non-dominated sorting genetic algorithm II (NSGA-II). The result of the optimal Pareto front showed that the optimal GC heating capacity changes from 19.2 to 56.7 kW, with a lowest cost of $7771 to a highest cost of $9742, respectively. When the lower bound of the GC outlet temperature was set to 32 °C, the Pareto front showed a maximum COP of 3.23, with a corresponding GC heating capacity of 44.36 kW.

Investigation of the emergency water supply schemes for youkou groundwater source field in Nanchang using a simulation–optimization model

To enhance the resilience of Nanchang’s water supply system and ensure a dependable emergency water supply. Taking youkou emergency groundwater source field as an example, a flow simulation model was developed through an analysis of the hydrogeological conditions in the study area. Additionally, an optimization model based on the genetic algorithm (GA) technique was constructed and integrated into the flow simulation model. Subsequently, various water supply schemes were simulated with the minimum cost of groundwater extraction as the objective function. The results show that the values of the objective function were reduced by 4.92%, 15.67%, and 42.35% for the three different optimization schemes, namely pumping rates, joint pumping rates and the number of wells, and joint pumping rates, number of wells and well location. Ultimately, the optimal emergency water supply scheme was determined by considering a comprehensive range of factors. These factors encompassed considerations such as the area of the water level depression funnel, the dewatering thickness of the aquifer and the recovery of the groundwater level. The practice shows that the simulation–optimization model could effectively simulate complex groundwater flow systems, meeting the objective function and constraints toachieve the optimal exploitation scheme.

An artificial intelligence-based model for optimal conjunctive operation of surface and groundwater resources

A hybrid simulation-optimization model is proposed for the optimal conjunctive operation of surface and groundwater resources. This second-level model is created by finding and combining the best aspects of two resilient metaheuristics, the moth swarm algorithm and the symbiotic organization search algorithm, and then connecting the resulting algorithm to an artificial neural network simulator. For assessment of the developed model efficiency, its results are compared with two first-level simulation-optimization models. The comparisons reveal that the operation policies obtained by the developed second-level model can reliably supply more than 99% of the total demands in the study regions, indicating its superior efficiency compared to the two other first-level models. In addition, the highest sustainability index in the study regions belongs to the proposed model. Comparing the results of this research with those of other recent studies confirm the supremacy of the developed second-level model over several previously developed models.

A Scientometric Review for Uncertainties in Integrated Simulation–Optimization Modeling System

Water resources management is a challenging task caused by huge uncertainties and complexities in hydrological processes and human activities. Over the last three decades, various scholars have carried out the study on hydrological simulation under complex conditions and quantitatively characterized the associated uncertainties for water resources systems. To keep abreast of the development of the collective knowledge in this field, a scientometric review and metasynthesis of the existing uncertainty analysis research for supporting hydrological modeling and water resources management has been conducted. A total of 2020 publications from 1991 to 2018 were acquired from the Web of Science. The scientific structure, cooperation, and frontiers of the related domain were explored using the science mapping software CiteSpace V5.4.R3. Through co–citation, collaboration, and co–occurrence network study, the results present the leading contributors among all countries and hotspots in the research domain. In addition, synthetical uncertainty management for hydrological models and water resource systems under climatic and land use change will continue to be focused on. This study comprehensively evaluates various aspects of uncertainty analysis in hydrologic simulation–optimization systems, showcasing advanced data analysis and artificial intelligence technologies. It focuses on current research frontiers, aiding decision–makers in better understanding and managing the complexity and uncertainties of water resource systems, thereby enhancing the sustainability and efficiency of responses to environmental changes.

Construction and application of a simulation optimization model of auxiliary guidance signs in enclosed public places

Many enclosed public spaces with multiple destinations often feature obstacles, such as columns within the building space and dense crowds, which adversely affect the recognition and guidance of pedestrian sign. To mitigate this issue, enhancing traffic efficiency necessitates the implementation of auxiliary guidance signs. However, experience-based schemes for positioning these signs often require iterative adjustments to achieve the desired effect. Thus, this paper endeavors to address this challenge by analyzing the relationship between obstacles and pedestrians’ visual blind areas. It establishes a mathematical model to describe pedestrian visual perception and its correlation with crowd density and individual visual field function. The model employs the effective aggregation area of the population exceeding a certain density threshold as its primary variable and enhances this variable’s function. Subsequently, it formulates an effect function that yields a substantial improvement in various combinations and permutations of auxiliary guidance signs. To exemplify this research, a large subway transfer hall with multiple destinations serves as a simulation model. The study calculates the optimal combination of auxiliary guidance sign placements that align with the enhancement effect function. Based on this combination, an optimized layout plan is derived, resulting in a significant improvement. The results demonstrate that the auxiliary guidance sign layout scheme proposed in this study markedly enhances traffic efficiency in enclosed public spaces. This research serves as a valuable reference for decision-makers seeking to optimize traffic flow in such spaces.

A Water Resources Management Simulation–Optimization Model: Application of Graph-Based Hypergame Model in Water Supply Conflicts Resolution

A Water Resources Management Simulation–Optimization Model: Application of Graph-Based Hypergame Model in Water Supply Conflicts Resolution

Development and optimization of simulation models and methods for controlling virtual artillery units in game scenarios

In the realm of modern video game development, special attention is given to the simulation of artillery systems, which play a crucial role in various military-themed games. This research presents a mathematical model for simulating the actions of a virtual artillery system. The model is designed to manage the execution of combat tasks, including targeting destruction with a specified number of shells and incorporating the strategic movement between firing positions to minimize detection and attack by enemy forces in the game. The model presumes that all shots are effective and equates the number of firing positions to the number of shots, with a minimum of one shot per position. The model's dynamics do not allow for returning to previous positions, adding a layer of complexity and realism to the gameplay. Movement simulations between positions are designed along virtual roads of varying quality, enhancing the strategic elements of the game. A method for determining the optimal strategy for the artillery system's actions has been developed, introducing the concept of the current structure of combat task execution. This problem-solving approach falls within the realm of Pareto-oriented tasks or dynamic programming challenges. The computational method of the model is based on a general algorithm, underpinned by specialized additional algorithms. Results from this model demonstrate the feasibility of completing combat tasks effectively, with a maximum of two shots per firing position. The research differentiates between defensive and offensive tactics in gameplay, suggesting that while a strategy involving ten shots per target aligns with defensive gameplay, a strategy with four shots per target aligns with offensive actions. Consequently, the "shot-and-scoot" tactic in an offensive context can be aptly termed as “hid-and-shot”.

Reactive power optimization control for multi-energy system considering source-load uncertainty

Reactive power control in power systems is an important way to ensure stable system operation. However, as the coupling between multi-energy systems increases, the uncertainty of supply and demand in the power system poses new challenges for reactive power control. Therefore, the main objective of this study is to perform reactive power balance control of the grid in a multi-energy system. Firstly, we studied the characteristics of reactive power demand during the energy transport of a multi-energy system. In addition, we studied the reactive power output characteristics of various power sources and their uncertainties. The reactive power balance of the multi-energy system and its uncertainty model are established. Then, we studied the adjustable pressure limits of the energy medium transmission pipelines of the heat supply network and the gas pipeline network. The reactive power demand capacity of the system is determined according to the system's energy condition and safe operation boundary. Finally, we established a reactive power optimization simulation model of the multi-energy system, considering the heat-gas network constraints. The study proposes that the optimal reactive power control method can improve the voltage stability of the multi-energy system.

Assessing Impacts of Flood Diversion on the Ecosystem of Brackish-Water Lakes through Simulation-Optimization Model

This study develops and evaluates a simulation-optimization approach to mitigate the environmental impacts of freshwater pulses in brackish-water lakes whilst maximizing flood diversion benefits. Lake Pontchartrain, located downstream of the Mississippi River, Mississippi, United States, is a brackish-water ecosystem threatened by reducing salinity concentrations due to freshwater pulses from the flood diversion project on the Mississippi River. An adaptive neuro-fuzzy-inference-system-based model was developed as a data-driven model for simulating salinity distribution at a representative station of Lake Pontchartrain. Then, the data-driven model was used as the simulator in the optimization system. Both single-objective and multi-objective particle swarm optimizations were used to find the optimal solutions. Results show that the data-driven model is robust at simulating the salinity time series in the brackish-water ecosystem of Lake Pontchartrain. The Nash–Sutcliffe efficiency index of the data-driven model between measured and modelled salinity is 0.85, which means the model is reliable for applying in further simulations. The proposed optimal solutions for the environmental management of the lake indicate that because of the magnitude of the volume of freshwater released, environmental impacts at this location cannot be optimized through varying the timing and volume of the releases. This work presents a novel contribution to science through developing an optimization framework for mitigating the impacts of flood management on changes in salinity in brackish-water systems.