A Novel Approach to Resource Allocation Problem delivered at ICBAI-2018 Conference jointly held by IISc Banglore and IIM Bangalore.

1. Resource Allocation
Problem
An extended application of Gale & Shapley
Algorithm
Ravi Vogeti Santhosh Raghavan

2. Delivery Organization Customer, VP

3. Tom
Delivery Manager
Kunal
Senior Engineer
Shreya
Engineer
Rajesh
Engineer
MBA : Premier
Institute Effective Communicator
Consulting, BFSI,
Telecom
18 Employees

4. Tom
Delivery Manager
Kunal
Senior Engineer
Shreya
Engineer
Rajesh
Engineer
Engineer
Extraordinaire
Go to Guy
any Technology
Fast Tracked in
4.5 years
Managerial
Aspirations

5. Tom
Delivery Manager
Kunal
Senior Engineer
Shreya
Engineer
Rajesh
Engineer
College
Topper
Lacking
Career Path
Early in
Career
Multiple Accounts
1year

6. Tom
Delivery Manager
Kunal
Senior Engineer
Shreya
Engineer
Rajesh
Engineer
Jack of
All Trades
Master of
None
2.5 years of
varied experience
Multiple Accounts

7. Tanya
Co-Founder & VP, NextEd
NextEd : Startup in highly competitive
Online education space
Tasked with scaling NextEd
for the next million Students
Try Fast and Fail Fast attitude
Focus on Content Creation and
Marketing to achieve the goal
Network Infrastructure as
a key Business Critical Service
A Partner who understands
their unique needs and who can
provide them a competitive difference

8. • Not just any Vendor
• A Trusted Partner
• My Specialization
• Burnout
• What’s next for me?
• When can I Lead?
• Where do I fit ?
• Career Plan
• Project Delays
• Team Morale

9. • Not just any Vendor
• A Trusted Partner
• My Specialization
• Burnout
• What’s next for me?
• When can I Lead?
• Where do I fit ?
• Career Plan
• Project Delays
• Team Morale

10. • Not just any Vendor
• A Trusted Partner
• My Specialization
• Burnout
• What’s next for me?
• When can I Lead?
• Where do I fit ?
• Career Plan
• Project Delays
• Team Morale

11. • Not just any Vendor
• A Trusted Partner
• My Specialization
• Burnout
• What’s next for me?
• When can I Lead?
• Where do I fit ?
• Career Plan
• Project Delays
• Team Morale

12. • Not just any Vendor
• A Trusted Partner
• My Specialization
• Burnout
• What’s next for me?
• When can I Lead?
• Where do I fit ?
• Career Plan
• Project Delays
• Team Morale

13. • Not just any Vendor
• A Trusted Partner
• My Specialization
• Burnout
• What’s next for me?
• When can I Lead?
• Where do I fit ?
• Career Plan
• Project Delays
• Team Morale

14. Due to Chaotic Assignments, Resources often find themselves
constrained by Deadlines, lacking Customer focus, lacking time
for Skill upgradation and for Team development.
Problem

15. The solution is to effectively deploy talent to customer accounts,
thereby promoting resource accountability, creating customer
focus, enhancing talent capabilities, creating mentoring
opportunities and shrinking resource deployment time
Great ! But how to implement ?
Solution

16. Traditional Approach
• Failure to exploit Strategic Capabilities, Tacit
Knowledge
• Preferences are ignored affecting Employee Morale
and Customer Satisfaction

17. Gale-Shapley algorithm takes the preferences of the agents into consideration and generates a stable matching,
so that all the agents are better off by the assignment, than any other possible matching
Dating Apps
Organ Donation
Residency Matching

18. College Admissions Problem
• Multiple Students compete for a
limited number of slots in their
preferred Colleges.
• The Students have a non-
overlapping priority for different
colleges and vice-versa.
Arpit Anu Vinoth Ria Anil
BMS RV PES

19. Arpit Anu Vinoth Ria Anil
BMS (2 Slots) RV (2 Slots) PES (2 Slots)
1. RV 1. RV 1. RV 1. BMS 1. RV
2. BMS 2. PES
3. BMS
2. RV
3. PES
2. BMS
3. PES
1. Anil 1. Anil 1. Anil
2. Vinoth 2. Arpit
3. Anu
4. Ria
5. Vinoth
2. Arpit
3. Vinoth
4. Ria
0
Arpit Anu Vinoth Ria Anil
BMS (2 Slots) RV (2 Slots) PES (2 Slots)
1. RV 1. RV 1. RV 1. BMS 1. RV
2. BMS 2. PES
3. BMS
2. RV
3. PES
2. BMS
3. PES
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5. Vinoth
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1

20. Arpit Anu Vinoth Ria Anil
BMS (2 Slots) RV (2 Slots) PES (2 Slots)
1. RV 1. RV 1. RV 1. BMS 1. RV
2. BMS 2. PES
3. BMS
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3. PES
2. BMS
3. PES
1. Anil 1. Anil 1. Anil
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BMS (2 Slots) RV (2 Slots) PES (2 Slots)
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2. BMS 2. PES
3. BMS
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21. Arpit Anu Vinoth Ria Anil
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1. RV 1. RV 1. RV 1. BMS 1. RV
2. BMS 2. PES
3. BMS
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3. PES
2. BMS
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5. Vinoth
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BMS (2 Slots) RV (2 Slots) PES (2 Slots)
1. RV 1. RV 1. RV 1. BMS 1. RV
2. BMS 2. PES
3. BMS
2. RV
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2. BMS
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PES (2 Slots)
2. PES
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22. Arpit Anu Vinoth Ria Anil
BMS (2 Slots) RV (2 Slots) PES (2 Slots)
1. RV 1. RV 1. RV 1. BMS 1. RV
2. BMS 2. PES
3. BMS
2. RV
3. PES
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2. Vinoth 2. Arpit
3. Anu
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5. Vinoth
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3. Vinoth
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BMS (2 Slots) RV (2 Slots) PES (2 Slots)
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2. BMS 2. PES
3. BMS
2. RV
3. PES
2. BMS
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PES (2 Slots)
4. Ria

23. Arpit Anu Vinoth Ria Anil
BMS (2 Slots) RV (2 Slots) PES (2 Slots)
1. RV 1. RV 1. RV 1. BMS 1. RV
2. BMS 2. PES
3. BMS
2. RV
3. PES
2. BMS
3. PES
1. Anil 1. Anil 1. Anil
2. Vinoth 2. Arpit
3. Anu
4. Ria
5. Vinoth
2. Arpit
3. Vinoth
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4
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2. BMS 2. PES
3. BMS
2. RV
3. PES
2. BMS
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24. ArpitAnu Vinoth Ria Anil
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2. BMS 2. PES
3. BMS
2. RV
3. PES
2. BMS
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2. Vinoth 2. Arpit
3. Anu
4. Ria
5. Vinoth
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4. Ria
NO MATCH MATCHED

25. College Pay-off
Student Pay-off
(Proxy: Account Hours)
(Proxy: Resource Hours)
Slots Colleges Students
Demand Forecast Accounts Resources

26. College Pay-off
Student Pay-off
(Proxy: Account Hours)
(Proxy: Resource Hours)
Slots Colleges Students
Demand Forecast Accounts Resources

27. Utility for Resource (µR)
𝐻𝑜𝑢𝑟𝑠 𝑙𝑜𝑔𝑔𝑒𝑑 𝑏𝑦 𝑅𝑒𝑠𝑜𝑢𝑟𝑐𝑒 𝑎𝑔𝑎𝑖𝑛𝑠𝑡 𝐴𝑐𝑐𝑜𝑢𝑛𝑡 2
𝑇𝑜𝑡𝑎𝑙 𝐻𝑜𝑢𝑟𝑠 𝑙𝑜𝑔𝑔𝑒𝑑 𝑏𝑦 𝑅𝑒𝑠𝑜𝑢𝑟𝑐𝑒
R1 R2 R3 . Rj . Rn
A1 u11 u12 u13 . . u1n
A2 u21 u22 u23 . . u2n
A3 u31 u32 u33 . . u3n
. . . . . . .
Ai . uij . .
. . . . . . .
Am um1 um2 um3 . . umn
µR
Resources
Accounts
Utility for Account (µA)
𝐻𝑜𝑢𝑟𝑠 𝑙𝑜𝑔𝑔𝑒𝑑 𝑢𝑛𝑑𝑒𝑟 𝐴𝑐𝑐𝑜𝑢𝑛𝑡 𝑏𝑦 𝑅𝑒𝑠𝑜𝑢𝑟𝑐𝑒 2
𝑇𝑜𝑡𝑎𝑙 𝐻𝑜𝑢𝑟𝑠 𝑙𝑜𝑔𝑔𝑒𝑑 𝑢𝑛𝑑𝑒𝑟 𝐴𝑐𝑐𝑜𝑢𝑛𝑡

28. • Used random allocation methodology over 5000 iterations for the mock data set and used a user defined metric Affinity
to benchmark the quality of the solution
• Using our methodology has resulted in affinities of 31% and 32% for resources and accounts respectively. While through
random allocation, the maximum affinities observed are only 1.1% and 1.2% respectively.
Benchmark

29. We wanted a key partner who
understands our unique needs
that’s who we got
I have a focused Specialization
and see myself becoming an
expert in the next 6-9 months
I have a customized career path
and am an integral part of my
customer’s success
I believe I can become a Team
Lead and handle my own team
My team works like a well-
oiled machine and I’ve heard
only compliments from
customers

30. Questions