Quantile-Based Simulation Optimization With Inequality Constraints: Methodology and Applications

被引:7
|
作者
Chang, Kuo-Hao [1 ]
Lu, Hou-Kuen [1 ]
机构
[1] Natl Tsing Hua Univ, Dept Ind Engn & Engn Management, Hsinchu 30013, Taiwan
关键词
Direct search method; quantile; simulation optimization; stochastic Nelder-Mead simplex method;
D O I
10.1109/TASE.2015.2406736
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Many automation or manufacturing systems are too complex to be modeled by analytical approaches and can only resort to fast-running simulation. Stochastic Nelder-Mead simplex method (SNM) is a newly developed methodology for simulation optimization with expected-value-based objective functions. Quantile, as an important alternative to the usual expected value, provides additional information about the distribution of system performance. In particular, it is useful in describing the tail behavior of the distribution. In this paper, we exploit the structure of SNM and extend it to solve simulation optimization problems with quantile-based objective functions and inequality constraints. The proposed method, called SNM-QC, utilizes the same search strategy as SNM but further incorporates effective quantile estimation techniques and penalty function approaches to solve the problem. We prove that SNM-QC has the desirable global convergence guarantee, i.e., the algorithm is guaranteed to converge to the true optima with probability one. One advantage of SNM-QC is that it is a direct search method that determines the moving direction simply by comparing a set of solutions rather than estimating gradient, thus it can handle many practical problems where gradient does not exist or is difficult to estimate. An extensive numerical study shows that the performance of SNM-QC is promising compared to the existing heuristics. Two illustrative applications are provided in the end to demonstrate the viability of SNM-QC in practical settings. Note to Practitioners-This paper proposes a direct search method, called SNM-QC, for solving quantile-based simulation optimization problems with inequality constraints. Compared to traditional methods that are largely focused on expected-value-based objective functions, SNM-QC complements the existing literature in simulation optimization. In particular, by adjusting the value in the quantile-based simulation optimization formulation, SNM-QC allows for more flexibility when seeking the optimal solution associated with the problem. The advantages of SNM-QC are that it is easy to implement and moreover, it does not require gradient estimation in the search process. In practice, SNM-QC can be applied, for example, to determine the staffing level in an emergency room of a hospital so as to maximize the service quality of an out-of-hospital system measured by the 90th percentile of the times taken to respond to emergency requests, subject to budget constraints.
引用
收藏
页码:701 / 708
页数:8
相关论文
共 50 条
  • [1] A direct search method for unconstrained quantile-based simulation optimization
    Chang, Kuo-Hao
    EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2015, 246 (02) : 487 - 495
  • [2] A quantile-based simulation optimization model for sizing hybrid renewable energy systems
    Chang, Kuo-Hao
    SIMULATION MODELLING PRACTICE AND THEORY, 2016, 66 : 94 - 103
  • [3] QUANTILE-BASED POLICY OPTIMIZATION FOR REINFORCEMENT LEARNING
    Jiang, Jinyang
    Peng, Yijie
    Hu, Jiaqiao
    2022 WINTER SIMULATION CONFERENCE (WSC), 2022, : 2712 - 2723
  • [4] Quantile-based clustering
    Hennig, Christian
    Viroli, Cinzia
    Anderlucci, Laura
    ELECTRONIC JOURNAL OF STATISTICS, 2019, 13 (02): : 4849 - 4883
  • [5] TRACTABLE SAMPLING STRATEGIES FOR QUANTILE-BASED ORDINAL OPTIMIZATION
    Shin, Dongwook
    Broadie, Mark
    Zeevi, Assaf
    2016 WINTER SIMULATION CONFERENCE (WSC), 2016, : 847 - 858
  • [6] On quantile-based dynamic survival extropy and its applications
    Khammar, Amir Hamzeh
    Jahanshahi, Seyyed Mahdi Amir
    Zarei, Hassan
    HACETTEPE JOURNAL OF MATHEMATICS AND STATISTICS, 2023, 52 (05): : 1349 - 1366
  • [7] Quantile-based classifiers
    Hennig, C.
    Viroli, C.
    BIOMETRIKA, 2016, 103 (02) : 435 - 446
  • [8] Quantile Learn-Then-Test: Quantile-Based Risk Control for Hyperparameter Optimization
    Farzaneh, Amirmohammad
    Park, Sangwoo
    Simeone, Osvaldo
    IEEE SIGNAL PROCESSING LETTERS, 2024, 31 : 3044 - 3048
  • [9] Quantile-Based Optimization of Noisy Computer Experiments With Tunable Precision
    Picheny, Victor
    Ginsbourger, David
    Richet, Yann
    Caplin, Gregory
    TECHNOMETRICS, 2013, 55 (01) : 2 - +
  • [10] Practical Nonparametric Sampling Strategies for Quantile-Based Ordinal Optimization
    Shin, Dongwook
    Broadie, Mark
    Zeevi, Assaf
    INFORMS JOURNAL ON COMPUTING, 2022, 34 (02) : 752 - 768