This document provides an overview of Optimization Direct, an IBM business partner that specializes in optimization software and consulting. It discusses Optimization Direct's experience implementing optimization technology for various industries. The document also summarizes Optimization Direct's product offerings, which focus on IBM ILOG CPLEX Optimization Studio. It then highlights several recent case studies where Optimization Direct helped customers solve scheduling, resource allocation, and pricing problems using analytics and optimization modeling approaches like MIP and heuristic algorithms. Finally, it shares an example of how Optimization Direct helped a retail client optimize markdown pricing and promotions to improve sales and margins.

1. Optimization Direct
Introduction & Recent Optimization Case Studies

2. Agenda
• Introduction, Recent Optimization Case Studies: Alkis
Vazacopoulos, Optimization Direct
• Recent advances and future direction(s) in IBM ILOG
CPLEX Optimization Studio, Xavier Nodet, IBM
• Analyzing uncertainty and optimizing: a case study in
retail, Robert Ashford, Optimization Direct
• Solving Planning and Scheduling with CPLEX. Filippo
Focacci, Decision Brain

3. Optimization Direct
• IBM Business Partner
• More than 30 years of experience in developingand selling
Optimization software
• Experience in implementingoptimization technology in all the
verticals
• Sold to end users – Fortune 500 companies
• Train our customers to get the maximum out of the IBM software
• Help the customersget a kick start and get the maximum from the
software right from the start

4. What software do we sell?
• IBM ILOG CPLEX Optimization Studio
• Cplex is the leader in optimization technology
• Cplex can handle large scale problems and solve them
very fast

5. Which markets
• Travel
• Retail
• Energy
• Financial
• Process
• Big data

6. Why IBM? Why Cplex?
• Fast
• Reliable
• IBM software
• Large scale
• Gives you the ability to model develop and solve your
decision problem
• Complete solution

7. What types of problems?
• Price & revenue optimization (Travel Industry, etc..,)
• Retail – optimization of campaigns
• Financial: trading, portfolio optimization
• Process industries: schedule your refinery
• Big Data: We see new innovations in human /machine
interface and how operation research Experts they
solve complicated problems in data mining

8. How can we help?
• Benchmark your problems
• Help you with next steps for developing your solution!
• Develop optimization prototypes using OPL

9. Why Optimization Direct?
• Experience
• Responsive
• Benchmark faster against competition
• Expertise
• 15 years of experience competing with CPLEX
• Understand differentiator
• Know how to sell against competitors

10. Recent Analytics & Optimization Case
Studies
• Hospital (OPL MODEL + MIP)
• DNA Screening Company (MIP + CP)
• Workforce scheduling Problem (CPLEX + ODH)
• Sports (MIP, MIP + Local Search, Regression)
• Customized Offers Company (Analytics + MIP)
• Packaging and Fulfillment (MIP, MIP+CP)
• Pharma Co (Analytics, Robust Opt, MIP)
• Energy Co (MIP, extend to Stochastic MIP)
• Financial company (Complex QCPs, MIP)
• Retail Clothing (Analytics, MIP)

11. Hospital Scheduling (non emergency
units)
• Patients
• Block is combination of Room/Day/week
• Surgeons, nurses and doctors
• PROBLEM A: COMPOSITION & ASSIGNMENTS: Create
teams of doctors + stuff to assign to patients
• PROBLEM B: BLOCK ASSIGNMENT: Assign Patients to
Block

12. Hospital Scheduling
• PROBLEM A: Starting to get attention lately in the
Health analytics area; Experiments to determine if
optimal composition will be beneficial (Health Analytics)
• PROBLEM B: Complex rules, Objective function
complexity;
• Objective function: Variation between Number of Patients
in Hospital, waiting for surgery and similar objectives
• Many experimentslarge Hospital
• Model develop with OPL and solved MIP with CPLEX

13. DNA Screening - Scheduling problems
• New Innovative DNA Screening Companies
• Goal: Make custom-built robots to turn blood and saliva
samples into purified DNA.
• Samples: These samples come from men and women
across the globe.
• DNA Sample and Robots: The robots can analyze
thousands of DNA samples at the same time, and can
work nonstop seven days a week.

14. DNA Screening Problem
• This is Flowshop scheduling problem with Many Side
Constraints
• Challenge: Increase Utilization of the robots –
decrease idle time
• Solver: Constrained programming & MIP combination
• Time Horizon: Determine easily Daily sequences and
develop a rolling horizon schedule

15. Workforce Scheduling
• Schedule entities over 64 periods
• Many Side constraints

16. Worksforce Scheduling Example: Large
Scale Scheduling models
• Schedule entities over 64 periods
• No usable (say within 30% gap) solution to small
model after 3 days run time on fastest hardware (Intel
i7 4790K ‘Devil’s Canyon’)
Model entities rows cols integers
Small 314 389560 94200 94200
Medium 406 371964 149132 149132
Large 508 554902 426390 426390

17. Solution: ODHeuristics
• Uses CPLEX as a solver
• Solves sequence of sub-models
• Delivers usable solutions (12%-16% gap)
• Takes 4-36 hours run time
• Multiple instances can be run concurrently with
different seeds
• Can run on only one core
• Can interrupt at any point and take best solution so far
time limit / call-back /SIGINT

18. Heuristic Results on Scheduling Models
Seed Solution Time Gap
Small 1234 118 65818 15.2%
5678 118 41122 15.2%
9012 117 38243 14.5%
21098 117 27623 14.5%
Medium 1234 703.32 100000
5678 728.64 100000
9012 718.23 100000
21098 832.43 100000
Large 1234 1039.67 60000
5678 1039.47 60000
9012 1039.43 60000
21098 1044.09 60000
Best
bound
of
100
established
by
separate
CPLEX
run
Times
are
in
seconds
on
Intel
i7-­‐4790K
@
4.4GHz
(1
core)

19. Small Model Heuristic Behavior
100
120
140
160
180
200
220
240
260
280
300
0 5000 10000 15000 20000 25000 30000
Solution value
Time in seconds
1234
5678
9012
21098
Seeds

20. Medium Model Heuristic Behavior
500
700
900
1100
1300
1500
1700
1900
0 20000 40000 60000 80000 100000 120000 140000 160000
Solution value
Time in seconds
1234
5678
9012
21098
Seeds

21. Large Model Heuristic Behavior
1020
1040
1060
1080
1100
1120
1140
1160
1180
0 10000 20000 30000 40000 50000 60000 70000
Solution value
Time in seconds
1234
5678
9012
21098
Seeds

22. Parallel Heuristic Approach
• Run several heuristic threads with different seeds
simultaneously
• CPLEX callable library very flexible, so
• Exchange solution information between runs
• Kill sub-model solves when done better elsewhere
• Improves sub-model selection
• 4 instances run on 4 core i7-4790K
• Each heuristic thread run with single CPLEX thread
i.e. 1 core each
• Compare with serial runs using a single CPLEX thread

23. Small Model Parallel Heuristic Behavior
100
120
140
160
180
200
220
240
260
280
300
0 5000 10000 15000 20000 25000 30000
Solution value
Time in seconds
1234
5678
9012
21098
Parallel
Seeds

24. Medium Model Parallel Heuristic Behavior
500
700
900
1100
1300
1500
1700
1900
0 20000 40000 60000 80000 100000 120000 140000 160000
Solution value
Time in seconds
1234
5678
9012
21098
Parallel
Seeds

25. Large Model Parallel Heuristic Behavior
1020
1040
1060
1080
1100
1120
1140
1160
1180
0 10000 20000 30000 40000 50000 60000 70000
Solution value
Time in seconds
1234
5678
9012
21098
Parallel
Seeds

26. Parallel Heuristic Advantages
• Better results
• Better objective value
• More consistent
• Faster
• Compare time to interesting(i.e. good) solutions
• Speedup depends on model (as with straight MIP)
• Depends on which serial run used for comparison
• A factor of 2 to 4 with 4 cores is typical
Model Speedup factor
Small 1.4 to 3.7
Medium 2.5 to 8.3
Large 1.8 to 2.8

27. Customized Offers Company:
• Products( portfolio of products)
• Customers
• More productsare added or deleted from the portfolio
• Customers are added or deleted every month; customers
have monthlyor yearly subscription s
• Objective : retain customers, increase sales
• Action: Send a package recommendation to a customer or–
customer has option to cancel or select other package

28. Customized Offers Company: Knapsack
Type problems
We have a set of
products
Assign it to a
CUSTOMER

29. Solution
• Use CPLEX libraries
• MIP
• Use ODHeuristics to solve some harder problems

30. Used Case: Dynamic Pricing Using Promotions
Markdowns and Clearance strategies

31. Retail Optimization Used Case
• Vertical: Retail
• Products: Apparel & Accessories
• Objective: Maximize Revenue, Maximize margin, Reduce Inventory
• Decisions: Dynamic Pricing
• What do I have: Initial Plan
• Status: Review the week
• Decisions: Pricing
• Dynamic Pricing
• Markdowns
• Price Points
• Clearance
• Promotions

32. PLAN – Last week
Sales $ Units Sold Margin
$7,689,140 568,000 53.73%
Our sales plan for last week was:
REVENUE TARGET

33. PLAN – Last week
Sales $ Units Sold Margin
$7,689,140 568,000 53.73%
Actual – Last Week
Sales $ Units Sold Margin
$7,083,935 559,390 51%
How did we do? Plan vs. Actual
REVENUE TARGET ACTUAL Revenue

34. PLAN – Last week
Sales $ Units Sold Margin
$7,689,140 568,000 54%
Actual – Last Week
Sales $ Units Sold Margin
$7,083,935 559,390 51%
How did we do? Plan vs. Actual
We missed both in
Sales revenue,
Units sold and
Margin

35. PLAN – Last week
Sales $ Units Sold Margin
$7,689,140 568,000 53.73%
Actual – Last Week
Sales $ Units Sold Margin
$7,083,935 559,390 51%
How did we do? Plan vs. Actual
Which Season/s was the problem?

36. PLAN – Last week – SPRING SEASON
Sales $ Units Sold Margin
$5,515,500 310,000 61.73%
Actual – Last Week
Sales $ Units Sold Margin
$4,571,196 269,470 61.48%
Where we miss?
We missed on Revenue
and on units
SPRING 2016 is the problem!

37. What can do?
• Using TM1 we can analyze the data and identify Variance in the Plan vs.
Actual
• How can we affect the demand?
• Promotions
• Markdowns
• Clearance
• How do we decide which products, groups, when to act?

38. Technology
• We use Predictive analytics
• To predict the sales for next week/s
• To identify slow and fast moving products
• To identify productsthat react well in markdowns and promotions
• We use Prescriptive analytics – optimization
• To decide optimal prices that maximize our revenue
• to decide when to offer promotionsto maximize our revenue

39. What are the data we need for each
SKU?
SKU
ID
Price Cost Days
on
the
Floo
r
Total
Quantity
Ordered
Revenues Cost
of
Sold
Current
Margin
Total
Sold
Units
Total
Length
of
Selling
period
Liquidatio
n price
SKU99
9
$24.0
4
$6.85 77
days
1794 $9969 $4247 57.4% 620 24
weeks
$6.85
Total Sold * Price IS
NOT EQUAL to
REVENUES SO FAR
Average
Price
$16.07
Avg. Salesthrough
3.14%

40. What is the output of the optimization?
SKU
ID
Price Cost Days
on
the
Floo
r
Total
Quantity
Ordered
Revenues Cost
of
Sold
Current
Margin
Total
Sold
Units
Total
Length
of
Selling
period
Liquidatio
n price
SKU99
9
$24.0
4
$6.85 77
days
1794 $9969 $4247 57.4% 620 24
weeks
$6.85
PROMOTE 30%
NEXT WEEK
Average
Price
$16.07 Avg. Salesthrough
3.14%

41. 50% off
20% off
30% off
40% off
50% off
$0
$2,000
$4,000
$6,000
$8,000
$10,000
$12,000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Revenues
Weeks
Markdown
&
Promotion
strategy for a
“slow-­‐moving”
product

42. 10% off
30% off
20% off 50% off
$0
$2,000
$4,000
$6,000
$8,000
$10,000
$12,000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Revenues
Weeks
Markdown
&
Promotion
strategy for a
“fast-­‐moving”
product

43. $79,000
$133,000
$187,000
$100,608
$174,846
$204,279
$60,000
$90,000
$120,000
$150,000
$180,000
$210,000
$240,000
Revenue
Effect of Markdowns & Promotionson Revenue
Revenue without Markdown & Promotion
Very slow
Slow
moving
Fast-
moving

44. 20.9%
53.0%
66.6%
37.9%
64.3%
69.4%
10.0%
25.0%
40.0%
55.0%
70.0%
Original Sales Rate
Effect of Markdowns & Promotions on Margin
Margin without Markdown & Promotion
Margin with Markdown & Promotion
VERY SLOW
SLOW
FAST

45. Learn more
Product
Slow
moving
Fast
Moving
Bad
Sales
Lift
Good
Sales
Lift
Bad
Sales
Lift
Good
Sales
Lift

46. 50% off 60% off
40% off
50% off
60% off
40% off
50% off
30% off
40% off
$0
$1,000
$2,000
$3,000
$4,000
$5,000
$6,000
$7,000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Revenues
Weeks

47. $79,000
$133,000
$100,608
$174,846
$81,064
$142,379
$60,000
$90,000
$120,000
$150,000
$180,000
$210,000
Revenue
Revenue without Markdown & Promotion Revenue with good sales lift
SLOW FAST
MOVING

48. 20.9%
53.0%
37.9%
64.3%
22.4%
56.1%
10.0%
25.0%
40.0%
55.0%
70.0%
Margin without Markdown & Promotion Margin with good sales lift
Margin with bad sales lift
FAST MOVING PRODUCTSLOW MOVING PRODUCT

49. Tutorial: Monday
• Optimization Direct will also be presenting a
technology tutorial at INFORMS 2016 on
• Monday April 11 at 3:40pm-4:30pm, Track 10 -
Technology Tutorials,
• Regency 6.
• Solving Large Scale Optimization Problems using
CPLEX Optimization Studio

50. Booth 25
• Stop by the booth ……..

51. To learn more
Contact
Alkis Vazacopoulos
201 256 7323
alkis@optimizationdirect.com
www.optimizationdirect.com