Absenteeism Prediction & Labor Force Optimization in Rail - - PowerPoint PPT Presentation

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Absenteeism Prediction & Labor Force Optimization in Rail - - PowerPoint PPT Presentation

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Absenteeism Prediction & Labor Force Optimization in Rail Dispatcher Scheduling Authors: Taylor Jensen & Qi Sun Advisor: Dr. Tony Craig MIT SCM ResearchFest May 22-23, 2013 31,000 Miles of Track Operates 24 hours a day, 365 days

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SLIDE 1

Absenteeism Prediction & Labor Force Optimization in Rail Dispatcher Scheduling

Authors: Taylor Jensen & Qi Sun Advisor: Dr. Tony Craig

MIT SCM ResearchFest May 22-23, 2013

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SLIDE 2 May 22-23, 2013 MIT SCM ResearchFest 2
  • 31,000 Miles of Track
  • Operates 24 hours a day, 365 days a year
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SLIDE 3

Dispatcher Scheduling

  • 270 positions must be staffed every day.
  • Each position has unique qualification requirements.
  • Unplanned absences complicate the scheduling task.
May 22-23, 2013 MIT SCM ResearchFest 3
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SLIDE 4

Research Questions

  • 1. Is it possible to predict unplanned

absences?

  • 2. How many extra employees should BNSF

have on staff?

May 22-23, 2013 MIT SCM ResearchFest 4
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SLIDE 5

1st Question: Predicting Unplanned Absences

May 22-23, 2013 MIT SCM ResearchFest 5
  • Unplanned absences are highly variable.
  • If BNSF could predict unplanned absences they

could adjust training schedules and planned vacation allotments.

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SLIDE 6

Modeling Unplanned Absences

  • Four years of Data: Jan 1, 2009 – Dec 31, 2012
  • Count unplanned absences by shift

– 4 years*365 days*3 shifts = 4,383 shifts

May 22-23, 2013 MIT SCM ResearchFest 6

*20% of all shifts have 3 absences, etc.

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SLIDE 7

Modeling Unplanned Absences

  • Four years of Data: Jan 1, 2009 – Dec 31, 2012
  • Count unplanned absences by shift

– 4 years*365 days*3 shifts = 4,383 shifts

May 22-23, 2013 MIT SCM ResearchFest 7

𝑄 𝑌 = 𝑙 =

𝜇𝑙𝑓−𝜇 𝑙!

*20% of all shifts have 3 absences, etc.

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SLIDE 8

What influences unplanned absences?

– Day of the week – Day of the month – Shift – Holidays – Football Games – Hunting Season – Snowstorms – Planned Absences

May 22-23, 2013 MIT SCM ResearchFest 8

= 66

Dummy Variables

Evaluate using Poisson Regression

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SLIDE 9

Results: Holidays

May 22-23, 2013 MIT SCM ResearchFest 9

Holiday Coef. Actual Effect

  • Std. Err.

z P>z Lower 95% int Upper 95% int

newyears

  • 0.722219
  • 1.930722

0.209295

  • 3.45

0.001

  • 1.132429
  • 0.312009

presidents

  • 0.420272
  • 1.122878

0.206649

  • 2.03

0.042

  • 0.825297
  • 0.015248

memorial

  • 0.418113
  • 1.115345

0.226170

  • 1.85

0.065

  • 0.861397

0.025172

independence

  • 0.916658
  • 2.448851

0.303559

  • 3.02

0.003

  • 1.511622
  • 0.321694

labor

  • 0.295194

0.000000 0.221066

  • 1.34

0.182

  • 0.728476

0.138088

thanksgiving

  • 1.171696
  • 3.104133

0.335387

  • 3.49 <.0001
  • 1.829043
  • 0.514350

thanksgivingfriday

  • 0.330449

0.000000 0.221151

  • 1.49

0.135

  • 0.763897

0.103000

christmaseve

  • 0.841878
  • 2.248154

0.260941

  • 3.23

0.001

  • 1.353313
  • 0.330443

christmas

  • 0.762535
  • 2.035175

0.252826

  • 3.02

0.003

  • 1.258065
  • 0.267006

federal 0.010323 0.000000 0.101771 0.10 0.919

  • 0.189144

0.209790

Less than .05 = Statistically Significant Holidays = Less absences

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SLIDE 10

Results: Football Games & Hunting Season

  • Football Games
  • Hunting Season
May 22-23, 2013 MIT SCM ResearchFest 10

Parameter Coef.

  • Std. Err.

z P>z Lower 95% int Upper 95% int NFL 0.01894 0.04944 0.38 0.702

  • 0.077954

0.115830 Super Bowl -0.19899 0.18556

  • 1.07

0.284

  • 0.562688

0.164712

Parameter Coef.

  • Std. Err.

z P>z Lower 95% int Upper 95% int Beg Hunt Season -0.096593 0.140805

  • 0.69

0.493

  • 0.372566

0.179380 End Hunt Season 0.217245 0.124163 1.75 0.080

  • 0.026110

0.460601

*Football Games & Hunting Season do not cause unplanned absences.

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SLIDE 11

Summary of Statistically Significant Factors

May 22-23, 2013 MIT SCM ResearchFest 11

Parameter

  • Avg. Effect
  • Std. Err.

z P>z Lower 95% int Upper 95% int jan 0.58494 0.13272 4.41 0.000 0.32481 0.84507 feb 0.67105 0.13414 5.00 0.000 0.40814 0.93396 mar 0.62630 0.12554 4.99 0.000 0.38025 0.87235 apr 0.65572 0.12724 5.15 0.000 0.40634 0.90510

  • ct

0.49693 0.12498 3.98 0.000 0.25198 0.74187 dec 0.32114 0.13089 2.45 0.014 0.06460 0.57768 shift2 0.17815 0.07013 2.54 0.011 0.04070 0.31560 shift3 0.27073 0.06340 4.27 0.000 0.14647 0.39500 snow 2.16735 0.24243 8.94 0.000 1.69220 2.64249 newyears

  • 1.93072

0.55983

  • 3.45

0.001

  • 3.02797
  • 0.83348

presidents

  • 1.12288

0.55258

  • 2.03

0.042

  • 2.20591
  • 0.03985

independence

  • 2.44885

0.81188

  • 3.02

0.003

  • 4.04011
  • 0.85759

thanksgiving

  • 3.10413

0.89692

  • 3.46

0.001

  • 4.86207
  • 1.34620

christmas

  • 2.03518

0.67619

  • 3.01

0.003

  • 3.36048
  • 0.70988

christmaseve

  • 2.24815

0.69794

  • 3.22

0.001

  • 3.61609
  • 0.88022

Statistically Insignificant

  • Day of the month
  • Day of the week
  • Hunting Season
  • Football Games
  • Months:

May, Jun, Aug, Jun, Aug, Sep, Nov

  • Planned Absences
  • Holidays

Memorial, Veterans, Labor, MLK

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SLIDE 12

How Useful are these Results?

  • Model has very weak predictive capability

(McFadden R-squared value of .018)

  • Conclusion:

We can identify factors that influence unplanned absences, but we cannot predict how many unplanned absences will occur

May 22-23, 2013 MIT SCM ResearchFest 12
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SLIDE 13

2nd Question

  • What is the appropriate number of extra

employees?

– Each position has unique qualifications – Extra employees earn a full-time salary even if they don't have an assignment every day – Extra cost to move employees from their regular position – Must pay overtime to call employees from home

May 22-23, 2013 MIT SCM ResearchFest 13
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SLIDE 14

Monte Carlo Simulation

  • Explore the relationship among overtime,

qualifications, and total labor cost.

  • Steps

– Set a number of extra board employees – Generate qualifications of regular employees from a probability distribution – Generate qualifications of extra employees from a probability distribution – Generate unplanned absences from a probability distribution – Use an optimization solver to find the minimum cost – Run 10,000 iterations to find the expected cost given the defined parameters

May 22-23, 2013 MIT SCM ResearchFest 14
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SLIDE 15

1st Input: Regular Employee Qualifications

May 22-23, 2013 MIT SCM ResearchFest 15
  • The distribution of qualifications of regular

employees can be modeled by a Negative Binomial distribution. Friday 3rd Shift

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SLIDE 16

2nd Input: Extra employee Qualifications

May 22-23, 2013 MIT SCM ResearchFest 16
  • The distribution of qualifications of extra employees

can be modeled by a Negative Binomial distribution. Friday 3rd Shift

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SLIDE 17

3rd Input: Absences by shift

May 22-23, 2013 MIT SCM ResearchFest 17
  • The distribution of unplanned absences can be

modeled by a Negative Binomial distribution.

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SLIDE 18

Assignment Problem

May 24-25, 2011 MIT SCM ResearchFest 18
  • The mathematical formulation of our problem.
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SLIDE 19

Qualification Matrix

May 24-25, 2011 MIT SCM ResearchFest 19
  • The qualification matrix describes who can work on which

position.

Position 1 2 3 4 …. N 1 1 1 … 2 1 1 … 1 3 1 … 4 1 1 … …. … … … … … … N 1 1 1 … 1 N+1 1 … N+2 1 1 … …. … … … … … … N+E 1 1 … Employee from Home N+E+1 1 1 1 1 … 1 Incumbent Employee Extra Board

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SLIDE 20

Cost Matrix

May 24-25, 2011 MIT SCM ResearchFest 20
  • The cost matrix describes the corresponding cost of each

single assignment.

Position 1 2 3 4 …. N 1 0 0.5 X X … X 2 X X 0.5 … 0.5 3 X X X … X 4 0.5 X X … X …. … … … … … … N 0.5 0 0.5 0.5 … N+1 X X X … X N+2 X X … X …. … … … … … … N+E X X … X Employee from Home N+E+1 1.5 1.5 1.5 1.5 1.5 1.5 Incumbent Employee Extra Board

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SLIDE 21

Solution Matrix

May 22-23, 2013 MIT SCM ResearchFest 21
  • The inputs are entered into a matrix and a solver

finds the best solution. Running many iterations produces an expected cost.

Position 1 2 3 4 …. N 1 1 … 2 … 3 1 … 4 … …. … … … … … … N … 1 N+1 1 … N+2 … …. … … … … … … N+E … Employee from Home N+E+1 1 Incumbent Employee Extra Board

Employees 2 and 4 are absent

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SLIDE 22

Solution Matrix

May 22-23, 2013 MIT SCM ResearchFest 22
  • The inputs are entered into a matrix and a solver

finds the best solution. Running many iterations produces an expected cost.

Position 1 2 3 4 …. N 1 1 … 2 … 3 1 … 4 … …. … … … … … … N … 1 N+1 1 … N+2 … …. … … … … … … N+E … Employee from Home N+E+1 1 Incumbent Employee Extra Board

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SLIDE 23

Extra Cost

May 22-23, 2013 MIT SCM ResearchFest 23
  • Extra cost always decreases as the number of extra

employees increases.

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SLIDE 24

Total Labor Cost

May 22-23, 2013 MIT SCM ResearchFest 24
  • Total labor cost always increases with more extra

board employees; qualification level does not make a large difference

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SLIDE 25

Total Labor Cost and Extra Cost

May 22-23, 2013 MIT SCM ResearchFest 25
  • Total cost always goes up even though the extra cost

is going down.

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SLIDE 26

Conclusion

May 22-23, 2013 MIT SCM ResearchFest 26
  • The savings in overtime costs from having extra

employees does not offset the fixed cost of extra employees.

  • However, there are other important

considerations, such as:

  • Employee morale
  • Union agreements
  • Training and Qualification requirements