Do We Choose Our Own Fate? Fatalism vs Determinism By Kerry Holmes.
CHOOSE YOUR RATE, CHOOSE YOUR FATE? Matching Navy Recruits to Jobs (MINIMUM-COST FLOW MODEL)
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Transcript of CHOOSE YOUR RATE, CHOOSE YOUR FATE? Matching Navy Recruits to Jobs (MINIMUM-COST FLOW MODEL)
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CHOOSE YOUR RATE, CHOOSE YOUR FATE?
Matching Navy Recruits to Jobs
(MINIMUM-COST FLOW MODEL)
Kyle Alcock Jemar Ballesteros
Tim Shaffer
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BACKGROUNDLabor Economics
Issues:• No lateral entry into the Navy• Attrition = loss of return on investment• Low retention = need to replace with new recruits• Expensive training cost per new recruit• Training cost is Navy’s investment; 4/5 yr. contract is
return on that investment • Better investment = reenlistment
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BACKGROUND• Rate selection Training pipeline Fleet– Wants of the recruit vs. Needs of the Navy
• Motivation Behind Model:– Sailors who remain happy with their job match are less
likely to attrite and more likely to reenlist and perform well
• Maximize Human Capital
• Right person, right place, right time…
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BACKGROUNDBUSINESS PROCESS
• Force management effects (PTS, ERB)
• Current process – FCFS, “needs of the Navy”
• Thought process for improving Human Capital:– Provide recruit’s top rate choices
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ASSUMPTIONS
• No attrition throughout the network• Have to set pool of recruits but not necessarily
use all into the model• Assignment process is discretized (in fact, it is
a time continuous process)• Pool is ‘high-quality’ recruits• Only 15 technical ratings
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Network Example (Simplified)
Objective: Deliver maximum Human Capital to the Fleet, subject to the network constraints.
(0, 0,
1)(-0.4, 0, 1) (0, 0, 45)
(0,29, 65)
(-1, 0, 1)
(0, 0, ∞)
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Network Example (n = 100)
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Intuition
• Possible Flow Inhibitors:– Recruit quality – High rating selectivity– Limited schoolhouse capacity– Fleet demand signal
• Rating demand vs. recruit preferences
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Min Cost Flow Objective
• Minimize the cost of flow from recruit pool to the Fleet– Equivalent to maximizing human capital delivery
subject to needs of the Navy
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Rating Preference Fulfillment
Capital = 415.6Recruits Assigned to Jobs = 460
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Network Design: Scenario
• Schoolhouse capacity limitations recognized• Naval Education and Training Command
(NETC) has proposed to augment traditional schoolhouse training with a limited number of computer-based training (CBT) courses to increase training throughput.
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Network Design: Action
• NETC can fund up to 5 CBT courses for the following ratings only:– ET, IT, OS, GSE, FC, CTT, EM
• Task: Determine optimal choice of CBT augmentation course offerings. – NETC wants to see the marginal improvement for
adding CBT courses, up to the max of 5.
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Network Design Example
Objective: Improve the delivery of Human Capital as much as possible by choosing which CBT augmentation courses to offer.
(0, 0,
1)(-0.4, 0, 1)
(0, 0, 45)(0.3, 0, 15)
(-1, 0, 1)
(0, 0, ∞)
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Rating Preference Fulfillment, 1 CBT
IT CBT course addedCapital delivered = 429.6% increase: 3.37%Recruits Assigned to Jobs = 480
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Rating Preference Fulfillment, 2 CBT
OS CBT course addedCapital delivered = 439.4% increase: 5.73%Recruits Assigned to Jobs = 493
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Rating Preference Fulfillment, 3 CBT
GSE CBT course addedCapital delivered = 445.4% increase: 7.17%Recruits Assigned to Jobs = 500
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Rating Preference Fulfillment, 4 CBT
EM CBT course addedCapital delivered = 448.3% increase: 7.87%Recruits Assigned to Jobs = 500
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Network Design Curve
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Network Design: Summary
• Objective function improves due to:– More people assigned jobs (more flow)– Higher preference fulfillment
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Schoolhouse Seat Allocation
• Multi-commodity flow model with 10 different recruit batches
• Schoolhouse capacities now decision variables• Total schoolhouse seating still constrained• Goal: Given recruit batches of varying AFQT
quality and different preferences, set optimal school seat capacities to fulfill projected future rating demands.
• Ran with batch sizes of 100 recruits
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Schoolhouse Scenario Results
AE AG AT CTI CTT EM ET FC GSE IS IT MT NUK OS STG0%
2%
4%
6%
8%
10%
12%
14%
7%
4%
7%
2%3%
12%
7%
10%11%
3%
11%
4%
2%
11%
6%
Schoolhouse Seat Allocation by Rating
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Limitations and Future Work
• Multi-objective utility function• Time-layered model– Recruiting is seasonal and time continuous
• Expand scope to include all ratings• Use real recruiting data and capacities• Proposal for business process change:– ‘Rack and Stack’ draft process
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QUESTIONS?