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  • 2020-2024  (3)
  • Economics  (3)
  • 1
    Online Resource
    Online Resource
    Elsevier BV ; 2024
    In:  Research in International Business and Finance Vol. 70 ( 2024-06), p. 102406-
    In: Research in International Business and Finance, Elsevier BV, Vol. 70 ( 2024-06), p. 102406-
    Type of Medium: Online Resource
    ISSN: 0275-5319
    RVK:
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2024
    detail.hit.zdb_id: 2165501-7
    detail.hit.zdb_id: 424514-3
    SSG: 3,2
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  • 2
    Online Resource
    Online Resource
    Institute for Operations Research and the Management Sciences (INFORMS) ; 2023
    In:  INFORMS Journal on Computing Vol. 35, No. 2 ( 2023-03), p. 386-402
    In: INFORMS Journal on Computing, Institute for Operations Research and the Management Sciences (INFORMS), Vol. 35, No. 2 ( 2023-03), p. 386-402
    Abstract: We consider performing simulation experiments in the presence of covariates. Here, covariates refer to some input information other than system designs to the simulation model that can also affect the system performance. To make decisions, decision makers need to know the covariate values of the problem. Traditionally in simulation-based decision making, simulation samples are collected after the covariate values are known; in contrast, as a new framework, simulation with covariates starts the simulation before the covariate values are revealed and collects samples on covariate values that might appear later. Then, when the covariate values are revealed, the collected simulation samples are directly used to predict the desired results. This framework significantly reduces the decision time compared with the traditional way of simulation. In this paper, we follow this framework and suppose there are a finite number of system designs. We adopt the metamodel of stochastic kriging (SK) and use it to predict the system performance of each design and the best design. The goal is to study how fast the prediction errors diminish with the number of covariate points sampled. This is a fundamental problem in simulation with covariates and helps quantify the relationship between the offline simulation efforts and the online prediction accuracy. Particularly, we adopt measures of the maximal integrated mean squared error (IMSE) and integrated probability of false selection (IPFS) for assessing errors of the system performance and the best design predictions. Then, we establish convergence rates for the two measures under mild conditions. Last, these convergence behaviors are illustrated numerically using test examples. History: Accepted by Bruno Tuffin, area editor for simulation. Funding: This work was supported in part by Singapore Ministry of Education Academic Research Funds [Tier 1 Grants R-155-000-201-114 and A-0004822-00-00], the City University of Hong Kong [Grants 7005269 and 7005568] , and the National Natural Science Foundation of China [Grant 72091211]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.1263 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2021.0329 ) at ( http://dx.doi.org/10.5281/zenodo.7344997 ).
    Type of Medium: Online Resource
    ISSN: 1091-9856 , 1526-5528
    RVK:
    Language: English
    Publisher: Institute for Operations Research and the Management Sciences (INFORMS)
    Publication Date: 2023
    detail.hit.zdb_id: 2070411-2
    detail.hit.zdb_id: 2004082-9
    SSG: 3,2
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  • 3
    Online Resource
    Online Resource
    Hindawi Limited ; 2022
    In:  Mobile Information Systems Vol. 2022 ( 2022-6-29), p. 1-14
    In: Mobile Information Systems, Hindawi Limited, Vol. 2022 ( 2022-6-29), p. 1-14
    Abstract: Systems are everywhere around us, such as products, production lines, the Internet, companies, schools, hospitals, airports, communities, cities, countries, governments, and military forces. System researchers and engineers always strive to achieve their desired system behaviors. Thus, this paper aims to solve two fundamental problems of systems engineering: what determines the behavior of a system and how to design a satisfactory system for the expected behavior. First, we determine the dimensions of a relationship flow, such as information flow, material flow, and capital flow. Second, we develop new theorems and propositions to solve the first problem. For example, they show that, for a given behavior of a system, if the dominant subsystem of the behavior has the basic level for the behavior, the behavior is only determined by its input flow and relationship flows at or above the basic level, all of which are collectively referred to as total relationship flow (TRF). Third, we develop a relationship flow diagram which is of great significance to system design, just as a circuit diagram to circuit design, and a TRF-oriented system design framework to solve the second problem. In order to show how the framework works, we apply it step by step to the schematic design of an airport system for the required passenger departure procedure service behavior, and get a system example; using this framework to design a system will greatly simplify the complexity of development and improve the maintainability of the system. Therefore, our findings provide a new theory for systems engineering and improve its methods and tools systematically.
    Type of Medium: Online Resource
    ISSN: 1875-905X , 1574-017X
    RVK:
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2187808-0
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