译文Simulation-based optimization for housekeeping in a container transshipment terminal
(复杂系统的性能评价与优化课件资料)overview_lecture
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Comparison with a CVDS Trajectory
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Modeling Ingredients
• Discrete States: combinatorial explosion • Stochastic Effects: unavoidable uncertainty • Continuous time and performance measure • Dynamical: • Hierarchical: • Computational vs conceptual
Untimed Timed
Finite State
Machines & Petri Nets
Finitely Recursive Processes
Min-Max Algebra
Generalized Semi-Markov
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GOAL: Finite representation. Qualitative properties, Quantitative Performance
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lifetime of
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Place the event in the future event list
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Automation in Construction
The growing diversity of disciplines, participants, tasks, tools and events associated with project management at the design and construction stages, the increasing pressure of costing competition and tighter production deadlines, as well as continually increasing quality requirements and the need for technological enhancements, are the driving force of information modeling and numerical simulation in the construction industry. When choosing the most effective investment project in construction, a major problem associated with the actual demand for resources is underestimated. In order to solve this problem in the most effective way, the application programs, covering virtually every phase of the specific construction product development, e.g. planning, design, cost estimation, scheduling, fabrication, construction, maintenance and facility management were developed and supplemented with the calculation of the demand for resources, comparison of alternatives and determination of the duration of all the stages of the project life. Theoretical principles and practical innovative applications of building information modeling and construction process simulation technique, used to determine the most effective alternative of the project by applying the appropriate multiple criteria evaluation methods, are considered in the article.Article Outline1. Introduction2. New concept in design and construction3. BIM as an approach to building design and management4. Computer-aided evaluation system in design and construction5. The development of virtual construction project6. Determining the most effective project variant7. ConclusionReferencesVariability in production is one of the largest factors that negatively impacts construction project performance. A common construction practice to protect production systems from variability is the use ofbuffers (Bf). Construction practitioners and researchers have proposed buffering approaches for different production situations, but these approaches have faced practical limitations in their application. A multiobjective analytic model (MAM) is proposed to develop a graphical solution for the design ofWork-In-Process (WIP) Bf in order to overcome these practical limitations to Bf application, being demonstrated through the scheduling of repetitive building projects. Multiobjective analytic modeling is based on Simulation–Optimization (SO) modeling and Pareto Fronts concepts. Simulation–Optimization framework uses Evolutionary Strategies (ES) as the optimization search approach, which allows for the design of optimum WIP Bf sizes by optimizing different project objectives (e.g., project cost, time and productivity). The framework is tested and validated on two repetitive building projects. The SO framework is then generalized through Pareto Front concepts, allowing for the development of the MAM as nomographs for practical use. The application advantages of the MAM are shown through a project scheduling example. Results demonstrate project performance improvements and a more efficient and practical design of WIP Bf. Additionally, production strategies based on WIP Bf and lean production principles in construction are discussed.Article Outline1. Introduction2. Research objective3. Research methodology4. Describing WIP Bf in repetitive construction processes5. WIP Bf design approach using Simulation–Optimization5.1. Simulation architecture and modeling assumptions5.2. General Simulation–Optimization approach to design WIP Bf5.3. Evolutionary Strategies in optimization problems5.4. WIP Bf optimization using Evolutionary Strategies in simulation approach6. Multiobjective model to design WIP Bf7. Testing and validation of the Simulation–Optimization approach7.1. Project description7.2. Project A7.3. Project B7.4. Discussion of SO testing and validation8. MAM application9. Conclusions10. NotationReferencesEvaluation and use of the standards in of the technical drawings in the final year project Original Research ArticleProcedia - Social and Behavioral SciencesThe use of a virtual building design and construction model for developing an effective project concept in 5D environment Original Research ArticleAutomation in ConstructionSimulation model incorporating genetic algorithms for optimal temporary hoist planning in high-rise building construction Original Research ArticleAutomation in ConstructionResearch highlightsWe propose a model for temporary hoist planning in high-rise building construction. >The model isconstructed with a discrete-event simulation and genetic algorithms. This model uses a simulation toverify various scenarios for vertical transportation. The GAs assists the planner to search for anoptimal scenario in the solution space. It will support hoist planners while preparing optimal plans with minimal time and effort.建筑/建材/工程环境艺术园林设计规划设计方案、景观设计方案绿植施工图选样及定板工作技术问题5,243 articles found for: pub-date > 2000 and tak(((Construction building materials) or (environmental art) or project or planning or design or (garden design) or (landscape design)) and (Plants or construction or drawings sampling or work or technical or problems or fixed or plate)) Edit this search | Save this search | Save as search alert | RSS FeedConstruction / building materials / environmental art project planning and design garden design, landscape designPlants and construction drawings sampling the work of technical problems fixed plateThe economics of native plants in residential landscape designs Original Research Article Landscape and Urban PlanningMultiobjective design of Work-In-Process buffer for scheduling repetitive building projects Original Research ArticleAutomation in ConstructionYard-scale landscape designs can influence environmental quality through effects on habitat, stormwater runoff, and water quality. Native plant gardens may have ecological benefits, and previous research has shown that yards using these plants can be designed in ways that people find attractive. This study examines whether people are willing to pay more for more ecologically benign designs than for a lawn. A contingent choice survey was conducted in southeast Michigan in which people were presented with four different yard designs (three of which included native plants) in three different settings, with different monthly maintenance costs for each design. Respondents were asked to rank their choices of the yards while considering the maintenance costs they were presented. Results suggest that people are willing to pay more for well-designed yards including native plants than for lawns, and that their increased willingness to pay exceeds any increase in costs associated with the native plantings. These results should encourage homeowners, landscape designers, and the landscape plant industry to work with native plants. In this study, people were willing to pay more for designs that present gains for the environment, without government intervention and without social cost.Article Outline1. Introduction2. Measurement of willingness to pay in theory and practice3. Survey design4. Results5. ConclusionAcknowledgementsAppendix A. Calculation of willingness to pay (WTP)ReferencesVitaeStudy of a historical garden soil at the Grand-Pressigny site (Indre-et-Loire, France): evidence of landscape management Original Research ArticleJournal of Cultural HeritageGarden archaeology is a new discipline in France, which mainly focuses on technical aspects of garden creation. Excavations reveal complex stratigraphic sequences and show that soils are strongly influenced by human activities linked to cultivation, including for aesthetic purposes. The objective of the research was firstly to better understand and explain the complex archaeological deposits of a historical garden, using various techniques such as soil micromorphology, image analysis and soil chemistry. The second objective was to show the composition of remains from one garden. Samples were taken from LeGrand-Pressigny site in Touraine, a French garden dating from the XVIth–XIXth centuries. The analyses of different anthropogenic levels in thin sections, the measurements of carbonate, phosphorus, carbon organic contents and soil porosity (image analysis) provided accurate information about the presence of an earlier garden made up of imported soil. The results also identified spatial changes over time. This study suggests an interesting approach to understanding soil care by early human communities and cancontribute to garden restoration projects considering the technical construction of these sites and historical techniques.Article Outline1. Introduction2. Study site and methods2.1. Study area2.2. Field data and sampling2.2.1. The natural soil2.2.2. The anthropic deposits2.3. Methods3. Results3.1. Micromorphological descriptions3.2. Analytical data3.2.1. Particle size distribution and chemical analyses3.3. Image analysis4. Interpretation and discussion4.1. Interpretation of the characteristics of natural subsoil4.2. Interpretation of the characteristics of anthropic deposits4.3. Imported soil as garden remains5. ConclusionAcknowledgementsReferencesRisk analysis in fixed-price design–build construction projects Original Research Article Building and EnvironmentA case study on the management of the development of a large-scale power plant project in East Asia based on design-build arrangement Original Research Article International Journal of Project ManagementTeaching construction project management with BIM support: Experience and lessons learned Original Research ArticleAutomation in ConstructionEnvironmental factors and work performance of project managers in the construction industry Original Research ArticleInternational Journal of Project Management。
使用MOEA的城市设计物理环境多目标寻优方法
使用MOEA的城市设计物理环境多目标寻优方法袁磊;冯锦滔;许雪松【摘要】This paper focuses on solving the ubiquitous disjunction between the performance evaluation and morphological design optimization for urban design with multi-physical criteria, and proposes an integrated and optimized design method for this purpose. The article explains the basic principles of this method, which is for both the design optimization and environmental performance optimization to be managed between regional planning and urban design via a two-stage workflow. The core of the method is a simulation-based optimizing engine using the multi-objective evolutionary algorithm (MOEA) to drive the optimization process and thereby achieve integrated prediction and optimization processes at the process level and multi-objective performance optimization simultaneously at the factor level. Using some examples, the article shows how the method works in two aspects of regional planning and urban design, and validates the effectiveness of the method and its efficiency in optimizing designs.%文章聚焦于解决城市设计中多种物理环境性能评价和形态优化设计之间普遍存在的脱节问题,提出了一种整合优化的设计方法.文章讲解了该方法的基本原理,是通过两阶段型的工作框架将环境性能优化的目标和数据在区域规划与城市设计的上下层次间实现传递.该方法的核心是基于模拟的优化引擎,使用多目标进化算法(MOEA)驱动多目标寻优过程,在过程层面实现了预测与优化的过程合一,在要素层面实现了多种性能共同优化的效果.文章通过案例展示了该方法在区域规划和城市设计两个层次的设计工作实验,验证了该方法的有效性和优化设计的效率.【期刊名称】《南方建筑》【年(卷),期】2018(000)002【总页数】5页(P41-45)【关键词】城市设计;环境性能;多目标优化算法;数值模拟;自动优化设计【作者】袁磊;冯锦滔;许雪松【作者单位】深圳大学建筑与城市规划学院;深圳大学建筑设计研究院有限公司;深圳大学建筑设计研究院有限公司【正文语种】中文【中图分类】TU11;TU-023引言城市作为人类建成环境的密集区域，其对人类生活环境和全球范围自然生态的影响殊为重大。
供应链优化(英文)
14
Cost/Resource Function
f(r) = sustaining cost
Fixed Cost (F2) Shutdown Cost (S) Fixed Cost (F1) Unit cost (c1) Unit cost (c2) Unit cost (c3)
{
Conditional Break last Minimum (m) Point (b) year
10
Scope Supply Chain Modeling System Hierarchy TopTop-Down View
Strategic Optimization Modeling System Tactical Optimization Modeling System
Strategic Analysis
5
Analytical IT
Concerned with developing and applying systems for evaluating and disseminating decisions based on models constructed from supply chain decision databases e.g., production scheduling systems, forecasting systems, supply chain network optimization systems
9
Models for Integrated Supply Chain Management
• Descriptive modeling - forecasting, data mining, activity-based costing, performance metrics, simulation, systems dynamics • Prescriptive modeling - optimization models (mathematical programming combined with heuristic methods)
基于模型参考自适应的永磁同步电机速度观测器中PI参数调节方法
基于模型参考自适应的永磁同步电机速度观测器中PI参数调节方法刘小俊;张广明;梅磊;王德明【摘要】永磁同步电机(PMSM)在有感控制方案中需安装编码器或霍尔传感器,增加了系统的设计成本,因此,研究PMSM的无感控制方案就显得有必要性.随着现代控制理论的发展,无传感器技术也日益发展.以磁场定向控制为控制策略,以模型参考自适应理论为基础,设计了一种速度观测器.侧重用现代控制理论知识分析了观测器的稳定性,并用传统控制理论知识分析了一种新的观测器中PI调节器参数整定方法.这种方法具有很强的适应性和移植性.最后,验证了这种方法的准确性和可行性.【期刊名称】《电机与控制应用》【年(卷),期】2016(043)007【总页数】6页(P1-6)【关键词】永磁同步电机;无感控制;模型参考自适应系统;稳定性;参数整定【作者】刘小俊;张广明;梅磊;王德明【作者单位】南京工业大学电气工程与控制科学学院,江苏南京210009;南京工业大学电气工程与控制科学学院,江苏南京210009;南京工业大学电气工程与控制科学学院,江苏南京210009;南京工业大学电气工程与控制科学学院,江苏南京210009【正文语种】中文【中图分类】TM341近年来，随着电力电子技术的发展，交流伺服系统越来越受到人们的关注。
其中永磁同步电机(Permanent Magnet Synchronous Motor， PMSM)具有体积小、效率高、功率密度高等特点，在交流伺服系统中占据着重要的地位，在高性能驱动系统中得到了广泛的应用[1-3]。
目前，PMSM的驱动通常使用磁场定向控制(Field Oriented Control， FOC)或者直接转矩控制(Direct Torque Control，DTC)。
但是，无论是针对哪种控制策略，都需要用到转速和转子位置角信息。
当然，这两个参数知道其中一个即可。
目前，对于这两个参数的获取有两种方案，即有传感器和无传感器。
电力英语文献---配电网络中较少损耗的实际方法
A realistic approach for reduction of energy losses in low voltage distribution networkabstractThis paper proposes reduction of energy losses in low voltage distribution network using Lab VIEW as simulation tool. It suggests a methodology for balancing load in all three phases by predicting and controlling current unbalance in three phase distribution systems by node reconfiguration solution for typical Indian scenario. A fuzzy logic based load balancing technique along with optimization oriented expert system for implementing the load changing decision is proposed. The input is the total phase current for each of the three phases. The average unbalance per phase is calculated and checked against threshold value. If the average unbalance per phase is below threshold value, the system is balanced. Otherwise, it goes for the fuzzy logic based load balancing. The output from the fuzzy logic based load balancing is the value of load to be changed for each phase. A negative value indicates that the specific phase is less loaded and should receive the load, while a positive value indicates that the specific phase is surplus load and should release that amount of load. The load change configuration is the input to the expert system which suggests optimal shifting of the specific number of load points, i.e., the consumers.1. IntroductionAmong three functional areas of electrical utility namely, generation, transmission and distribution, the distribution sector needs more attention as it is very difficult to standardize due to its complexity. Transmission and distribution losses in India have been consistently on the higher side in the range of 21–23%. Out of these losses, 19% is at distribution level in which 14% is contributed by technical losses. This is due to inadequate investments for system improvement work. To reduce technical losses, the important parameters are inadequate reactive compensation, unbalance of current and voltage drops in the system. There are two main distribution network lines namely, primary distribution lines (33 kV/22 kV/11 kV) and secondary distribution lines (415 V line voltage). Primary distribution lines are feeding HT consumers and are regularized by insisting the consumers to maintain power factor of 0.9 and above and their loads in all three phases is mostly balanced. The energy loss control becomes a critical task in secondary distribution network due to the very complex nature of the network.Distribution Transformer caters to the needs of varying consumers namely Domestic, Commercial, Industrial, Public lighting, Agricultural, etc. Nature of load also varies as single phase load and three phase load. The system is dynamic and ever expanding. It requires fast response to changes in load demand, component failures and supply outages. Successful analysis of load data at the distribution level requiresprocedures different from those typically in use at the transmission system level. Several researchers have proposed methods for node reconfiguration in primary distribution network [1–11]. Two types of switches used in primary distribution systems are normally closed switches (sectionalizing switches) and normally open switches (tie switches). Those two types of switches are designed for both protection and configuration management and by altering the open/ closed status of switches loss reduction and optimization of primary distribution network can be achieved. Siti et al. [12] discussed reconfiguration algorithms in secondary distribution network with load connections done via a switching matrix with triacs and hence costly alternative for developing countries. Much work needs to be done in the secondary distribution network where lack of information is an inherent characteristic. For example in most of the developing countries (India, China, Brazil, etc.) the utilities charge the consumers based on their monthly electric energy consumption. It does not reflect the day behaviour of energy consumption and such data are insufficient for distribution system analysis.Conventionally, to reduce the unbalance current in a feeder the load connections are changed manually after field measurement and software analysis. Although this process can improve the phase current unbalance, this strategy is more time consuming and erroneous. The more scientific process of node reconfiguration of LV network which involves thearrangement of loads or transfer of load from heavily loaded area to the less loaded area is needed. This paper focuses on this objective. In the first stage, the energy meter reading from secondary of Distribution Transformer is downloaded and is applied as input to Lab-VIEW based distribution simulation package to study the effects of daily load patterns of a typical low voltage network (secondary distribution network). The next stage is to develop an intelligent model capable of estimating the load unbalance on a low voltage network in any hour of day and suggesting node reconfiguration to balance the currents in all three phases.Objectives are to:Study the daily load pattern of low voltage network of Distribution Transformer by using Lab VIEW.Study the unbalance of current in all three phases and power factor compensation in individual phases.Develop distribution simulation package.The distribution simulation package contains fuzzy logic based load balancing technique and fuzzy expert system to shift the number of consumers from over loaded phase to under loaded phase.2. Existing systemIn the existing system of distribution network, the energymeters are provided for energy accounting, but there is no means of sensingunbalance currents, voltage unbalance and power factor correction requirement for continuous 24 h in three phases of LT feeder. In other words, instantaneous load curves, voltage curves, energy curves and power factor curves for individual three phases are not available for monitoring, analyzing and controlling the LV network. The individual phase of Distribution Transformer could be monitored only by taking reading whenever required and if there is unequal distribution of load in three phases, the consumer loads are shifted from overloaded phase to under loaded phase of distribution LT feeder by the field staff in charge of the Distribution Transformer. There is no scientific methodology at present.3. Proposed systemIn the proposed system, Lab VIEW is used as software simulation tool [13]. In the existing system of distribution network, the Distribution Transformers are fixed with energy meters in the Secondary of the Distribution Transformer and energy meter readings can be downloaded with Common Meter Reading Instrument (CMRI instrument). The energy meter reading includes VI profile and it can be used for the power measurement.4. Monitoring parametersThe phase voltages and the line currents of all three phases are available every half an hour and the voltage curve and load curve are obtained fromthese values. The active, the reactive and the apparent power are computed from these quantities after the phase angle is determined. The following parameters are plotted:1. Individual phase voltage.2. Individual phase current.3. Individual phase active power.4. Individual phase reactive power.5. Individual phase apparent power.6. Individual phase power factor.With the above concepts, the front panel and block diagram are developed for unbalanced three phase loads by downloading the VI profile from energy meter installed in the Distribution Transformer and simulating the setup using practical values. From the actual values obtained load unbalance is predicted using fuzzy logic and node reconfiguration is suggested using expert system.The Lab VIEW front panel displays the VI profile on a particular date with power and energy measurement as in Table 1. The Lab VIEW reads the VI profile and computes the real power, reactive power, apparent power and energy, kWh.4.1. Prediction of current unbalanceThe maximum current consumption in each phase is IRmax, IYmax, and IBmax. The optimum current (Iopt) is given in the following equation:()3max max max B Y R opt I I I I ++=The difference between opt I and m ax R I is then determined. Similarly thedifference between opt I and max Y I , opt I and max B I is computed. If thedifference is positive then that phase is considered as overloaded and if the difference is negative then that phase is considered to be under loaded. If the difference is within the threshold value, then that load is perfectly balanced.To balance the current in three phases, if the difference between opt I and m ax R I is less than threshold value then that phase is left as such.Otherwise, if the difference is greater than threshold value, some of the consumers are suggested reconfiguration from overloaded phase to under loaded phase using expert system.5. Fuzzy based load balancingA fuzzy logic based load balancing technique is proposed along with combinatorial optimization oriented expert system for implementing the load changing decision. The flowchart of the proposed system is shown in Fig. 1. Here the input is the total phase current for each of the three phases. Typical loads on low voltage networks are stochastic by nature. However it has been ensured that there is similarity in stochastic nature throughout the day as seen from load graph of Distribution Transformer as shown in Fig. 6. It has been verified that if R phase is overloaded followed by Y phase and thenB phase the same load pattern continuesthroughout the day.The average unbalance per phase is calculated as (IRmax _ Iopt) for R phase, (IYmax _ Iopt) for Y phase and (IBmax _ Iopt) for B phase and is checked against a threshold value (allowed unbalance current) of 10 A. If the average unbalance per phase is below 10 A, it can be assumed that the system is more or less balanced and discard any further load balancing. Otherwise, it goes for the fuzzy logic based load balancing. The output from the fuzzy logic based load-balancing step is the load change values for each phase.This load change configuration is the input to the expert system, which tries to optimally suggest shifting of specific number of load points. However, sometimes the expert system may not be able to execute the exact amount of load change as directed by the fuzzy step. This is because the actual load points for any phase might not result in a combination which sums up to the exact change value indicated by the fuzzy controller however optimization is achieved because of balancing attempted during peak hours of the day of the load graph.5.1. Fuzzy controller: input and outputTo design the fuzzy controller, at first the input and output variables are to be designed. For the load balancing purpose, the inputs selected are ‘phase current’ i.e., the individual phase current for each of the three phases and optimum current required and the output as ‘change’, i.e., thechange of load (positive or negative) to be made for each phase. For the input variable, Table 2 and Fig. 2 show the fuzzy nomenclature and the triangular fuzzy membership functions. And for the output variable, Table 3 shows the fuzzy nomenclature and Fig. 3 the corresponding triangular fuzzy membership functions.The IF-THEN fuzzy rule set governing the input and output variable is described in Table 4.5.2. Fuzzy expert systemA fuzzy expert system is an expert system that uses a collection of fuzzy membership functions and rules, instead of Boolean logic, to reason out data. The rules in a fuzzy expert system are usually of a form similar to the following:If x is low and y is high then z = mediumwhere x and y are input variables (names for known data values), z is an output variable (a name for a data value to be computed), low is a membership function (fuzzy subset) defined on x, high is a membership function defined on y, and medium is a membership function defined on z .The antecedent (the rule’s premise) describes to what degree the rule applies, while the conclusion (the rule’s consequent) assigns a membership function to each of one or more output variables. Most tools for working with fuzzy expert systems allow more than one conclusion per rule. The set of rules in a fuzzy expert system is known as the rulebase or knowledge base.The load change configuration is the input to the expert system which tries to optimally shift the specific number of load points. The following are the objectives of the expert system:_ Minimum switching._ Minimum losses._ Satisfying the voltage and current constraints.Fg. 4 shows the block diagram of the expert system. The inputs to the expert system are the value added or subtracted to that particular phase from the fuzzy controller and the current consumption of the individual consumers on that particular phase. The expert system should display which of the consumers are to be shifted from the overloaded phase to under loaded phase and also displays the message NO CHANGE if that phase is balanced.6. Simulation resultsTable 1 shows the display of output of Lab-VIEW based power and energy measurement [14]. It asks for the Distribution Transformer secondary reading, date, tolerance value (threshold value), and fuzzy conditioner of three phases for load balancing. It then displays the date, time, voltage, current, power factor, real power, reactive power, apparent power.Fig. 5 shows the line voltage curve for R, Y and B phases. It alsoindicates the voltage drop during peak hours of the day. The current curve for R, Y and B phases is shown in Fig. 6. It indicates the current unbalance in the existing supply network. The load graph from typical Distribution Transformer for entire day indicates interesting similarity in load patterns of consumers. Hence load balancing attempted during peak load band yielded fruitful result for the entire day.Fig. 7 displays the results of fuzzy logic based load balancing technique. Fuzzy toolkit in Lab VIEW is used for simulation. Mamdani fuzzy inference technique is applied and centroid based defuzzication technique is employed in the load balancing system. The output from the fuzzy controller is the value that is to be subtracted or added to a particular phase. The positive value indicates that the specific phase is overloaded and it should release the amount of load. The negative value indicates that the specific phase is under loaded and it should receive the amount of load. The value less than 10 A indicate that phase is perfectly loaded. Fig. 8 show the expert system output for all three phases. It gives the Service connection number (SC No.) and current consumption of individual consumer. The output of the fuzzy controller is applied as the input to the expert system. If the output of the fuzzy controller is a positive value then the expert system should inform which of the consumers are to be shifted from that phase.From Fig. 8, the R phase is overloaded, so the expert system informs thatthe SC No.’s 56 and 23 should be shifted. The output of the fuzzy controller for the Y phase is less than threshold value 10 A so that phase is perfectly loaded. The output of the fuzzy controller for B phase is a negative value; hence it receives the load from R-phase. There is no shifting of consumer in Y phase and B phase therefore the entries are indicated by zero values. There is no switching arrangement in secondary low voltage distribution network in Indian scenario and hence shifting to be done manually.The suggested approach has been tested practically on 70 nodes (70 consumers) low voltage distribution network and results are as shown in Fig. 9 (before balancing) and Fig. 10 (after balancing). Single phase customers physically reconfigured from overloaded phase into under loaded phase and then test results studied. Unbalancing has been observed for 10 days and then balancing attempted. Balanced network was studied and then results obtained. There is a percentage reduction in Energy loss from 9.695% to 8.82% though there is increase in cumulative kWh from 1058.95 to 1065.9. This Distribution Transformer belongs to urban area of a typical Indian city and has 41 single phase consumers and 29 three-phase consumers and three-phase consumers have balanced loads. In rural areas where number of single phase consumers are predominant and scattered around lengthy distribution lines this balancing technique will be much more beneficial than the tested study indicates.This research is significant to the Indian scenario considering the fact that there are 180,763 Distribution Transformers (www.tneb.in) and 2,07,00,000 consumers and length of secondary distribution network 5,17,604 km in one state, Tamil Nadu alone, 1% saving in energy loss per transformer per day will save few crores of rupees for a month to electrical utility.7. ConclusionIn this paper, the complete online monitoring of low voltage distribution network is done by using Lab VIEW and the fuzzy logic based load balancing technique is presented. With the results obtained from Lab VIEW, currents in individual phases are predicted and unbalance pattern is studied without actually measuring instantaneous values from consumer premise.A fuzzy logic based load balancing is implemented to balance the current in three phases and expert system to reconfigure some of the consumers from over loaded phase to under loaded phase. The input to the fuzzy controller is the individual phase current. The output of the fuzzy controller is the load change value, negative value for load receiving and positive value for load releasing. Expert system performs the optimal interchanging of the load points between the releasing and receiving phases.The proposed phase balancing system using fuzzy logic and expertsystem is effective for reducing the phase unbalance in the low voltage secondary distribution network. The energy losses are reduced and efficiency of the distribution network is improved and has been practically studied in typical Distribution Transformer of electrical utility.图一图2图3 图4图5图6图7图8图9图10。
仿真流程集成与自动化优化设计PIDO方案
Mass per PET [kg]
1ヶ月あたりの必要原材料[kg]Necessary raw materials per month [kg]
667939.726
343920.5479
1ヶ月あたりのPETコスト[億円]PET cost per month [100 million yen]
0.922157586
确定参数相关性降低复杂性了解您的设计以最少的工作量优化您的 产品验证鲁棒性和可靠性为数字孪生创建高保真度 的ROM模型
PET瓶制造工艺案例- Background
6
预制加热
旨在优化PET瓶的制造工艺
Objectives・PET bottle optimum thickness・Preform thickness・Preform heater output
12 parameters: number of fins, fin thickness, fan flow rates and positions
20
鲁棒性设计
对于每个优化运行，都会根据关键模型响应调 整安全系数公差对材料、几何和产量的影响有多大？
21
鲁棒性和可靠性仿真结果
汽车雷达可靠性设计案例—Infineon针对三种不同的设计方案进行可靠性分析，得到方案3的可靠 性最高；利用MOP完成几千个设计点计算，大大提升计算速度
新一代最佳元模型技术-集成深度学习算法
Integration of Keras & Tensorflow Libraries
Implementation in custom surrogate interface of optiSLang:Automatic configuration of neurons and layersCross validation to estimate Coefficient of PrognosisAvailable as external python environmentNeural networks are treated as one of a library of approximation models
基于flexsim的仓库作业流程的仿真与优化
1.2.2
本文将以A公司配送仓为研究对象，研究内容如下图1-1所示
图1-1研究内容
先实地考察A公司配送仓，了解总体情况并获取模型搭建所需的信息，比如进出库的作业流程，货架的数量，打包台的数量、员工的数量以及货Байду номын сангаас出库所需的时间等等。对资料进行分析整合，利用Felxsim软件搭建初步模型，对仓库作业中存在的问题进行优化分析，提出优化方案和改进措施，对模型进行仿真优化，得出最优方案。
2.1.3
物流仿真系统主要用于对物流系统的作业流程进行仿真建模，从而在模型中找出各个环节的潜在问题，让我们可以更直观更方便的对整个物流系统进行评价。
它是一种运用电脑技术，数学技术和人工智能技术对现实中的环境进行模拟仿真的软件，最后以3D的形式把模拟的环境整个呈现出来。
目前，市面上运用得最多的仿真软件主要有Flexsim、Vensim、Supply chain guru、Classwarehouse等等。
2
2.1
2.1.1
仓储管理，顾名思义，是对仓库和仓库中储存物料的管理。现代企业的仓库已成为企业核心。在新的经济和新的竞争形势下，企业在注重效益的同时，更加注重合理的仓储管理，不断挖掘和发展自身的竞争力。准确的仓库管理能够有效地控制和降低流通和库存成本，是企业保持优势的关键支撑和保证。仓储管理的重点不仅在于物料的安全储存，还在于如何运用信息技术、自动化技术等现代技术，提高仓储作业的速度和效率[5]。这也是自动化立体仓库有自己的方式的原因。
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Simulation-based optimization for housekeeping in a container
transshipment terminal
基于仿真技术的集装箱转运终端的业务优化
ABSTRACT
An improtant activity in container transshipment terminals consists in transferring container in the yard from their current temporary positions to different positions closer to the point along the berth from which the containers will be boarded on dapating vessels.This housekeeping process aim at speeding-up discharge and loading operations and,thus,relieving congestion.This paper introduces a heuristic procedure to manage the routing of multi-trailer systems and straddle carriers in a maritime terminal.A simulation modle embedded in a local search heuristic allows a proper evaluation of the impact of different vehicle schedules on congestion and putational experiments proformed on test instances dercied from real-life data show that improtant
集装箱转运码头的一个重要任务是将集装箱从当前的临时存放地点转移到距离其需要装载的对应船只更近的位置。
这一业务过程旨在加速集装箱的装卸过程，进而缓解拥堵现象。
本文介绍了一种启发式方法来管理沿海转运码头常见的多拖车系统和跨运车的路线选择。
嵌入在局部搜索的仿真模型，启发性的给出一个合理的关于不同车辆调度方案对于拥堵和吞吐量的影响的评估。
在来自现实数据的测试实例上的计算实验表明，相比于标准的调度策略，在路途和等待时间方面的重大提升是可以实现的。
CONCLUSION
本文介绍了一种解决内务处理问题的基于仿真的优化（SO）方法，该方法针对集装箱转运中心的整体目标，包括寻找最小在途时间和最小等待时间的调度方案。
嵌入的启发性方法依赖于简单的局部搜索，但是他们使用了一个详细的仿真模型来评估上述的在途时间和等待时间，这是由于拥堵的现象出现在同时的无冲突的车辆进入集装箱终端存放地的码排。
QUESTION
Housekeeping 内务处理/业务；
Local serach 局部搜索；
Yard rows 码排；。