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Computing Across Curricula (CAC) The Final Report of Fellow Action Research Project

Fellow Information Name: Reha Uzsoy Position (Job Title): Professor

Department: Industrial & Systems Eng’g CAC Fellowship Year and Semester:

Email: ruzsoy@ncsu.edu Phone: 3-1681

Years Employed at NCSU: 3 Years of Teaching (Total): 25

Courses you teach (on average) Number of Fall Sections: Undergraduate Level ___0____ Graduate Level ____1__ Number of Spring Sections: Undergraduate Level ___1____ Graduate Level ___1___

Project Information Action Research Project Title: Modelling for Computer Problem Solving

Course Information Course Title: Computer-based Modelling for Engineers

Course Number: ISE110 Number of Students: 39 at start of semester

PURPOSE & OBJECTIVES

The primary objectives of the class are to provide students with a comprehensive basis in programming skills which subsequent classes in the curriculum can build on, using the medium of Excel and Visual basic for Applications. Based on my experience sitting in the class this semester, the class does an admirable job of enhancing studentsʼ computing and problem skills, as ongoing assessment has demonstrated. However, one of the most useful skills our students acquire throughout their education is in the area of modeling - isolating the critical and noncritical components of a problem, abstracting these into a representation that is amenable to mathematical and/or computational analysis, and extracting and presenting insights from the results of the analysis. Previous studies in this class have examined the effect of the computer technology on the studentsʼ problem solving ability. However, there was no explicit component of the course aimed at enhancing problem solving ability – the benefits were all incidental. The purpose of this project is to examine whether a specific component aimed at enhancing studentsʼ problem solving skills would show benefits over the course of a semester.

METHOD Students were organized into groups of three, which will also be their teams for the class projects. In the first phase, to establish a baseline, the teams were given an appropriately

   

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selected, ill-structured realistic problem description and asked to prepare a written report discussing how they could use computation to study this situation. One class session was devoted to an interactive discussion of the various proposals, for the purpose of idea sharing and illustrating the different approaches that could be taken. After this discussion, the students were asked to develop an improved model and validate it to some degree. In both these assignments, students were provided with a list of guiding questions they needed to address. The questions were aimed at making students think about several different aspects of the problem, such as the presence of multiple stakeholders with different goals, different types of uncertainty present in the problem, missing data and related issues. All groups submitted written reports, which were returned with extensive feedback, and made oral presentations to the whole class, thus allowing every group to see what the other groups had done. The studentsʼ level of problem solving proficiency was evaluated using the written reports based on Wolcottʼs Steps to Better Thinking rubric, which had also been used for the previous problem-solving studies in this class. The difference between the baseline and the second project was assessed to examine the benefit of the modeling exercise for the studentʼs understanding of basic modeling issues. The project was implemented in both the Fall 2009 and Spring 2010 semesters. Harvard Business School case studies were used to provide the problem environment in both cases. In the Fall 2009 semester, Case No. HBS 9-681-061, “University Health Services: Walk In Clinic” was used. This case study focuses on attempts to reduce the average waiting time for patients in a university health clinic while maintaining the support of different stakeholder groups including doctors, nurses and the clinic administration. In the Spring 2010 semester, in an attempt to link the project more closely to the Excel content of the class, Case 9-698-053 Hamptonshire Express was used. This latter case is essentially a case in supply chain coordination delivered through a simple two-stage system and a simple newsboy inventory model. This second case proved to be very structured in nature, and together with the extensive Excel spreadsheets provided, resulted in the project being highly structured, with little of the ambiguity and problem definition requirement that would require student problem solving skills. Hence the analysis for this study focused on the projects from Fall 2009. Copies of the assignment sheets and the grading rubrics used for the phases are included in the appendix. The study was conducted in a pre-post design. In the initial phase, the written reports submitted for the first phase were analyzed according to the Wolcott scale. This was done by the instructor, after some instruction from Dr. Raubenheimer in how to use the Wolcott rubric. The written reports for the second phase were then reviewed, after extensive feedback had been given. The basic research question was whether or not the students exhibited improved scores in the second phase of the project. Students worked on the project in teams of three. These teams were partially self-selected; some students formed groups, while others asked the instructor to assign them partners. Several teams began with three students but did the second phase with only two, due to students dropping the class over the semester. The students were mostly sophomores, but with several juniors, seniors and even a graduate student included, and highly diverse backgrounds in terms of prior computing and problem solving experience. It is also notable that the project is a relatively small portion of the course grade (8%), and the class has a very heavy workload in terms of homework, with many students also taking heavy course loads outside this class. Hence the degree of emphasis and effort students devoted to this class varied quite considerably. In some groups there

   

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was real evidence of teamwork and collaboration; in many teams, one student took the lead and others contributed minimally; while in yet others the group turned in a very minimal effort. The results in the following section should thus be read with this background in mind. RESULTS In the Fall 2009 semester, 9 groups completed Phase 1, and 8 completed Phase 2. The results of the first phase of the project according to the Wolcott scale are summarized in Figure 1 below. The Wolcott rubric assigns scores on a scale of 1-5 for each of the categories indicated on the horizontal axis. As can be seen, in Phase 1student teams performed quite poorly. In the instructorʼs estimation, no team did well in terms of eliciting the relevant information; a very rudimentary understanding of uncertainty was displayed, with most groups failing to recognize the presence of uncertainty and attributing this to lack of data. One group did somewhat better than the others in terms of organizing information and laying out solution alternatives, but this success did not go beyond 2 on a five point scale. These results are to be expected in a lower level class, where students have had little exposure to problem solving except in highly structured environments.

Figure 1: Wolcott Scores for Project Teams in Phase 1 The scores obtained by the teams completing Phase 2 are shown in Figure 2 below. The results are considerably improved, although statistical significance cannot be claimed and was not explored. The average score of each team over all categories has more than doubled in several cases. The elicitation of relevant information is now scoring higher, and there is better performance across the board in interpreting and organizing information and systematically judging options. Two groups have actually addressed more advanced issues of identifying the limitations of their analysis, which was not attempted in the Phase 1 submissions.

   

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Figure 2: Wolcott Scores for Project Teams in Phase 2 Figure 3 compares the average scores over all teams for each category in Phases 1 and 2. These indicate that there were apparent improvements from Phase 1 to Phase 2. This clearly cannot be attributed to more effective problem solving skills on the part of the students. The questions the students were required to answer in Phase 2 were designed to guide them to address certain of the Wolcott categories, and there are many sources of variability between groups. The absence of a control group is also a major limitation, which will need to be addressed in this work in the following semesters.

Figure 3: Comparison of Average Wolcott Scores by Category

   

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CONCLUSIONS   Given the critical foundation of computing skills ISE110 provides for the rest of the ISE curriculum, it is not feasible to dedicate a large amount of time to discussion of modelling, nor is it realistic to expect students to become expert modelers after brief exposure in one class. However, exposing the students to the logic of modeling has a number of advantages. Much of the later ISE curriculum involves the development and application of various kinds of mathematical models, ranging from statistical tools like regression and control charts to numerical optimization techniques like linear programming and purely computational approaches like Monte Carlo simulation. Secondly, much ISE work in industry is conducted in environments with heavy business and personnel aspects, leading to the need to build models that abstract key features of a situation that are of importance in the business context, involving non-technical aspects of the environment that must be considered for the solution to be successful. Finally, ISE 110 provides an environment where, once basic computing skills have been acquired, students can implement the models they develop and perform some basic validation of the models against data obtained from the real system, or from some surrogate for the real system provided by the instructor. The results of this project are clearly limited in scope, but suggest that students can at least be made aware of the need for a structured approach to problem solving, which can help them do better than when they are left to flounder without guidance. The experience of being faced with a seemingly chaotic situation and finding that they can make sense of it and suggest meaningful solutions is a strong motivator and confidence builder for some students, while others are content to go through the motions and show little interest. In this aspect, these students were no different from the senior and masters student the instructor has applied this project approach to in prior years. A few broad observations are that students concept of uncertainty is very limited, which is to expected to some degree since many have not been exposed to probability and statistics; it was interesting that several students who were taking probability did not appear to relate it to the project until forcibly made aware of the relation. The ability to view the problem from multiple perspectives was also something lacking in most groups, although the project guided them through this experience it is not clear how well this was internalized.    

NEXT STEP/LESSONS LEARNED The results of this preliminary study suggest that the exposure of low level students to

problem solving issues can yield benefits, if only in making them aware of the issues they need to consider. As suggested by several texts on teaching problem solving, providing students with a systematic procedure with which to approach ill-structured problems has benefits. While the nature of ISE110 is such that a large portion of the class cannot be devoted to this issue, it appears perfectly feasible to include such exercises as a minor portion of the class, exposing students to issues that can be further developed throughout the rest of the undergraduate curriculum. For this approach to yield maximum benefit, however, subsequent courses must maintain these themes and build upon these concepts.

   

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In the immediate future, we plan to continue refining the presentation and design of these projects to support problem solving skills. In Fall 2010 the course instructor, Ms. Kuang-Hao Yeh, is planning an alternative design where students will be presented with a problem, and must submit a formal proposal describing how they will use the computing technology taught in the class to develop a solution. They will then work in teams to develop the solution and evaluate its effectiveness. Another interesting direction will be to use the Wolcott approach to assess the degree of problem-solving competency in a masters level class on supply chain management, where three such projects constitute 25% of the grade. Finally, the assessment of the impact of these projects on student problem solving ability needs to be conducted with a formal control group, to help achieve a clearer understanding of to what degree the results are due to the projects, and what to other environmental factors.

   

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APPENDICES

ISE 110 Computer Modeling for Engineers 

Fall 2009 – Dr. Reha Uzsoy 

Term Project Part I 

Due Tuesday, September 29, 2009 

Overview:   The  purpose  of  the  class  project  is  to  give  you  hands‐on  experience  with 

taking  a  realistic,  practical  engineering  problem  situation,  determining  what  the 

problem is and what constitutes an acceptable solution, analyzing  it quantitatively 

using  the computer  tools presented  in class, and recommending solutions  that are 

implementable. This involves a number of different areas: different people involved 

will have different ideas as to what the problem is, and what the solution ought to 

be; all the data required may not be available, especially data regarding the future; 

and there may exist a range of possible solutions that may all be reasonable. This is 

precisely  the  type of problem‐solving activity  that will  be  required of  you  in your 

professional  practice.    Also,  consistent  with  professional  practice,  you  will  be 

required  to work  in  a  team,  and  present  your  results  both  orally  and  in  a  formal 

technical report. 

  All  questions,  discussions  and  grading  regarding  the  project  will  be 

handled by Professor Uzsoy; apart from technical issues of using Excel, the TA 

will not be involved in the project. 

 

The Problem: 

  The costs of healthcare are currently the focus of an intense national debate, 

so  we  shall  consider  a  problem  in  healthcare  management:  how  to  organize  the 

operations of a university walk‐in clinic, described in the Harvard Business School 

Case Study No. 9‐681‐061, “University Health Services: Walk‐In Clinic”. The case will 

be available from the campus bookstore for your purchase; it is part of the required 

material for the class. 

 

 

 

Project Team: 

  You will work on this project in a team of three students. Please submit the 

name  of  one  person  you  would  prefer  to  be  teamed  with  to  Professor  Uzsoy  by 

Tuesday, September 15, 2009. We will then form teams of three people, trying our 

best to respect everyone’s preferences. However, bear in mind that it is impossible 

to satisfy everyone! Once teams are formed, they are fixed for the remainder of 

the semester!  

 

Our Approach: 

  We will work on the project in two separate phases. The first phase focuses 

on exploring the problem with your team, reaching a consensus among yourselves 

as  to what  the  problem  is, what  a  good  solution would  look  like,  and  performing 

some basic data analysis (yes, with Excel!) to develop an idea of what solutions may 

be  possible.  Each  team will make  a  formal  Powerpoint‐supported  presentation  in 

class summarizing the results of this analysis on Tuesday, September 29, 2009. This 

will be accompanied by a written report on your analysis and conclusions submitted 

the same day. I will work with all teams in helping to develop the presentation and 

report before its submission. I will also meet with each team after the presentation 

to  discuss  the  presentation  and  report,  answer  questions,  and  suggest 

improvements. 

  In  the  second phase of  the project you will build on your  initial  analysis  to 

develop  a  set  of  recommendations  to  clinic  management  on  how  to  improve  the 

operations of the clinic, and justify these based on cost and performance. This will 

require addressing the concerns of the different constituencies involved, as well as 

practical  issues  to  make  your  recommendations  implementable  in  practice.  This 

second phase will involve an in‐class discussion on Thursday November 5, 2009; a 

second presentation on Tuesday December 1, 2009; and  the  final written report 

on Thursday December 4, 2009. Again, I will work with your team as you develop 

both report and presentations. 

 

 

 

Project Phase 1: 

  This  phase  of  the  project  is  aimed  at  becoming  familiar with  the  problem, 

identifying  what  is  needed  and  what  makes  it  difficult  to  solve,  and  doing  some 

initial, exploratory data analysis to set the stage for your more complete analysis in 

the later phases. 

Exploring the Problem: 

1) Who are  the different  stakeholders  in  the problem who may have different 

ideas  as  to  what  a  good  solution  is?  Obviously,  the  clinic  management,  as 

represented  by  Angell,  is  one  such;  what  other  groups  with  different 

perspectives  need  to  be  considered?  In  other words, who  are we  trying  to 

make happy, or at least, less unhappy? 

2) For  each group of  stakeholders  you  identified  in Question 1,  describe  their 

idea  of  a  “good  solution”.  Remember  people will  have  different  incentives, 

such  as  financial  rewards,  job  satisfaction,  etc.  Hence  for  a  solution  to  be 

implementable in practice, i.e., all the main stakeholders need to be willing to 

at least “live with it”.  

3) List the data items given in the case that you feel are relevant to obtaining a 

good solution, together with one or two sentences explaining why each item 

is needed. 

4) Is there any data that is not given but you wish you had? List these items, and 

explain how they would help your analysis if they were available. 

5) List the primary sources of uncertainty in the problem. One way to think of 

this  is  to  think of  items  that  if you knew them perfectly now, your solution 

would always operate exactly as planned. 

6) Based  on  your  responses  to  these  questions,  summarize  in  at  most  two 

sentences  what  it  is  that  makes  this  problem  difficult  –  why  is  Angell 

struggling so much? 

 

 

 

Exploratory Analysis: 

7) The clinic has just moved from a pre‐triage system to implementing a triage 

system.  Has  the  triage  system  yielded  the  expected  improvements?  What 

stakeholders are unhappy about it, and why? How would you use the data in 

the case to examine what might be causing the dissatisfaction? 

8) How does the demand for MD and NP services vary within a day, and across 

days of the week? Estimate the average utilization of the MDs for each day of 

the week,  and  for each morning and afternoon period. Do  the  same  for  the 

NPs.  Is  there  sufficient  NP  and  MD  time  available  to  serve  the  demand 

throughout  the week? Relate  this  analysis  to  the waiting  times  incurred by 

patients. Does the clinic really have the MD/NP capacity that it is supposed to 

have  (see p.2 of  case,  first  full paragraph)(Hint: This  is  an EXCEL question; 

use worksheets to do the analysis, and graphs/charts to present the results.) 

9) What  do  you  think  are  the  problems  caused  by  the walk‐in  appointments? 

Justify your answer clearly and concisely based on the information from the 

case.  Discuss  this  issue  from  the  perspectives  of  the  different  stakeholders 

affected. 

 

Report Format: 

  Your report should be submitted as a Word or PDF file, appropriately spell‐

checked,  formatted and prepared  in a professional manner.  It should consist of no 

more  than  six  double  spaced  pages  in  12  point  font,  organized  by  the  question 

numbers above. You may include charts, tables and graphs in the body of the report 

as you see fit, or in an appendix which must be clearly labelled and cross‐referenced 

with the main body.  As a rough guideline, try initially for half a page for each of the 

questions above, which will  leave you some room for graphs etc. There  is no page 

limit  on  the  Appendix,  but  be  warned  –  I  will  read  the  main  body,  and  use  the 

appendix mainly for clarification of details. Both the report and the presentation will 

be due in class Tuesday September 29, 2009. 

 

ISE  110  Computer  Modeling  for  Engineers  

Fall  2009  –  Dr.  Reha  Uzsoy  

Term  Project  Part  II  

Due  Tuesday,  December  1,  2009  

Project  Phase  2:  

  Your  first  task  in  the  second  phase  of  the  project  is  to  revisit  your  responses  

to   the   questions   raised   in   Phase   I,   responding   to   the   concerns   raised   in   the  

presentations  and  in  the  comments  and  discussion  of  your  written  responses.  This  

material  should  now  be  converted  into  the   introduction  of  a  technical  report,  with  

the  following  sections:  

  Introduction:  A  high-­‐level  overview  of  the  context  for  the  project;  why  is  it  

being  undertaken,  what  was  achieved,  and  concluding  with  a  brief  overview  of  the  

remaining  sections  of  the  report.  (1.5-­‐2  pages)  

  Problem  Environment:  Who  are   the   stakeholders,  what   are   the  perceived  

problems   by   each   group,   what   steps   have   been   taken   to   address   these   recently  

(triage!),   and   data-­‐driven   discussion   of   whether   they   have   been   successful.   (2-­‐3  

pages)  

  Analysis:   Using   the  data   given   in   the   exhibits,   does   the   clinic   have   enough  

capacity   to  process   the  number  of  patients  arriving  within  desired  wait   times   in  a  

consistent  manner  throughout  the  week?  (4-­‐5  pages)  

  Recommendations:  What   steps   should   clinic  management   take   to   address  

the   problems   they   face,   both   in   the   short   term   and   the   longer   term,   to   improve  

things?  You  need  to  justify  these  recommendations  base  don  the  data  in  the  case  and  

the  analysis  you  have  performed.  

  A  sample  report  will  be  posted  on  the  Moodle  site  for  you  review.  

 

Analysis:  

  The   analysis   in   this   phase   of   the   project  will   proceed   in   two   stages.   In   the  

first,  you  will  extend  the  simple,  average-­‐based  analysis  you  have  done  in  Phase  1  to  

examine  whether  the  clinic  has  sufficient  resources  to  treat  the  arriving  patients.  In  

the  second  stage,  you  will  enhance  the  analysis  using  simple  queuing  concepts  given  

below.  Finally,  you  will  explore  a  number  of  specific  questions  using  both  analysis  

approaches  and  make  additional  recommendations  based  on  your  analysis.  

 

Basic  queuing  concepts:  

  The   basic   idea   of   queuing   models   is   simple:   customers   arrive   at   a   server  

according   to   some   random   pattern   over   time,   and   the   server   serves   them,   also  

taking  some  random  time  to  perform  each  service.  The  easiest  way  to  envision  this  

is   to   think   of   a   bank  where   a   teller  must   serve   customers   arriving   over   time.  We  

don’t  know  exactly  when  customers  arrive,  nor  do  we  know  beforehand  exactly  how  

long  each  customer  will  take  to  complete  their  transaction.  Queuing  models  proceed  

by   assuming   that   the   time   between   customer   arrivals   (interarrival   time)   and   the  

time   to  process  a   customer   (service   time)   follow  some  statistical  distribution,  and  

try  to  draw  conclusions  about  some  critical  performance  measures.    

  While  there  are  many  different  queuing  models  that  have  been  studied  over  

the   last   fifty   years,   many   for   specialized   applications   like   telecommunications  

networks   (many   of   the   protocols   that   run   the   Internet   were   developed   by  

researchers    in  queuing,  such  as  Leonard  Kleinrock  of  the  University  of  California),  

we   shall   focus   on   one   such   model   that   is   adequate   to   our   purposes.   To   use   this  

model,  we  will  assume  we  know  the  mean  and  standard  deviation  of  two  quantities:  

the  interarrival  time  (mean  ta,  standard  deviation  sa),  and  the  service  time  (mean  te,  

standard  deviation  se).  We  shall  define  one  intermediate  quantity  –  the  coefficient  of  

variation,   defined   by   the   ratio   of   the   standard   deviation   to   the   mean.   Thus,   the  

coefficient   of   variation   of   the   interarrival   times   is   given   by   ca   =   sa/ta;   that   for   the  

service  times  by  ce  =  se/te.  

  A  key  concept  in  queuing  analyses  is  that  of  the  average  utilization,  which  is  

given  by  u  =   te/ta;   the  ratio  of   the  average  service   time   to   the  average   interarrival  

time.  In  order  for  a  queueing  system  to  be  well-­‐behaved,  we  must  have  u  <  1  (What  

happens  if  u  >1  on  average  over  a  long  period  of  time?)  The  utilization  corresponds  

to  the  fraction  of  time  a  resource  is  busy  over  a  long  period  of  time;  most  queuing  

analyses  assume  the  system  will  be  running   in   its  current  configuration   for  a   long  

time,  and  try  to  estimate  average  performance  over  this  time  frame.  

  Under   these   assumptions   we   can   estimate   the   key   quantity   of   interest   for  

Angell  which  is  the  average  time  a  patient  may  be  expected  to  wait,  as  follows:  

T = (ca2 + ce

2 )2

( u1− u

)te + te .  

Another   important   relationship   in   queuing,   which   holds   under   very   general  

conditions,   is   Little’s   Law,  which   states   that   the   average  Number   of   customers   in  

queue  =  T/ta.  

  We  shall  now  use  these  concepts  to  examine  the  problems  faced  by  the  Walk-­‐

In  Clinic.  For  more   information  on  queuing  models,  a  good  reference   is  (Hopp  and  

Spearman  2001).  

 

Guidelines  for  Analysis  

  Your   report   and   presentation   should   make   specific   recommendations   to  

Angell   as   to  what   she  can  do   to   improve   the  operations  of   the  clinic   to   satisfy   the  

various  stakeholders.  Your  conclusion  s  and  recommendations  need  to  based  as  far  

as  possible  on  the  data  in  the  case,  and  on  the  analyses  you  perform  using  this  data.  

You  are  expected  to  support  your  arguments  with  tables  and  graphs  as  appropriate;  

in   Excel,   you   have   a   powerful   tool   for   performing   the   analysis   and   displaying   the  

results.  I  will  be  available  for  consultation  on  an  ongoing  basis,  both  in  office  hours  

and  by  email,  for  your  questions  regarding  the  project,  from  now  until  the  due  date.  

Specifically,  I  will  be  glad  to    

Your  report  needs  to  address  at  least  the  following  issues:  

1) Revisit  your  analysis  from  Phase  1  to  estimate  the  average  utilization  of  MDs  

and   nurse   practitioners   in   each   time   period   each   day   of   the  week.   Display  

these   results   in   an   appropriate   table     (Conditional   formatting   may   be  

helpful…).  

2) Based   on   this   analysis,   identify   some   short-­‐term   steps   Angell   can   take   to  

improve  things.  

3) It   is   clear   from   your   analysis   in   Phase   1   that   the   clinic   has   an   issue   with  

limited   MD   capacity.   Recall   that   the   MDs   are   supposed   to   be   working   12  

hours   per   week   in   the   clinic.   Examine   the   data   in   the   case   to   determine  

whether   this   is,   in   fact,   the   case.   If   this   is   not   the   case,   how   would   the  

situation   change   if   they   were   able   to   get   all   the   MD   capacity   they   are  

supposed  to  have?  Use  your  calculations  from  1)  above  to  address  this.  

4) Develop  a  rough-­‐cut  queuing  analysis  to  estimate  the  average  wait  times  for  

an  MD  using  the  relationships  given  above.  Assume  initially  that  ca  =  ce  =  1;  

this   corresponds   to   an   exponential   distribution   of   service   and   inter   arrival  

times,  which  is  generally  a  reasonable,  somewhat  conservative  assumption  in  

service  systems  of  this  nature.  Set  these  calculations  up  in  Excel;  you’ll  need  

to  reuse  them  for  other  portions  of  your  analysis.  Be  sure  to  explain  how  you  

calculate   the   various   parameter   values   clearly,   and   state   explicitly   all  

assumptions  you  make.  

a. To  guide  you,  consider  the  following  steps:  

b. Estimate  the  average  arrival  rate  of  patients  (patients/hr)  to  the  clinic  

for  the  entire  year.  

c. Estimate  the  average  service  time  over  all  MDs;  remember  to  factor  in  

the  fact  that  MDs  are  not  available  all  week,  but  only  limited  hours.  

d. Estimate  the  average  time  in  queue  from  the  equation  above.  (What  is  

the   relation   between   average   time   in   system   and   average   time   in  

queue?)  

e. Compare  the  rate  at  which  different  MDs  serve  patients,  and  use  this  

data  to  estimate  the  standard  deviation  of  the  service  time  seen  by  a  

random  patient  walking  in  at  a  random  time  to  see  a  random  doctor.  

Recalculate   your   estimate   of   the   average   waiting   time   using   this  

information,  and  discuss  its  implications  for  Angell.  

f. What   are   some   realistic   options   that   Angell   and   the   clinic  

management  can  pursue  to  reduce  the  workload  on  the  MDs?    Discuss  

the   possible   effects   of   such   decisions   on   the   average   waiting   times  

using  the  queuing  model  you  have  developed  above.  

g. What   are   the   implications   of   these   average   waiting   times   for   the  

number  of  patients  waiting  at  the  clinic  at  a  given  time?  (Think  space  

in  waiting  rooms…)  

Report  Format:  

  Your  report  should  be  submitted  as  a  Word  or  PDF  file,  appropriately  spell-­‐

checked,   formatted  and  prepared   in  a  professional  manner.   It  should  consist  of  no  

more  than  10  double  spaced  pages  in  12  point  font,  organized  by  the  sections  above.  

You  may  include  charts,  tables  and  graphs  in  the  body  of  the  report  as  you  see  fit,  or  

in  an  appendix  which  must  be  clearly   labelled  and  cross-­‐referenced  with  the  main  

body.     As   a   rough   guideline,   try   initially   for   half   a   page   for   each   of   the   questions  

above,  which  will  leave  you  some  room  for  graphs  etc.  There  is  no  page  limit  on  the  

Appendix,  but  be  warned  –  I  will  read  the  main  body,  and  use  the  appendix  mainly  

for   clarification   of   details.   The   presentation   will   be   due   in   class   Tuesday  

December  1,  2009;  the  report  Thursday,  December  3,  2009.    

 

References  

Hopp,   W.   J.   and   M.   L.   Spearman   (2001).   Factory   Physics   :   Foundations   of  Manufacturing  Management.  Boston,  Irwin/McGraw-­‐Hill.  

   

Oral Presentation - 40 Points

Organization of topics (10):

Presentation of information (10):

Observation of time limits (10):

Preparation of presenters(10):

ISE 110 Computer-Based Modeling for EngineersFall 2009 - Dr. Reha UzsoyProject Phase 2 Reports

ISE110 Computer-Based Modeling for EngineersFALL 2009 - Dr. Reha Uzsoy

Project Phase II Written report

GROUP:

Introduction: out of 10 points:! Overview of company background, summarizing the business need for the ! project:! Brief overview of project relative to business needs: ! Outline of rest of report:.

Problem Environment: out of 10 Points! Stakeholders:! Perceived Problems by groups: ! Recent efforts to address problems:! Data driven assessment of success:

Analysis: out of 45 Points! Describes symptoms of problem;! Suggests hypotheses as to causes.! Appropriate analysis to test hypothesis, with appropriate use of available data: ! Assumptions clearly explained and justified. ! Appropriately organized - different aspects of analysis presented in a logical, ! integrated manner.

Conclusions and Future Recommendations: out of 15 Points! Effective, concise summary of the results of the analysis - what were the ! problems?.! Recommended solutions:! Suggestions for next steps after current study: N/A

Presentation and Writing: out of 20 points! Grammar, spelling:! Sentences, paragraphs; ! Organization and transitions between sections:!Integration of figures and tables into text:! Use of appendices. N/A

Total Points:

Steps for Better Thinking Rubric ←Less Complex Performance Patterns More Complex Performance Patterns→

Steps for Better Thinking SKILLS

"Confused Fact Finder" Performance Pattern 0—How performance might appear when Step 1, 2, 3, and 4 skills are weak

"Biased Jumper" Performance Pattern 1—-How performance might appear when Step 1 skills are adequate, but Step 2, 3, and 4 skills are weak

"Perpetual Analyzer" Performance Pattern 2—-How performance might appear when Step 1 and 2 skills are adequate, but Step 3 and 4 skills are weak

"Pragmatic Performer" Performance Pattern 3—-How performance might appear when Step 1, 2, and 3 skills are adequate, but Step 4 skills are weak

"Strategic Re-Visioner" Performance Pattern 4—-How performance might appear when one has strong Step 1, 2, 3, and 4 skills

Step 1: IDENTIFY A—Identify and use relevant information B—Articulate uncertainties

A0—Uses very limited information; primarily "facts," definitions, or expert opinions

B0—Either denies uncertainty OR attributes uncertainty to temporary lack of information or to own lack of knowledge

A1—Uses limited information, primarily evidence and information supporting own conclusion*

B1—Identifies at least one reason for significant and enduring uncertainty*

A2—Uses a range of carefully evaluated, relevant information

B2—Articulates complexities related to uncertainties and the relationships among different sources of uncertainty

A3—Uses a range of carefully evaluated, relevant information, including alternative criteria for judging among solutions

B3—Exhibits complex awareness of relative importance of different sources of uncertainties

A4—Same as A3 PLUS includes viable strategies for GENERATING new information to address limitations

B4—Exhibits complex awareness of ways to minimize uncertainties in coherent, on-going process of inquiry

Step 2: EXPLORE C—Integrate multiple perspectives and clarify assumptions D—Qualitatively interpret information and create a meaningful organization

C0—Portrays perspectives and information dichotomously, e.g., right/wrong, good/bad, smart/stupid

D0—Does not acknowledge interpretation of information; uses contradictory or illogical arguments; lacks organization

C1—Acknowledges more than one potential solution, approach, or viewpoint; does not acknowledge own assumptions or biases

D1—Interprets information superficially as either supporting or not supporting a point of view; ignores relevant information that disagrees with own position; fails to sufficiently break down the problem

C2—Interprets information from multiple viewpoints; identifies and evaluates assumptions; attempts to control own biases*

D2—Objectively analyzes quality of information; Organizes information and concepts into viable framework for exploring realistic complexities of the problem*

C3—Evaluates information using general principles that allow comparisons across viewpoints; adequately justifies assumptions

D3—Focuses analyses on the most important information based on reasonable assumptions about relative importance; organizes information using criteria that apply across different viewpoints and allow for qualitative comparisons

C4—Same as C3 PLUS argues convincingly using a complex, coherent discussion of own perspective, including strengths and limitations

D4—Same as D3 PLUS systematically reinterprets evidence as new information is generated over time OR describes process that could be used to systematically reinterpret evidence

Step 3: PRIORITIZE E—Use guidelines or principles to judge objectively across the various options F—Implement and communicate conclusions for the setting and audience

E0—Fails to reason logically from evidence to conclusions; relies primary on unexamined prior beliefs, clichés, or an expert opinion

F0—Creates illogical implementation plan; uses poor or inconsistent communication; does not appear to recognize existence of an audience

E1—Provides little evaluation of alternatives; offers partially reasoned conclusions; uses superficially understood evidence and information in support of beliefs

F1—Fails to adequately address alternative viewpoints in implementation plans and communications; provides insufficient information or motivation for audience to adequately understand alternatives and complexity

E2—Uses evidence to reason logically within a given perspective, but unable to establish criteria that apply across alternatives to reach a well-founded conclusion OR unable to reach a conclusion in light of reasonable alternatives and/or uncertainties

F2—Establishes overly complicated Implementation plans OR delays implementation process in search of additional information; provides audience with too much information (unable to adequately prioritize)

E3—Uses well-founded, overarching guidelines or principles to objectively compare and choose among alternative solutions; provides reasonable and substantive justification for assumptions and choices in light of other options*

F3—Focuses on pragmatic issues in implementation plans; provides appropriate information and motivation, prioritized for the setting and audience*

E4—Articulates how a systematic process of critical inquiry was used to build solution; identifies how analysis and criteria can be refined, leading to better solutions or greater confidence over time

F4—Implementation plans address current as well as long-term issues; provides appropriate information and motivation, prioritized for the setting and audience, to engage others over time

Step 4: ENVISION G—Acknowledge and monitor solution limitations through next steps H—Overall approach to the problem

G0—Does not acknowledge significant limitations beyond temporary uncertainty; next steps articulated as finding the “right” answer (often by experts)

H0—Proceeds as if goal is to find the single, "correct" answer

G1—Acknowledges at least one limitation or reason for significant and enduring uncertainty; if prompted, next steps generally address gathering more information

H1—Proceeds as if goal is to stack up evidence and information to support own conclusion

G2—Articulates connections among underlying contributors to limitations; articulates next steps as gathering more information and looking at problem more complexly and/or thoroughly

H2—Proceeds as if goal is to establish an unbiased, balanced view of evidence and information from different points of view

G3—Adequately describes relative importance of solution limitations when compared to other viable options; next steps pragmatic with focus on efficiently GATHERING more information to address significant limitations over time

H3—Proceeds as if goal is to come to a well-founded conclusion based on objective consideration of priorities across viable alternatives

G4—Identifies limitations as in G3; as next steps, suggests viable processes for strategically GENERATING new information to aid in addressing significant limitations over time*

H4—Proceeds as if goal is to strategically construct knowledge, to move toward better conclusions or greater confidence in conclusions as the problem is addressed over time*

© 2006, Susan K. Wolcott. All rights reserved. Materials herein may be reproduced within the context of educational practice or classroom education, provided that reproduced materials are not in any way directly offered for sale or profit. Please cite this source: Wolcott, S. K. (February 9, 2006). Steps for Better Thinking Rubric [On-line]. Available: http://www.WolcottLynch.com. Based in part on information from Reflective Judgment Scoring Manual With Examples (1985/1996) by K. S. Kitchener & P. M. King. Grounded in dynamic skill theory (Fischer & Bidell, 1998). * Shaded cells most closely related to "stair step" model. Performance descriptions to the left of a shaded cell characterize skill weaknesses. Performance descriptions to the right of a shaded cell characterize skill strengths.