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simulation optimization for Inpatient Bed Allocation with Sharing

Journal of Systems Science and Systems Engineering 2025年第1期34卷 55-77页

作者： Jie Li Sichen Li Jun Luo Haihui Shen Sino-US Global Logistics Institute Antai College of Economics and ManagementShanghai Jiao Tong UniversityShanghai200240China Antai College of Economics and Management Shanghai Jiao Tong UniversityShanghai200240China Data-Driven Management Decision Making Lab Shanghai Jiao Tong UniversityShanghai200240China

The inpatient bed allocation that allows beds shared among different departments is an important and challenging problem for a healthcare system. When the objective function(s) and (some) constraints need to be estimated via expensive and noisy stochastic simulation, a simulation optimization algorithm is required to solve this problem. In literature, there is a heuristic algorithm highly customized for one specific inpatient bed allocation problem, and it performs quite well on that problem. However, its lack of theoretical convergence and high specialization may not give practitioners enough confidence to apply it on real inpatient bed allocation problems. To mitigate such issues, this paper proposes to use the empirical stochastic branch-and-bound (ESB&B) algorithm, which is theoretically convergent and suitable for relatively general problems. A modest improvement for the original ESB&B algorithm is made and how to adapt the ESB&B algorithm to inpatient bed allocation problems is presented. Numerical experiments reveal the generality and fairly satisfying performance of the ESB&B algorithm, and the superiority of the improved ESB&B algorithm over the original one.

关键词： Healthcare management resource sharing bed allocation simulation optimization empirical stochastic branch-and-bound(ESB&B)

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simulation optimization of Shovel-Truck System in Open-Pit Mines Considering Rockmass Parameters

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JOURNAL OF ADVANCED TRANSPORTATION 2025年第1期2025卷

作者： Pathan, Shafi Muhammad Pathan, Abdul Ghani Memon, Muhammad Saad Mehran Univ Engn & Technol Dept Min Engn Jamshoro 76062 Sindh Pakistan Mehran Univ Engn & Technol Dept Ind Engn & Management Jamshoro 76062 Sindh Pakistan

The shovel-truck system remains a popular method for overburden removal and mineral excavation in open-pit mines, needing rigorous logistical management to achieve required productivity levels and maximize resource utilization. Fixed truck assignment (FTA) models represent a prevalent method for truck allocation in open-pit mining, owing to their simplified fleet operational management. However, existing FTA models often overlook the simultaneous minimization of both trucks' waiting time and shovels' idle time. Consequently, these oversights lead to suboptimal allocation of trucks to shovels, resulting in either trucks queuing or shovels idling while awaiting trucks. Such inefficiencies contribute to fleet underutilization and increased fuel costs. To tackle the above issue, this research introduces a novel truck dispatching rule, MFTA, which integrates geotechnical parameters and excavating equipment performance to optimize truck allocation in open-pit mining. Geotechnical parameters across various rock and soil formations reveal significant variability, influencing shovel performance assessed through the total loading time (TLT) indicator. Utilizing TLT and travel times of loaded and empty trucks, the study determines the optimal number of fixed trucks allocated to each shovel by minimizing the total waiting time (TWT). A case study conducted in an open-pit coal mine in Thar, Pakistan, validates the approach, demonstrating that adjusting truck allocations based on TLT significantly reduces operational inefficiencies and enhances productivity. The findings highlight the effectiveness of this method in improving overall operational efficiency and economics in open-pit mining. Integrating real-time data and advanced simulation techniques, this research enhances the competitiveness and sustainability of mining operations. These outcomes are particularly relevant for mining professionals aiming to optimize mining operations for improved efficiency and sustainability.

关键词： open-pit mining shovel-truck system simulation optimization total loading time truck allocation

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simulation optimization of Spatiotemporal Dynamics in 3D Geometries

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 10442-10456页

作者： Yao, Bing Leonelli, Fabio Yang, Hui Univ Tennessee Knoxville Dept Ind & Syst Engn Knoxville TN 37996 USA James A Haley Vet Hosp Cardiac Electrophysiol Lab Tampa FL 33620 USA Penn State Univ Harold & Inge Marcus Dept Ind & Mfg Engn University Pk PA 16802 USA

Many engineering and healthcare systems are featured with spatiotemporal dynamic processes. The optimal control of such systems often involves sequential decision making. However, traditional sequential decision-making methods are not applicable to optimize dynamic systems that involves complex 3D geometries. simulation modeling offers an unprecedented opportunity to evaluate alternative decision options and search for the optimal plan. In this paper, we develop a novel simulation optimization framework for sequential optimization of 3D dynamic systems. We first propose to measure the similarity between functional simulation outputs using coherence to assess the effectiveness of decision actions. Second, we develop a novel Gaussian Process (GP) model by constructing a valid kernel based on Hausdorff distance to estimate the coherence for different decision paths. Finally, we devise a new Monte Carlo Tree Search (MCTS) algorithm, i.e., Normal-Gamma GP MCTS (NG-GP-MCTS), to sequentially optimize the spatiotemporal dynamics. We implement the NG-GP-MCTS algorithm to design an optimal ablation path for restoring normal sinus rhythm (NSR) from atrial fibrillation (AF). We evaluate the performance of NG-GP-MCTS with spatiotemporal cardiac simulation in a 3D atrial geometry. Computer experiments show that our algorithm is highly promising for designing effective sequential procedures to optimize spatiotemporal dynamics in complex geometries. Note to Practitioners-This article proposes a novel simulation optimization framework for sequential decision making to optimize spatiotemporal dynamics in complex geometries. This framework incorporates the advantage of Bayesian modeling and Gaussian Process inference into Monte-Carlo tree search to effectively solve the sequential optimization problem. It has significant potential to contribute to the emerging discipline of computational engineering and medicine, and further realize precision control/treatment planning in various manu

关键词： Spatiotemporal phenomena optimization Three-dimensional printing Geometry Heuristic algorithms Decision making Computational modeling Solid modeling Planning Numerical models simulation optimization Monte-Carlo tree search Gaussian process Bayesian analysis sequential decision making spatiotemporal dynamic systems

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Measuring the effectiveness and efficiency of simulation optimization metaheuristic algorithms

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JOURNAL OF HEURISTICS 2025年第1期31卷 1-21页

作者： Thengvall, Benjamin G. Hall, Shane N. Deskevich, Michael P. Opttek Syst Inc Boulder CO USA Montana State Univ Jake Jabs Coll Business & Entrepreneurship Bozeman MT 59717 USA

Metaheuristic algorithms have proven capable as general-purpose algorithms for solving simulation optimization problems. Researchers and practitioners often compare different metaheuristic algorithms by examining one or more measures that are derived through empirical analysis. This paper presents a single measure that can be used to empirically compare different metaheuristic algorithms for optimization problems. This measure incorporates both the effectiveness and efficiency of the metaheuristic algorithm, which is especially important in simulation optimization applications because the number of simulation runs available to the analyst (i.e., the run budget) can vary significantly with each simulation study. Therefore, the trade-off between the effectiveness and efficiency of a metaheuristic algorithm must be examined. This single measure is especially useful for multi-objective optimization problems;however, determining this measure is non-trivial for two or more objective functions. Additional details for calculating this measure for multi-objective optimization problems are provided as well as a procedure for comparing two or more metaheuristic algorithms. Finally, computational results are presented and analyzed to compare the performance of metaheuristic algorithms using knapsack problems, pure binary integer programs, traveling salesman problems, and the average results obtained across a diverse set of optimization problems that include simulation and multi-objective optimization problems.

关键词： Binary integer program Efficiency and effectiveness of metaheuristics Knapsack problem Metaheuristic performance measures Multi-objective optimization simulation optimization Traveling salesman problem

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Genetic Multi-Armed Bandits: A Reinforcement Learning Inspired Approach for simulation optimization

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2025年第2期29卷 360-374页

作者： Preil, Deniz Krapp, Michael Univ Augsburg Dept Quant Methods D-86159 Augsburg Germany EON Digital Technol D-30539 Hannover Germany

Many real-world problems are inherently stochastic, complicating, or even precluding the use of analytical methods. These problems are often characterized by high dimensionality, large solution spaces, and numerous local optima, which make finding optimal solutions challenging. Therefore, simulation optimization is frequently employed. This article specifically focuses on the discrete case, also known as discrete optimization via simulation. Despite their adaptions for stochastic problems, previous evolutionary algorithms face a major limitation in these problems. They discard all information about solutions that are not involved in the most recent population. However, this is ineffective, as each simulation observation gathered over the course of iterations provides valuable information that should guide the selection of subsequent solutions. Inspired by the domain of reinforcement learning (RL), we propose a novel memory concept for evolutionary algorithms that ensures global convergence and significantly improves their finite time performance. Unlike previous evolutionary algorithms, our approach permanently preserves simulation observations to progressively improve the accuracy of sample means when revisiting solutions in later iterations. Moreover, the selection of new solutions is based on the entire memory rather than just the last population. The numerical experiments demonstrate that this novel approach, which combines a genetic algorithm (GA) with such memory, consistently outperforms popular convergent state-of-the-art benchmark algorithms in a large variety of established test problems while requiring considerably less computational effort. This marks the so-called genetic multi-armed bandit (MAB) as one of the currently most powerful algorithms for solving stochastic problems.

关键词： Evolutionary computation Convergence optimization Genetics Resource management Complexity theory Search problems Program processors Genetic algorithms (GAs) multi-armed bandits (MABs) reinforcement learning (RL) simulation simulation optimization simulation simulation optimization

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Maximizing network reliability through simulation optimization: a component configuration strategy

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ANNALS OF OPERATIONS RESEARCH 2025年 1-36页

作者： Chang, Ping-Chen Yeh, Cheng-Ta Yu, Vincent F. Chiu, Sung-Fan Natl Taipei Univ Technol Dept Ind Engn & Management Taipei 106 Taiwan Fu Jen Catholic Univ Dept Informat Management New Taipei City 242 Taiwan Natl Taiwan Univ Sci & Technol Dept Ind Management Taipei 106 Taiwan

This study introduces an innovative simulation optimization framework to address network reliability optimization through component configuration. Unlike traditional approaches, this methodology uniquely integrates metaheuristic algorithms-simulated annealing (SA) and genetic algorithm (GA)-with Monte Carlo simulation (MCS), creating hybrid frameworks termed SA-MCS and GA-MCS. A key innovation lies in the framework's ability to optimize network reliability by appropriately selecting the types and quantities of components within predefined constraints, achieving high efficiency without altering the network topology. Furthermore, the proposed approaches estimate network reliability without relying on the enumeration of lower-bound states or upper-bound states for demand d, significantly promoting computational efficiency. Numerical experiments on various network scenarios, including two real-world case studies of a wind power supply network and an academic computer network, demonstrate the effectiveness of these approaches. The results indicate that the proposed GA-MCS performs best in all experimental scenarios regarding computational efficiency. This work offers a novel tool for system administrators to enhance the robustness and performance of critical networks in industries such as power distribution and data transmission.

关键词： Network reliability Component configuration simulation optimization Metaheuristic algorithms Monte Carlo simulation

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Preparation and simulation optimization of 3D modified PVA carbon-based photothermal evaporation materials with high evaporation rate and exceptional photocatalytic effects

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SURFACES AND INTERFACES 2025年 56卷

作者： Li, Guangxue Chen, Zhili Tang, Shan Chen, Li Zhao, Songhao Zhu, Zhaolong Guilin Univ Technol Coll Environm Sci & Engn Guilin 541000 Peoples R China Guilin Univ Technol Guangxi Key Lab Environm Pollut Control Theory & T Guilin 541006 Peoples R China Guilin Univ Technol Collaborat Innovat Ctr Water Pollut Control & Wate Guilin 541006 Peoples R China

Solar-driven interfacial evaporation for seawater desalination is a promising strategy to mitigate the global water resource crisis. However, developing materials that simultaneously achieve efficient desalination and degrade organic pollutants in seawater remains a significant challenge. In this study, we fabricated a three-dimensional modified polyvinyl alcohol (PVA) based photothermal evaporation material by doping with few-layer graphene, known for its excellent photothermal conversion properties, and alpha-Bi2O3 recognized for its photocatalytic efficiency. The material was synthesized using in-situ polymerization and repeated freeze-thaw methods, resulting in a structure with a high evaporation rate and effective photocatalytic degradation of organic pollutants. Furthermore, COMSOL simulation software was employed to iteratively optimize the system, taking into account factors such as material shape, surface emissivity, relative humidity, pressure, water vapor temperature and escape velocity, under controlled evaporation conditions. The optimized model demonstrated an impressive evaporation rate of 4.03 kg & sdot;m-2h-1 under one sun illumination, reflecting a 25.16 % increase compared to the preoptimization rate of 3.22 kg m-2 h-1. Additionally, in a simulated evaporation experiment using North Sea seawater contaminated with 8 mg L-1 of methylene blue, the material maintained a stable evaporation rate of 3.06 kg m-2 h-1. Continuous exposure to light further revealed that 81 % of methylene blue in the water was photocatalytically degraded. These results indicate that this novel evaporation material offers high efficiency for seawater desalination and holds significant potential for treating seawater polluted with organic dyes, broadening its application scope.

关键词： Interfacial evaporation 3D evaporative material Photocatalysis simulation optimization PVA

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A simulation optimization method for coordination of production, transportation and sales

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FUNDAMENTAL RESEARCH 2025年第2期5卷 473-485页

作者： Zheng, Yi Lei, Ming Peng, Yijie Peking Univ Guanghua Sch Management Beijing 100871 Peoples R China

This study considers a problem of coordinating production, transportation and sales in a multi-echelon supply chain network. A simulation model is built to generate the random customer demands at different locations, which are affected by a marketing strategy. Customer demands need to be satisfied by the supply chain through production, transportation and distribution. The optimization problem for coordination of production, transportation and distribution is first formulated as a linear programming with demands as input parameters in the constraint. Our objective is to maximize the expectation of the optimal profit of the supply chain given random demands by selecting an optimal marketing strategy. A simulation optimization technique is proposed to control the generation of random demands and solve the linear programming for efficiently learning the optimal marketing strategy. Numerical results show that our method can significantly improve the expected profit of the supply chain and reduce the computational burden of solving linear programming for achieving a given level of probability of correct selection of the optimal marketing strategy. Furthermore, we extend the optimization problem to a mixed integer programming and also demonstrate the computational efficiency of our proposed method.

关键词： Supply chain coordination Marketing strategy simulation optimization Linear programming Mixed integer linear programming

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simulation and optimization of HTPB propellant aging detection sensor based on cantilever structure

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SENSOR REVIEW 2025年

作者： Gui, Xiaohong Yan, Jin Xue, Wei Zhan, Xingrui China Univ Min & Technol Beijing Sch Emergency Management & Safety Engn Beijing Peoples R China China Univ Min & Technol Beijing Beijing Peoples R China

PurposeThe aging detection of solid rocket-terminated hydroxy polybutadiene (HTPB) propellant is crucial for improving the reliability and safety of rocket motors, and cantilever beam sensors have been widely used in the detection ***/methodology/approachIn this paper, the cantilever structure is selected as the elastic element, and the deflection of straight and curved cantilever beams are derived and calculated by using the differential equation of the deflection curve and Carpenter's theorem, respectively. At the same time, the cantilever structure detection unit is analyzed by numerical simulation using ABAQUS, and the sensor results are calibrated by simulation with modulus change. The modulus change of HTPB propellant aging process is detected by structural design, the factors affecting the measuring range of the cantilever structure detection unit are analyzed, and the effects of different stiffness ratios on the measuring range are obtained, the changes in the mechanical properties of HTPB propellant aging are tested by gel fraction test and the performance of the cantilever structure sensors is finally fabricated and *** study shows that modeling analysis using ABAQUS numerical simulation software can verify the accuracy of the sensor design scheme, the elasticity coefficient ratio of the center cantilever straight beam and the outer ring cantilever curved beam of the cantilever structural detection unit should be in the range of 1.5-2, and the stiffness ratio should be in the range of 2.5-3.5, and the initial modulus of HTPB propellant within the range of effective service should be in the range of 3-7 MPa, the results can be used to guide the structural design and physical fabrication of HTPB propellant aging detection sensor. 7 MPa range, the results can be used to guide the structural design and physical fabrication of the HTPB propellant aging detection ***/valueTests were conducted on HTPB propellants with dif

关键词： HTPB solid propellant Cantilever structure Numerical simulation Aging detection simulation optimization

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Robust simulation-based optimization for multiobjective problems with constraints

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ANNALS OF OPERATIONS RESEARCH 2025年第2期346卷 1897-1927页

作者： Zheng, Liang Bao, Ji Tan, Zhen Cent South Univ Sch Traff & Transportat Engn Changsha Peoples R China Hainan Univ Mech & Elect Engn Coll Haikou 570228 Peoples R China Univ Nottingham Ningbo China Nottingham Univ Business Sch China Ningbo Peoples R China Univ Nottingham Ningbo China Nottingham Ningbo China Beacons Excellence Res & I Ningbo 315199 Peoples R China

This study proposes a constrained multiobjective robust simulation optimization (CMRSO) method to address black-box problems with multiple objectives and constraints under uncertainties, especially when multiple objectives and constraints are evaluated by costly simulations. Neighborhood exploration is first performed for each iterate to search for its infeasible neighbors and worst-case feasible neighbors with the help of kriging surrogate models of constraints and multiple objectives. Next, a local move direction and a proper step size are determined to obtain an updated iterate that stays away from previous infeasible neighbors and worst-case feasible neighbors. These two steps are repeated until no feasible local move direction exists or the computational budget is exhausted. By evolving iteratively and independently from a set of initial solutions, multiple final solutions will generate a set of robust efficient solutions. Finally, the CMRSO method is applied to a synthetic constrained biobjective optimization problem and a network-wide signal timing simulation optimization (SO) problem under cyber-attacks. Our study shows the effectiveness of CMRSO even with a limited computational budget, indicating that it may be a promising tool for solving simulation-based problems with multiple objectives and constraints under uncertainties.

关键词： simulation optimization Multiobjective Robust efficiency Constraints Uncertainties

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