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Automatic information gain-guided convergence for refining building design parameters: Enhancing effectiveness and interpretability in simulation-based optimization

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BUILDING AND ENVIRONMENT 2025年 275卷

作者： Zhou, Qianyun Xue, Fan Univ Hong Kong Dept Real Estate & Construct Pokfulam Hong Kong Peoples R China Univ Hong Kong Natl Ctr Technol Innovat Digital Construct Hong Ko Pokfulam Hong Kong Peoples R China

simulation-based optimization (SBO) is widely applied to building designs by iteratively tuning design parameters towards sustainable goals. However, numerous design parameters in exploratory stages lead to design uncertainty and exponentially increase optimization search space's dimensionality. The non-linear, non-derivative nature of objective functions determines SBO tasks a black box, which lacks interpretability for design decisions or optimization strategies. This study introduces an Automatic Information Gain-guided Convergence (AIGGC) method for refining critical design parameters in building performance SBO. The AIGGC method extends the generic SBO process with interpretable information gain analysis for each design parameter and component, to converge to the most promising domain sub-intervals prior to traditional SBOs. Experimental results evaluated the robustness and scalability of AIGGC across two design scales. Under the same iteration budgets, AIGGC significantly enhanced three SBO algorithms, i.e., RBFOpt, CMAES, and GA, by 0.62 similar to 0.67% less energy use intensity and 2.14 similar to 4.74% more direct sunlight hours against the baseline solutions, respectively. The contribution of this study involves two aspects, including introducing a novel information-theory-based method for optimizing design parameters in high-dimensional SBO tasks of sustainable building designs, and a novel perspective in guiding stakeholders with interpretable analysis of building design parameters.

关键词： Information gain simulation-based optimization Sustainable building design Interpretability analysis

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Dedicated or shared? simulation-based optimization of autonomous driving's operational design domain considering network efficiency

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TRANSPORTMETRICA B-TRANSPORT DYNAMICS 2025年第1期13卷

作者： Wu, Xuemian Wu, Jiyan Sun, Jian Tian, Ye Tongji Univ Dept Traff Engn 4800 Caoan Rd Shanghai 201804 Peoples R China Tongji Univ Key Lab Rd & Traff Engn Minist Educ 4800 Caoan Rd Shanghai 201804 Peoples R China Dalian Univ Technol Sch Econ & Management Dalian 116024 Liaoning Peoples R China Tongji Univ Urban Mobil Inst Shanghai Peoples R China

Operational Design Domain (ODD) refers to the specific condition set under which an autonomous driving system is designed to operate. Currently, ODD deployment primarily focuses on safety factors, such as lighting and road surface conditions. However, the impact of ODD deployment on network efficiency is equally significant. To efficiently optimize ODD deployment in terms of network efficiency, we propose a surrogate model that incorporates multi-user structural information and integrate it into an efficient simulation-based optimization framework. The proposed surrogate model can accurately estimate the complex simulation model and construct a hyperplane containing the efficient descent direction for simulation-based optimization. And we compare two common deployment forms, namely, Dedicated Zone (DZ) and Shared Zone (SZ). The results reveal that DZ and SZ have different deployment tendencies. And DZ facilitates the spatial reallocation of travel demand, effectively alleviating congestion, while SZ reduces the temporal continuity of congestion, thus accelerating traffic flow.

关键词： Operational design domain large-scale network modelling simulation-based optimization surrogate model

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simulation-based optimization for design parameter exploration in hybrid system: a defense system example

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simulation-TRANSACTIONS OF THE SOCIETY FOR MODELING AND simulation INTERNATIONAL 2013年第3期89卷 362-380页

作者： Hong, Jeong Hee Seo, Kyung-Min Kim, Tag Gon Korea Adv Inst Sci & Technol Dept Elect Engn Taejon 305701 South Korea

This paper presents a method for solving the optimization problems that arise in hybrid systems. These systems are characterized by a combination of continuous and discrete event systems. The proposed method aims to find optimal design configurations that satisfy a goal performance. For exploring design parameter space, the proposed method integrates a metamodel and a metaheuristic method. The role of the metamodel is to give good initial candidates and reduced search space to the metaheuristic optimizer. On the other hand, the metaheuristic method improves the quality of the given candidates. This proposal also demonstrates a defense system that illustrates the practical application of the presented method. The optimization objective of the case study is to find the required operational capability configurations of a decoy that meet the desired measure of effectiveness. Through a comparison with a full search method, two metamodeling methods without the aid of metaheuristics and a metaheuristic method without the support of metamodels, we confirmed that the proposed method provides same high-quality solutions as those of the full search method at a small computational cost.

关键词： simulation-based optimization design parameter exploration hybrid system defense system metamodel metaheuristics

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simulation-based optimization approach for material dispatching in continuous mining systems

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2019年第3期275卷 1108-1125页

作者： Shishvan, Masoud Soleymani Benndorf, Joerg Delft Univ Technol Resource Engn Sect Dept Geosci & Engn NL-2628 CD Delft Netherlands Freiberg Univ Technol Geomonitoring & Mine Surveying Inst Mine Surveying & Geodesy D-09599 Freiberg Germany

This paper examines a problem related to dispatching materials to spreaders in coal (lignite) mines operated under the paradigm of continuously excavated material flow. In the considered particular case, complexity arises from different material types of overburden to be placed on the dump site in predefined patterns that guarantee geotechnical safety. These types include wet, semi-wet and dry material, which are accessed on the excavation site according to the geological deposition and the mining plan. Controlling of the dispatch system has to take into account the extraction sequence and geological stratification on the excavation site and the available dump space per material on the dumping site. With eight excavators on the excavation site and seven spreaders on the dump site the problem is already complex, having not stated yet that random breakdowns may make some options temporarily unavailable. To optimize the dispatch system in terms of minimum idle time due to unavailability of dumping space, a new multi-stage simulation-based optimization approach is proposed. This approach consists of running alter-natingly a deterministic optimization model and a stochastic simulation model. It combines simulation and algorithms to solve a transportation problem and a job-shop scheduling problem. The proposed approach is tested on a large continuous mine under given different dumping sequences, and results are reported. The merits and limitations of the proposed approach as pinpointed and farsighted operations management are discussed. (C) 2018 Elsevier B.V. All rights reserved.

关键词： OR in natural resources simulation-based optimization Opencast coal mining Short-term scheduling Material dispatching

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simulation-based optimization of process control policies for inventory management in supply chains

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AUTOMATICA 2006年第8期42卷 1311-1320页

作者： Schwartz, Jay D. Wang, Wenlin Rivera, Daniel E. Arizona State Univ Dept Chem & Mat Engn Control Syst Engn Lab Tempe AZ 85287 USA

A simulation-based optimization framework involving simultaneous perturbation stochastic approximation (SPSA) is presented as a means for optimally specifying parameters of internal model control (IMC) and model predictive control (MPC)-based decision policies for inventory management in supply chains under conditions involving supply and demand uncertainty. The effective use of the SPSA technique serves to enhance the performance and functionality of this class of decision algorithms and is illustrated with case studies involving the simultaneous optimization of controller tuning parameters and safety stock levels for supply chain networks inspired from semiconductor manufacturing. The results of the case studies demonstrate that safety stock levels can be significantly reduced and financial benefits achieved while maintaining satisfactory operating performance in the supply chain. (c) 2006 Elsevier Ltd. All rights reserved.

关键词： supply chain management simulation-based optimization inventory control internal model control model predictive control

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simulation-based optimization in a Bidirectional A/B Skip-Stop Bus Service

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IEEE ACCESS 2017年 5卷 15478-15489页

作者： Huang, Qingxia Jia, Bin Jiang, Rui Qiang, Shengjie Beijing Jiaotong Univ Sch Traff & Transportat Beijing 100044 Peoples R China East China Jiaotong Univ Sch Transportat & Logist Nanchang 330013 Jiangxi Peoples R China

A two-way bus corridor system always suffers severe demand imbalance between their two operational directions during the peak hours. This paper intends to minimize the average passenger travel time by applying the A/B skip-stop strategy in such an imbalance situation. This strategy defined three types of stations: A , B , and AB . In the service, the buses depart alternately from the original station as type A and B , and A (or B ) buses serve A (or B ) stations, as well as AB stations. Then the problem becomes determining the skip-stop patterns for both directions. A heuristic genetic algorithm is adopted to solve this problem with a kernel of a precise simulation model depicting the bus system. Finally, we apply the optimization method to a realistic bus corridor of BRT line 1 in Beijing, China. Results demonstrate that the bidirectional A/B skip-stop service prevails over the unidirectional services applying A/B skip-stop only on one direction, and the common used regular service visiting all stations. It is certificated that the bidirectional skip-stop service reduces bus bunching, yields a more balanced bus load and provides a smooth bus service with lower cycle time and variability. Moreover, a sensitivity analysis is conducted to show the impacts of some key attributes on potential benefits of bidirectional skip-stop service. Finally, the elastic demand case where transferring passengers may change their origins or destinations has been discussed.

关键词： Bus operation bus route model service level simulation-based optimization transportation management

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simulation-based optimization for a capacitated multi-echelon production-inventory system

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JOURNAL OF simulation 2015年第4期9卷 325-336页

作者： Gueller, M. Uygun, Y. Noche, B. Tech Univ Dortmund D-44227 Dortmund Nrw Germany MIT Cambridge MA 02139 USA Univ Duisburg Essen Duisburg Germany

One of the most important aspects affecting the performance of a supply chain is the management of inventories. Managing inventory in complex supply chains is typically difficult, and may have a significant impact on the customer service level and system-wide costs. The main challenge of inventory management is that almost every inventory problem involves multiple and conflicting objectives that need to be optimized simultaneously. In this paper, we present an efficient way using simulation-based optimization approach to determine the optimal inventory control parameters of a multi-echelon production-inventory system under a stochastic environment. The Pareto dominance concept is implemented to find a set of near optimal solutions for determining the best trade-off between objectives. The Multi-objective Particle Swarm optimization (MOPSO) algorithm is used to determine the appropriate inventory control parameters to minimize the total inventory cost and maximize the service level. To evaluate the control parameters generated by the MOPSO, an object-oriented framework for developing the simulation model is presented. Finally, we provide a real-world case study of a major food product supply chain to demonstrate the use of proposed approach and enable decision making at inventory management. The proposed algorithm is compared with existing multi-objective genetic algorithm (NSGA-II).

关键词： simulation-based optimization metaheuristics multi-objective particle swarm inventory object-oriented

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simulation-based optimization for resectorization in healthcare systems

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JOURNAL OF simulation 2024年第3期18卷 311-330页

作者： Teymourifar, Aydin Catolica Porto Business Sch CEGE Ctr Estudos Gestao & Econ Porto Portugal

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.

关键词： Resectorization balancing accessibility of healthcare units contract mechanisms simulation-based optimization

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simulation-based optimization vs. mathematical programming: A hybrid approach for optimizing scheduling problems

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ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING 2009年第6期25卷 917-925页

作者： Klemmt, Andreas Horn, Sven Weigert, Gerald Wolter, Klaus-Juergen Tech Univ Dresden Fac Elect Engn & Informat Technol Elect Packaging Lab D-01062 Dresden Germany

With the increasing computing power of modern processors, exact solution methods (solvers) for the optimization of scheduling problems become more and more important. based on the mixed integer programming (MIP) formulation of a scheduling problem, it will be analyzed how powerful the present solvers of this problem class are and up to which complexity real scheduling problems are manageable. initially some common benchmark problems are investigated to find out the boundaries for For this, practical application. Then, the acquired results will be compared with the results of a conventional simulation-based optimization approach under comparable time restrictions. As a next step, the general advantages and disadvantages of both approaches were analyzed. As the result, a coupling of the discrete event simulation system and an MIP solver is presented. This coupling automatically generates an MIP-formulation for the present simulation model which can be solved externally by an MIP solver. After the external optimization process follows a backward transformation of the results into the simulation system. All features of the simulation system (like Gantt-Charts, etc.) could be used to check or to illustrate these results. To perform the coupling for a wide range of simulation models, it has to be defined which general constraints the model has to satisfy. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Complex scheduling Job Shop scheduling Mixed integer programming simulation-based optimization

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simulation-based optimization framework for multi-echelon inventory systems under uncertainty

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COMPUTERS & CHEMICAL ENGINEERING 2015年 73卷 1-16页

作者： Chu, Yunfei You, Fengqi Wassick, John M. Agarwal, Anshul Northwestern Univ Dept Chem & Biol Engn Evanston IL 60208 USA Dow Chem Co USA Midland MI 48674 USA

Inventory optimization is critical in supply chain management. The complexity of real-world multi-echelon inventory systems under uncertainties results in a challenging optimization problem, too complicated to solve by conventional mathematical programing methods. We propose a novel simulation-based optimization framework for optimizing distribution inventory systems where each facility is operated with the (r, Q) inventory policy. The objective is to minimize the inventory cost while maintaining acceptable service levels quantified by the fill rates. The inventory system is modeled and simulated by an agent-based system, which returns the performance functions. The expectations of these functions are then estimated by the Monte-Carlo method. Then the optimization problem is solved by a cutting plane algorithm. As the black-box functions returned by the Monte-Carlo method contain noises, statistical hypothesis tests are conducted in the iteration. A local optimal solution is obtained if it passes the test on the optimality conditions. The framework is demonstrated by two case studies. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： simulation-based optimization Supply chain management Agent-based modeling Cutting plane algorithm Statistical hypothesis tests

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