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Hyperparameter Tuning with Gaussian Processes for Optimal Abstraction Control in simulation-based optimization of Smart Semiconductor Manufacturing Systems

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ACM TRANSACTIONS ON MODELING AND COMPUTER simulation 2024年第1期35卷 1-21页

作者： Seok, Moon gi Tan, Wen jun Su, Boyi Cai, Wentong Kwon, Jisu Choi, Seon han Dongguk Univ Dept Comp Sci & Engn Seoul South Korea Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore Kyungpook Natl Univ Sch Elect & Elect Engn Daegu South Korea Ewha Womans Univ Grad Program Smart Factory Seoul South Korea

Smart manufacturing utilizes digital twins that are virtual forms of their production plants for analyzing and optimizing decisions. Digital twins have been mainly developed as discrete-event models (DEMs) to represent the detailed and stochastic dynamics of productions in the plants. The optimum decision is achieved after simulating the DEM-based digital twins under various what-if decision candidates;thus, simulation acceleration is crucial for rapid optimum determination for given problems. For the acceleration of discrete-event simulations, adaptive abstraction-level conversion approaches have been previously proposed to switch active models of each machine group between a set of DEM components and a corresponding lookup table-based mean-delay model during runtime. The switching is decided by detecting the machine group's convergence into (or divergence from) a steady state. However, there is a tradeoff between speedup and accuracy loss in the adaptive abstraction convertible simulation (AACS), and inaccurate simulation can degrade the quality of the optimum (i.e., the distance between the calculated optimum and the actual optimum). In this article,

关键词： simulation-based optimization runtime abstraction-level conversion discrete-event modeling manufacturing-system simulation

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A taxonomy of constraints in black-box simulation-based optimization

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optimization AND ENGINEERING 2024年第2期25卷 605-+页

作者： Le Digabel, Sebastien Wild, Stefan M. Polytech Montreal GERAD & Dept Math & Genie Ind Montreal PQ H3C 3A7 Canada Lawrence Berkeley Natl Lab Appl Math & Computat Res Div Berkeley CA 94720 USA Northwestern Univ Ind Engn & Management Sci Dept Evanston IL 60208 USA

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.

关键词： Taxonomy of constraints Black-box optimization simulation-based optimization

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A derivative-free approach for a simulation-based optimization problem in healthcare

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optimization LETTERS 2016年第2期10卷 219-235页

作者： Lucidi, Stefano Maurici, Massimo Paulon, Luca Rinaldi, Francesco Roma, Massimo Univ Roma La Sapienza Dipartimento Ingn Informat Automat & Gestionale A Via Ariosto 25 I-00185 Rome Italy Univ Roma La Sapienza ACTOR Spin Off Via Ariosto 25 I-00185 Rome Italy Univ Roma Tor Vergata Dipartimento Biomed & Prevenz Via Montpellier 1 I-00133 Rome Italy Univ Roma Tor Vergata Lab Simulat & Optimizat Hlth Serv Via Montpellier 1 I-00133 Rome Italy Univ Padua Dipartimento Matemat Via Trieste 63 I-35121 Padua Italy Via Luigi Vittorio Bertarelli 169 I-00159 Rome Italy

Hospitals have been challenged in recent years to deliver high quality care with limited resources. Given the pressure to contain costs, developing procedures for optimal resource allocation becomes more and more critical in this context. Indeed, under/overutilization of emergency room and ward resources can either compromise a hospital's ability to provide the best possible care, or result in precious funding going toward underutilized resources. simulation-based optimization tools then help facilitating the planning and management of hospital services, by maximizing/minimizing some specific indices (e.g. net profit) subject to given clinical and economical constraints. In this work, we develop a simulation-based optimization approach for the resource planning of a specific hospital ward. At each step, we first consider a suitably chosen resource setting and evaluate both efficiency and satisfaction of the restrictions by means of a discrete-event simulation model. Then, taking into account the information obtained by the simulation process, we use a derivative-free optimization algorithm to modify the given setting. We report results for a real-world problem coming from the obstetrics ward of an Italian hospital showing both the effectiveness and the efficiency of the proposed approach.

关键词： Healthcare problems simulation-based optimization Derivative-free methods

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EVALUATION OF simulation-based optimization IN GRAFTING LABOR ALLOCATION

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APPLIED ENGINEERING IN AGRICULTURE 2018年第3期34卷 479-489页

作者： Masoud, S. Son, Y. J. Kubota, C. Tronstad, R. Univ Arizona Dept Syst & Ind Engn Tucson AZ 85721 USA Univ Arizona Sch Plant Sci Tucson AZ USA Univ Arizona Dept Agr & Resource Econ Tucson AZ USA

Vegetable grafting is a labor-intensive operation with many management decisions. Labor management and resource planning are critical allocations in grafting nurseries, yet optimization is challenging due to the dynamic nature of workers' performance in vegetable seedling propagation. To this end, we developed a simulation-based optimization framework for labor management to optimize labor allocation. This approach was evaluated by comparing its result with those suggested by selected domain experts (a plant scientist and a nursery manager). Furthermore, the simulation models were validated with a dataset from a developing tomato grafting company. simulation-based optimization is demonstrated as an effective approach to find the optimal/near optimal labor allocation for horticultural nurseries, where discrete event simulation is used to represent the dynamics of the grafting work environment and meta-heuristics are used to devise optimal/near optimal resource allocation strategies. Results reveal that a potential annual savings between $2,510 (0.6%) and $97,388 (20%) can be achieved for a grafting facility of 6 million plants per year.

关键词： simulation-based optimization Grafting Labor allocation Discrete event simulation

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A conservative multi-fidelity surrogate model-based robust optimization method for simulation-based optimization

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STRUCTURAL AND MULTIDISCIPLINARY optimization 2021年第4期64卷 2525-2551页

作者： Hu, Jiexiang Zhang, Lili Lin, Quan Cheng, Meng Zhou, Qi Liu, Huaping Huazhong Univ Sci Technol Sch Aerosp Engn Wuhan 430074 Peoples R China Huazhong Univ Sci Technol Sch Mech Sci & Engn Wuhan 430074 Peoples R China

Multi-fidelity (MF) surrogate model-based robust optimization has been used to deal with engineering design and optimization problems that have uncertainty in their inputs. However, the MF surrogate model constructed by a limited number of samples ineluctable has prediction uncertainty, which often leads to the optimal solutions becoming infeasible. In this paper, a MF surrogate model-assisted semi-nested variable adjustment robust optimization (CMF-SN-VARO) method is proposed to address the impact of prediction uncertainty of the MF surrogate model during the optimization process. A piecewise conservative MF surrogate modeling method is proposed to replace the objective functions and constraints, in which the safety margin is calculated by different error metrics according to their performance in problems with different dimensions. The variable adjustment robust optimization (VARO) framework is adopted to solve the robust optimization problem by adjusting the preexisting design. A switch criterion is utilized to adaptively determine when to use surrogate models or inner optimization problems to evaluate the robustness index of design alternatives. The performance of the proposed method is illustrated with an analytical example, a torque arm design problem, and a micro aerial vehicle fuselage design problem. Results show that the proposed method achieves better optimal design solutions that are both objective robust and feasibility robust.

关键词： Multi-fidelity surrogate model Robust optimization Conservative surrogate model simulation-based optimization Interval uncertainty

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A framework for simulation-based optimization of business process models

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simulation-TRANSACTIONS OF THE SOCIETY FOR MODELING AND simulation INTERNATIONAL 2012年第7期88卷 852-869页

作者： Kamrani, Farzad Ayani, Rassul Moradi, Farshad KTH Royal Inst Technol Sch Informat & Commun Technol Stockholm Sweden Swedish Def Res Agcy Div Informat Syst FOI Stockholm Sweden

The Assignment Problem is a classical problem in the field of combinatorial optimization, having a wide range of applications in a variety of contexts. In general terms, the Assignment Problem consists of determining the best assignment of tasks to agents according to a predefined objective function. Different variants of the Assignment Problem have been extensively investigated in the literature in the last 50 years. In this work, we introduce and analyze the problem of optimizing a business process model with the objective of finding the most beneficial assignment of tasks to agents. Despite similarities, this problem is distinguished from the traditional Assignment Problem in that we consider tasks to be part of a business process model, being interconnected according to defined rules and constraints. In other words, assigning a business process to agents is a more complex form of the Assignment Problem. Two main categories of business processes, assignment-independent and assignment-dependent, are distinguished. In the first category, different assignments of tasks to agents do not affect the flow of the business process, while processes in the second category contain critical tasks that may change the workflow, depending on who performs them. In each category several types of processes are studied. Algorithms for finding optimal and near-optimal solutions to these categories are presented. For the first category, depending on the type of process, the Hungarian algorithm is combined with either the analytical method or simulation to provide an optimal solution. For the second category, we introduce two algorithms. The first one finds an optimal solution, but is feasible only when the number of critical tasks is small. The second algorithm is applicable to large number of critical tasks, but provides a near-optimal solution. In the second algorithm a hill-climbing heuristic method is combined with the Hungarian algorithm and simulation to find an overall near-opt

关键词： Assignment Problem business process optimization Hungarian algorithm simulation-based optimization

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Improving configuration of complex production lines via simulation-based optimization

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COMPUTERS & INDUSTRIAL ENGINEERING 2017年 109卷 295-312页

作者： Yegul, Mustafa Fatih Erenay, Fatih Safa Striepe, Soeren Yavuz, Mustafa Univ Waterloo Dept Mech & Mechatron Engn Waterloo ON Canada Univ Waterloo Dept Management Sci Waterloo ON Canada 200 Univ Ave West E5-3011 Waterloo ON N2L3G1 Canada 18579 Ctr St Mt Albert ON L0G1M0 Canada

Optimizing the configuration of a complex production line is an NP-hard problem in various machine settings. Solving real-life-size instances of this problem becomes a more common challenge because the current trend of reshoring induces multi-national firms to transfer manufacturing facilities from workforce intensive to capital-intensive production environments which usually require re-configuration of the transferred manufacturing systems according to the availability of better machinery in a capital intensive environment. This paper focuses on the problem of optimizing production line configuration and proposes several simulation-based optimization approaches based on myopic search, ant-colony, simulated annealing, and response-surface methodologies. We investigate the relative performances of these proposed algorithms on a real-life manufacturing system transfer case in automotive industry according to solution quality and computation-time metrics under different parameter scenarios. Thus, our numerical results may guide the decision makers in choosing a suitable solution approach for this problem depending on the problem size and time availability. Our results also illustrate that ant colony optimization, a methodology not widely applied in simulation-based optimization, provides high-solution quality for this problem when matched-up with a myopic search to find a good initial solution. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Production line configuration simulation-based optimization Ant-colony optimization Response-surface method Simulated-annealing

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Analyzing a lost-sale stochastic inventory model with Markov-modulated demands: A simulation-based optimization study

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JOURNAL OF MANUFACTURING SYSTEMS 2016年 38卷 1-12页

作者： Diaz, Rafael Bailey, Mike P. Kumar, Sameer Zaragoza Logist Ctr MIT Zaragoza Int Logist Program Global Scale Zaragoza 50197 Spain US Marine Corps Combat Dev Command Combat Dev & Integrat Quantico VA 22134 USA Univ St Thomas Opus Coll Business Operat & Supply Chain Management 1000 LaSalle Ave Minneapolis MN 55403 USA

A consumer demand that presents auto-correlated components is a class of demand commonly found in competitive markets in which consumers may develop preferences for certain products which influence their willingness to purchase them again. This behavior may be observed in inventory systems whose products are subject to promotion plans in which mechanisms that incentivize the demand are implemented. Inventory systems that ignore these dependency components may severely impair their performance. This paper analyzes a stochastic inventory model where the control review system is periodic, is categorized as a lost-sale case, and is exposed to this class of auto-correlated demand pattern. The demand for products is characterized as a discrete Markov-modulated demand in which product quantities of the same item may relate to one another according to an empirical probability distribution. A simulation-based optimization that combines simulated annealing, pattern search, and ranking and selection (SAPS&RS) methods to approximate near-optimal solutions to this problem is employed. Lower and upper bounds for a range of near-optimal solutions are determined by the pattern search step enhanced by ranking and selection indifferent zone. Results indicate that inventory performance significantly declines as the autocorrelation increases and is disregarded. (C) 2015 Published by Elsevier Ltd on behalf of The Society of Manufacturing Engineers.

关键词： Stochastic inventory model Autocorrelation simulation-based optimization Simulated annealing Ranking and selection methods Pattern search

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Solving a class of simulation-based optimization problems using "optimality in probability"

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DISCRETE EVENT DYNAMIC SYSTEMS-THEORY AND APPLICATIONS 2018年第1期28卷 35-61页

作者： Mao, Jianfeng Cassandras, Christos G. Chinese Univ Hong Kong Sch Sci & Engn Shenzhen 518172 Peoples R China Boston Univ Div Syst Engn Brookline MA 02446 USA

We consider a class of simulation-based optimization problems using optimality in probability, an approach which yields what is termed a "champion solution". Compared to the traditional optimality in expectation, this approach favors the solution whose actual performance is more likely better than that of any other solution;this is an alternative complementary approach to the traditional optimality sense, especially when facing a dynamic and nonstationary environment. Moreover, using optimality in probability is computationally promising for a class of simulation-based optimization problems, since it can reduce computational complexity by orders of magnitude compared to general simulation-based optimization methods using optimality in expectation. Accordingly, we have developed an "Omega Median Algorithm" in order to effectively obtain the champion solution and to fully utilize the efficiency of well-developed off-line algorithms to further facilitate timely decision making. An inventory control problem with nonstationary demand is included to illustrate and interpret the use of the Omega Median Algorithm, whose performance is tested using simulations.

关键词： simulation-based optimization Optimality in probability Nonstationary inventory control

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Redundancy allocation of heterogeneous components by simulation-based optimization

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART O-JOURNAL OF RISK AND RELIABILITY 2021年第6期235卷 1090-1101页

作者： Chambari, Amirhossein Azimi, Parham Najafi, Amir Abbas Islamic Azad Univ Fac Ind & Mech Engn Dept Ind Engn Qazvin Branch Nokhbegan Blvd Qazvin *** Iran KN Toosi Univ Technol Fac Ind Engn Tehran Iran

The present study proposes a novel simulation-based optimization model for a series-parallel redundancy allocation problem (RAP) under heterogeneous components for reliability maximization subjected to system-level constraints through the identification of the optimal redundancy strategy, component type, and subsystem component count. To obtain higher practicality, active, cold-standby, mixed, and K-mixed redundancy strategies are incorporated as the decision variables. In general, as it is difficult to determine a closed-form (excluding active redundancy strategies), it is not possible to analytically assess system reliability. Earlier studies on system reliability optimization applied convenient lower bound approximation. This limits higher reliability levels. In order to tackle this problem, this study adopted simulation sampling to make unbiased efficient reliability estimates. To this end, 4Dscript interpreting programming was utilized to develop a simulation model. As RAP has a combinatorial nature and is a random and NP-hard problem, this study adopted the genetic algorithm (GA) optimization. To validate the model and assess GA efficiency, the numerical findings of some benchmark tests were employed. The proposed approach outperformed earlier approaches in reliability and confidence.

关键词： simulation-based optimization redundancy allocation genetic algorithm redundancy strategy

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