Simulation-based Optimization Method Research Articles

A novel inlet air cooling system based on liquefied natural gas cold energy utilization for improving power plant performance

The performance of gas turbine-based power plants decreases significantly as ambient temperature increases. For improving power plant performance, this paper proposes a novel inlet air cooling system based on liquefied natural gas cold energy utilization. The novel system consists of two organic Rankine cycles in series and a mechanical vapor compression chiller. A simulation-based optimization method is developed to optimize its design parameters for maximizing the power plant performance. Moreover, the off-design performances of the proposed system and the power plant with and without inlet air cooling are evaluated under different ambient conditions. The results show that the novel inlet air cooling system gives larger air temperature drop (2.1–5.3 °C) and higher cooling duty (6.9–7.3 MW) than a literature design. Furthermore, for 28–40 °C ambient temperatures, the power plant with the novel inlet air cooling produces 1.83–14.4% (6.4–52.6 MW) higher power output than that without inlet air cooling. Importantly, the novel cooling system improves the plant power output by 0.79–3.04% (2.8–11.1 MW) when compared with the literature design. Finally, the economic analysis shows that the proposed system is economically viable with a net present value of $1.6–34.4 million higher than the literature design.

A stochastic simulation-based optimization method for equitable and efficient network-wide signal timing under uncertainties

The equity of right-of-way is an important topic in traffic management and control. With the balance consideration of traffic equity and efficiency, which are respectively evaluated by the Atkinson index and average travel time, this study proposes a bi-objective signal timing simulation-based optimization (SO) model under uncertainties, and solve it by a bi-objective stochastic simulation-based optimization (BOSSO) method. In this method, two types of surrogate models (i.e., regressing Kriging model and quadratic regression model) are used to capture the complicated mapping relationship between decision variables and bi-objectives, respectively in the whole variable domain and in the local trust-region. Meanwhile, the incorporation of the global regressing Kriging model and an adaptive selector helps to predict bi-objective values of untested samples and re-estimate simulated samples in the local trust-region, which can save great computational costs and smooth stochastic noises. Moreover, the non-interactive role of a decision maker is taken to generate more Pareto optimal solutions around his/her desired bi-objective values. Through the algorithm comparison for a benchmark bi-objective stochastic optimization problem, the proposed BOSSO method is validated to outperform three other counterparts (i.e., NSGA-II, BOTR and BOEGO) under the same simulation costs. In real-field experiments, an urban road network with 15 signalized and five non-signalized intersections in Changsha, China is modeled by VISSIM. After the well calibration of the microscopic traffic simulation model, the network-wide bi-objective signal timing stochastic SO problems with and without coordination are solved by BOSSO. Numerical results indicate that compared with the real-field case, the average travel time and Atkinson index are reduced respectively by at most 13.48% and 23.49% for optimized non-coordinated signal plans, and respectively by at most 25.58% and 2.83% for optimized coordinated ones. It is further validated that under variable traffic volumes, the non-coordinated signal plan can well improve both traffic efficiency and equity, and the coordinated one is capable to improve traffic efficiency at a larger degree but sacrifice traffic equity. Moreover, the balance analyses show the existence of competing relationship between bi-objectives, and BOSSO is confirmed to outperform NSGA-II, BOTR and BOEGO in searching the better Pareto optimal signal plans under the same budged simulations. In conclusion, BOSSO is promising to address bi-objective optimization problems characterized by costly evaluation, high dimensions and stochastic noises.

Network-wide signal timing stochastic simulation optimization with environmental concerns

This study addresses a network-wide signal timing optimization problem with environmental concerns by a bi-objective stochastic simulation-based optimization (BOSSO) method. In this method, the global samples evaluated by costly simulation are used to build a type of surrogate model named the regressing Kriging model, which are then employed to predict bi-objectives of untested samples or filter noises from the evaluated samples. An adaptive selector is incorporated to determine which samples in the local trust-region are evaluated by costly simulation and which ones by the built regressing Kriging model. This helps to balance computational costs and accuracies of three quadratic regression models, especially when the variable dimension is high. The non-interactive role of a decision maker is taken to generate more non-dominated solutions around the desired bi-objective point. In the field experiments, an urban road network with 15 signalized and five non-signalized intersections in Changsha, China, is modeled as the simulation scenario by VISSIM. Then, the traffic simulation model is firstly calibrated from two aspects by the BOSSO method, which can well reproduce the reality. After that, the network-wide bi-objective signal timing optimization problem is also solved by the BOSSO method. Numerical results show that compared with the real-field traffic states, the total delay and vehicular emissions are reduced by at most 16.90% and 32.22% respectively under the budged number of simulations. Balance analyses also show the existence of a competing relationship between bi-objectives. Finally, the BOSSO method is validated to outperform three other counterparts (NSGA-II, BOTR and BOEGO) from various aspects.

Adaptive State Space Partitioning for Dynamic Decision Processes

With the rise of new business processes that require real-time decision making, anticipatory decision making becomes necessary to use the available resources wisely. Dynamic real-time problems occur in many business fields, for example in vehicle routing applications with stochastic customer service requests expecting a fast response. For anticipatory decision making, offline simulation-based optimization methods like value function approximation are promising solution approaches. However, these methods require a suitable approximation architecture to store the value information for the problem states. In this paper, an approach is proposed that finds and adapts this architecture iteratively during the approximation process. A computational proof of concept is presented for a dynamic vehicle routing problem. In comparison to conventional architectures, the proposed method is able to improve the solution quality and reduces the required architecture size significantly.

Discrete simulation-based optimization methods for industrial engineering problems: A systematic literature review

In recent years, some attention has been driven to modeling, simulation, and optimization techniques capable of representing and improving discrete event systems. These techniques can support decision making helping to determine the best scenario on a combinatorial search space with stochastic variables. This paper presents findings from a systematic literature review of discrete simulation-based optimization applied to industrial engineering problems. It indicates the most frequent contexts, problems, methods, tools, and intended results of discrete-simulation based studies published in the last 25 years (1991–2016) in scientific journals and conference proceedings. The four research questions presented a scenario to help practitioners and researchers to develop simulation optimization projects for industrial engineering problems. A conclusion presented the gap and prospects found during the writing of the research.

Potential for Machine Learning in Optimized Production Planning with Hybrid Simulation

Advanced production planning and scheduling approaches increasingly rely on simulation-based optimization methods. This entails the problem of a high computational effort due to complex models, resulting in limitations for the practical application of otherwise powerful methods. While machine-learning methods offer a potential for performance improvement, approaches for real-life applications with a high complexity are still lacking. This paper explores the potential for machine learning, especially artificial neural networks, used as surrogate models, to improve the performance of a recently developed planning method for real life production planning applications. The simulation considered in this paper is a complex hybrid discrete-continuous model, enabling the method to pursue energy efficiency simultaneously with economic goals, in a complex multi-criteria goal system. The artificial neural network is trained via offline learning and is meant to provide a computationally cheap evaluation of intermediate planning solutions, compiled by an optimization algorithm during an optimization run.The approach is developed and evaluated in a case-study on the food industry, indicating a basic feasibility of the approach but also pointing out necessary future challenges to be solved towards practical applicability.

Increasing productivity in heavy machining using a simulation based optimization method for porcupine milling cutters with a modified geometry

Porcupine milling cutters offer a high potential for increasing the metal removal rate in heavy machining of steel and titanium. Here, the available machine power and the maximum radial force represent important process limits. According to the current state of the art, mainly rectangular indexable inserts are used. Investigations show that the use of round inserts can significantly reduce the resulting radial force and cutting torque similar to serrated endmills. However, the design of such tools is a major challenge due to the complicated shape of cross-section of the undeformed chip. Therefore, this paper presents a new method for optimizing the position of individual indexable inserts by means of geometric material removal simulations. With the new method, the radial force can be reduced by 14%.

Identifying the Relationship between Assignments of Scenario Weights and their Positions in the Derivation of Reservoir Operating Rules under Climate Change

In order to mitigate the adverse impacts of climate change, adaptive operating rules (AOR) are generally derived using an ensemble of General Circulation Models (GCMs). Up to date, most of related literatures only focus on one fold of the following issues concerning the derivation of AOR using the GCMs ensemble, including: (1) consideration of different scenario weighing methods, or (2) analysis of different positions to locate scenario weights. And less concern is given to the latter compared with the former. However, few studies identify the relationship between (1) and (2) in the derivation of AOR based on the GCMs ensemble. In this study, we attempt to investigate where to use Equal and REA scenario weights in the derivation of reservoir operating rules under climate change. Equal weights (EW) and unequal weights based on the reliability ensemble average (REA) method are used in two positions: (I) the optimization objective of the reservoir operation model, which is to maximize the weighted average hydropower generation for all future scenarios; and (II) the incorporation of GCMs ensemble climate projections into the weighted climate conditions, and then it is input into the reservoir operation model with the objective of maximizing annual hydropower generation. Four AORs, including EW-AOR(I), REA-AOR(I), EW-AOR(II) and REA-AOR(II), are derived, and their optimized parameters are obtained by the simulation-based optimization (SBO) method with the Complex algorithm. The case study in the Jinxi Reservoir in China indicates that REA-AOR(I) outperforms the other three operation schemes, and EW-AOR(II) performs better than REA-AOR(II). Therefore, equal weights are preferably used to incorporate climate conditions, while unequal weights based on REA method can improve the performance of the reservoir operation model. Generally, REA-AOR(I) and EW-AOR(II) are suggested for adaptive reservoir management under climate change.

Efficient calibration of microscopic car-following models for large-scale stochastic network simulators

This paper proposes a simulation-based optimization methodology for the efficient calibration of microscopic traffic flow models (i.e., car-following models) of large-scale stochastic network simulators. The approach is a metamodel simulation-based optimization (SO) method. To improve computational efficiency of the SO algorithm, problem-specific and simulator-specific structural information is embedded into a metamodel. As a closed-form expression is sought, we propose adopting the steady-state solution of the car-following model as an approximation of its simulation-based input-output mapping. This general approach is applied for the calibration of the Gipps car-following model embedded in a microscopic traffic network simulator, on a large network. To this end, a novel formulation for the traffic stream models corresponding to the Gipps car-following law is provided.The proposed approach identifies points with good performance within few simulation runs. Comparing its performances to that of a traditional approach, which does not take advantage of the structural information, the objective function is improved by two orders of magnitude in most experiments. Moreover, this is achieved within tight computational budgets, i.e., few simulation runs. The solutions identified improve the fit to the field measurements by one order of magnitude, on average. The structural information provided to the metamodel is shown to enable the SO algorithm to become robust to both the quality of the initial points and the simulator stochasticity.

Fluid arrivals simulation for choice network revenue management

Since the beginning of revenue management, simulation has been used to estimate the expected revenue resulting from an availability policy. It has also been used to verify the quality of forecasts by projecting them onto past availability policies. Recently, it has been used in simulation-based optimization approaches to find the best policy. Simulation thus has a central role in revenue management. We focus on the choice network revenue management (CNRM) problem that incorporates multiple resources and customer behavior. The traditional CNRM simulation is based on discrete customer arrivals; we propose a new approach based on fluid arrivals. Our estimator is biased, but we observe that the bias is often insignificant in practice and appears to be asymptotically null. Our approach consistently outperforms the traditional simulation in terms of estimation time and is thus a better choice for large instances. We also prove that it is equivalent to an approximation for the CNRM availability policy optimization problem. This equivalence limits the value of simulation-based optimization methods but allows us to propose heuristics to rapidly support the optimization.

A simulation-based optimization method for designing energy efficient buildings

Buildings have a remarkable impact on the environment, therefore finding efficient design configurations satisfying conflicting criteria such as, economic and environmental performance has become an important task. The aim of this study is to propose a methodology to aid designers in satisfying the requirements of government regulations and green building certification programs, while optimizing the energy consumption and maintaining the thermal comfort. In this context, a software package combining a tailor-made thermal simulation software and Matlab Optimtool is developed to implement proposed methodology. The developed software package offers an effective method to perform large number of simulations to find optimal building configuration. The software package was tested on a reference building that represents a typical residential building in Turkey. The testing process is conducted on a five story building located in Istanbul and building information such as building size, location and occupation schedule were used within the developed software. Based on an extensive market search for building materials, cost functions for each parameter were developed as well as a database required for optimization process. A genetic algorithm optimization technique was utilized to minimize the objective function and find the optimal building configuration for the selected building. Development of a simulation-based optimization method fulfills the need for a tool that assists designers to find better design alternatives at the conceptual design stage. The software package requires least amount of data input for energy simulation process to improve usability. A tailor-made energy simulation module was developed to significantly reduce optimization time period. Besides, instead of coupling two separate software packages, performing the energy simulation and optimization processes on a single platform (MATLAB) reduces the time required to find optimal design and eliminates compatibility issues. Developing simulation-based optimization software on a single platform increased the flexibility and user-friendliness of the software. The effectiveness of the approach for finding optimal building configuration is demonstrated in the presented test cases.

Modeling and Evaluating Traffic Flow at Sag Curves When Imposing Variable Speed Limits on Connected Vehicles

Sag curves, road segments where the gradient changes from downwards to upwards, generally reduce the roadway capacity and cause congestion. This results from a change in longitudinal driving behavior when entering a sag curve as drivers tend to reduce speeds or increase headways as vehicles reach the uphill section. In this research, a control strategy is investigated through manipulating the speed of connected vehicles (CVs) in the upstream of the sag curve to avoid the formation of bottlenecks caused by the change in driver behavior. Traffic flow along a sag curve is simulated using the intelligent driver model (IDM), a time-continuous car-following model. A feedback control algorithm is developed for adjusting the approach speeds of CVs so that the throughput of the sag curve is maximized. Depending on the traffic density at the sag curve, adjustments are made for the speeds of the CVs. A simulation-based optimization method using a meta-heuristic algorithm is employed to determine the critical control parameters. Various market penetration rates for CVs are also considered in the simulations. Even at relatively low market penetration rates (e.g., 5–10%), significant improvements in travel times and throughput are observed.

Compensating Pose Uncertainties through Appropriate Gripper Finger Cutouts

Abstract The gripper finger design is a recurring problem in many robotic grasping platforms used in industry. The task of switching the gripper configuration to accommodate for a new batch of objects typically requires engineering expertise, and is a lengthy and costly iterative trial-and-error process. One of the open challenges is the need for the gripper to compensate for uncertainties inherent to the workcell, e.g. due to errors in calibration, inaccurate pose estimation from the vision system, or object deformation. In this paper, we present an analysis of gripper uncertainty compensating capabilities in a sample industrial object grasping scenario for a finger that was designed using an automated simulation-based geometry optimization method (Wolniakowski et al., 2013, 2015). We test the developed gripper with a set of grasps subjected to structured perturbation in a simulation environment and in the real-world setting. We provide a comparison of the data obtained by using both of these approaches. We argue that the strong correspondence observed in results validates the use of dynamic simulation for the gripper finger design and optimization.

A multi-objective Environmental Hedging Point Policy with customer satisfaction criteria

Abstract Recently, a new feedback control policy, named the Environmental Hedging Point Policy, to control the inventory and production was introduced. In this policy, inventory, backlog, and emission costs were taken into account. Employing low-emitting technology, this study aimed at developing this policy in order to integrate three objectives of costs, emissions, and customers’ satisfaction. Two prominent environmental control rules, namely command-and-control and cap-and-trade were examined by applying the proposed policy. To solve the problem, a new simulation-based optimization method combining OptQuest, experimental design, variance analysis, and response surface methodology, was conducted. This method improved the procedure of finding the second-order responses. Compromise solutions of a multiple-response problem were calculated thanks to the desirability function approach. Accordingly, the Pareto optimal surface was generated by changes in the weights or shape factors. The effectiveness of introduced policy was examined by sensitivity analysis. Thereupon, the shadow prices for both low-emitting technology and emission penalty were obtained. The results showed that considering shadow prices imposed by the regulatory authorities, the cap-and-trade rule was more effective in comparison with other rules.

Simulation-Based Optimization on Control Strategies of Three-Echelon Inventory in Hybrid Supply Chain With Order Uncertainty

Multi-echelon inventory control is the main form of the management of supply chain inventory, and it has been paid extensive attention by scholars because of its importance in supply chain management. Process industries, represented by metallurgy, petrochemical and pharmaceutical industry, have typical characteristics such as high investment, high costs, and high resource utilization, they play a key role in industrialization. In this paper, the simulation model of a multiproduct three-echelon inventory control system is established for the hybrid supply chain of the process industry. Based on the idea of control engineering, a feedback control law is designed for inventory control. Several mixed inventory control strategies were applied to the model. The proportional plus integral control algorithm is used to modify the inventory control strategies. Based on this simulation model, a simulation-based optimization method is applied to the three-echelon inventory control system. The results show that the reciprocal of total entropy ratio and customer satisfaction are optimized by the mixed inventory control strategies when the hybrid supply chain is affected by order uncertainty.

Simulation‐Based Optimization on the System‐of‐Systems Model via Model Transformation and Genetic Algorithm: A Case Study of Network‐Centric Warfare

Simulation of a system‐of‐systems (SoS) model, which consists of a combat model and a network model, has been used to analyze the performance of network‐centric warfare in detail. However, finding the combat model parameters satisfying the required combat power using simulation can take a long time for two reasons: (1) the prolonged execution time per simulation run and (2) the enormous number of simulation runs. This paper proposes a simulation‐based optimization method for the SoS‐based simulation model to overcome these problems. The method consists of two processes: (1) the transformation of the SoS‐based model into an integrated model using the neural network to reduce the execution time and (2) the optimization of the integrated model using the genetic algorithm with ranking and selection to decrease the number of simulation runs. The experimental result reveals that the proposed method significantly reduced the time for finding the optimal combat parameters with an acceptable level of accuracy.

Simulation-Based Multiobjective Optimization of Timber-Glass Residential Buildings in Severe Cold Regions

In the current context of increasing energy demand, timber-glass buildings will become a necessary trend in sustainable architecture in the future. Especially in severe cold zones of China, energy consumption and the visual comfort of residential buildings have attracted wide attention, and there are always trade-offs between multiple objectives. This paper aims to propose a simulation-based multiobjective optimization method to improve the daylighting, energy efficiency, and economic performance of timber-glass buildings in severe cold regions. Timber-glass building form variables have been selected as the decision variables, including building width, roof height, south and north window-to-wall ratio (WWR), window height, and orientation. A simulation-based multiobjective optimization model has been developed to optimize these performance objectives simultaneously. The results show that Daylighting Autonomy (DA) presents negative correlations with Energy Use Intensity (EUI) and total cost. Additionally, with an increase in DA, Useful Daylighting Illuminance (UDI) demonstrates a tendency of primary increase and then decrease. Using this optimization model, four building performances have been improved from the initial generation to the final generation, which proves that simulation-based multiobjective optimization is a promising approach to improve the daylighting, energy efficiency, and economic performances of timber-glass buildings in severe cold regions.

Simulation-based pricing optimization for improving network-wide travel time reliability

ABSTRACTTravel time variability in a network is an important measure of the transportation system performance and a major factor influencing the decision-making of system management such as congestion pricing. The congestion-pricing problem with reliability maximization as the goal of a transportation network is characterized by expensive-to-evaluate objective functions without closed forms. In this paper, an effective simulation-based optimization (SBO) method is utilized to solve the problem. The network reliability is measured by a weighted average link travel time coefficient of variation (CV) with link traffic flows as the weights. We employ DynusT to evaluate the system reliability as the objective function corresponding to different toll charges for a new toll road in Maryland. The results show that the two optimal toll charge strategies improve the network-wide reliability, reducing the weighted travel time CV by 9.60% and 1.16%, respectively, when compared to the baseline toll.

Solving a class of simulation-based optimization problems using “optimality in probability”

We consider a class of simulation-based optimization problems using optimality in probability, an approach which yields what is termed a “champion solution”. Compared to the traditional optimality in expectation, this approach favors the solution whose actual performance is more likely better than that of any other solution; this is an alternative complementary approach to the traditional optimality sense, especially when facing a dynamic and nonstationary environment. Moreover, using optimality in probability is computationally promising for a class of simulation-based optimization problems, since it can reduce computational complexity by orders of magnitude compared to general simulation-based optimization methods using optimality in expectation. Accordingly, we have developed an “Omega Median Algorithm” in order to effectively obtain the champion solution and to fully utilize the efficiency of well-developed off-line algorithms to further facilitate timely decision making. An inventory control problem with nonstationary demand is included to illustrate and interpret the use of the Omega Median Algorithm, whose performance is tested using simulations.

Reservoir adaptive operating rules based on both of historical streamflow and future projections

Climate change is affecting hydrological variables and consequently is impacting water resources management. Historical strategies are no longer applicable under climate change. Therefore, adaptive management, especially adaptive operating rules for reservoirs, has been developed to mitigate the possible adverse effects of climate change. However, to date, adaptive operating rules are generally based on future projections involving uncertainties under climate change, yet ignoring historical information. To address this, we propose an approach for deriving adaptive operating rules considering both historical information and future projections, namely historical and future operating rules (HAFOR). A robustness index was developed by comparing benefits from HAFOR with benefits from conventional operating rules (COR). For both historical and future streamflow series, maximizations of both average benefits and the robustness index were employed as objectives, and four trade-offs were implemented to solve the multi-objective problem. Based on the integrated objective, the simulation-based optimization method was used to optimize the parameters of HAFOR. Using the Dongwushi Reservoir in China as a case study, HAFOR was demonstrated to be an effective and robust method for developing adaptive operating rules under the uncertain changing environment. Compared with historical or projected future operating rules (HOR or FPOR), HAFOR can reduce the uncertainty and increase the robustness for future projections, especially regarding results of reservoir releases and volumes. HAFOR, therefore, facilitates adaptive management in the context that climate change is difficult to predict accurately.