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simulation-based optimization with surrogate models - Application to supply chain management

COMPUTERS & CHEMICAL ENGINEERING 2005年第6期29卷 1317-1328页

作者： Wan, XT Pekny, JF Reklaitis, GV Purdue Univ Sch Chem Engn W Lafayette IN 47906 USA

simulation is widely used in the decision-making processes associated with supply chain management. In this paper, we present an extension of the simulation-based optimization framework which has been previously proposed for analyzing supply chains. The extension consists of the iterative construction of a surrogate model based on systematically accumulated simulation results to capture the causal relation between the key decision variables and supply chain performance. The decision variables can then be optimized using the surrogate model in place of individual simulation runs to economize on the overall computational effort. Several techniques are embedded in the framework to achieve the targeted objective: least square support vector machine (LSSVM), Bayesian evidence framework, and design and analysis of computer experiment (DACE). The extended framework is illustrated using two small examples and then applied to optimize the inventory levels in a three-stage supply chain. The results show that the framework identifies good solutions efficiently, can accommodate chance constraints and scales up well. (c) 2005 Elsevier Ltd. All rights reserved.

关键词： simulation-based optimization supply chains surrogate model support vector machine design and analysis of computer experiment safety stocks

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simulation-based optimization approach for software cost model with rejuvenation

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5th International Conference on Autonomic and Trusted Computing

作者： Eto, Hiroyuki Dohi, Tadashi Ma, Jianhua Hiroshima Univ Dept Informat Engn Grad Sch Engn 1-4-1 Kagamiyama Higashihiroshima 7398527 Japan Hosei Univ Grad Sch Comp & Informat Sci Tokyo Japan

ISBN: (纸本)9783540692942

Software rejuvenation is a preventive and proactive maintenance solution that is particularly useful for counteracting the phenomenon of software aging. In this paper we consider an operational software system with multiple degradations and derive the optimal software rejuvenation policy minimizing the expected operation cost per unit time in the steady state, via the dynamic programing approach. Especially, we develop a reinforcement learning algorithm to estimate the optimal rejuvenation schedule adaptively and examine its asymptotic properties through a simulation experiment.

关键词： software aging software rejuvenation semi-Markov decision process Q-learning adaptation non-parametric statistics simulation-based optimization

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simulation-based optimization Method for Three-echelon Network Inventory System of A Supply Chain

Simulation-based Optimization Method for Three-echelon Netwo...

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20th Chinese Control and Decision Conference

作者： Gao, Jingmei Wang, Dingwei Northeastern Univ Dept Syst Engn Shenyang 100004 Peoples R China

ISBN: (纸本)9781424417339

A single-item, three-echelon network inventory system of a supply chain is considered that consists of two manufactures, two distribution centers and three retailers and implements the reorder point, order quantity (r, Q) inventory policy. The policy decision problem is formulated in order to minimize the total cost of the system subject to customer arrival Possion process, stochastic demand, production capacity and other uncertain factors. Because most of such complex and stochastic problems are not simple to apply analytic method only and simulation is not an optimization tool, a simulation-based optimization method consisting of a simulation model and particle swarm optimization algorithm is developed to obtain the optimal (r, Q) policies. A numerical experiment is given to illustrate the simulation-based optimization method efficient.

关键词： Multi-echelon Inventory simulation-based optimization Particle Swarm optimization

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simulation-based optimization on RFID-Enabled Hybrid Push/Pull Control Strategy for Multi-Echelon Inventory of Supply Chain 11

Simulation-based Optimization on RFID-Enabled Hybrid Push/Pu...

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11th World Congress on Intelligent Control and Automation

作者： Li, Guojia Wang, Dingwei Northeastern Univ Inst Syst Engn Shenyang Liaoning Peoples R China

ISBN: (纸本)9781479958252

The research on control methods of multi-echelon inventory systems is very significant to reduce inventory cost, to improve service level and to enhance the key competitive ability. Internet to things and Radio Frequency Identification (RFID) provides an effective way to the innovation of inventory control strategies. In this paper, the Push/Pull control strategy based on RFID is applied to multi-echelon inventory of supply chain, and three-echelon inventory of supply chain is exemplified and eight kinds of RFID-enabled hybrid Push/Pull control strategies are designed. These strategies are optimized by the simulation-based particle-swarm optimization method. The results demonstrate the feasibility and the effectiveness of the method. The simulation results after optimization show that the hybrid Push/Pull control strategy that manufactures are controlled by the Push strategy, the distributions and retailers are controlled by the Pull strategy performs best among the eight kinds of strategies.

关键词： RFID Three-echelon inventory simulation-based optimization Particle swarm optimization Push/Pull

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simulation-based optimization for the Integrated Control of Production and Logistics: A Performance Comparison 21st

Simulation-Based Optimization for the Integrated Control of ...

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21st IFAC World Congress on Automatic Control - Meeting Societal Challenges

作者： Pires, Matheus C. Frazzon, Enzo M. Quadras, Djonathan Broda, Eike Freitag, Michael Univ Fed Santa Catarina Dept Prod Engn & Syst EPS Florianopolis SC Brazil Univ Fed Santa Catarina Grad Program Prod Engn Florianopolis SC Brazil Univ Bremen Fac Prod Engn Bremen Germany Univ Bremen BIBA Bremer Inst Prod & Logist GmbH Bremen Germany

Manufacturing systems' efficiency depends on the proper assignment of orders to resources. Due to existing interdependencies, the integrated consideration of production, inventory and delivery processes can improve the overall manufacturing performance. However, the integration can result in high complexity and stochasticity. Thus, the three areas are rarely addressed together. Thereof, this paper proposes an integrated simulation-based optimization method to cope with uncertainty and complexity. The proposed approach was compared to a benchmark approach and the obtained results show that the first is able to handle the complexity and stochasticity of real-world manufacturing systems, surpassing the performance of the latter. Copyright (C) 2020 The Authors.

关键词： Supply Chain Planning and Control simulation-based optimization Integrated Planning simulation optimization Simulated Annealing

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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

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simulation-based optimization approach with scenario-based product sequence in a reconfigurable manufacturing system (RMS): A case study 9

Simulation-based optimization approach with scenario-based p...

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9th IFAC/IFIP/IFORS/IISE/INFORMS Conference on Manufacturing Modelling, Management and Control (IFAC MIM)

作者： Petroodi, S. Ehsan Hashemi Eynaud, Amelie Beauville Dit Klement, Nathalie Tavakkoli-Moghaddam, Reza IMT Atlantique LS2N UMR CNRS 6004 4Rue Alfred Kastler BP 20722 F-44307 Nantes 3 France HeSam Ecole Natl Super Arts & Metiers LISPEN Arts & Metiers 8 Blvd Louis XIV F-59046 Lille France Grp PSA Route Gisy F-78943 Velizy Villacoublay France Univ Tehran Sch Ind Engn Coll Engn Tehran Iran

In this study, we consider a production planning and resource allocation problem of a Reconfigurable Manufacturing System (RMS). Four general scenarios are considered for the product arrival sequence. The objective function aims to minimize total completion time of jobs. For a given set of input parameters defined by the market, we want to find the best configuration for the production line with respect to the number of resources and their allocation on workstations. In order to solve the problem, a hybridization approach based on simulation and optimization (Sim-Opt) is proposed. In the simulation phase, a Discrete Event simulation (DES) model is developed. On the other hand, a simulated annealing (SA) algorithm is developed in Python to optimize the solution. In this approach, the results of the optimization feed the simulation model. On the other side, performance of these solutions are copied from simulation model to the optimization model. The best solution with the best performance can be achieved by this manually cyclic approach The proposed approach is applied on a real case study from the automotive industry. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： simulation-based optimization Reconfigurable Manufacturing System (RMS) Reconfigurability Simulated Annealing (SA)

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simulation-based optimization on Quay Crane Scheduling of Container Terminals 29

Simulation-based Optimization on Quay Crane Scheduling of Co...

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第29届中国控制与决策会议

作者： Li Haoyuan Sun Qi Dalian Neusoft University of Information

ISBN: (纸本)9781509046584

By applying the object-oriented simulation modeling method of discrete event system,this paper establishes an simulating model of container terminal operating system,including vessels,anchorages,berths,gantry cranes,internal and external container trucks and gate *** order to solve the quay crane scheduling problem of container terminal,a lot of stochastic factors in the problem are taken into ***-based optimization(SBO) method is proposed to solve the *** algorithm,particle swarm algorithm and simulated annealing algorithm are used respectively as the superior optimizer,and their application performance is compared and analyzed.

关键词： simulation-based optimization Container Terminal Quay Crane Scheduling

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simulation-based optimization over discrete sets with noisy constraints

Simulation-based optimization over discrete sets with noisy ...

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作者： Luo, Yao University of Miami

学位级别：Ph.D.

The first part of this dissertation studies a constrained simulation-based optimization problem over a discrete set where noise-corrupted observations of the objective and constraints are available. The problem is challenging because the feasibility of a solution cannot be known for certain. The uncertainty, in turn, arises from the noisy measurements of the constraints. To tackle this issue, we propose an innovative method that converts constrained optimization into the unconstrained optimization problem of finding a saddle point of the Lagrangian. The method applies stochastic approximation to the Lagrangian in search of the saddle point. We prove that the proposed method converges to the optimal solution almost surely (a.s.) under suitable conditions as the number of iterations grows. We present the effectiveness of the proposed method numerically in four examples, with applications in inventory control, call center staffing and emergency room management. The second part of this dissertation discusses the problem of fitting a convex function based on noisy data from simulation output. The traditional way of fitting a convex function to data, which is done by computing a convex function minimizing the sum of least squares, takes too long to compute the fit. It also runs into an "out of memory" issue when the number of data points exceeds a few hundred. In this dissertation, we propose a computationally efficient way by minimizing the sum of least absolute deviations rather than the sum of squares. The least absolute deviations estimator we introduce in this dissertation is posed via a solution to a linear program (LP) while the traditional least squares estimator is posed via a solution to a quadratic program (QP). Furthermore, our LP formulation has a dual problem that exhibits a block-angular form in its constraints. This enables one to apply decomposition techniques such as Dantzig-Wolfe decomposition to solve the dual problem. Thus the proposed estimator can b

关键词： simulation-based optimization discrete optimization Lagrangian duality convex regression least absolute deviation

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simulation-based optimization of a Piezoelectric Energy Harvester using Artificial Neural Networks and Genetic Algorithm 28

Simulation-Based Optimization of a Piezoelectric Energy Harv...

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28th IEEE International Symposium on Industrial Electronics (IEEE-ISIE)

作者： Bagheri, Shahriar Wu, Nan Filizadeh, Shaahin Univ Manitoba Dept Mech Engn Winnipeg MB Canada Univ Manitoba Dept Elect & Comp Engn Winnipeg MB Canada

ISBN: (纸本)9781728136660

The problem of finding optimal design parameters for a piezoelectric energy harvester is studied. An accurate iterative numerical simulation model based on Euler-Bernoulli beam theory is used as the basis for defining a simulation-based optimization problem. Due to the complexity of the simulation model, evaluation of the Objective Function (OF) is difficult and computationally expensive. In order to remedy this problem, an Artificial Neural Network (ANN) model is trained based on a dataset obtained from the iterative numerical simulation. ANN is then used during the optimization process instead of the original expensive-to-evaluate simulation model. Performance evaluation for the ANN is performed using a set of test data. Genetic Algorithm (GA) optimization method based on the trained ANN model is further developed and optimum system parameters are obtained for an energy harvester based on piezoelectric patches and cantilever aluminum beam.

关键词： Artificial Neural Network (ANN) Energy Harvesting Genetic Algorithm (GA) Piezoelectricity simulation-based optimization

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