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Solutions Manual

Discrete-Event System Simulation

Fifth Edition

Jerry Banks

John S. Carson II

Barry L. Nelson

David M. Nicol

August 10, 2009

This work is protected by United States copyright laws and is provided solely for the use of instructors in teaching their

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优化sql：SELECT we.organization_id ,we.wip_entity_id ,case when wl.line_id is null then we.wip_entity_name else '' end wip_entity_name ,we.primary_item_id ,mtt.transaction_type_name ,mmt.transaction_date ,bd.department_code ,mmt.inventory_item_id ,mmt.subinventory_code ,mta.reference_account ,br.resource_code ,lu2.meaning as line_type_name ,mta.base_transaction_value ,mta.cost_element_id ,flv.meaning as cost_element ,wdj.class_code job_type_code ,ml.meaning job_type_name FROM (select * from gerp.mtl_material_transactions where substr(transaction_date,1,7) >= '2023-06' and transaction_source_type_id = 5) mmt inner join gerp.wip_entities we on mmt.organization_id = we.organization_id inner join gerp.mtl_transaction_accounts mta on mta.transaction_source_id = we.wip_entity_id and mta.transaction_id = mmt.transaction_id and mta.transaction_source_type_id = 5 inner join gerp.mtl_transaction_types mtt on mtt.transaction_type_id = mmt.transaction_type_id inner join mfg_lookups lu2 on lu2.lookup_code = mta.accounting_line_type and lu2.lookup_type = 'CST_ACCOUNTING_LINE_TYPE' inner join gerp.mtl_system_items_b msi on msi.inventory_item_id = mmt.inventory_item_id and msi.organization_id = mta.organization_id left join gerp.bom_departments bd on bd.department_id = mmt.department_id left join gerp.bom_resources br on br.resource_id = mta.resource_id left join gerp.wip_lines wl on wl.line_id = mmt.repetitive_line_id left join gerp.wip_discrete_jobs wdj on wdj.wip_entity_id = mta.transaction_source_id left join gerp.fnd_lookup_values_vl flv on cast(mta.cost_element_id as string) = flv.lookup_code and flv.lookup_type = 'CST_COST_CODE_TYPE' left join mfg_lookups ml on ml.lookup_code = wdj.job_type and ml.lookup_type = 'WIP_DISCRETE_JOB' 。其中mmt,we,mta,msi,wdj数据量很大

1. 使用JOIN语句时，可以根据数据量大小，将数据量大的表放在后面，这样可以避免在连接时出现性能问题。 2. 使用子查询时，可以在子查询中添加条件筛选，减少返回的数据量。 3. 可以对查询中的日期条件进行优化，...

discrete-event-system-simulation-fifth-edition

《离散事件系统模拟第五版》是由Jerry Banks等人合著的一本关于离散事件系统模拟的教材。本书主要介绍了离散事件系统模拟的概念、原理和方法，并提供了大量的实际案例和应用。这本教材首先介绍了离散事件系统模拟...

优化以下Oracle语句： SELECT SUBSTR(msn.serial_number, 1, 10) genset_sn, msi2.segment1 Genset_BOM_NUM, msi2.inventory_item_id, msi.segment1 key_component, mut1.serial_number component_sn, msi.description component_desc, wdj.date_completed, (SELECT MAX(aps.vendor_name) FROM ap_suppliers aps, bom_resources bor, mtl_unit_transactions mut, po_headers_all poh, po_lines_all pol, wip_osp_resources_val_v wor WHERE aps.vendor_id = poh.vendor_id AND bor.resource_id = wor.resource_id AND poh.po_header_id = pol.po_header_id AND pol.item_id = bor.purchase_item_id AND wor.wip_entity_id = mut.transaction_source_id AND mut.serial_number = mut1.serial_number AND mut.inventory_item_id = mut1.inventory_item_id AND mut.organization_id = mut1.organization_id AND mut.receipt_issue_type = 2 AND mut.transaction_source_type_id = 5 ) supplier FROM mtl_material_transactions mmt1, mtl_material_transactions mmt2, mtl_parameters mpa, mtl_serial_numbers msn, mtl_system_items msi, mtl_system_items msi2, mtl_transaction_types mtt1, mtl_transaction_types mtt2, mtl_unit_transactions mut1, mtl_unit_transactions mut2, wip_discrete_jobs_v wdj WHERE mmt1.inventory_item_id = mut1.inventory_item_id AND mmt1.organization_id = mut1.organization_id AND WDJ.PRIMARY_ITEM_ID = msi2.INVENTORY_ITEM_ID AND mmt1.transaction_id = mut1.transaction_id AND mmt1.transaction_source_id = wdj.wip_entity_id AND mmt1.transaction_type_id = mtt1.transaction_type_id AND mtt1.transaction_type_name = 'WIP Issue' AND NOT EXISTS (SELECT 'WIP Negative Issue or WIP Return' FROM mtl_material_transactions mmt3, mtl_transaction_types mtt3, mtl_unit_transactions mut3 WHERE mmt3.transaction_id = mut3.transaction_id AND mmt3.transaction_type_id = mtt3.transaction_type_id AND mmt3.transaction_date > mmt1.transaction_date AND mtt3.transaction_type_name IN ('WIP Negative Issue', 'WIP Return') AND mut3.serial_number = mut1.serial_number AND mut3.inventory_item_id = mut1.inventory_item_id) AND mmt2.transaction_id = mut2.transaction_id AND mmt2.transaction_source_id = wdj.wip_entity_id AND mmt2.transaction_type_id = mtt2.transaction_type_id AND mtt2.transaction_type_name = 'WIP Completion' AND mpa.organization_code = 'WHP' AND msn.current_organization_id = mpa.organization_id AND LENGTH(msn.serial_number) >= 10 AND msi.inventory_item_id = mmt1.inventory_item_id AND msi.organization_id = mmt1.organization_id AND (msi.planning_make_buy_code = 2 OR msi.segment1 LIKE 'SO%') AND mut2.serial_number = msn.serial_number AND mut2.inventory_item_id = msn.inventory_item_id AND mut2.organization_id = mpa.organization_id AND msi2.ORGANIZATION_ID = '323'

1. 在以下表中创建索引：mtl_material_transactions、mtl_unit_transactions、mtl_system_items、mtl_parameters、mtl_transaction_types、mtl_serial_numbers、wip_discrete_jobs_v 和 bom_resources。 - 在 mtl_...

用python Price discrete-monitored barrier options (e.g. at the closing of the last day of each month or each day), and compare with the price under the continuous-time version.

To price discrete-monitored barrier options in Python, we can use the Monte Carlo simulation method. Here are the steps to do so: 1. Define the parameters of the option, such as the strike price, ...

import serial,time,struct import modbus_tk.defines as cst from modbus_tk import modbus_rtu # from modbus_tk.utils import create_master_string def main(): # 打开两个串口 com1 = serial.Serial(port='com2', baudrate=38400, bytesize=8, parity='N', stopbits=1) com2 = serial.Serial(port='com3', baudrate=38400, bytesize=8, parity='N', stopbits=1) # 创建Modbus RTU主机(master) master = modbus_rtu.RtuMaster(com1) master.set_timeout(1.0) master.set_verbose(True) # time.sleep(2) # 读取保持寄存器数据 red = master.execute(1, cst.READ_HOLDING_REGISTERS, 0, 9) print(red) red1 = master.execute(1, cst.READ_HOLDING_REGISTERS, 0, 9)[0] # 这里可以修改需要读取的功能码 print(red1) new = int(red1 * 0.98) print(new) master.execute(1, function_code=cst.WRITE_MULTIPLE_REGISTERS, starting_address=0, output_value=[new]) time.sleep(0.5) red = master.execute(1, cst.READ_HOLDING_REGISTERS, 0, 9) print(red,0) # 读取输入寄存器数据 input_regs = master.execute(1, cst.READ_INPUT_REGISTERS, 0, 9) print(input_regs, 1) # 读取输入线圈数据 input_bits = master.execute(1, cst.READ_COILS, 0, 9) print(input_bits, 2) # 读取输出线圈数据 output_bits = master.execute(1, cst.READ_DISCRETE_INPUTS, 0, 9) print(output_bits, 3)。现在将所有读取数据打包为Modbus RTU消息并写入com2串口。要求打包的Modbus RTU消息格式要跟 master = modbus_rtu.RtuMaster(com1)一样

可以使用modbus_tk.modbus_rtu.master._compute_crc函数计算Modbus RTU消息的校验码，并使用struct.pack()函数将消息... output_bits = master.execute(1, cst.READ_DISCRETE_INPUTS, 0, 9) print(output_bits, 3) ```

ionort_discrete_grid_nf.for

ionort_discrete_grid_nf.for是一个Fortran程序文件，属于离散格点型电离层模型IONORT。该程序文件主要用于生成电离层参数的离散化格点数据。离散格点型电离层模型是一种电离层参数化方法，通过将电离层参数离散化...

-i I, --indir I Input genome fasta directory (.fa|.fna[.gz]|*.fasta) -o O, --outdir O Output directory -m , --mode ANI calculation mode ('fastani'[default]|'skani') -t , --thread_num Thread number parameter (Default: MaxThread - 1) --overwrite Overwrite previous ANI calculation result (Default: OFF) --fig_width Figure width (Default: 10) --fig_height Figure height (Default: 10) --dendrogram_ratio Dendrogram ratio to figsize (Default: 0.15) --cmap_colors cmap interpolation colors parameter (Default: 'lime,yellow,red') --cmap_gamma cmap gamma parameter (Default: 1.0) --cmap_ranges Range values (e.g. 80,90,95,100) for discrete cmap (Default: None) --annotation Show ANI value annotation (Default: OFF) -v, --version Print version information -h, --help Show this help message and exit

这段代码是 ANIcalculator 工具的命令行参数，用于计算基因组序列的平均核酸同源性（ANI）。具体来说，它的含义如下： - -i I, --indir I 指定输入的基因组序列文件所在的目录，可以是 *.fa、*.fna[.gz] 或 *.fasta...

import serial import modbus_tk.defines as cst from modbus_tk import modbus_rtu import struct # 打开两个串口 com1 = serial.Serial(port='com2', baudrate=38400, bytesize=8, parity='N', stopbits=1) com2 = serial.Serial(port='com3', baudrate=38400, bytesize=8, parity='N', stopbits=1) # 创建Modbus RTU主机(master) master = modbus_rtu.RtuMaster(com1) master.set_timeout(1.0) master.set_verbose(True) # 读取保持寄存器数据 red = master.execute(1, cst.READ_HOLDING_REGISTERS, 0, 9) print(red) # 将字节数组划分为若干个长度为4的子数组 # def hex_f(a,b): # # g1=b # # z=a+g1#高低16位组合 # # z1=hex(z)[2:]#取0x后边的部分 # # # print(z1) # # return struct.unpack('!f', z1.decode('hex'))[0]#返回浮点数 # # e = hex_f(red[0],red[1]) # # print(e) red1 = master.execute(1, cst.READ_HOLDING_REGISTERS, 0, 9)[0] # 这里可以修改需要读取的功能码 print(red1) new = int(red1 * 0.8) print(new) red =master.execute(1, function_code=cst.WRITE_MULTIPLE_REGISTERS, starting_address=0, output_value=[new]) # 读取输入寄存器数据 input_regs = master.execute(1, cst.READ_INPUT_REGISTERS, 0, 9) print(input_regs,1) # 读取输入线圈数据 input_bits = master.execute(1, cst.READ_COILS, 0, 9) print(input_bits,2) # 读取输出线圈数据 output_bits = master.execute(1, cst.READ_DISCRETE_INPUTS, 0, 9) print(output_bits,3) # 将所有读取数据打包为Modbus RTU消息并写入com2串口 data = red + input_regs + input_bits + output_bits com2.write(master._do_crc(data)) # 关闭串口 com1.close() com2.close()AttributeError: 'RtuMaster' object has no attribute '_do_crc'咋修改不报错

output_bits = master.execute(1, cst.READ_DISCRETE_INPUTS, 0, 9) print(output_bits, 3) # 将所有读取数据打包为Modbus RTU消息并写入com2串口 data = red + input_regs + input_bits + output_bits com2.write...

matlab 生成 discrete cosine transform 矩阵

在 MATLAB 中，可以使用 `dctmtx` 函数来生成离散余弦变换（DCT）矩阵。该函数的语法如下： ```matlab T = dctmtx(N) ``` 其中，`N` 是 DCT 矩阵的大小（即维度），`T` 是生成的 DCT 矩阵。 ...

from sklearn.datasets import load_iris data,target=load_iris(return_X_y=True) print('feature_value:',data.shape) print('target:',target) from sklearn.cluster import KMeans import numpy as np for i in range(0,30): kmeans=KMeans(n_clusters=3,max_iter=30,tol=0.0001, random_state=i).fit(data) label=kmeans.labels_ #print('label:',label) center=kmeans.cluster_centers_ #print('center:',center) predict=kmeans.predict(data) print('i=',i) print('predict:',predict) accuracy=np.mean(predict==target)100 print('accuracy',accuracy) #模型预测 predict=kmeans.predict(data) print('predict:',predict) import numpy as np accurancy=np.mean(predict==target)100 print('i=',i) print('accurancy',accurancy) import matplotlib.pyplot as plt import mglearn plt.figure(figsize=(10,8)) plt.subplot(221) mglearn.discrete_scatter(data[:,0],data[:,1],target,markers='^') plt.xlabel('data') plt.ylabel('origin') plt.subplot(222) mglearn.discrete_scatter(data[:,0],data[:,1],y_predict,markers='^') mglearn.discrete_scatter(kmeans.cluster_centers_[:,0],kmeans.cluster_centers_[:,1],[0,1,2],markers='o',markeredgewidth=2) plt.xlabel('data') plt.ylabel('y_predict') plt.show()写一下注释，要非常详细

mglearn.discrete_scatter(kmeans.cluster_centers_[:,0],kmeans.cluster_centers_[:,1],[0,1,2],markers='o',markeredgewidth=2) plt.xlabel('data') plt.ylabel('y_predict') plt.show() ``` 其中，注释解释...

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