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Operations Analytics: Simulation
Creation date：2025-02-20 14:57:18
Belonging category：商业分析business analysis
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Operations Analytics: Simulation

As you read the case, consider how you would utilize Discrete Event Simulation to model services provided by the  company’s tech teams. There are several challenges and issues one has to consider in analyzing the case. The following  guidelines should help in developing key assumptions of the modeling process.

The first task is to model the current state. The case provides some information to assist in making  assumptions, although the data is  incomplete. The key results are that technicians dedicated to traditional servers are utilized more than 90% of the  time  and  customers  of traditional severs wait over 2.5 hours, on average, for a technician. Mainframe. customers wait one hour, on average, while these technicians face much lower utilization.

1.   One important issue to address is the length of each replication in Arena (under Run, Setup). I assumed that each replication is 240 hours. Reflecting a schedule of about 1.5 weeks.

2.   Service times for technicians follow an exponential distribution with a mean of 0.8 hours for service (including travel time), for both types of servers.  In the current state there are 4 technicians dedicated to traditional servers, and one technician dedicated to mainframes.

3.   Instead of analyzing the individual machine, you may assume that service requests are distributed over the course of the simulation trial. In other words, machines are the customers. Arrival times for service follow an exponential distribution.

a.    For traditional servers: In a 24 hour period there should be about 114 arrivals. Choose the mean inter-arrival time accordingly (= 0.21 hours). There are 4 technicians.

b.   For mainframes: In a 24 hour period there should be about 17 arrivals. Choose the mean inter-arrival time accordingly (= 1.4 hours), with one technician.

c.    Growth  over  the  next  few  years.  The  increase  in  mainframes  and   reduction  in traditional servers will impact the inter-arrival times each year.  Use the following inter-arrival times to evaluate technician utilization and the waiting time for each time of server:

 

Current Year

Year + 1

Year +2

Year +3

Traditional servers

0.21

0.22

0.23

0.24

Mainframes

1.4

1.15

0.9

0.8

4.    Analyze a single region. From there, you can extrapolate to the entire company.

5.   Approach Suggested by Paul Phixitt: implies that there may be a need to increase  the number of dedicated technicians for both traditional and mainframe. servers in each shift. Find the number of these technicians required to meet the objectives laid out by the committee. Then calculate the implications for increasing technicians in each region  and extrapolate to the entire company. Also verify that in the current format, waiting time for mainframes is about 1 hour.

Evaluate the number of technicians of each type needed each year. If needed, increase the number of technicians, of either type, to meet the objectives. Once a technician is added, they should not be reduced in future years.

6.   Approach Suggested  by the Vice President for Engineering: This implies changing the distribution of service time. Assume a Triangular Distribution for service times. Focus only on the traditional servers. For the base case, assume parameters (0, 0.4,  2.05). This  should generate results similar to the base case (with the exponential distribution). Verify this. To model  the  approach suggested by Engineering, lower only the maximum value of the distribution to achieve the target. What implications are there for the new technology? Don’t worry about mainframes in this alternative.

7.   Approach Suggested by the Chief Financial Officer: In this scenario all technicians are the same and repair both types of servers. There are 5 technicians available to repair traditional and mainframe. servers. Evaluate if you need to add technicians in each of the following years, to meet the objectives. Is it true that in the first year, wait time for mainframes increase, because the mainframe. technicians also repair traditional servers (as stated by the marketing department)?

8.   Approach Suggested by the Vice President for Marketing: At the current staffing levels wait times for mainframes will exceed the target soon. To resolve this problem, they recommend adding a mainframe. technician. This is similar to the recommendation by Mr.  Phixxit, although less expensive, since only one type of technician is being added. Your answer here can be based on your previous analysis. Is it true that the reduction in traditional servers will shorten wait times sufficiently to meet the targets?

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关键词一 关键词二 关键词三 关键词四 关键词五 和法规和的发更好的风格和 ECON 615 FINC 612 CSC148 COMP 639 ACCT 203 ACCT 211 PSYC 101 MTH2009 COMP52315 PSYCO 432 statistics MATH4063 11 xxx 1 MA1MSP MAST30028 MATH3888 CS341 EEE30002 ES2F5 MCD4160 CC0933 ECON 1101 MCHM3001 CHEM3118 COMMGMT 2511 COMP41280 STAT 631 24T1 IRE379 SENG365 ECON433 FIT3139 MATH4602 LING5621M FINS5516 ECON3208 MN2177 L2 SM EEET1415 Finance DS4023 COMP3004 7CCSMRTS K1S5B6 Stat230 CSCI4211 CSI2132 ECON30130 MAS6100 ENVS222 COM2107 INFO1112 STAT 4004 FIT2100 comp2022 COMP4691 Physics 307 Econ 659 probability CSCI316 fir29 CSCI203 COMP3160 COMP6714 INFO1113 Physics 7B Physics Photonics S203031 STAT2005 COMP90015 CSCI 2110 CSCC46 Information CSC477 COMP4650 Statistical COMP 346 COMP1017 ELEC340 MATH1007 Programming function STAT 244 CS 134 Java cse13s Math 108A FW20 ECON3389 CSE 130 Science 331 COMP9020 engineering COSC 445 CSE 5523 GAME2012 visualization MATH 235 Math 370 CSC343 COGS 108 EE524 COMP10200 STAT 416 3FK4 MSCI 311 ECON6113 ACC40700 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MOEC0021 STA 106 ENGG 1300A ENGG 1300 OLET1139 STAT 3503 STAT 206 FEM11149 ELEN 4720 COMP20003 SOLA1070 CS574 C11CS LINB29H3 MAST90083 ECON7200 STAT 610 LINB29 DSM-5 COMP3161 EEEN40010 MATH 4431 K353 STA 3386 IERG4300 COMS W4167 ENG5298 AMME 2302 CS3910 COMPSCI 320 STATS 369 GR5242 COSC 328 CSCI435 MGTS1301 INFS2200 COMP3900 CIS 375 Module AE03 COMP3121 CS260 HAM503 MGMT5050 INT3075 COSC 285 GEOL0029 GEGE2X01 STAT1201 MATH1061 MMF1941H STAT 5060 CS 520 COMP 4320 MSIN0104 000T FINS3648 ECMT2160 ECON4003 Phys 129L CSE 142 CS 3130 MGEB01 EECS 340 Finance (20443/20444) ECMM149 COMP5310 EEEN60180 CSC 111 AT2 CSCI 4430 STAB57 MA/CSC 427 MATH322 D100 CSCI544 CITS3004 CI6227 CSE 565 APCO 1P01 852L1 ELEC S411F COMP3207 STATS330 ECON7530 FIT5149 CSI4104 CMPT 371 COMP201 SEHS4696 FI4003 MATH6182 CG1 WS MATH401 MGT 205 QTM 110 COMP 0137 MACE12201 FIT5210 ECE 544 BISM7206 HUDM 6055 ECON3203 STATS 210 MSCI 570 MATH6173 COMPSCI4039 Accounting COM3503 AOE2024 ECON213 PSYC10004 Control Data 100/200A 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FN1024 MATH0094 MANG 6554 ECOS3003 ELEC6252 C2511C PHY328 EC1060 ECON32202 COMP4002-E1 MATH3063 EFIMM0124 QBUS2310 ECON4411 FUNDAMENTALS ACCT20001 18YX RPM COMP 2711 QBUS6600 BUSS5221 ST3189 FIT1047 MAT344 EGB375 MATH60013 MATH96011 COMP3557 MAST20029 ECM159 EAE1011 ENSCEN 105 SECTION A STA 141C SOSE 2022 PAM006 COMP0048 M40007 ISOM2500 CMPE 142 A06472 MCC150 ACST 8083 GEOG6087 ECON920 FINM7008 ENVENG 4110 COMPSCI 361 ENGRCEE 21 PHYS 1112 ACF2100 ECMM108 COMPS267F MAT00050M MAST30025 CSIT970 MSCI 224 INT202 PUBL0054 MSDM5004 BMAN 21020 BMAN21020 MGMT1002 MATH11150 LAWS7023 ACTL30002 CIVL3340 MATH1002 ECON 8069 COMPSCI 351 IBUS7302 STAT4528 CHEM1101 STAT3925 BUSN 7008 Comp 3120 ECOS2040 SWEN90010 ENGN6528 ECM158 ECON20022 COMP6452 Calculation CITS1401 S1889112 BUS 351 ACTL30008 FIT2091 ECON6002 IBUS6020 PHI 001 ECON30290 MATH7501 ECON10004 MFIN7017 ISYS90043 LAWS7012 ECON30025 COMP30023 COMM1190 ECOM20001 GSBS6009 STAT 337 EMET8002 ECOM90001 ECOM30001 FINC 2012 BUSINESS ENGCV512 ECO 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SVM. (10 points) Consider an SVM classifier using RBF kernel, and finish the following python code to search for the optimum kernel parameter γ. You are required to calculate the RBF function and search yourself, instead of using functions in Scikit-Learn (sklearn). def parameter search(gamma list ,Xtr,ytr,Xts,yts ,...): """ Parameters gamma list: A list of RBF kernel parameter gamma to search for Xtr: Training data, shape (ntr,nrow,ncol) ytr: Training labels, shape (ntr,) Xts: Test data, shape (nts,nrow,ncol) yts: Test labels, shape (nts,) ... Add other parameters as needed Returns: gamma optimum: The gamma with minimum error on Xts, yts """ ... return gamma optimum
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