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simulation-based optimization for the Fast Fashion Replenishment

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Journal of Donghua University(English Edition) 2016年第3期33卷 495-500页

作者：齐洁张晶 School of Information Science and Technology Donghua University Engineering Research Center of Digitized Textile and Fashion Technology Ministry of EducationDonghua University

Fast fashion is a commercial pattern which provides fashionable clothes at affordable *** mode needs rapid response supply chain to respond to varying fashion *** styles are introduced in every sale period to cover fashion *** order to maximize profits,replenishment quantity of each style should be decided in every *** purchasing and replenishing process over multiple periods based on uncertainty customer demand is modeled,which is formulated by a stochastic choice *** consumers visit a store in a stochastic sequence and choosing dynamically from the available fashion styles(buy or not buy) according to a utility maximization *** purchase process in a retail shop for multi-period is *** algorithm which combines simulated anneal(SA) with gradient estimation is proposed to find the optimal replenishing strategy from the simulation program.

关键词： fast fashion supply chain stochastic choice replenishment simulation-based optimization simulated annealing

Synergistic optimization framework for the process synthesis and design of biorefineries

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Frontiers of Chemical Science and Engineering 2022年第2期16卷 251-273页

作者： Nikolaus IVollmer Resul Al Krist V.Gernaey Gürkan Sin Process and Systems Engineering(PROSYS)Research Center Department of Chemical and Biochemical EngineeringTechnical University of Denmark2800 Kgs.LyngbyDenmark Novo Nordisk A/S 2880 BagsværdDenmark

The conceptual process design of novel bioprocesses in biorefinery setups is an important task,which remains yet challenging due to several *** propose a novel framework incorporating superstructure optimization and simulation-based optimization *** this context,several approaches for superstructure optimization based on different surrogate models can be *** means of a case study,the framework is introduced and validated,and the different superstructure optimization approaches are *** results indicate that even though surrogate-based optimization approaches alleviate the underlying computational issues,there remains a potential issue regarding their *** development of appropriate surrogate models,comprising the selection of surrogate type,sampling type,and size for training and cross-validation sets,are essential *** this aspect,satisfactory validation metrics do not ensure a successful outcome from its embedded use in an optimization ***,the framework’s synergistic effects by sequentially performing superstructure optimization to determine candidate process topologies and simulationbased optimization to consolidate the process design under uncertainty offer an alternative and promising *** findings invite for a critical assessment of surrogatebased optimization approaches and point out the necessity of benchmarking to ensure consistency and quality of optimized solutions.

关键词： biotechnology surrogate modelling superstructure optimization simulation-based optimization process design

Optimal synthesis of heat-integrated distillation configurations using the two-column superstructure

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Chinese Journal of Chemical Engineering 2024年第2期66卷 238-249页

作者： Xiaodong Zhang Lu Jin Jinsheng Sun School of Chemical Engineering and Technology Tianjin UniversityTianjin 300072China

In the realm of the synthesis of heat-integrated distillation configurations,the conventional approach for exploring more heat integration possibilities typically entails the splitting of a single column into a twocolumn ***,this approach frequently necessitates tedious enumeration procedures,resulting in a considerable computational *** surmount this formidable challenge,the present study introduces an innovative remedy:The proposition of a superstructure that encompasses both single-column and multiple two-column ***,a simultaneous optimization algorithm is applied to optimize both the process parameters and heat integration structures of the twocolumn *** effectiveness of this approach is demonstrated through a case study focusing on industrial organosilicon *** results underscore that the superstructure methodology not only substantially mitigates computational time compared to exhaustive enumeration but also furnishes solutions that exhibit comparable performance.

关键词： Superstructure Process synthesis Heat integration simulation-based optimization Industrial organosilicon separation

simulation-based Optimal Design of α-β-γ-δ Filter

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International Journal of Automation and computing 2010年第2期7卷 247-253页

作者： Chun-Mu Wu Paul P. Lin Zhen-Yu Han Shu-Rong Li Department of Mechanical and Automation Engineering Kao Yuan University Taiwan 821 Taiwan China College of Engineering Cleveland State University Cleveland Ohio 44115-2214 USA College of Information and Control Engineering China University of Petroleum Dongying 257061 China

The existing third-order tracker known as α-β-γ-δ filter has been used for target tracking and predicting for years. The filter can track the target＇s position and velocity, but not the acceleration. To extend its capability, a new fourth-order target tracker called α-β-γ-δ filter is proposed. The main objective of this study was to find the optimal set of filter parameters that leads to minimum position tracking errors. The tracking errors between using the α-β-γ-δ filter and the α-β-γ-δ filter are compared. As a result, the new filter exhibits significant improvement in position tracking accuracy over the existing third-order filter, but at the expense of computational time in search of the optimal filter. To reduce the computational time, a simulation-based optimization technique via Taguchi method is introduced.

关键词： Filter design simulation-based optimization Taguchi methods position tracking velocity tracking acceleration tracking.

Remanufacturing planning based on constrained ordinal optimization

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Frontiers of Electrical and Electronic Engineering in China 2011年第3期6卷 443-452页

作者： Chen SONG Xiaohong GUAN Qianchuan ZHAO Qing-Shan JIA Ubiquitous Energy Research Center ENN Research and Development CorporationLangfang 065001China Center for Intelligent and Networked Systems Department of AutomationTNLISTTsinghua UniversityBeijing 100084China State Key Laboratory for Manufacturing Systems Engineering(SKLMS Lab)and Ministry of Education Key Laboratory for Intelligent Network and Network Security(MOE KLINNS Lab) Xi’an Jiaotong UniversityXi’an 710049China

Resource planning for a remanufacturing system is in general extremely difficult in terms of problem size,uncertainties,complicated constraints,*** this paper,we present a new method based on constrained ordinal optimization(COO)for remanufacturing *** key idea of our method is to estimate the feasibility of plans by machine learning and to select a subset with the estimated feasibility based on the procedure of horse racing with feasibility model(HRFM).Numerical testing shows that our method is efficient and effective for selecting good plans with high *** is thus a scalable optimization method for large scale remanufacturing planning problems with complicated stochastic constraints.

关键词： remanufacturing systems constrained ordinal optimization(COO) simulation-based optimization machine learning

simulation modeling and optimization for ambulance allocation considering spatiotemporal stochastic demand

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Journal of Management Science and Engineering 2019年第4期4卷 252-265页

作者： Wei Yang Qiang Su Samuel HHuang Qiugen Wang Yanhong Zhu Ming Zhou College of Management Shenzhen University518060ShenzhenChina School of Economics and Management Tongji University200092ShanghaiChina Department of Mechanical and Materials Engineering University of CincinnatiCincinnatiOH45221USA Trauma Center Shanghai General Hospital200127ShanghaiChina

To ensure a timely response to emergencies,governments are obliged to implement effective ambulance allocation *** practice,an emergency medical service(EMS)system works in an uncertain environment,with stochastic demand,response-times,and *** uncertainty significantly affects ambulance allocation ***-ever,few studies in this field adequately consider the effect of spatiotemporal uncertainty in demand,because it is difficult to measure it *** a result,few analytic models capture the dynamic nature of an EMS system and,thus,the allocation plans they generate are not efficient in ***,this study proposes a simulation-based optimization method for ambulance allocation.A simulation model is constructed to mimic the operational processes of an EMS system,and to evaluate the performance of an ambulance allocation plan in an uncertain *** mixture model clus-tering is used to derive the uncertain spatial ***,the simulation generates emergency demand based on the obtained spatial distribution.A Gaussian-process-based search algorithm is used together with the simulation model to identify optimal *** validate the proposed method,a case study is conducted using data on emergency patients in the Shanghai Songjiang *** with the current plan adopted in Songjiang,the experimental results demonstrate that the delay time and frequency of the EMS system can be reduced significantly by employing the proposed ***-more,nearly 41%of the allocation cost can be saved.

关键词： Ambulance allocation Spatiotemporal randomness simulation-based optimization Gaussian process

Cyber-Physical System-based optimization for Multiple Energy Systems with Imperfect State-of-Charge Knowledge of Storage Devices

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Cyber-Physical System-Based Optimization for Multiple Energy...

On Efficient Sampling in Supervisory Reinforcement Learning

第37届中国控制会议

作者： Zhanbo Xu Qing-Shan Jia Xiaohong Guan MOE KLINNS Lab of Xi'an Jiaotong University Center for Intelligent and Networked Systems Department of Automation BNRist Tsinghua University

The optimization of the multiple energy systems consisting of renewable energy resources, storage devices, various loads, etc., could significantly facilitate the energy efficiency and has thus attracted lots of attention. Since a perfect knowledge of the state of charge（SOC） of the storage devices may be difficult or costly in practice, in this paper we consider the optimization problem of the multiple energy systems with imperfect SOC knowledge of storage devices. The problem is formulated as a partially observable Markov decision process（POMDP）. A cyber-physical simulation-based optimization algorithm is developed to solve the problem. The effectiveness of the developed method under uncertainties in the supply and demand and the performance degradation compared to the case with perfect SOC knowledge are demonstrated based on numerical case studies.

关键词： Cyber-physical energy system storage devices renewable supply POMDP simulation-based optimization

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On Efficient Sampling in Supervisory Reinforcement Learning

分销网络RFID使能的CONWIP策略的仿真与优化

第43届中国控制会议

作者： Qing-Shan Jia Qi Guo Yongcai Wang CFINS Department of Automation Tsinghua University China Southern Power Grid Electric Power Research Institute Department of Computer Science School of Information Renmin University of China

Supervisory reinforcement learning is a process to learn the best policy from interaction with the environment and from the supervisor. These two ways of interaction usually provide guidance with different level of accuracies. Feedback from the environment is usually noisy and random. Guidance from the supervisor is usually experienced and accurate. It is of practical interest to understand how to efficiently obtain samples from both channels. We consider this important problem in this work, and make the following contributions. First,we formulate the sample optimization problem in supervisory reinforcement learning. Second, we convert that into the maximization of the probability of correctly selecting(PCS) the best policy under limited sample budget in both channels. Third, we analyze how to best utilize the guidance from the supervisor,and how to interact with the environment regarding selected(state, action) pairs. An algorithm is presented and shown to asymptotically maximize the PCS. We hope this work may shed light on sample efficiency study in more general settings.

关键词： supervisory reinforcement learning simulation-based optimization Markov decision process

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