Simulation Tool For Optimization Research Articles

From scenario to roadmap: Design and evaluation of a web-based participatory watershed planning system for optimizing multistage implementation plans of management practices under stepwise investment

Planning multistage implementation plans, or roadmaps, based on the spatial distribution of a best management practice (BMP) scenario is essential for achieving watershed management goals under realistic conditions, such as stepwise investment plans that involve multiple stakeholders, including investors, economic and environmental beneficiaries. The state-of-the-art BMP roadmap optimization method can address this need for optimization but is over-specialized and complex to non-expert stakeholders. This study designed a user-friendly web-based participatory watershed planning system to assist a diverse group of stakeholders in reaching a consensus on optimal roadmaps. The participatory process of stakeholders includes iteratively proposing stepwise investment constraints, submitting roadmap optimization tasks, analyzing spatiotemporal results from multiple perspectives, and selecting preferred roadmaps. The proposed system design separates the participatory process of non-expert stakeholders from the specialized modeling process of constructing simulation-optimization tools for BMP roadmaps, which is done in advance by professional modelers and encapsulated as webservices on the server side. The webservices expose a small set of essential parameters to lower barriers to use. The interactive participatory process is presented to stakeholders through web browsers with an easy-to-use interface. The system design was evaluated by implementing an agricultural watershed planning system for soil erosion reduction and conducting a role-playing experiment involving three groups of stakeholders with different standpoints in proposing investment constraints. The experimental results show that the optimal roadmap sets exhibit progressive improvements across three-round optimizations started by different stakeholders, effectively capturing the varying perspectives of stakeholders and facilitating consensus-building among them. The idea of system design and example implementation can serve as a valuable reference for developing related user-friendly environmental decision support systems.

Reinforcement Learning Based Topology Control for UAV Networks

The recent development of unmanned aerial vehicle (UAV) technology has shown the possibility of using UAVs in many research and industrial fields. One of them is for UAVs moving in swarms to provide wireless networks in environments where there is no network infrastructure. Although this method has the advantage of being able to provide a network quickly and at a low cost, it may cause scalability problems in multi-hop connectivity and UAV control when trying to cover a large area. Therefore, as more UAVs are used to form drone networks, the problem of efficiently controlling the network topology must be solved. To solve this problem, we propose a topology control system for drone networks, which analyzes relative positions among UAVs within a swarm, then optimizes connectivity among them in perspective of both interference and energy consumption, and finally reshapes a logical structure of drone networks by choosing neighbors per UAV and mapping data flows over them. The most important function in the scheme is the connectivity optimization because it should be adaptively conducted according to the dynamically changing complex network conditions, which includes network characteristics such as user density and UAV characteristics such as power consumption. Since neither a simple mathematical framework nor a network simulation tool for optimization can be a solution, we need to resort to reinforcement learning, specifically DDPG, with which each UAV can adjust its connectivity to other drones. In addition, the proposed system minimizes the learning time by flexibly changing the number of steps used for parameter learning according to the deployment of new UAVs. The performance of the proposed system was verified through simulation experiments and theoretical analysis on various topologies consisting of multiple UAVs.

Applications and Perceptions of Workforce Management Systems for Warehouse Operation - Results and Findings from Expert Interviews

Workforce management represents a crucial element of today's warehouse operations. Due to volatile market dynamics and uncertain customer demands, the need for flexible and adaptive warehouse practices is especially urgent in the retail sector. High fluctuation rates in workforce demand are just one side effect of these developments. The current paper analyzes the application and perception of Workforce Management Systems (WMS) in practice as one approach to counteract these issues and enable flexible warehouse operations. By means of an online survey and a detailed set of expert interviews, we analyze the current use of WMS and information items in retail. We investigate design and time horizons of workforce planning processes as well as forecasts and summarize main expectations and requirements of WMS from practitioners’ point-of-view. Our results show highly diverse usage patterns of systems with a strong emphasis on spreadsheet tools like MS Excel. The integration of legislation on working hours, qualification of employees and individual working time arrangements / contract details were mentioned as exemplary key information items of WMS. The typical time horizon of workforce planning for most participants was three months. Key requirements for an ideal WMS include the depiction of qualification profiles, productivity of individual employees, forecasting features, simulation-optimization tools to find best possible solutions with given workforce, automated planning as well as bottleneck reduction and handling. The results of our paper provide practitioners with an overview of expert opinions, present opportunities for system development, and provide future research tasks in WMS for warehouse operation.

Optimal siting of rainwater harvesting systems for reducing combined sewer overflows at city scale

The installation of green infrastructure (GI) is an effective approach to manage urban stormwater and combined sewer overflow (CSO) by restoring pre-development conditions in urban areas. Research on simulation-optimization techniques to aid with GI planning decision-making is expanding. However, due to high computational expense, the simulation-optimization methods are often based on design storm events, and it is unclear how much different rainfall scenarios (i.e., design storm events vs. long-term historical rainfall data) impact the optimal siting of GI. The Parallel Pareto Archived Dynamically Dimensioned Search (ParaPADDS) algorithm in a novel simulation-optimization tool OSTRICH-SWMM was used to leverage distributed computing resources. A case study was conducted to optimally site rainwater harvesting cisterns within 897 potential subcatchments throughout the City of Buffalo, New York. Seven design storm events with different return periods and rainfall durations and a one-month historical rainfall time series were considered. The results showed that the optimal solutions of siting cisterns using event-based scenarios, though less computationally expensive, may not perform well under continuous rainfall scenarios, suggesting design rainfall scenarios should be carefully considered for optimizing GI planning. The impact of rainfall scenarios was particularly significant in the middle region of the Pareto front of multi-objective optimization. Utilizing high-performance parallel computing, OSTRICH-SWMM is a promising tool to optimize GI at large spatial and temporal scales.

Posterior-Based Stopping Rules for Bayesian Ranking-and-Selection Procedures

Sequential ranking-and-selection procedures deliver Bayesian guarantees by repeatedly computing a posterior quantity of interest—for example, the posterior probability of good selection or the posterior expected opportunity cost—and terminating when this quantity crosses some threshold. Computing these posterior quantities entails nontrivial numerical computation. Thus, rather than exactly check such posterior-based stopping rules, it is common practice to use cheaply computable bounds on the posterior quantity of interest, for example, those based on Bonferroni’s or Slepian’s inequalities. The result is a conservative procedure that samples more simulation replications than are necessary. We explore how the time spent simulating these additional replications might be better spent computing the posterior quantity of interest via numerical integration, with the potential for terminating the procedure sooner. To this end, we develop several methods for improving the computational efficiency of exactly checking the stopping rules. Simulation experiments demonstrate that the proposed methods can, in some instances, significantly reduce a procedure’s total sample size. We further show these savings can be attained with little added computational effort by making effective use of a Monte Carlo estimate of the posterior quantity of interest. Summary of Contribution: The widespread use of commercial simulation software in industry has made ranking-and-selection (R&S) algorithms an accessible simulation-optimization tool for operations research practitioners. This paper addresses computational aspects of R&S procedures delivering finite-time Bayesian statistical guarantees, primarily the decision of when to terminate sampling. Checking stopping rules entails computing or approximating posterior quantities of interest perceived as being computationally intensive to evaluate. The main results of this paper show that these quantities can be efficiently computed via numerical integration and can yield substantial savings in sampling relative to the prevailing approach of using conservative bounds. In addition to enhancing the performance of Bayesian R&S procedures, the results have the potential to advance other research in this space, including the development of more efficient allocation rules.

Building Retrofitting through Coupling of Building Energy Simulation-Optimization Tool with CFD and Daylight Programs

Simultaneous satisfaction of both thermal and visual comfort in buildings may be a challenging task. Therefore, this paper suggests a comprehensive framework for the building energy optimization process integrating computational fluid dynamics (CFD) daylight simulations. A building energy simulation tool, IDA Indoor Climate and Energy (IDA-ICE), was coupled with three open-source tools including GenOpt, OpenFOAM, and Radiance. In the optimization phase, several design variables i.e., building envelope properties, fenestration parameters, and Heating, Ventilation and Air-Conditioning (HVAC) system set points, were selected to minimize the total building energy use and simultaneously improve thermal and visual comfort. Two different scenarios were investigated for retrofitting of a generic office building located in Oslo, Norway. In the first scenario a constant air volume (CAV) ventilation system with a local radiator in each zone was used, while an all-air system equipped with a demand control ventilation (DCV) was applied in the second scenario. Findings showed that, compared to the reference design, significant reduction of total building energy use, around 77% and 79% in the first and second scenarios, was achieved respectively, and thermal and visual comfort conditions were also improved considerably. However, the overall thermal and visual comfort satisfactions were higher when all-air system was applied.

A review of conjunctive GW-SW management by simulation–optimization tools

Abstract The conjunctive use of groundwater and surface water (GW-SW) resources has grown worldwide. Optimal conjunctive water use can be planned by coupling hydrologic models for the simulation of water systems with optimization techniques for improving management strategies. The coupling of simulation and optimization methods constitutes an effective approach to determine sustainable management strategies for the conjunctive use of these water resources; yet, there are challenges that must be addressed. This paper reviews (1) hydrologic models applied for the simulation of GW-SW interaction in the water resources systems, (2) conventional optimization methods, and (3) published works on optimized conjunctive GW-SW use by coupling simulation and optimization methods. This paper evaluates the pros and cons of GW-SW simulation tools and their applications, thus providing criteria for selecting simulation–optimization methods for GW-SW management. In addition, an assessment of GW-SW simulation–optimization tools applied in various studies over the world creates valuable knowledge for selecting suitable simulation–optimization tools in similar case studies for sustainable water resource management under multiple scenarios.

Integrated hydro-economic modeling for optimal design of development scheme of salinity affected irrigated agriculture in Helleh River Basin

Helleh River, a southern vital agricultural ecosystem in Iran, has been suffering from both water shortage and quality degradation in recent decades. To deal with the problems, different stakeholders have been suggesting the central government some compartment development plans including construction of dams and irrigation districts, installation of modern irrigation technologies, and modification of crop patterns and irrigation–leaching strategies. Accordingly, this paper proposes a generic novel hydro-economic methodology for basin-scale optimal planning of an integrated development scheme for Helleh River Basin. Mentioned measures in different spatial scales from farm-level to basin-level as well as distinct time horizons of short-term and long-term are holistically evaluated and optimized based on technical, hydrologic, agronomic, and economic aspects. In this regard, a water allocation simulation model, i.e. WEAP software, improved here by some extra modules for salt routing and economic evaluation in Python Programming Environment is coupled with an optimization algorithm, i.e. Particle Swarm Optimization, which results in a new holistic hydro-economic simulation–optimization tool. Results demonstrate the economic efficiency of constructing three new dams, improvement and development of 19930 ha (ha) of irrigation area, irrigation technology, and strategy change, and crop pattern modification. Furthermore, results show that optimizing irrigation–leaching schedule leads to significant improvement of the economic value of water compared to the status quo, while construction and operation of structural projects result in a dramatic decrease in the economic value of water due to increase in both costs and leaching-related water usage. We discuss how irrigation technology improvement in salinity-affected basin may lead to a decrease in the economic value of water. Moreover, we advocate for the application of deficit irrigation strategy while respecting salt leaching requirements and cultivation of lower water-consuming crops. Results demonstrate how reservoirs’ operation takes a minor role in the regulation of rivers’ salinity, while we anticipate a significant increase in salt concentration of crops’ root zones due to more efficient water use.

FUZZY MONTE CARLO SIMULATION OPTIMIZATION FOR SELECTING MATERIALS IN GREEN BUILDINGS

Global interest in sustainable and green building design has been increasing in the last few decades. This interest is strengthened by the fact that sustainable measures help in reducing negative social and environmental impacts of buildings. For that, this paper aims to develop a mixed integer optimization model that aids architects/designers and owner representatives during design stage in selecting building materials taking into consideration costs and risks that are involved in the selection process. The model is developed as a simulation optimization tool based on the Leadership in Energy and Environmental Design (LEED) rating system for new construction. The developed model allows deterministic and probabilistic cost analysis of various design alternatives. In addition, it identifies the least possible cost to gain the LEED credits and the risks associated with materials’ quantities and materials’ unit prices. To illustrate the use of the proposed tool, a case study of an office building project constructed in Egypt is presented. An integrated Fuzzy Monte Carlo Simulation (FMCS) analysis is performed to account for the associated risks of using new materials in the considered case study. The proposed model is capable to capture the cost uncertainty of building materials and to identify the cost and sustainability performance of various building materials by relating the LEED rating system for new construction.

Application of Clustered-NA-ACO in three-objective optimization of Water Distribution Networks

ABSTRACTOptimal operation of pumping stations in Water Distribution Networks (WDNs) is presented based on three conflicting objectives, including 1) pumping energy costs, 2) hydraulic reliability, and 3) quality reliability. We proposed a three-objective optimization algorithm called Clustered Non-dominated Archiving Ant Colony Optimization (Clustered-NA-ACO) and evaluated the efficiency of this algorithm through DTLZ test functions. Incorporating k-means clustering strategy with NA-ACO can greatly improve the distribution of solutions and reduce the run time compared to NA-ACO. A pressure-driven analysis tool called EPANET-Iterative Modifications to Nodal Outflows (EPANET-IMNO) is developed for dynamic analysis of WDNs and linked directly to Clustered-NA-ACO. We applied this simulation-optimization tool to two WDNs. The results show that it is necessary to consider hydraulic reliability and quality reliability as two separate objective functions in WDNs with storage tanks. We also suggested the combined form of the two reliability indicators in the optimization of WDNs without any storage tanks.

Simulation–Optimization Tool for Multiattribute Reservoir Systems

AbstractReservoirs, as the largest water systems for controlling water and supply demands, have been particularly important due to their operational requirements. Applying practical tools to evalua...

Quantitative management of groundwater resources in regional aquifers under uncertainty: A retrospective optimization approach

Simulation-optimization models have been widely developed and used for many decades in groundwater resource management. However, simulation-optimization models in most applications encompass data that are subject to uncertainty. Groundwater aquifer hydrogeology condition is highly uncertain; hence it is important that this uncertainty is taken into account when managing groundwater resource pumping rates. Various methodologies have been developed and used by researchers to tackle uncertainty, simulation-optimization models are often used for groundwater resource management. However, direct application of such an approach in which all realizations are considered at each iteration of the optimization process leads to a very expensive optimization, particularly when the number of realizations is large. This, therefore, highlights the need for an efficient simulation-optimization tool that can be used to sustainably manage the limited water resource under uncertainty conditions. In recent times, retrospective optimization approximation (ROA) approach has emerged to be useful simulation-optimization tool that can efficiently incorporate uncertainty. This paper, introduces a procedure which is new within the context of regional groundwater resource management – retrospective optimization approximation approach. ROA procedure solves and evaluates a sequence of optimization sub-problems in an increasing number of realizations (sample sizes). Response matrix technique was used to combine simulation model with optimization procedure (model). We adopted k-means clustering sampling technique for realizations mapping. By using k-means clustering sampling the ROA-Active Set procedure was able to find a (nearly) converged solution within a relatively few number of iterations (within 6–7 iterations). The methodology is demonstrated through an application to a real-world aquifer system found in the Great Letaba River catchment located at Mopani district in South Africa. The results demonstrate that ROA sampling-based approach is capable of reproducing indicative optimal (sustainable) groundwater pumping rates which can be useful for managing regional aquifers groundwater resource under geological uncertainty conditions.

A multiobjective simulation-optimization tool for assisting in urban watershed restoration planning

Significant efforts are being made to restore urban watersheds. Stormwater control measures (SCMs) reduce runoff and provide water quality treatment, and are the predominate means of achieving restoration objectives. Determining an effective implementation strategy for these measures often requires a simulation model. The Storm Water Management Model (SWMM) is a hydrologic-hydraulic and water quality model specifically developed for use in urban watersheds. We enhanced an existing external control program for SWMM by adding automated calibration for user-selected events, sensitivity analysis capability, and a multiobjective cost-optimization feature using a well-known evolutionary solver, the Non-dominated sorting genetic algorithm (NSGA-II). The enhanced program, RSWMM-Cost, was then applied to a previously developed SWMM model of the Difficult Run watershed of Fairfax County, Virginia. Calibration and verification were performed using the program. Sensitivity analysis was conducted by varying SCM attributes as a function of performance metrics. Cost-optimization was conducted on a 123.4 ha headwater subcatchment of the watershed using generalized cost relationships and constraints based upon SCM design guidance and water quality requirements. A cost-effectiveness curve was generated, consisting of sets of SCMs that either are optimal or nearly so. Thus, RSWMM-Cost can assist decision makers identify watershed management strategies that meet water quality goals.

SWAT-MODSIM-PSO optimization of multi-crop planning in the Karkheh River Basin, Iran, under the impacts of climate change

Agriculture is one of the environmental/economic sectors that may adversely be affected by climate change, especially, in already nowadays water-scarce regions, like the Middle East. One way to cope with future changes in absolute as well as seasonal (irrigation) water amounts can be the adaptation of the agricultural crop pattern in a region, i.e. by planting crops which still provide high yields and so economic benefits to farmers under such varying climate conditions. To do this properly, the whole cascade starting from climate change, effects on hydrology and surface water availability, subsequent effects on crop yield, agricultural areas available, and, finally, economic value of a multi-crop cultivation pattern must be known. To that avail, a complex coupled simulation-optimization tool SWAT-LINGO-MODSIM-PSO (SLMP) has been developed here and used to find the future optimum cultivation area of crops for the maximization of the economic benefits in five irrigation-fed agricultural plains in the south of the Karkheh River Basin (KRB) southwest Iran. Starting with the SWAT distributed hydrological model, the KR-streamflow as well as the inflow into the Karkheh-reservoir, as the major storage of irrigation water, is calibrated and validated, based on 1985–2004 observed discharge data. In the subsequent step, the SWAT-predicted streamflow is fed into the MODSIM river basin Decision Support System to simulate and optimize the water allocation between different water users (agricultural, environmental, municipal and industrial) under standard operating policy (SOP) rules. The final step is the maximization of the economic benefit in the five agricultural plains through constrained PSO (particle swarm optimization) by adjusting the cultivation areas (decision variables) of different crops (wheat, barley, maize and “others”), taking into account their specific prizes and optimal crop yields under water deficiency, with the latter computed in the LINGO-sub-optimization module embedded in the SLMP-tool. For the optimization of the agricultural benefits in the KRB in the near future (2038–2060), quantile-mapping (QM) bias-corrected downscaled predictors for daily precipitation and temperatures of the HadGEM2-ES GCM-model under RCP4.5- and RCP8.5-emission scenarios are used as climate drivers in the streamflow- and crop yield simulations of the SWAT-model, leading to corresponding changes in the final outcome (economic benefit) of the SLMP-tool. In fact, whereas for the historical period (1985–2004) a total annual benefit of 94.2 million US$ for all multi-crop areas in KRB is computed, there is a decrease to 88.3 million US$ and 72.1 million US$ for RCP4.5 and RCP8.5, respectively, in the near future (2038–2060) prediction period. In fact, this future income decrease is due to a substantial shift from cultivation areas devoted nowadays to high-price wheat and barley in the winter season to low-price maize-covered areas in the future summers, owing to a future seasonal change of SWAT-predicted irrigation water available, i.e. less in the winter and more in the summer.

Environmental performance of speed optimization strategies in offshore supply vessel planning under weather uncertainty

Supply vessels serving oil and gas installations at sea are a major emission source of greenhouse gases in offshore logistics. Speed optimization strategies applied in tactical vessel planning significantly reduce fuel consumption and thus emissions. Nevertheless, the environmental performance of these strategies in rough weather conditions is difficult to estimate as the duration of supply vessel operations is weather dependent. In this paper we develop a simulation-optimization tool which estimates the average fuel consumption for weekly vessel schedules. The schedules are constructed by using speed optimized vessel voyages which are simulated under different weather conditions. Results of experiments on real instances show that rough weather conditions increase fuel consumption and costs. The application of speed optimization strategies reduces fuel consumption both in winter and summer, but the reduction may be at the expense of a fixed vessel cost increase in the winter season.

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This paper presents the development of a new methodology for relative permeabilities estimation, through the match of the simulation results with the experimental data, obtained from unsteady state displacements. The development of the simulationoptimization tool, in Matlab environment, necessary for the automatic match is presented too. Six unsteady state displacements are performed; four of two-phase and two of three-phase, the experimental data obtained are analyzed using the proposed algorithm. The results from two-phase experiments are compared with those of Sendra software and it is found that the proposed algorithm has a good performance respect to this commercial tool. The results from the three-phase cases show that the algorithm achieves effectively estimate the relative permeability that produce the history match; It is found that there are differences with respect to the estimated curves for two-phase systems.Keywords: Relative Permeability Three-Phase, Coreflooding Simulation, Numerical Optimization, History Match.

Optimization of thermal and daylight performance of school buildings based on a multi-objective genetic algorithm in the cold climate of China

A high demand for heating energy in winter, overheating in summer and visual discomfort are important issues in school buildings in the cold climate of China. This study presents the use of simulation–optimization tools to find the optimal trade-off between minimizing energy use for heating and lighting, reducing summer discomfort time and maximizing the Useful Daylight Illuminance (UDIavg, between 100 and 2000lx). Different spatial configurations were investigated, including a single-sided open corridor type, a single-sided enclosed corridor type and a double-sided corridor type school. The following passive design parameters were considered in the optimization analysis: orientation, room depth and corridor depth, window-to-wall ratio of different interfaces, glazing materials and shading types.The results of the optimization study show that the energy demand for heating and lighting can be reduced by 24–28% and summer thermal discomfort by 9–23% while the UDIavg (100–2000lx) can simultaneously increase by 15–63%. Considering all three aspects, the double-sided enclosed corridor type performs best due to its energy performance while the one-sided enclosed corridor type is the least suitable for the cold climate because of its relatively poor visual comfort quality. In addition, the passive design parameters should be carefully considered as each spatial configuration has its own optimal passive design parameters.

An integrated assessment approach to optimal forest bioenergy production for young Scots pine stands

Bioenergy is re-shaping opportunities and imperatives of forest management. This study demonstrates, through a case study in Scots pine (Pinus sylvestris L.), how forest bioenergy policies affect stand management strategies. Optimization studies were examined for 15 Scots pine stands of different initial stand densities, site types, and temperature sum regions in Finland. Stand development was modelled using the PipeQual stand simulator coupled with the simulation-optimization tool OptiFor Bioenergy to assess three forest bioenergy policies on energy wood harvest from early thinnings. The optimal solutions maximizing bare land value indicate that conventional forest management regimes remain optimal for sparse stands. Energy harvests occurred only when profitable, led to lower financial returns. A forest bioenergy policy which included compulsory energy wood harvesting was optimal for denser stands. At a higher interest rate (4 %), increasing energy wood price postponed energy wood harvesting. In addition, our results show that early thinning somewhat reduced wood quality for stands in fertile sites. For less fertile sites, the changes were insignificant. A constraint of profitable energy wood harvest is not rational. It is optimal to carry out the first thinning with a flexible forest bioenergy policy depending on stand density.

Simulation tool for optimization and performance prediction of a generic hybrid electric series powertrain

Hybrid and electric vehicles are taking an increasingly important slice of the market, gaining much interest from major car manufacturers which have decided to invest in this sector, taking as example the pioneers like Toyota. The key factor to hybrid and electric vehicle success is a good overall mileage achieved from the battery back or powertrain. The purpose of this work is to provide a support to design, testing, and development of such vehicles through the implementation of a mathematical model in order to simulate the operation and predict the performance of a generic ground vehicle equipped with either a purely electric or a hybrid-electric type powertrain. The model should enable the user to estimate the impact of various control strategies on mileage range, efficiency, energy consumption, etc. The model should also allow for a significant time to market reduction with all the related benefits in terms of cost etc. A validation is also provided, based on the application of this tool on a so-called micro-car (0.5t GVW class). Thanks to a joint research project with the manufacturer it has been possible to compare model results with real-world data directly obtained during road testing with the help of a data acquisition system.

Derivative-based hybrid heuristics for continuous-time simulation optimization

The topic of simulation–optimization has not been fundamentally tackled by many continuous-time modeling and simulation tools, yet. Common simulation-based optimization problems are usually coupled with standard optimization algorithms like any other simulation-free nonlinear optimization problems. While such couplings are usually based on many state-of-the-art software engineering concepts with a high-level user interface for flexible incorporation of simulation and optimization, the design of specialized optimization strategies targeting simulation-based objective functions is lacked within many simulation–optimization tools. In this work, new redefinition of Non Linear Programming (NLP) problems in the context of continuous-time simulation optimization is presented. Then, the modified optimization problems are efficiently tackled using derivative-based hybrid heuristics. In order to specify, illustrate and implement such heuristics, a new terminology is proposed. According to the proposed terminology, derivative-based hybrid strategies are implemented by hybridizing naive multistart derivative-based optimization methods with population-based metaheuristics. It is shown that the adoption of derivative-based optimization methods within hybrid optimization strategies significantly improves the solution quality of continuous-time simulation optimization problems.