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Simulation-based optimization is an effective solution to complex transportation problems relying on stochastic simulations. However, existing studies generally perform a fixed number of evaluations for each decision vector across the design space, overlooking simulation heteroscedasticity and its effects on solution efficiency and robustness. In this paper, we treat the number of evaluations as an adaptive variable depending upon the simulation heteroscedasticity and the potential optimality of each decision vector. A statistical method, which automatically determines the variable number of evaluations, is presented for a range of derivative-free optimization methods. By fusing Bayesian inference with the probability of correct selection, it permits adaptive allocation of budgeted computational resources to achieve improved solution efficiency and robustness. The method is integrated with the deterministic global optimizer DIviding RECTangles (DIRECT) to yield NoisyDIRECT as a continuous simulation-based robust optimization method (which is open sourced). The key properties of the method are proved and discussed. Numerical experiments on difficult test functions are first conducted to verify the improvement of NoisyDIRECT compared with DIRECT and Bayesian optimization. Given the same computational budget, NoisyDIRECT can better locate the global optimum than the other two alternatives. Applications to representative simulation-based transportation problems, including an M/M/1 queueing problem and a parking pricing problem, are then presented. The results demonstrate the ability of NoisyDIRECT to pinpoint the optimal solution via adaptive computational resources allocation, achieving the desired level of robustness. Funding: This work was supported by the Youth Program [Grant 52102375] and the Key Project [Grant 52131203] of the National Natural Science Foundation of China, the Youth Program [Grant BK20210247] of the Natural Science Foundation of Jiangsu Province, and the High-Level Personnel Project of Jiangsu Province [Grant JSSCBS20220099]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0485. [ABSTRACT FROM AUTHOR]

Flexible flat cables (FFCs) are a typical form of interconnect in modern electrical and electronic systems that facilitate signal transmission between components while minimizing harness volume. FFCs offer a practical connectivity solution in energy management applications, where sensors and displays are essential for monitoring power consumption and performing advanced digital control. In FFCs, signal lines run parallel to each other, and the proximity between lines can cause interference among adjacent signals. Therefore, the arrangement of signals along different lines can significantly influence the overall transmission performance. In this paper, the order of signals within the FFC is optimized to ensure optimal transmission performance, avoiding electromagnetic compatibility (EMC) and signal integrity (SI) issues. The problem is tackled by implementing a multi-objective optimization (MOO) approach, whose aim is to minimize near-end and far-end crosstalk, namely NEXT and FEXT. The effectiveness of the proposed approach is verified by considering a minimized interconnection system involving an FFC. The Pareto-optimal solutions are identified, and worst-case and best-case conditions are highlighted. The results show improvements in EMC and SI, underlining the relevance of the proposed optimization strategy. The proposed strategy provides a valuable tool for designing high-performance interconnections in electrical and electronic systems. [ABSTRACT FROM AUTHOR]

The main objective of the sectorization approach is to divide a vast region into symmetric sectors. Balancing accessibility of healthcare units, which is commonly between the goals of governments is closely related to the concept of sectorization. When the desired equilibrium between sectors is lost resectorization is applied. In this study, we propose a new model based on the resectorization concept for balancing the accessibility of healthcare services in a region. We consider each hospital and the patients that choose it as a sector. We define a new bi-objective function, which includes the goal of improving quality and accessibility. We use simulation-based optimization for resectorization and we portray it as a tool for policymaking based on contract mechanisms. A case study is utilized in the section on experimental results. The outcomes demonstrate that it is achievable to balance the accessibility of healthcare units with a suitable contract mechanism. [ABSTRACT FROM AUTHOR]

The types of constraints encountered in black-box simulation-based optimization problems differ significantly from those addressed in nonlinear programming. We introduce a characterization of constraints to address this situation. We provide formal definitions for several constraint classes and present illustrative examples in the context of the resulting taxonomy. This taxonomy, denoted KARQ, is useful for modeling and problem formulation, as well as optimization software development and deployment. It can also be used as the basis for a dialog with practitioners in moving problems to increasingly solvable branches of optimization. [ABSTRACT FROM AUTHOR]

An integrated energy system (IES) plays a key role in transforming energy consumption patterns and solving serious environmental and economic problems. However, the abundant optional schemes and the complex coupling relationship among each piece of equipment make the optimization of an IES very complicated, and most of the current literature focuses on optimization of a specific system. In this work, a simulation-based two-step decoupling method is proposed to simplify the optimization of an IES. The generalized IES is split into four subsystems, and a two-layer optimization method is applied for optimization of the capacity of each piece of equipment. The proposed method enables fast comparison among abundant optional configurations of an IES, and it is applied to a hospital in Beijing, China. The optimized coupling system includes the gas-fired trigeneration system, the GSHP, and the electric chiller. Compared with the traditional distributed systems, the emission reduction rate of CO2 and NOX for the coupling system reaches 153.8% and 314.5%, respectively. Moreover, the primary energy consumption of the coupling system is 82.67% less than that of the traditional distributed energy system, while the annual cost is almost at the same level. [ABSTRACT FROM AUTHOR]

This paper presents a method to design a static output feedback active suspension controller for ride comfort improvement and motion sickness reduction in a real vehicle system. Full-state feedback controller has shown good performance for active suspension control. However, it requires a lot of states to be measured, which is very difficult in real vehicles. To avoid this problem, a static output feedback (SOF) controller is adopted in this paper. This controller requires only three sensor outputs, vertical velocity, roll and pitch rates, which are relatively easy to measure in real vehicles. Three types of SOF controller are proposed and optimized with linear quadratic optimal control and the simulation optimization method. Two of these controllers have only three gains to be tuned, which are much smaller than those of full-state feedback. To validate the performance of the proposed SOF controllers, a simulation is carried out on a vehicle simulation package. From the results, the proposed SOF controllers are quite good at improving ride comfort and reducing motion sickness. [ABSTRACT FROM AUTHOR]

In the face of substantial environmental impact, energy-intensive industries are compelled to deal with a spectrum of future energy scenarios, including the adoption of electrification and hydrogen use. In addition, these sectors are under pressure to enhance energy efficiency and adaptability to reduce costs. The integrated planning and control of production and energy system components is challenging due to the complexity and lack of available methods, though. This paper addresses this gap, by introducing a simulation-based method that optimizes production and energy system components. The planning method is based on a multicriteria meta-heuristic with an integrated simulation of production and energy system behavior. It evauates different future energy scenarios with electrification and hydrogen use including variable energy markets and storage systems. The method is applied and validated in a case study in an energy-intensive steel production. The results show the significant benefits of the dynamic planning and optimization for various variables in two different heat-treatment process technologies. [ABSTRACT FROM AUTHOR]

This paper presents a method for designing an active suspension controller for ride comfort enhancement and motion sickness mitigation. For this, it is necessary to design an active suspension controller, which aims to reduce the vertical acceleration and pitch rate of a sprung mass in a vehicle. A half-car vehicle model was selected. For the controller design, a static output feedback (SOF) control was selected instead of a full-state feedback control because it is hard to measure all state variables in real vehicles. With the available signals, three types of SOF controller were proposed. To determine the gains of the SOF controllers, a linear quadratic optimal control methodology and a simulation-based optimization method were adopted. To validate the proposed method, a simulation was carried out using vehicle simulation software. The simulation results show that the proposed method is quite effective for ride comfort enhancement and motion sickness mitigation. [ABSTRACT FROM AUTHOR]

A joint policy of quality control, condition-based maintenance and spare ordering is proposed for a degradation system subject to the delay time concept. First, considering the fact that product quality is largely dependent on system state, a two-stage inspection policy is proposed, in which the system state is detected during the initial deterioration process, but the product quality is checked after the defective state is found by an inspection. Then, based on the condition inspection information, quality information and failure information, the corresponding maintenance activity is chosen. Combining the state of the spare part when the system replacement is required, all possible events during an inspection interval are discussed and then a mathematical model of average cost rate is established. Afterwards, a simulation-based optimization approach coupling discrete event simulation and response surface methodology is devised to obtain a near optimal joint policy. Finally, a numerical example is provided to demonstrate the effectiveness and applicability of the proposed policy by comparing it with the comparative policy.

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Autonomous driving technology heavily depends on accurate and smooth path tracking. Facing complex urban driving scenarios, developing a suite of high-performance and robust parameters for controllers becomes imperative. This paper proposes a stochastic simulation-based optimization model for optimizing the Proportional–Integral–Differential (PID) controller parameters, with tracking accuracy and smoothness as bi-objectives, and solves it using a domination-measure-based efficient global optimization (DMEGO) algorithm. In this model, the tracking accuracy and smoothness are indexed by the normalized dynamic time warping (NDTW) and the mean absolute lateral acceleration (MALA), respectively. In addition, we execute the PID controller in a realistic simulation environment using a CARLA simulator, which consider various city scenes, diverse routes, different vehicle types, road slopes, etc., to provide a comprehensive and reliable evaluation for the designed PID controller. In the DMEGO method, each solution undergoes evaluation using a fixed number of costly simulations. Then, utilizing the solutions and their estimated bi-objective values, two surrogate models for the bi-objectives are constructed using the Gaussian process (GP) model. The preliminary nondominated solutions can be obtained by optimizing the two surrogate models. Finally, a novel performance metric known as the domination measure is employed to evaluate the quality of each solution. This metric is then integrated with the crowding distance to selectively retain a candidate solution exhibiting superior performance and good diversity for the next iteration. In our numerical experiments, we first test the DMEGO algorithm against three other counterparts using a stochastic FON benchmark. The proposed approach is then employed to optimize the PID parameters considering the complexity and uncertainty of urban traffic. The numerical results demonstrate that the nondominated solutions obtained by DMEGO exhibit excellent performance in terms of tracking accuracy and smoothness under limited simulation budgets. Overall, the proposed approach may be a viable tool for solving multi-objective simulation-based optimization problem under uncertainties. [ABSTRACT FROM AUTHOR]

A significant number of sample points are often required for surrogate-based optimization when utilizing process simulations to cover the entire system space. This necessity is particularly pronounced in complex simulations or high-dimensional physical experiments, where a large number of sample points is essential. In this study, we have developed an adaptive Latin hypercube sampling (LHS) method that generates additional sample points from areas with the highest output deviations to optimize the required number of samples. The surrogate model used for the optimization problem is artificial neural networks (ANNs). The standard for measuring solution accuracy is the percent error of the optimal solution. The outcomes of the proposed algorithm were compared to those of random sampling for validation. As case studies, we chose three different chemical processes to illustrate problems of varying complexity and numbers of variables. The findings indicate that for all case studies, the proposed LHS optimization algorithm required fewer sample points than random sampling to achieve optimal solutions of similar quality. To extend the application of this methodology, we recommend further applying it to fields beyond chemical engineering and higher-dimensional problems. [ABSTRACT FROM AUTHOR]

Low-energy cases can be designed more efficiently by using analytical optimization. The non-linear thermal performance of buildings has led to the development of optimization techniques based on simulation. In building optimization, it is important to achieve superior solutions while minimizing calculation expenses. This study aims to optimize an Australian office building using the Balanced Tree Growth Optimizer (BTGO). It has resulted that more than 11.7 % of energy can be saved by the optimization process and also some energy-saving measures. A comparison of the utilized algorithm with benchmark algorithms including the Nelder-Mead method, hybrid Particle Swarm Optimization, Hooke-Jeeves, and Ant Colony Optimization for continuous domain showed that the BTGO can achieve better solutions and needs less computational time.

Flexible flat cables (FFCs) are a typical form of interconnect in modern electrical and electronic systems that facilitate signal transmission between components while minimizing harness volume. FFCs offer a practical connectivity solution in energy management applications, where sensors and displays are essential for monitoring power consumption and performing advanced digital control. In FFCs, signal lines run parallel to each other, and the proximity between lines can cause interference among adjacent signals. Therefore, the arrangement of signals along different lines can significantly influence the overall transmission performance. In this paper, the order of signals within the FFC is optimized to ensure optimal transmission performance, avoiding electromagnetic compatibility (EMC) and signal integrity (SI) issues. The problem is tackled by implementing a multi-objective optimization (MOO) approach, whose aim is to minimize near-end and far-end crosstalk, namely NEXT and FEXT. The effectiveness of the proposed approach is verified by considering a minimized interconnection system involving an FFC. The Pareto-optimal solutions are identified, and worst-case and best-case conditions are highlighted. The results show improvements in EMC and SI, underlining the relevance of the proposed optimization strategy. The proposed strategy provides a valuable tool for designing high-performance interconnections in electrical and electronic systems.

This paper presents a method for designing an active suspension controller for ride comfort enhancement and motion sickness mitigation. For this, it is necessary to design an active suspension controller, which aims to reduce the vertical acceleration and pitch rate of a sprung mass in a vehicle. A half-car vehicle model was selected. For the controller design, a static output feedback (SOF) control was selected instead of a full-state feedback control because it is hard to measure all state variables in real vehicles. With the available signals, three types of SOF controller were proposed. To determine the gains of the SOF controllers, a linear quadratic optimal control methodology and a simulation-based optimization method were adopted. To validate the proposed method, a simulation was carried out using vehicle simulation software. The simulation results show that the proposed method is quite effective for ride comfort enhancement and motion sickness mitigation.

Terminal towing and barging operations play a critical role in cargo transfer within ports, especially in complex waterways. Scheduling of barges and tugs is influenced by various factors, such as speed, and the operation planning must consider the constraints of channel traffic. Additionally, the transfer system involves constructing and operating berths, transshipment equipment, barges, tugboats, and other resources. Effective scheduling and resource planning are crucial for achieving sustainability. To illustrate this point, we analyze the estuary barging process in a real-world case. We utilize the Anylogic platform to simulate the entire process of ore terminal loading, river transportation, and sea transshipment. Furthermore, we employ simulation-based optimization methods to enhance the system’s performance. Our case study has yielded valuable management insights, including: (1) Key variables related to shipping speeds and resource allocation are identified and optimized; (2) A specific tug dispatch rule has been verified to outperform other options in terms of efficiency and emissions; (3) The potential benefits of constructing and enabling an additional terminal have been identified. Our exploration provides quantitative support for decision-making related to vessel scheduling and resource planning in engineering projects for ore terminals.

Organ transplantation is a crucial task in the healthcare supply chain, which organizes the supply and demand for various vital organs. In this regard, dealing with uncertainty is one of the main challengings in designing an organ transplant supply chain. To address this gap, in the present research, a mathematical formulation and solution method is proposed to optimize the organ transplants supply chain under shipment time uncertainty. A possibilistic programming model and simulation-based solution method are developed for organ transplant center location, allocation, and distribution. The proposed mathematical model optimizes the overall cost by considering the fuzzy uncertainty of organ demands and transportation time. Moreover, a novel simulation-based optimization is applied using the credibility theory to deal with the uncertainty in the optimization of this mathematical model. In addition, the proposed model and solution method are evaluated by implementing different test problems. The numerical results demonstrate that the optimal credibility level is between 0.2 and 0.6 in all tested cases. Moreover, the patient's satisfaction rate is higher than the viability rate in the designed organ supply chain. [ABSTRACT FROM AUTHOR]

In this work, we propose a novel methodology in order to automatically optimize the location of the sampling points for a water quality monitoring network in an estuary, in such a way that any unknown pollution source can be identified (both in intensity and location) from the data supplied by those sampling points. In the central part of the article, after a rigorous mathematical formulation of the environmental problem, the full details of its numerical implementation are given. Finally, we present and analyze the results when applying the above proposed technique to study a real case in Ría of Vigo (northwestern Spain). [ABSTRACT FROM AUTHOR]

Inspired by the bionic Bouligand structure, helicoidal carbon fiber-reinforced polymer composite (CFRPC) laminates have been proven to own outstanding out-of-plane mechanical properties. This work aims to further explore the excellent bending characteristics of helicoidal CFRPC laminated plates and find out the optimal helicoidal layup patterns. The optimization design of laminated plates stacked with single-form and combination-form helicoidal layup sequences are carried out by using the finite element method (FEM) and adaptive simulated annealing (ASA) optimization algorithm on the Isight platform. Then, the nonlinear bending responses of optimal helicoidal CFRPC laminated plates are investigated via the FEM for the first time. The helicoidal CFRPC laminated plates under three different types of boundary conditions subjected to transverse uniformly distributed load are considered. The numerical results reveal that the combination-form helicoidal layup sequences can decrease the dimensionless bending deflection of laminated plates by more than 5% compared with the quasi-isotropic plate and enhance the out-of-plane bending characteristics of CFRPC laminated plates effectively. The boundary conditions can significantly influence the nonlinear bending responses of helicoidal CFRPC laminated plates. [ABSTRACT FROM AUTHOR]

Commercialization of lithium-oxygen batteries faces many challenges, such as electrolyte decomposition, short cycle life, low energy and power density, and high cell mass. However, the commercialization of LiO2 batteries for aeronautics is more challenging due to additional safety constraints on cyclability and performance (high specific-power and specific-energy). In this paper, we perform experiments and use the results to calibrate a 1-D finite element model to simulate discharge profiles up to 5 mA cm-2. The calibrated model is used to perform parametric studies of geometrical, microstructural, transport, and material properties for different discharge times, and discharge current densities. Next, a simulation-based optimization study shows optimal cell designs for various electrolytes for high specific-power. Results show that a high specific-power LiO2 cell needs to have a cathode with a thickness equal to oxygen diffusion length, thin separator, optimized anode, and other components with low mass. The specific-power is sensitive to discharge time, discharge current density, and microstructural and geometrical properties. Also, in some cases, electrolytes with high oxygen solubility or high external partial pressure can compensate for electrolytes with low oxygen diffusivity. However, reducing the cell mass is the most straight forward path to improving specific-power.

Commercialization of lithium-oxygen batteries faces many challenges, such as electrolyte decomposition, short cycle life, low energy and power density, and high cell mass. However, the commercialization of LiO2 batteries for aeronautics is more challenging due to additional safety constraints on cyclability and performance (high specific-power and specific-energy). In this paper, we perform experiments and use the results to calibrate a 1-D finite element model to simulate discharge profiles up to 5 mA cm-2. The calibrated model is used to perform parametric studies of geometrical, microstructural, transport, and material properties for different discharge times, and discharge current densities. Next, a simulation-based optimization study shows optimal cell designs for various electrolytes for high specific-power. Results show that a high specific-power LiO2 cell needs to have a cathode with a thickness equal to oxygen diffusion length, thin separator, optimized anode, and other components with low mass. The specific-power is sensitive to discharge time, discharge current density, and microstructural and geometrical properties. Also, in some cases, electrolytes with high oxygen solubility or high external partial pressure can compensate for electrolytes with low oxygen diffusivity. However, reducing the cell mass is the most straight forward path to improving specific-power.

We study an inbound container housekeeping problem in an automated container terminal. We propose an integer programming model to characterize the problem and provide a meta-heuristic algorithm to solve it. To tackle the random factors in the future retrieval process, a simulation module is embedded in the process of evaluating the objective value. By computational experiments, we demonstrate the effectiveness and efficiency of our method. Moreover, we also conduct sensitivity analysis to examine the impacts of different algorithm parameters. [ABSTRACT FROM AUTHOR]

Public transport network design deals with finding efficient network solution(s) from a set of alternatives that best satisfies the often-conflicting objectives of stakeholders like passengers and operators. This work presents a simulation-based optimization (SBO) model for designing public transport networks. The work's novelty is in developing such a network design model that fully accounts for the stochastic behavior of commuters on the transit network. The SBO discipline solves decision-based problems like the transit network design problem (TNDP) by combining simulation and optimization models. The proposed model integrates a disaggregated activity-based travel demand simulation with a multi-objective network optimization algorithm. Trip-based travel demand models are commonly used to represent traveler behavior in the literature. The approach limits its ability to accommodate the stochastic realities of traveler behavior in a transit network design solution. Using activity-based simulation instead makes it possible to account for a more realistic traveler behavior, especially real-time decisions made in response to changing network dynamics which ultimately affect the distribution of demand over time on the network. The proposed model is applied to the improved design of the integrated public transport network in the City of Cape Town, South Africa. The results show SBO can design efficient network solutions that reflect the objectives of network stakeholders. [ABSTRACT FROM AUTHOR]

Summary: China's building energy consumption accounts for 27.5% of China's total energy demand. This amount is increasing because of the considerable increase in the rate of household equipment mostly in air‐conditioning systems. In this study, the TRNSYS simulation tool and improved Battle Royal optimizer (IBRO) is coupled for energy optimization of the building based on the simulation to study the impact of the overhangs optimization. The thermal comfort enhancement is evaluated in three case studies of three different weather conditions in China. To validate the achieved results, a comparison of the optimum values with benchmark cases is performed with consideration of heating and cooling demands, and yearly discomfort percentage. According to the achievements, an improvement can be seen in the comfort level. Also, the cooling demand is decreased by 4.2% for Shanghai. [ABSTRACT FROM AUTHOR]

Appointment scheduling is one of the critical factors for improving patient satisfaction with healthcare services. A practical and robust appointment scheduling solution allows clinics to efficiently utilize medical devices, equipment, and other resources. This study introduces a Multi-Objective Patient Appointment Scheduling (MO-PASS) framework to enhance clinic operations and quality of care. The proposed framework integrates three modules: (1) Optimization (using MATLAB), (2) Data-Exchange (MS Excel), and (3) Simulation (Simio). To implement MO-PASS, the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm is coded in MATLAB, and a Simio API is developed, which exchanges simulated scenarios with MOPSO via Excel. The efficiency of the proposed framework is evaluated in a breast cancer clinic with multiple physicians and patient types. Two objective functions are defined for evaluating the solutions of the AS problem: (1) minimizing the total service time and (2) maximizing the number of (admitted) patients with zero overtime. Finally, the performance of MO-PASS is tested against three heuristic approaches with respect to objective functions. The computational experiment results show that the proposed MO-PASS outperforms the existing heuristic benchmarks. Also, the framework is accompanied by all the necessary details to make it practical and easy to implement. [ABSTRACT FROM AUTHOR]

This paper introduces a simulation-based planning method that metaheuristically optimizes production planning and the control of components of a flexible energy system for energy intensive production. Significantly increasing energy efficiency and energy flexibility are key requirements and success factors, especially for energy-intensive manufacturers, amid rising energy prices, volatility, and the overall need to reduce the energy and pollution footprint. The considerable potential for an optimized integrated planning and control of production and energy system components remains unexhausted, due to the underlying planning complexity, combined with a lack of available planning methods. This paper aims to introduce a novel planning approach that provides an automated integrated planning functionality to improve energy efficiency and flexibility alongside production goals. The method considers fluctuating short-term energy markets as well as local hydrogen-based energy storage components. Both method development and evaluation are based on a case study in energy-intensive steel production. The achieved results show a significant benefit from the developed dynamic planning and optimization for different variables particularly in two different heat-treatment process technologies. [ABSTRACT FROM AUTHOR]

The integration of supply chain decisions aims to reduce costs and delivery time for customers. However, uncertainty in supply chain parameters, particularly demand, can disrupt this integration. The increased interest in probabilistic planning and simulation models in supply chain modeling is a response to this demand uncertainty. Therefore, the main objective of this study was to develop a multi-level, multi-product, multi-period supply chain network model that considers conflicting objectives such as cost minimization, delivery time minimization, and system-wide reliability maximization. The supply chain network under investigation consisted of four levels or subsystems: suppliers, manufacturers, distributors, and retailers. In this study, it was assumed that demand follows a random probabilistic distribution function. Consequently, simulation techniques were employed to estimate costs, including shipping costs, lost sales costs, and other expenses. After developing the multi-objective model, various scenarios were created based on different perspectives of inventory levels, namely minimum inventory, maximum inventory, and average inventory level. For each scenario, objective-related values were estimated. Ultimately, based on the Pareto optimal solutions obtained for each case of the model, the Vickor decision-making method was used to rank the answers and select the best solution from the proposed model. The results indicated that the second scenario, considering the average inventory level, was identified as the optimal solution for the described model.IntroductionToday, supply chain management (SCM) encompasses the entire production planning process for the supply chain, from raw material suppliers to the final customer. This has become a focal point for numerous researchers. In most supply chain designs, the objective has been to transfer products from one layer to another in order to meet strategic, tactical, and operational demands while minimizing complications arising from interrelationships and uncertainties across the chain. These challenges have posed significant decision-making hurdles in the supply chain domain. Supply chains can be regarded as complex systems wherein various factors interact with each other, resulting in emergent properties. Designing a versatile supply chain to address conflicting and diverse objectives requires considering them simultaneously and striking a balance among different criteria. The dynamic and intricate nature of the supply chain introduces a high level of uncertainty, thereby impacting the decision-making process in supply chain planning and influencing overall network performance. Based on the aforementioned issues, the focus of investigation includes the following: The examined supply chain network comprises four levels or subsystems, namely suppliers, manufacturers, distributors, and retailers. Raw materials are sourced from suppliers and sent to production factories, where each product is manufactured using a specific combination of raw materials. The products are then transported from manufacturers to distribution centers, and subsequently forwarded to retailers. The market is divided into different regions, and customer demands are fulfilled through visits to the retailers. Demand is assumed to be random and follows a probability distribution pattern. Consequently, simulation techniques are employed to estimate costs, including transportation costs, lost sales costs, and other expenses. Scenarios are created based on different perspectives at each level, focusing on inventory levels (minimum, maximum, and average). For each scenario, the values associated with the investigated objectives are estimated.Materials and methods In this research, data collection involved the examination of relevant literature, including articles published in international journals, books, and treatises. Documentary studies were conducted to gather information. To analyze the collected data, simulation and multi-objective programming concepts and methods were employed. Minitab and ED software were utilized for statistical analysis and simulation purposes.ConclusionsConsidering that the model can be solved under different conditions, including the current situation and various scenarios, the answers obtained for each state are Pareto optimal. This means that it is not possible to determine a single best answer for each state of the model. Therefore, before comparing the scenarios with each other, the Pareto optimal answers for each scenario should be ranked to identify the best options. In this research, a model for designing the supply chain network was presented, taking into account demand randomness. To better understand the proposed model and demonstrate its practicality, numerical examples were examined and evaluated using different scenarios and the Lingo software. It is important to note that the developed model in this study is independent of the number of facilities at each level of the supply chain and the parameter values. Therefore, the general form of this model can be applied to any production environment that aligns with the patterns presented in this research. The proposed model initially employed the design of experiments to estimate the mathematical relationship related to the cost objective function. After developing the multi-objective model, the Lingo software was used to solve the sample problem and evaluate the results under different scenarios. Finally, based on the Victor decision-making method, the Pareto optimal solutions for each state of the model were used to rank the answers and determine the best mode for the proposed models. Based on the obtained results, the third option or the second scenario is suggested as the preferred choice for the described model, considering the index values associated with each option

This paper proposes an ex post path choice estimation framework for urban rail systems using an aggregated time-space hypernetwork approach. We aim to infer the actual passenger flow distribution in an urban rail system for any historical day using the observed automated fare collection (AFC) data. By incorporating a schedule-based dynamic transit network loading (SDTNL) model, the framework captures the crowding correlation among stations and the interaction between the path choice and passenger left behind, which is important for the path choice estimation in a "near-capacity" operated urban rail system. The path choice estimation is formulated as an optimization problem, which aims to minimize the difference between the model-derived and observed information with path choice parameters as decision variables. The original problem is intractable because of nonlinear (logit model) and nonanalytical (SDTNL) constraints. A solution procedure is proposed to decompose the original problem into three tractable subproblems, which can be solved efficiently. Solving the decomposed problem is equivalent to finding a fixed point. We prove that the solution to the original problem is the same as the decomposed problem (i.e., the fixed point) when passenger path choices follow the predefined behavior model. If this condition does not hold, the solution of the original problem is proved to be an "almost fixed point" for the decomposed problem. The model is validated using both synthetic and real-world AFC data from a major urban railway system. The analysis with synthetic data validates the model's effectiveness in estimating path choice parameters and left behind probabilities, which outperforms state-of-art simulation-based optimization methods and probabilistic models in both accuracy and efficiency. The analysis using actual data shows that the estimated path shares are more reasonable than the baseline uniform path shares and survey-derived path shares. The model estimation is robust to different initial parameter values and AFC data from various dates. Funding: The project is partially funded by the TRENoP Strategic Research funding at KTH. Supplemental Material: The online appendices are available at https://doi.org/10.1287/trsc.2022.1177. [ABSTRACT FROM AUTHOR]

This paper developed a new framework for better knowledge management through computational simulations, enabling both human knowledge creation and updating simulation models. The framework was developed based on the concepts of the socialization, externalization, combination, and internalization (SECI) model, and it contains a structured workshop that includes the four types of Ba the model requires. To evaluate this framework, the planning of post-disaster restoration of water distribution networks was employed as a case study. In addition, a new optimization method was developed using empirical heuristics obtained from practitioners, aiming for meaningful feedback from the practitioners working in a water management company. Based on our simulation, three workshops were conducted to create new knowledge, and new features were added to the simulation. In these workshops, practitioners performed simulation-based training in planning the restoration and then discussed their decisions. Afterward, it was concluded that the proposed framework was sufficient for updating the simulation. However, it required additional methods to provide practitioners with opportunities to obtain new insights. [ABSTRACT FROM AUTHOR]

In this paper, important cross-section profile and layout design formulas developed for the lace design simulation of rock cutterheads (LDR) are described. In this regard, the main objectives are to optimize the cutterhead cross-section, to generate the desired combination of different cutters (single-edge, twin, multiple center cutters) in the profile of the cutterhead, to investigate how to prevent the overlap of the cutter boxes with each other and with buckets and pedestals, and to generate relationships to provide different optimized layout plans for the cutterhead with minimum off-axis forces and moments and with no boundary overlap among structural elements. The outcomes of LDR provide optimized design alternatives that enable engineers to select the best plan based on their project requirements. An example is also presented to explain the major steps followed by an application user to optimize the cross-section profile and layout plan for a micro-TBM. [ABSTRACT FROM AUTHOR]

Accurate modeling of the patient flow within an Emergency Department (ED) is required by all studies dealing with the increasing and well-known problem of overcrowding. Since Discrete Event Simulation (DES) models are often adopted with the aim of assessing solutions for reducing the impact of this worldwide phenomenon, an accurate estimation of the service time of the ED processes is necessary to guarantee the reliability of the results. However, simulation models concerning EDs are frequently affected by missing data, thus requiring a proper estimation of some unknown parameters. In this paper, a simulation-based optimization approach is used to estimate the incomplete data in the patient flow within an ED by adopting a model calibration procedure. The objective function of the resulting minimization problem represents the deviation between simulation output and real data, while the constraints ensure that the response of the simulation is sufficiently accurate according to the precision required. Data from a real case study related to a big ED in Italy is used to test the effectiveness of the proposed approach. The experimental results show that the model calibration allows recovering the missing parameters, thus leading to an accurate DES model. [ABSTRACT FROM AUTHOR]

We develop an augmented simulation approach to solve discrete stochastic optimization problems by converting them into a grand simulation problem in the joint space of random and decision variables. The optimal decision is obtained via the mode of the augmented probability model, using a new multivariate extension of the classic Barker's algorithm. Illustrations on different versions of univariate and multivariate discrete news-vendor problems with exogenous and endogenous uncertainties are detailed. We contrast our method with the Metropolis–Hastings algorithm, the nested sampling-based augmented simulation method, and traditional Monte Carlo simulation-based optimization schemes. The proposed method is shown to be computationally efficient and could serve as another tool to solve discrete stochastic optimization problems with recourse. [ABSTRACT FROM AUTHOR]

Autonomous driving technology heavily depends on accurate and smooth path tracking. Facing complex urban driving scenarios, developing a suite of high-performance and robust parameters for controllers becomes imperative. This paper proposes a stochastic simulation-based optimization model for optimizing the Proportional–Integral–Differential (PID) controller parameters, with tracking accuracy and smoothness as bi-objectives, and solves it using a domination-measure-based efficient global optimization (DMEGO) algorithm. In this model, the tracking accuracy and smoothness are indexed by the normalized dynamic time warping (NDTW) and the mean absolute lateral acceleration (MALA), respectively. In addition, we execute the PID controller in a realistic simulation environment using a CARLA simulator, which consider various city scenes, diverse routes, different vehicle types, road slopes, etc., to provide a comprehensive and reliable evaluation for the designed PID controller. In the DMEGO method, each solution undergoes evaluation using a fixed number of costly simulations. Then, utilizing the solutions and their estimated bi-objective values, two surrogate models for the bi-objectives are constructed using the Gaussian process (GP) model. The preliminary nondominated solutions can be obtained by optimizing the two surrogate models. Finally, a novel performance metric known as the domination measure is employed to evaluate the quality of each solution. This metric is then integrated with the crowding distance to selectively retain a candidate solution exhibiting superior performance and good diversity for the next iteration. In our numerical experiments, we first test the DMEGO algorithm against three other counterparts using a stochastic FON benchmark. The proposed approach is then employed to optimize the PID parameters considering the complexity and uncertainty of urban traffic. The numerical results demonstrate that the nondominated solutions obtained by DMEGO exhibit excellent performance in terms of tracking accuracy and smoothness under limited simulation budgets. Overall, the proposed approach may be a viable tool for solving multi-objective simulation-based optimization problem under uncertainties.

A discrete network design problem (DNDP) is conventionally formulated as an analytical bi-level programming problem to acquire an optimal network design strategy for an existing traffic network. In recent years, multimodal network design problems have benefited from simulation-based models. The nonconvexity and implicity of bi-level DNDPs make it challenging to obtain an optimal solution, especially for simulation-related models. Bayesian optimization (BO) has been proven to be an effective method for optimizing the costly black-box functions of simulation-based continuous network design problems. However, there are only discrete inputs in DNDPs, which cannot be processed using standard BO algorithms. To address this issue, we develop a hybrid method (BO-B&B) that combines Bayesian optimization and a branch-and-bound algorithm to deal with discrete variables. The proposed algorithm exploits the advantages of the cutting-edge machine-learning parameter-tuning technique and the exact mathematical optimization method, thereby balancing efficiency and accuracy. Our experimental results show that the proposed method outperforms benchmarking discrete optimization heuristics for simulation-based DNDPs in terms of total computational time. Thus, BO-B&B can potentially aid decision makers in mapping practical network design schemes for large-scale networks. [ABSTRACT FROM AUTHOR]

This paper focuses on the timetabling problem for a congested urban rail transit network. During peak hours, the situation of premature full load of trains and passenger stranded on platforms is serious. To minimize the total passenger's travelling time and optimize resource allocation from a planning and management perspective, we develop an optimization model considering dwelling time as one of the decision variables to control the number of boarding passengers. In the proposed model, the network internal passenger flow is quantitatively characterized by three types of flows according to its origin and destination under the distributed structure. And the mixed logic dynamic model describes the operation of network, especially the interaction between lines. Furthermore, the number of time-varying transfer passengers is calculated which has never been studied before. At last, we adopt a simulation-based approach to solve the model and prove the effectiveness through two cases. [ABSTRACT FROM AUTHOR]

This work deals with the optimal design for the location of the exit doors at meeting places (such as sports centers, public squares, street markets, transport stations, etc.) to guarantee a safer emergency evacuation in events of a sporting, social, entertainment or religious type. This problem is stated as an optimal control problem of nonlinear partial differential equations, where the state system is a reformulation of the Hughes model (coupling the eikonal equation for a density-weighted walking velocity of pedestrians and the continuity equation for conservation of the pedestrian density), the control is the location of the exit doors at the domain boundary (subject to several geometric constraints), and the cost function is related to the evacuation rate. We provide a full numerical algorithm for solving the problem (a finite element technique for the discretization and a gradient-free procedure for the optimization), and show several numerical results for a realistic case. [ABSTRACT FROM AUTHOR]

Summary: The use of liquefied natural gas (LNG) has grown over the last decade owing to an increase in the global energy demand. This work presents a pioneering approach for rigorous simulation of an actual cold section of an LNG plant and provides a potential process optimization to achieve a higher production rate (PR) while reducing the specific power consumption (SPC). The concept of upfront nitrogen removal (UNrem) from the natural gas (NG) feed was introduced, and the impact on the PR and SPC was presented. We considered seven scenarios with varying percentages of UNrem and compared them with the conventional plant design (base case) while maintaining the product specifications (LNG high heating value [HHV] of 1105 Btu/Scf and NGL reid vapor pressure [RVP] of 2042 psia). The results show that UNrem of 87.5% from the NG feed can reduce the total power requirements by 0.24%, increase the production flow rate by 4.4%, and decrease the exergy losses by 0.23% compared with the base case. The UNrem can also have a significant improvement on the plant capacity without the need for structural modifications in the cold section. Future research should focus on the efficiency and feasibility of different UNrem processes as well as examine the use of a large‐scale system. [ABSTRACT FROM AUTHOR]

This paper proposes a simulation-based optimization (SbO) approach for multi-objective runway operations scheduling problem. The SbO approach utilizes a discrete-event simulation component to account for uncertain conditions, and an optimization component for finding the best-known Pareto set of solutions. This approach explicitly considers uncertainty to decrease the real operational cost of the runway operations as well as maximize fairness among aircraft as part of the optimization process. Due to the problem's large, complex, and unstructured search space, a hybrid Tabu/Scatter Search algorithm is developed to find solutions using an elitist strategy to preserve non-dominated solutions, a dynamic update mechanism to produce high-quality solutions, and a rebuilding strategy to promote solution diversity. The proposed algorithm maximizes both objectives of runway utilization and fairness. Computational experiments are conducted using real-life data sets for a major US airport to demonstrate that the proposed approach is effective and computationally tractable in a practical sense. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Railway Science & Engineering is the property of Journal of Railway Science & Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Railway Science & Engineering is the property of Journal of Railway Science & Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper investigates the continuous network design problem (CNDP) and proposes a simulation-based bi-level model and solution framework based on Bayesian optimization. In the bi-level model, the upper level minimizes the total system cost, and the lower level assigns traffic under an approximated dynamic equilibrium condition corresponding to the given network design strategy. A simulation tool integrated micro- and macro- traffic dynamics, namely SUMO, is employed to solve the lower-level problem. The embedded high-fidelity, non-linear understanding of traffic in the simulator oftentimes gains additional complexity due to the lack of tractable mathematical representation. Thus, a Bayesian machine learning technique is utilized to build the link between simulation and optimization. The proposed solution framework takes both the advantages of fine-grained simulations and the efficiency of surrogate-based optimization techniques. Moreover, lane width expansion is innovatively proposed as the decision variable of CNDP to bridge the gap between theory and practice. The relationship between link free-flow speed (FFS) and lane width is also explored based on real data and established to accurately calibrate the simulation input. For demonstrative purposes, numerical experiments on the optimal lane width expansion design were conducted in the inner city of Suzhou, China. The results show that with proper parameter settings, the proposed method is capable to find the global optimal solution within a very tight computational budget, which makes the simulation-based framework an encouraging option for policymakers to enhance transportation network performance. [ABSTRACT FROM AUTHOR]

Bayesian optimization (BO) is an attractive method for tackling transportation optimization problems due to its ability to balance exploitation and exploration. However, scaling BO to solve high-dimensional problems is a major challenge which has remained unsolved. Since transportation problems can be high-dimensional, the use of BO to solve transportation problems has been limited. This paper explores the use of BO with Gaussian process (GP) models to tackle high-dimensional transportation problems. It proposes formulations of the prior mean function and covariance function of the GP that enable BO to incorporate problem-specific transportation information, while remaining computationally tractable. This is done through the use of an analytical surrogate model. We validate the method with the 1-D and 100-D Griewank functions. The impact of different forms of bias in the surrogate model was also evaluated using the 100-D Griewank function example. We then apply the method to a high-dimensional traffic signal control problem in New York City. The results indicate the added value of using the problem-specific information in the prior mean and/or the covariance function. Importantly, for surrogate models that are not accurate approximations of, but have significant (anti-)correlation with the true objective function, the better approach is to embed the information in the covariance function, rather than in the prior mean function. More generally, the use of problem-specific information in the covariance function is robust to the accuracy of the surrogate model. This opens the way for a variety of low-resolution analytical transportation models to be used to tackle high-dimensional simulation-based optimization problems. • Problem-specific information can be used for exploration and exploitation in BO. • Using the surrogate model in the GP prior mean function improves exploitation. • Using the surrogate model in the GP covariance function improves exploration. • The surrogate model-based covariance function is robust to biases in the model. [ABSTRACT FROM AUTHOR]