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

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 a year May 22-23, 2013 MIT SCM ResearchFest 2

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

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

1 st Question: Predicting Unplanned Absences  Unplanned absences are highly variable.  If BNSF could predict unplanned absences they could adjust training schedules and planned vacation allotments. May 22-23, 2013 MIT SCM ResearchFest 5

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 *20% of all shifts have 3 absences, etc. May 22-23, 2013 MIT SCM ResearchFest 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 *20% of all shifts have 3 absences, etc. 𝜇 𝑙 𝑓 −𝜇 𝑄 𝑌 = 𝑙 = 𝑙 ! May 22-23, 2013 MIT SCM ResearchFest 7

What influences unplanned absences? – Day of the week = 66 – Day of the month – Shift – Holidays Dummy – Football Games Variables – Hunting Season – Snowstorms – Planned Absences Evaluate using Poisson Regression May 22-23, 2013 MIT SCM ResearchFest 8

Results: Holidays Holidays = Less absences 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 May 22-23, 2013 MIT SCM ResearchFest 9

Results: Football Games & Hunting Season  Football Games 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 *Football Games & Hunting Season do not cause unplanned absences.  Hunting Season 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 May 22-23, 2013 MIT SCM ResearchFest 10

Summary of Statistically Significant Factors Statistically Insignificant 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 -Day of the month 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 -Day of the week apr 0.65572 0.12724 5.15 0.000 0.40634 0.90510 -Hunting Season oct 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 -Football Games 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 -Months: snow 2.16735 0.24243 8.94 0.000 1.69220 2.64249 May, Jun, Aug, Jun, Aug, Sep, Nov 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 -Planned Absences 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 -Holidays christmas -2.03518 0.67619 -3.01 0.003 -3.36048 -0.70988 christmaseve -2.24815 0.69794 Memorial, Veterans, Labor, MLK -3.22 0.001 -3.61609 -0.88022 May 22-23, 2013 MIT SCM ResearchFest 11

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

2 nd 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

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

1st Input: Regular Employee Qualifications  The distribution of qualifications of regular employees can be modeled by a Negative Binomial distribution. Friday 3 rd Shift May 22-23, 2013 MIT SCM ResearchFest 15

2nd Input: Extra employee Qualifications  The distribution of qualifications of extra employees can be modeled by a Negative Binomial distribution. Friday 3 rd Shift May 22-23, 2013 MIT SCM ResearchFest 16

3 rd Input: Absences by shift  The distribution of unplanned absences can be modeled by a Negative Binomial distribution. May 22-23, 2013 MIT SCM ResearchFest 17

Assignment Problem  The mathematical formulation of our problem. May 24-25, 2011 MIT SCM ResearchFest 18

Qualification Matrix  The qualification matrix describes who can work on which position. Position 1 2 3 4 …. N … 1 1 1 0 0 0 Incumbent Employee … 2 0 1 0 1 1 … 3 0 0 1 0 0 … 4 1 0 0 1 0 … … … … … … …. … N 1 0 1 1 1 … N+1 1 0 0 0 0 Extra Board … N+2 0 0 1 1 0 …. … … … … … … … N+E 1 0 0 1 0 Employee from Home N+E+1 1 1 1 1 … 1 May 24-25, 2011 MIT SCM ResearchFest 19

Cost Matrix  The cost matrix describes the corresponding cost of each single assignment. Position 1 2 3 4 …. N … 1 0 0.5 X X X Incumbent Employee … 2 X 0 X 0.5 0.5 … 3 X X 0 X X … 4 0.5 X X 0 X … … … … … … …. … N 0.5 0 0.5 0.5 0 … N+1 0 X X X X Extra Board … N+2 X X 0 0 X …. … … … … … … … N+E 0 X X 0 X Employee from Home N+E+1 1.5 1.5 1.5 1.5 1.5 1.5 May 24-25, 2011 MIT SCM ResearchFest 20

Solution Matrix  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 Employees 2 and … 1 1 0 0 0 0 Incumbent Employee 4 are absent … 2 0 0 0 0 0 … 3 0 0 1 0 0 … 4 0 0 0 0 0 …. … … … … … … … N 0 0 0 0 1 … N+1 0 1 0 0 0 Extra Board … N+2 0 0 0 0 0 …. … … … … … … … N+E 0 0 0 0 0 Employee from Home N+E+1 0 0 0 1 0 0 May 22-23, 2013 MIT SCM ResearchFest 21

Solution Matrix  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 0 0 0 0 Incumbent Employee … 2 0 0 0 0 0 … 3 0 0 1 0 0 … 4 0 0 0 0 0 …. … … … … … … … N 0 0 0 0 1 … N+1 0 1 0 0 0 Extra Board … N+2 0 0 0 0 0 …. … … … … … … … N+E 0 0 0 0 0 Employee from Home N+E+1 0 0 0 1 0 0 May 22-23, 2013 MIT SCM ResearchFest 22

Extra Cost  Extra cost always decreases as the number of extra employees increases. May 22-23, 2013 MIT SCM ResearchFest 23

Total Labor Cost  Total labor cost always increases with more extra board employees; qualification level does not make a large difference May 22-23, 2013 MIT SCM ResearchFest 24

Total Labor Cost and Extra Cost  Total cost always goes up even though the extra cost is going down. May 22-23, 2013 MIT SCM ResearchFest 25

Conclusion  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 May 22-23, 2013 MIT SCM ResearchFest 26