Simulation-optimization Framework Research Articles

Multiobjective layout optimization for low impact development considering its ecosystem services

The augmented proportion of impervious surfaces and heavy rainfall have resulted in waterlogging, nonpoint source pollution, and environmental degradation. Low impact development (LID) is an effective storm management practice. Based on six different LID paving scenarios, a four objective simulation-optimization framework coupled with stormwater management model (SWMM) and NSGA-Ⅱ that incorporates the ecosystem service value (ESV) was proposed for the optimal layout of LID facilities. A case study in Beijing, China, was taken as an example. The results showed that the rain garden paving scenario is remarkable compared to the other scenarios in realizing ESV, with the dominant ESV of net carbon sinks. The optimization revealed that the change in pollution control rate was more sensitive than the runoff reduction rate. Simultaneously, the optimization of these two indicators will be accompanied by more cost and lower ESV. The optimal solution achieved 32.48 % of runoff reduction rate, 82.22 % of water pollution control rate, 0.73×105 CNY of ESV, and 2.29×106 CNY of cost. The framework provides reference and technical support to achieve the synergistic objectives among cost, water quality and quantity control, and ecosystem services.

Integration of deep learning and improved multi-objective algorithm to optimize reservoir operation for balancing human and downstream ecological needs

Dam (reservoir)-induced alterations of flow and water temperature regimes can threaten downstream fish habitats and native aquatic ecosystems. Alleviating the negative environmental impacts of dam-reservoir and balancing the multiple purposes of reservoir operation have attracted wide attention. While previous studies have incorporated ecological flow requirements in reservoir operation strategies, a comprehensive analysis of trade-offs among hydropower benefits, ecological flow, and ecological water temperature demands is lacking. Hence, this study develops a multi-objective ecological scheduling model, considering total power generation, ecological flow guarantee index, and ecological water temperature guarantee index simultaneously. The model is based on an integrated multi-objective simulation-optimization (MOSO) framework which is applied to Three Gorges Reservoir. To that end, first, a hybrid long short-term memory and one-dimensional convolutional neural network (LSTM_1DCNN) model is utilized to simulate the dam discharge temperature. Then, an improved epsilon multi-objective ant colony optimization for continuous domain algorithm (ε-MOACOR) is proposed to investigate the trade-offs among the competing objectives. Results show that LSTM _1DCNN outperforms other competing models in predicting dam discharge temperature. The conflicts among economic and ecological objectives are often prominent. The proposed ε-MOACOR has potential in resolving such conflicts and has high efficiency in solving multi-objective benchmark tests as well as reservoir optimization problem. More realistic and pragmatic Pareto-optimal solutions for typical dry, normal and wet years can be generated by the MOSO framework. The ecological water temperature guarantee index objective, which should be considered in reservoir operation, can be improved as inflow discharge increases or the temporal distribution of dam discharge volume becomes more uneven.

Boosting hydropower generation of mixed reservoirs for reducing carbon emissions by using a simulation–optimization framework

Abstract The optimization operation of reservoir seasonal Flood-Limited Water Levels (FLWLs) can counterbalance the hydropower generation and flood prevention in the flood season. This study proposes a multi-objective optimization operation model to optimize the reservoir seasonal FLWLs for enhancing synergies of hydropower generation and flood prevention. The integration of the Non-dominated Sorting Genetic Algorithm-II and a simulation-optimization framework is applied for optimizing the joint operation of reservoirs meanwhile achieving the Pareto solutions to reduce computation complexity and time. And then, the Technique for Order of Preference by Similarity to Ideal Solution is utilized to identify the best seasonal FLWL scheme grounded on multi-criteria decision-making analysis. The mixed reservoirs located in the upstream Yangtze River of China constitute the case study. The results showed that: compared with the annual FLWL scheme, the proposed seasonal FLWL schemes without increasing flood prevention risk could facilitate the joint operation of the mixed reservoirs to achieve 868 million kW·h (5.1% improvement) in average hydroelectricity production during the flood season, meanwhile reducing 681 million kg in carbon emissions accordingly. The results support that the proposed methods can boost hydropower production to benefit China's national tactics in accomplishing peak carbon dioxide emissions before 2030.

Study on the Basic Form of Reservoir Operation Rule Curves for Water Supply and Power Generation

Reservoir operation rule curves are crucial for managing water supply and power generation in reservoirs. As the number of objectives and management requirements increase, there is a growing demand for optimized operation rule curves. The objective of this study is to explore the most effective forms of reservoir operation rule curves, focusing on the case of the Nierji Reservoir and considering the dual objectives of water supply and power generation. The parameter–simulation–optimization framework, specifically employing the NSGA-II algorithm, was used to analyze and compare two basic forms of operation rule curves: the shared type and independent type. The impact of these curves on water supply potential and multi-objective optimization results with various water demand scenarios was assessed. The analysis revealed that the choice of operation rule curve form can influence the maximum water supply potential of the reservoir to some extent. The independent type operation rule curve was significantly more effective in enhancing the water supply potential for industrial and domestic users, resulting in a notable increase of 3.5 × 108 m3. Additionally, it also proved beneficial for environmental users, with an increase of 1 × 108 m3. Conversely, the shared type operation rule curve demonstrated similar functionality to the independent type curve with fewer decision variables, particularly when the water demand was relatively low. In scenarios with high water demand, the independent type curve outperformed the shared type curve by generating 6549 superior, non-dominated solutions for multi-objective optimization, specifically focused on maximizing reservoir operation benefits. In conclusion, selecting the appropriate form of reservoir operation rule curve is crucial to balance different reservoir functional objectives and achieve optimal results. Further research could focus on quantifying the specific benefits and trade-offs associated with each type of curve in order to provide more robust evidence for the advantages of a complex reservoir system.

Allocation and smart inverter setting of ground-mounted photovoltaic power plants for the maximization of hosting capacity in distribution networks

As the integration of solar photovoltaic (PV) power plants into distribution networks grows, quantifying the amount of PV power that distribution networks can host without harmfully impacting power quality becomes critical. This work aims to determine the best number, location, and size of PV systems to be installed on a distribution feeder, as well as the best control set-points of the PV inverters, to maximize the PV hosting capacity (HC). Therefore, a simulation-optimization framework is proposed for siting and sizing ground-mounted PV power plants equipped with smart inverters (SIs). Single (decentralized) and multiple (distributed) allocations are analyzed by considering the connection of one, two, and three PV systems. Genetic algorithm (GA) and particle swarm optimization (PSO) metaheuristics are employed to solve the optimization problem. The simulation-optimization framework is tested on a real-world feeder model from an Ecuadorian utility. Installing two PV systems with their SIs operating with the Volt-VAr control function yields maximum PV HC, which is increased by 32.1 % compared to a single PV power plant operating at a unity power factor. Moreover, a comparative analysis of the two metaheuristic algorithms reveals that the PSO method provides better results than GA.

A simulation optimization framework to solve Stochastic Flexible Job-Shop Scheduling Problems—Case: Semiconductor manufacturing

This paper addresses a Stochastic Flexible Job-Shop Scheduling Problem (SFJSSP) in the context of semiconductor manufacturing. Semiconductor industry is among the most capital-intensive businesses whose operational excellence is of vital importance. Within the front-end fab of the semiconductor industry, the photolithography workstation is the well-known bottleneck process. To elevate the performance of the whole semiconductor manufacturing system, developing a competent schedule for its bottleneck is essential. However, the re-entrant product flows, high uncertainties in operations times, and rapidly changing products and technologies within the photolithography, make it difficult to develop a schedule for the whole semiconductor fab. Considering Industry 4.0, hybrid methods such as Simulation Optimization (SO) have proven their applicability in addressing complex production scheduling problems. Thus, this paper develops a mathematical model for SFJSSP of the semiconductor manufacturing considering special constraints of the photolithography workstation (machine process capability, machine dedication, and maximum reticles (masks) sharing constraints). Next, we transform the developed model into an SO model integrated with a computer simulation model capable of modeling the photolithography workstation. The simulation model develops an initial schedule based on the Least Work Remaining (LWR) dispatching rule. Moreover, the simulation model calculates the objective function of the SFJSSP. A tailored Genetic Algorithm (GA) is then developed, which attempts to optimize the initially proposed schedule. To validate the superiority of the presented SO methodology in addressing SJSSPs, it is compared with previously proposed methods. Furthermore, to assess the impact of the three special constraints of the photolithography work area on system performance, two sets of experiments are proposed. In the first set of experiments, the performance of two SFJSS environments, one with the special constraints and one without, is compared. The second set of experiments involves observing the system’s performance while systematically varying the severity of the special constraints. The results indicate that improved performance levels can be accomplished by enhancing flexibility within both the operations of individual jobs and the machines within the manufacturing system.

A spatiotemporal optimization method for nutrient control in lake watersheds

Developing an efficient strategy for managing nutrients in less-developed lake watersheds that can balance the need for socioeconomic progress with the protection of aquatic ecosystems has become an urgent research subject for achieving sustainable development. This paper improves the optimization method for environmental and economic management of lake watersheds proposed in our previous research. A spatiotemporal optimization method based on a coupling model consisting of the Soil and Water Assessment Tool, system dynamics model, and objective programming model was applied to an agricultural non-point source (ANPS) pollution control program and a rural sewage treatment program at the Yilong Lake watershed as a case study. A simulation evaluation showed that the efficiency of the previous scheme was significantly improved after conducting spatiotemporal optimization. This scheme was dynamic and distributed, demonstrating an annual and high-resolution control program that can provide a basis for the precise management of ANPS. Although it still requires improvement, a framework for coupling simulation and two-step optimization was achieved in this study.

Optimal layout design of groundwater pollution monitoring network using parameter iterative updating strategy-based ant colony optimization algorithm.

The scientific layout design of the groundwater pollution monitoring network (GPMN) can provide high quality groundwater monitoring data, which is essential for the timely detection and remediation of groundwater pollution. The simulation optimization approach was effective in obtaining the optimal design of the GPMN. The ant colony optimization (ACO) algorithm is an effective method for solving optimization models. However, the parameters used in the conventional ACO algorithm are empirically adopted with fixed values, which may affect the global searchability and convergence speed. Therefore, a parameter-iterative updating strategy-based ant colony optimization (PIUSACO) algorithm was proposed to solve this problem. For the GPMN optimal design problem, a simulation-optimization framework using PIUSACO algorithm was applied in a municipal waste landfill in BaiCheng city in China. Moreover, to reduce the computational load of the design process while considering the uncertainty of aquifer parameters and pollution sources, a genetic algorithm-support vector regression (GA-SVR) method was proposed to develop the surrogate model for the numerical model. The results showed that the layout scheme obtained using the PIUSACO algorithm had a significantly higher detection rate than ACO algorithm and random layout schemes, indicating that the designed layout scheme based on the PIUSACO algorithm can detect the groundwater pollution occurrence timely. The comparison of the iteration processes of the PIUSACO and conventional ACO algorithms shows that the global searching ability is improved and the convergence speed is accelerated significantly using the iteration updating strategy of crucial parameters. This study demonstrates the feasibility of the PIUSACO algorithm for the optimal layout design of the GPMN for the timely detection of groundwater pollution.

An assets maintenance-workforce planning problem under uncertainty: A chance constraints assisted simulation-optimization approach

Preventive and corrective maintenance-workforce planning for assets is a growing area of research, given the presence of uncertainty. We model a Simulation-Optimization framework to solve this challenging large-scale maintenance-workforce planning problem while considering its stochastic nature inside both the simulation and optimization models. A holistic simulation model is proposed through multiple modules to imitate different aspects of the maintenance process, i.e., spare facilities/storage and workforce. The workforce module represents technicians’ types, skills and levels, and employment life-cycle, including recruitment, promotion, and separation/retirement. The present uncertainty in the simulation model demands its corresponding optimization model to cope with the nature of the problem and guarantee a degree of confidence level of the optimal decisions. Therefore, a Chance Constraints Programming (CCP) method is integrated with an evolutionary optimization algorithm to limit the risk level of violating the random constraints while minimizing the total maintenance cost. Discrete Event Simulation (DES) and Self-Adaptive Differential Evolution assisted with Chance Constraints (SA-DE-CC) algorithm are coupled to solve the current problem. The optimization model is proposed to optimize the preventive maintenance frequency and workforce planning problem while implicitly considering corrective maintenance in the simulation model. The Taguchi method performs scenario and risk analysis for multiple CC probability distributions (i.e., Weibull and Normal), confidence levels, and critical parameter combinations. The model’s sensitivity analysis and managerial insights are also discussed.

Assessing the impacts of connected-and-autonomous vehicle management strategy on the environmental sustainability of urban expressway system

Urban expressway network has become essential for over 30% of road trips in urban areas, making them significant in shaping environmental sustainability. Recently connected-and-autonomous vehicle (CAV) technology is expected to significantly transform the urban expressway systems, but little attention has been attributed to quantitatively assessing the impacts of managing CAV in mixed traffic flow on urban expressway environmental sustainability. To address this research gap, this study proposes a comprehensive simulation-optimization framework. The simulation component involves multiclass cell transmission model that simulates and evaluates the traffic flow of mixed human-driven vehicles and CAVs on the expressway, whereas the optimization component proposes a novel management strategy that jointly considers the dedicated lanes and speed management. An analytical tool is developed and validated in North-South Elevated Expressway in Shanghai, China, and further investigated the sensitivity of environmental impacts to vehicle headway, travel demand, and CAV market penetration rate. The results indicate that use of the proposed tool can reduce the total fuel consumption and exhaust emissions compared to baseline scenario. Overall, the insights from this study are valuable for policymakers of transportation in future cities, who develop management strategies on urban expressways that enhance the potential impact of CAVs on environmental sustainability.

Hypolimnetic aeration optimization based on reservoir thermal stratification simulation

Hypolimnetic aeration is an effective management strategy for mitigating oxygen deficiency in dam reservoirs caused by thermal stratification (ThS). However, designing an optimal hypolimnetic aeration system based on dissolved oxygen (DO) concentration evaluation has yet to be considered in the literature. This study proposed an innovative optimization model utilizing the non-dominated sorting genetic algorithm-II (NSGA-II) for up to 4 aerators, considering two main objectives: (i) minimizing DO concentration deviation from the standard limit and (ii) minimizing aerator(s) operation energy. This optimization model was developed based on five primary decision variables: the location of the aerator(s) (length and depth), the aeration rate, and the operating time (start and end date). The NSGA-II optimization approach was coupled with the CE-QUAL-W2 simulation model for each identified period of ThS to simulate DO concentrations. To satisfy the conflicting objectives of the primary beneficiaries involving the Ministry of Energy and the Regional Water Organization, a two-step decision-making framework was introduced, utilizing the Nash bargaining (NB) and weighting method (WM) techniques. In order to show the practicality and effectiveness of the suggested framework in enhancing the DO concentration within a dam reservoir, the novel simulation optimization framework was implemented in a real-world case study of the Karkheh dam reservoir (KDR) in Iran. The findings revealed that applying an optimal hypolimnetic aeration system significantly improved the DO concentration, reaching at least twice the level without an aerator. However, the influence of aerators was not long-lasting, gradually diminishing when their operations ceased. Considering the importance of increasing DO concentration to mitigate the adverse effects of ThS, the four-aerators system was chosen as the optimal compromise design for the KDR.

A data-driven simulation-optimization framework for generating priority dispatching rules in dynamic job shop scheduling with uncertainties

Modeling and optimizing dynamic job shop scheduling problems (DJSSP) without ample assumptions is inherently challenging due to the increasing complexity and uncertainty arising from various disruptive events. Moreover, establishing job due dates in dynamic job shop environments has become increasing difficult due to unforeseen events, while it remains a pivotal factor in determining tardiness-related objectives. A research gap exists in exploring the potential synergy between the two aforementioned aspects and its resulting implications. In this study, we propose a novel framework that combines data-driven simulation, machine learning, and evolutionary algorithm to generate new Priority Dispatching Rules (PDRs) for optimizing makespan and total tardiness. The framework incorporates a data-exchange mechanism for real-time updates of the simulation model based on the shop floor. Moreover, an online due date prediction model using XGBoost is developed, replacing the previous rough estimation of the Total Work Content (TWK) rule. Simulation experiments of various scales demonstrate that XGBoost-assisted due date prediction leads to an average reduction of 1.6% in makespan and 65.0% in total tardiness compared to the TWK rule. The proposed method outperforms existing well-known dispatching rules, yielding considerable improvements with average reductions of 90.8%, 40.3%, and 44.6% in total tardiness for vary-sized experiments. Finally, the approach is validated through a real industrial case study.

Machine Learning–Based Source Identification in Sewer Networks

Motivated by the valuable epidemiological information it reveals, wastewater surveillance has received significant attention in recent years. Furthermore, monitoring the water quality in sewer systems has been shown to provide useful information to support wastewater treatment operations. Yet, a critical need still exists for developing novel approaches for rapid and efficient source identification of chemical and biological species of interest in sewer systems. A limited number of source identification approaches have been proposed in previous literature, and the majority of these approaches employed various simplifying assumptions that limit their usage in real-life applications. In this study, a machine learning–based simulation-optimization framework was developed to determine the characteristics (i.e., concentration and loading pattern) of multiple simultaneous injection sources in sewer systems. The simulation was conducted using a surrogate model in the form of a multilayer perceptron neural network, which was trained using simulation results derived from the Storm Water Management Model (SWMM). The simulation model was then coupled with a genetic algorithm to reveal the characteristics of multiple sources that reproduce the concentration patterns observed at one or more monitoring locations in the sewer system. The proposed framework was applied to a range of injection scenarios and was able to identify the characteristics of multiple simultaneous injection sources under different conditions. The results showed that the residence time plays a significant role in the identifiability of the injection source location. The proposed framework is applicable to a wide number of source identification applications, including contamination source identification and wastewater-based epidemiology.

COVID-19: Agent-based simulation-optimization to vaccine center location vaccine allocation problem

This article presents an agent-based simulation-optimization modeling and algorithmic framework to determine the optimal vaccine center location and vaccine allocation strategies under budget constraints during an epidemic outbreak. Both simulation and optimization models incorporate population health dynamics, such as susceptible (S), vaccinated (V), infected (I) and recovered (R), while their integrated utilization focuses on the COVID-19 vaccine allocation challenges. We first formulate a dynamic location–allocation Mixed-Integer Programming (MIP) model, which determines the optimal vaccination center locations and vaccines allocated to vaccination centers, pharmacies, and health centers in a multi-period setting in each region over a geographical location. We then extend the agent-based epidemiological simulation model of COVID-19 (Covasim) by adding new vaccination compartments representing people who take the first vaccine shot and the first two shots. The Covasim involves complex disease transmission contact networks, including households, schools, and workplaces, and demographics, such as age-based disease transmission parameters. We combine the extended Covasim with the vaccination center location-allocation MIP model into one single simulation-optimization framework, which works iteratively forward and backward in time to determine the optimal vaccine allocation under varying disease dynamics. The agent-based simulation captures the inherent uncertainty in disease progression and forecasts the refined number of susceptible individuals and infections for the current time period to be used as an input into the optimization. We calibrate, validate, and test our simulation-optimization vaccine allocation model using the COVID-19 data and vaccine distribution case study in New Jersey. The resulting insights support ongoing mass vaccination efforts to mitigate the impact of the pandemic on public health, while the simulation-optimization algorithmic framework could be generalized for other epidemics.

Evaluation and optimization of a horizontal well-triplet for CO2 plume geothermal harvest: Comparison between open and close reservoirs

Depleted oil/gas reservoirs can be used for CO2 sequestration and utilization; CO2 plume geothermal (CPG) production from a CO2 geological storage reservoir has emerged as a viable approach owing to its favorable thermodynamic properties. However, most oil/gas reservoirs are separated by faults into geological blocks, and the impact of these fault boundaries on CPG performance remains unclear. This study evaluated and compared the technical performances of a horizontal well-triplet placement and CPG operation in a thin geothermal reservoir between open and close boundaries. A meta-model simulation–optimization framework was also applied for the economic assessment of both systems. For each boundary system, 200 samples of the horizontal well-triplet design, including injection well overpressure, horizontal perforation length of injection and production wells, and distance between injection and production well, were derived from a 4D-parameter space constrained by particular ranges. These 200 samples of input parameters yielded 200 numerical models for the simulation of CPG operations. A suite of technical metrics, including lifespan, injected, produced, and stored CO2, recovered heat energy, and produced heat flux, were computed from the model simulations for technical performance evaluations. Meta models for economic objectives were developed based on the 200 input–output datasets and used in optimization. A multi-objective particle swarm optimization (MOPSO) approach was utilized to obtain the appropriate optimal designs of the well-triplet for the two boundary systems. The results indicated that performance of both systems was controlled by the well space, which was positively correlated to CO2 storage and total heat recovery but negatively to power plant capacity. High horizontal well lengths and overpressure facilitated CO2 storage, heat recovery, and plant capacity. The open system could generate much more overall profit than the close system owing to CO2 storage income.

Routing battery-constrained delivery drones in a depot network: A business model and its optimization–simulation assessment

This paper proposes a novel business model for on-demand package shipment services using drones, and evaluates different modeling and solution approaches for the drone routing problem that underpins the service operation. In the proposed service, customers’ shipment orders of arbitrary origins and destinations, payload weights and bid values are collected every five minutes, and available drones from multiple depots are then dispatched to fulfill a subset of these orders in a way to maximize profit. A drone path starts from a depot, serves one or more customer orders in sequence, and ends at a depot for battery recharging, which incurs a fixed cost. Two mixed integer programming (MIP) formulations are presented to model the drone dispatch and routing problem. To improve solution efficiency, three computational approaches, including two column generation based algorithms and a brute-force path enumeration algorithm, are developed and compared. Computational experiments suggest, somewhat surprisingly, that the brute-force approach is the most effective and most scalable one, outperforming other alternatives by a substantial margin in both computing time and solution quality. Furthermore, an optimization–simulation framework is proposed to assess the system performance over a long horizon that spans multiple dispatch periods without complicating the optimization model. Using the simulation framework, useful managerial insights including the effects of battery capacity, wind condition and computing capability on the fleet dispatch operation, are generated, which will guide real-world implementations of the new business model.

One-way station-based electric carsharing service design considering route selection based on road congestion

ABSTRACT In the field of one-way station-based electric carsharing systems (OSECSs), determining the optimal service design is a major challenge faced by operators. To address this issue, This study proposes a simulation-optimisation framework that takes into account several factors, such as fleet size, station capacity, rental pricing, vehicle relocation, and staff rebalancing to determine the optimal service design. Specially, the simulation module of this framework is used to evaluates the performance of different carsharing service designs by taking into account the selection of optimal routes based on road congestion, nonlinear charging profiles, and elastic demand influenced by price and travel time. In the optimisation module of this framework, the study introduces a novel greedy genetic search algorithm to solve the optimisation problem. The proposed simulation-optimisation framework is tested on a carsharing network with 15 stations in Chengdu, China, and the numerical experiment shows its efficiency. Additionally, the study analyzes the impact of different degrees of demand and travel time variation, demand scenarios, and road congestion scenarios on the OSECS. By doing so, it provides a more comprehensive understanding of the system and the factors that affect its performance.

A parallel workflow framework using encoder-decoder LSTMs for uncertainty quantification in contaminant source identification in groundwater

Groundwater contamination is a major problem and it can be contaminated by natural or manmade activities. Contaminant sources cannot be identified promptly after a contamination event due to slow groundwater movement in aquifers. To limit the further spread of the plume and to take appropriate remedial measures, it is essential to determine the release rates associated with source locations as soon as a contaminant is detected in one of the observation wells in the aquifer. Conventional techniques for contaminant source identification such as the simulation–optimization (SO) uses a simulation model, which is based on estimated or, measured hydrogeological parameters, for replicating the aquifer response to contamination at source locations. However, the associated intrinsic uncertainty in the parameters can affect the estimated release histories at the source locations. To assess confidence in the results, it is important to consider stochasticity in the hydrogeological parameters for contaminant release history estimation requiring numerous runs of the SO model for corresponding breakthrough curves. The present study proposes a novel surrogate simulation optimization (SSO) framework which utilizes an Encoder-Decoder Long Short-Term Memory (ED-LSTM) based surrogate model along with Multiverse Optimization (MVO) for contaminant source identification. The comparison of ED-LSTM with other surrogate models shows its better accuracy and significantly low computational time requirement compared to the simulation model. To enhance the computational efficiency further, the SSO model is embedded in a parallel workflow for the quantification of uncertainty associated with the inverse modelling. The study shows that the proposed workflow can be efficiently used for uncertainty quantification in contaminant source identification due to stochastic parameters with a reduced computational cost.

Multi-objective optimization of hydrant flushing in a water distribution system using a fast hybrid technique

As a critical element in preserving the health of urban populations, water distribution systems (WDSs) must be ready to implement emergency plans when catastrophic events such as contamination events occur. A risk-based simulation-optimization framework (EPANET–NSGA-III) combined with a decision support model (GMCR) is proposed in this study to determine optimal locations for contaminant flushing hydrants under an array of potentially hazardous scenarios. Risk-based analysis using Conditional Value-at-Risk (CVaR)-based objectives can address uncertainties regarding the mode of WDS contamination, thereby providing a robust plan to minimize the associated risks at a 95% confidence level. Conflict modeling by GMCR achieved an optimal compromise solution within the Pareto front by identifying a final stable consensus among the decision-makers involved. A novel hybrid contamination event grouping-parallel water quality simulation technique was incorporated into the integrated model to reduce model runtime, the main deterrent in optimization-based methods. The nearly 80% reduction in model runtime made the proposed model a viable solution for online simulation-optimization problems. The framework's capacity to address real-world problems was evaluated for the WDS operating in Lamerd, a city in Fars Province, Iran. Results showed that the proposed framework was capable of highlighting a single flushing strategy, which not only optimally reduced risks associated with contamination events, but provided acceptable coverage against such threats, flushing 35–61.3% of input contamination mass on average, and reducing average time-to-return to normal conditions by 14.4–60.2%, while employing less than half of the initial potential hydrants.

Flood Management with SUDS: A Simulation–Optimization Framework

Urbanization and climate change are the main driving force in the development of sustainable strategies for managing water in cities, such as sustainable urban drainage systems (SUDS). Previous studies have identified the necessity to develop decision-making tools for SUDS in order to adequately implement these structures. This study proposes a simulation–optimization methodology that aims to ease the decision-making process when selecting and placing SUDS, with the specific goal of managing urban flooding. The methodology was applied to a real case study in Dresden, Germany. The most relevant variables when selecting SUDS were the spatial distribution of floods and the land uses in the catchment. Furthermore, the rainfall characteristics played an important role when selecting the different SUDS configurations. After the optimal SUDS configurations were determined, flood maps were developed, identifying the high potential that SUDS have for reducing flood volumes and depth, but showing them to be quite limited in reducing the flooded areas. The final section of the study proposes a combined frequency map of SUDS implementation, which is suggested for use as a final guide for the present study. The study successfully implemented a novel methodology that included land-use patterns and flood indicators to select SUDS in a real case study.