1. No category

Models and Algorithms for Manpower Planning and
Personnel Rostering:
Towards An Integrated Approach
Komarudin
Department of Business Technology and Operations
Vrije Universiteit Brussel
[email protected]
Promotor: Prof. dr. Marie-Anne Guerry
February 19, 2015
Backgrounds
Research motivation
Research backgrounds
Unattractive
working
schedule2
Over/under
qualified
personnel1
Personnel
planning
research
Qualified
personnel
shortage3
Ageing
workforce4
1
International Labour Organization. 2014. Skills mismatch in Europe.
New York Times. Aug. 14, 2014. Starbucks to Revise Policies to End Irregular
Schedules for Its 130,000 Baristas.
3
The European Commission. 2012. Employment and Social Developments in Europe
2012.
4
Erixon & van der Marel. 2011. What is driving the rise in health care expenditures?
An inquiry into the nature and causes of the cost disease.
2
(BUTO)
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February 19, 2015
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Backgrounds
Research motivation
Research backgrounds
Unattractive
working
schedule2
Over/under
qualified
personnel1
Personnel
planning
research
Quantitative
approach
Research
Limitation
Qualified
personnel
shortage3
Ageing
workforce4
Focus on
corporate/
organization
1
International Labour Organization. 2014. Skills mismatch in Europe.
New York Times. Aug. 14, 2014. Starbucks to Revise Policies to End Irregular
Schedules for Its 130,000 Baristas.
3
The European Commission. 2012. Employment and Social Developments in Europe
2012.
4
Erixon & van der Marel. 2011. What is driving the rise in health care expenditures?
An inquiry into the nature and causes of the cost disease.
2
(BUTO)
Integrated Manpower Planning & Personnel Rostering
February 19, 2015
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Overview
Overview of Our Work
Manpower Planning
and
Personnel Rostering
Personnel
Rostering
Integrated
model
Three-step
evaluation
(Chap. 2)
Optimization
approach
(Chap. 3)
Balancing
three
criteria
(Chap. 4)
(BUTO)
Manpower
planning
individual
fairness
(Chap. 6)
Stochastic
Model
(Chap. 5)
Integrated Manpower Planning & Personnel Rostering
February 19, 2015
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Introduction
Manpower planning
Manpower planning
Manpower planning
aims to meet medium-to-long term human resource requirements
involves personnel supply-and-demand prediction
controls recruitments, personnel transition and wastage (depending on the
chosen strategy)
Personnel
structure
2015
90
30
65
Hospital
(BUTO)
Integrated Manpower Planning & Personnel Rostering
February 19, 2015
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Introduction
Manpower planning
Manpower planning
Manpower planning
aims to meet medium-to-long term human resource requirements
involves personnel supply-and-demand prediction
controls recruitments, personnel transition and wastage (depending on the
chosen strategy)
Personnel
structure
2015
90
30
65
2016
95
35
75
Hospital
t + ..
(BUTO)
Integrated Manpower Planning & Personnel Rostering
February 19, 2015
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Introduction
Manpower planning
Manpower planning
Manpower planning
aims to meet medium-to-long term human resource requirements
involves personnel supply-and-demand prediction
controls recruitments, personnel transition and wastage (depending on the
chosen strategy)
Personnel
structure
2015
90
30
65
95
35
75
Recruitment
2016
Wastage
Hospital
t + ..
(BUTO)
Integrated Manpower Planning & Personnel Rostering
February 19, 2015
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Introduction
Personnel Rostering
Personnel rostering
Personnel rostering
short term planning to allocate personnel into shifts
involves coverage requirements, legal and personal constraints
aims to meet coverage requirements and avoid unattractive rosters.
Coverage
Reqs.
Feb.2015
Personnel
Contract
Rest
time
Absence
Request
(BUTO)
Integrated Manpower Planning & Personnel Rostering
Consecutive
Shift
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Introduction
Personnel Rostering
Personnel rostering - Illustration
Nurse
1
2
3
4
5
3
4
4
2
4
4
2
February 2015
4
5
6
Score
7
...
28
...
Cov.
Req.
5
4
2
5
4
2
5
5
3
4
5
2
4
4
3
Score
(BUTO)
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Introduction
Personnel Rostering
Personnel rostering - Illustration
Nurse
1
2
3
4
5
3
4
4
2
4
4
2
February 2015
4
5
6
Score
7
...
28
...
Cov.
Req.
5
4
2
5
4
2
5
5
3
4
5
2
4
4
3
Score
(BUTO)
Integrated Manpower Planning & Personnel Rostering
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Introduction
Personnel Rostering
Personnel rostering - Illustration
Nurse
1
2
3
4
5
3
4
4
2
4
4
2
February 2015
4
5
6
Score
7
...
28
...
Cov.
Req.
5
4
2
5
4
2
5
5
3
4
5
2
4
4
3
Score
Over/Under
Coveraged
(BUTO)
Absence
Request
Integrated Manpower Planning & Personnel Rostering
Unattractive
Roster
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Integrated model (Chaps. 2 & 3)
Integrated model (Chaps. 2 & 3)
Manpower Planning
and
Personnel Rostering
Personnel
Rostering
Integrated
model
Three-step
evaluation
(Chap. 2)
Optimization
approach
(Chap. 3)
Balancing
three
criteria
(Chap. 4)
(BUTO)
Manpower
planning
individual
fairness
(Chap. 6)
Stochastic
Model
(Chap. 5)
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Integrated model (Chaps. 2 & 3)
Motivation and Contribution
Challenges and Motivation
Personnel Planning
demand
Personnel supplydemand projection
t
2015
90
30
65
2016
95
35
75
Manpower
planning
Different time
frame
Personnel
rostering
Dept.1
(BUTO)
Conducted
sequentially
Dept.2
Dept.3
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Integrated model (Chaps. 2 & 3)
Motivation and Contribution
Challenges and Motivation
Personnel Planning
demand
Personnel supplydemand projection
t
2015
90
30
65
2016
95
35
75
Conducted
sequentially
Manpower
planning
Different time
frame
Potentially produce
poor results
Personnel
rostering
Poor rosters
Dept.1
(BUTO)
Dept.2
Dept.3
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Integrated model (Chaps. 2 & 3)
Motivation and Contribution
Challenges and Motivation
Personnel Planning
demand
Personnel supplydemand projection
t
2015
90
30
65
2016
95
35
75
Conducted
sequentially
Manpower
planning
Personnel
rostering
Different time
frame
Potentially produce
poor results
feedback
Our motivation:
provide feedbacks
Poor rosters
Dept.1
(BUTO)
Dept.2
Dept.3
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Integrated model (Chaps. 2 & 3)
Motivation and Contribution
Contributions
The State-of-The-Art:
No
1
2
3
4
Approach
Sequential planning
Integrated
staffing
and
rostering
Part-time/
Annualized
personnel
Staff allocation
(BUTO)
References
Abernathy et al. (1973);
Ozcan (2009)
Mundschenk and Drexl
(2007); Beli¨en et al. (2012)
Bard and Purnomo (2006);
Corominas and Pastor
(2010)
Haspeslagh (2012); Maenhout and Vanhoucke (2013)
Integrated Manpower Planning & Personnel Rostering
Review
Suboptimal
solutions
Constraints
are
rather limited; Less
personalized rosters
Only temporary solution
Focus only on staff
allocation
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Integrated model (Chaps. 2 & 3)
Motivation and Contribution
Contributions
The State-of-The-Art:
No
1
2
3
4
Approach
Sequential planning
Integrated
staffing
and
rostering
Part-time/
Annualized
personnel
Staff allocation
References
Abernathy et al. (1973);
Ozcan (2009)
Mundschenk and Drexl
(2007); Beli¨en et al. (2012)
Bard and Purnomo (2006);
Corominas and Pastor
(2010)
Haspeslagh (2012); Maenhout and Vanhoucke (2013)
Review
Suboptimal
solutions
Constraints
are
rather limited; Less
personalized rosters
Only temporary solution
Focus only on staff
allocation
Our Contributions:
Three-step approach for limited personnel structure modification.
Optimization approach for arbitrary personnel structure modification.
(BUTO)
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Integrated model (Chaps. 2 & 3)
Three-step approach
Three-step approach (Chap. 2)
Step 1. Consistency check
Compare the required and the available resources
Disregard many rostering constraints
(BUTO)
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Integrated model (Chaps. 2 & 3)
Three-step approach
Three-step approach (Chap. 2)
Step 1. Consistency check
Compare the required and the available resources
Disregard many rostering constraints
Step 2. Quality evaluation of the current personnel
structure
Solve the rostering problems
Identify which personnel group inappropriate
(BUTO)
Integrated Manpower Planning & Personnel Rostering
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Integrated model (Chaps. 2 & 3)
Three-step approach
Three-step approach (Chap. 2)
Step 1. Consistency check
Compare the required and the available resources
Disregard many rostering constraints
Step 2. Quality evaluation of the current personnel
structure
Solve the rostering problems
Identify which personnel group inappropriate
Current
personnelstructure
Step 3. Quality evaluation of the neighboring personnel
structures
Small modification to the personnel structure
Evaluate the roster qualities
(BUTO)
Integrated Manpower Planning & Personnel Rostering
Neighbor 1
Neighbor 2
Neighbor 3
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Integrated model (Chaps. 2 & 3)
Optimization approach (Chap. 3)
Optimization approach (Chap. 3)
Initial
solution
Response surface meth. (RSM)
Sampling the neighborhood of the
current solution
New
solution
Perform regression analysis to find
new direction
(BUTO)
Integrated Manpower Planning & Personnel Rostering
Neighborhood
sampling
Regression
generate
February 19, 2015
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Integrated model (Chaps. 2 & 3)
Optimization approach (Chap. 3)
Optimization approach (Chap. 3)
Response surface meth. (RSM)
Sampling the neighborhood of the
current solution
Perform regression analysis to find
new direction
Initial
solution
New
solution
Regression
generate
Initial
solution
Simulated Annealing (SA)
Local search to generate
neighboring solutions
Current
solution
Move to the best neighboring
solution with a certain probability
(BUTO)
Neighborhood
sampling
Integrated Manpower Planning & Personnel Rostering
Local
search
Accept with
SA criteria
move to
February 19, 2015
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Integrated model (Chaps. 2 & 3)
Case study
Case studies
The data set originating from real-world nurse rostering problems is taken
from Bilgin et al. (2012).
The three-step approach is provided for the Emergency ward only
No
1
2
3
4
5
6
Ward
Emergency
Geriatrics
Meal Preparations
Psychiatry
Reception
Palliative Care
k
FTE
qv
¯ T0s ,T1s (R(n))
P
(BUTO)
:
:
:
Abbr.
Em
Gr
MP
Ps
Re
PC
k
8
8
8
9
9
20
P
FTE
26.25
17.06
16.84
16.50
13.75
21.70
qv
¯ T0s ,T1s (R(n))
17,309
11,423
13,223
8,733
14,447
130,579
Number of subgroups
The current total of full time equivalent in the ward
The value of the roster quality (the smaller the better)
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Integrated model (Chaps. 2 & 3)
Case study
Case study results (three-step approach)
Step 1. Consistency check
Ratio =
Required resources
Available resources
Proportion of the
total weighted constraint violations
(BUTO)
Subgroup:
Ratio:
1
0.95
2
0.85
3
0.85
4
0.85
Step 2. Quality evaluation of the current personnel
structure
Coverage constraint:
Skill type 1: 1.6%
Skill type 3: 2.2%
Skill type 4: 80.1%
Counter constraint: 10.2%
Series constraint: 2.9%
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Integrated model (Chaps. 2 & 3)
Case study
Case study results (three-step approach)
Step 1. Consistency check
Ratio =
Required resources
Available resources
Reasonable, some slack
for vacation/illness
Proportion of the
total weighted constraint violations
Skill 4 understaffed
(BUTO)
Subgroup:
Ratio:
1
0.95
2
0.85
3
0.85
4
0.85
Step 2. Quality evaluation of the current personnel
structure
Coverage constraint:
Skill type 1: 1.6%
Skill type 3: 2.2%
Skill type 4: 80.1%
Counter constraint: 10.2%
Series constraint: 2.9%
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Integrated model (Chaps. 2 & 3)
Case study
Step 3. Quality evaluation of the neighboring personnel structures
Increase the number of nurses with skill type 2, 3 or 4.
(BUTO)
Integrated Manpower Planning & Personnel Rostering
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Integrated model (Chaps. 2 & 3)
Case study
Optimization approach (RSM & SA)
No
Ward
P
FTE
qv
¯ T0s ,T1s (R(n))
1
2
3
4
5
6
Em
Gr
MP
Ps
Re
PC
26.25
17.06
16.84
16.50
13.75
21.7
13,176
4,508
4,436
5,182
9,750
50,583
RSM
ratio
76.13%
39.47%
33.54%
59.34%
67.49%
38.74%
P¯
FTE
qv
¯ T0s ,T1s (R(n))
29.20
15.05
15.05
14.35
14.65
17.42
13,301
3,728
3,949
4,060
10,212
20,111
SA
ratio
P¯
FTE
76.85%
32.64%
29.86%
46.49%
70.69%
15.40%
29.35
15.35
15.33
14.20
14.45
15.85
Small modification on the total FTE can lead to a better roster quality
SA performs slightly better
(BUTO)
Integrated Manpower Planning & Personnel Rostering
February 19, 2015
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Manpower planning (Chaps. 4 & 5)
Overview Chapters 4 & 5
Manpower Planning
and
Personnel Rostering
Personnel
Rostering
Integrated
model
Three-step
evaluation
(Chap. 2)
Optimization
approach
(Chap. 3)
Balancing
three
criteria
(Chap. 4)
(BUTO)
Manpower
planning
individual
fairness
(Chap. 6)
Stochastic
Model
(Chap. 5)
Integrated Manpower Planning & Personnel Rostering
February 19, 2015
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Manpower planning (Chaps. 4 & 5)
Challenge and motivations
What if the desired
personnel structure is
difficult to reach?
2015
How we ensure that the
promotion policy does not
harm the personnel
motivation?
How we deal with
uncertainties?
90
30
65
95
35
75
Recruitment
2016
Wastage
t + ..
Motivation: develop models and
algorithms for manpower planning problems
(BUTO)
Integrated Manpower Planning & Personnel Rostering
February 19, 2015
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Manpower planning (Chaps. 4 & 5)
Contributions
The State-of-The-Art:
The degree of desirability and the degree of attainability have been
introduced in Guerry (1999); De Feyter and Guerry (2009)
Previous approaches use an unrestricted promotion strategy that can lead to
personnel dissatisfaction (Song and Huang, 2008; Nilakantan et al., 2011)
No method has been proposed to find the most beneficial manpower planning
policy with stochastic wastage (Davies, 1982; Guerry, 1993)
(BUTO)
Integrated Manpower Planning & Personnel Rostering
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Manpower planning (Chaps. 4 & 5)
Contributions
The State-of-The-Art:
The degree of desirability and the degree of attainability have been
introduced in Guerry (1999); De Feyter and Guerry (2009)
Previous approaches use an unrestricted promotion strategy that can lead to
personnel dissatisfaction (Song and Huang, 2008; Nilakantan et al., 2011)
No method has been proposed to find the most beneficial manpower planning
policy with stochastic wastage (Davies, 1982; Guerry, 1993)
Our contributions
Introduce MIP models for manpower planning problems that include the
promotion steadiness degree (Chap. 4 & 5)
Introduce a stochastic optimization problem and its complexity analysis to
deal with wastage uncertainties (Chap. 5)
(BUTO)
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Manpower planning (Chaps. 4 & 5)
MP with three criteria - Chap. 4
Manpower planning with three criteria (Chap. 4)
Optimizing three criteria
The degree of
α
ni (t) (si ) =

 0,s −s
i
LL,i
ni (t)−sLL,i ,

s
−s
 i UL,i ,
ni (t)−sUL,i
attainability
if si < sLL,i or si > sUL,i
if sLL,i ≤ si ≤ ni (t)
if ni (t) ≤ si ≤ sUL,i
The degree of desirability βi (ni (t))
The degree of promotion steadiness
γij (f¯ij (t − 1, t))
(BUTO)
Integrated Manpower Planning & Personnel Rostering
Difference between n(t)
and the attainable set
Difference between
n(t) and the desired
personnel structure
Difference between the
personnel transition and the
’common’ promotion policy
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Manpower planning (Chaps. 4 & 5)
MP with three criteria - Chap. 4
The model of Chap. 4
Optimizing three criteria
Mixed integer nonlinear program (MINLP)
Piecewise linear approximation (PLA) to handle nonlinearity
The model is solved using an iterated PLA
(BUTO)
Integrated Manpower Planning & Personnel Rostering
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Manpower planning (Chaps. 4 & 5)
MP with three criteria - Chap. 4
The model of Chap. 4
Optimizing three criteria
Mixed integer nonlinear program (MINLP)
Piecewise linear approximation (PLA) to handle nonlinearity
The model is solved using an iterated PLA
Set lower &
upper bounds
Increase #
segments
Solve PLA model
Low accuracy?
(BUTO)
Integrated Manpower Planning & Personnel Rostering
Yes
February 19, 2015
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Manpower planning (Chaps. 4 & 5)
MP with three criteria - Chap. 4
Experiments for model of Chap. 4
PLA
IPLA
a
No Problem
k
κ a Comp. time
κ
Comp. time
1
P03
3 0.633
2.67 0.633
1.09
2
P04
4 0.547
15.57 0.547
11.04
3
P05
5 0.111
11.64 0.111
1.13
4
P10
10 0.466
18.45 0.466
1.76
5
P11
11 0.627
20.00 0.627
20.55
b
6
P12
12 0.601
3600.00 0.601
13.50
c
7
P20
20
0.307
3601.77
c
8
P25
25
0.000
32.57
c
9
P30
30
0.184
2.74
a
In seconds
b
Solver is terminated after 1 hour
c
Solver terminates: out of memory without encountering a feasible solution
κ: the overall objective function value
with similar value of κ, IPLA model is more efficient than the PLA
(BUTO)
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Manpower planning (Chaps. 4 & 5)
MP with stochastic wastage - Chap. 5
Manpower planning with stochastic wastage (Chap. 5)
Optimizing partially stochastic system
The degree of desirability
Difference between
n(t) and the desired
personnel structure
The degree of promotion steadiness
wastage is considered stochastic, following a
probability distribution
(BUTO)
Integrated Manpower Planning & Personnel Rostering
Difference between the
personnel transition and the
’common’ promotion policy
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Manpower planning (Chaps. 4 & 5)
MP with stochastic wastage - Chap. 5
Partially stochastic system
The stochastic wastage is modeled using a finite number of scenarios
The MIP model is solved using a MIP solver
The problem is proved to be NP-hard based on reduction from 3-SAT
Case study with 3 subgroups
Using the deterministic case w = w,
¯ then the result is tested under stochastic
case w, the overall degree κ
¯ = 0.78099.
Using the stochastic case wi with 1000 scenarios, the overall degree
κ
¯ = 0.8019
(BUTO)
Integrated Manpower Planning & Personnel Rostering
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Personnel rostering (Chapter 6)
Overview Chapter 6
Manpower Planning
and
Personnel Rostering
Personnel
Rostering
Integrated
model
Three-step
evaluation
(Chap. 2)
Optimization
approach
(Chap. 3)
Balancing
three
criteria
(Chap. 4)
(BUTO)
Manpower
planning
individual
fairness
(Chap. 6)
Stochastic
Model
(Chap. 5)
Integrated Manpower Planning & Personnel Rostering
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Personnel rostering (Chapter 6)
Motivation and Contribution
Challenges and Motivation
Nurse
1
2
3
4
5
3
4
4
2
4
4
2
February 2015
4
5
6
Score
7
...
28
...
Cov.
Req.
5
4
2
5
4
2
5
5
3
4
5
2
4
4
3
Score
(BUTO)
Integrated Manpower Planning & Personnel Rostering
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Personnel rostering (Chapter 6)
Motivation and Contribution
Challenges and Motivation
Nurse
1
2
3
4
5
3
4
4
2
4
4
2
February 2015
4
5
6
Score
7
...
28
...
Cov.
Req.
5
4
2
5
4
2
5
5
3
4
5
2
Score
4
4
3
Low individual
fairness
Motivation:
incorporate fairness in personnel rostering model
(BUTO)
Integrated Manpower Planning & Personnel Rostering
February 19, 2015
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Personnel rostering (Chapter 6)
Motivation and Contribution
Contributions
The State-of-The-Art:
Approach
The maximum and the range (Stolletz
and Brunner, 2012; Smet et al., 2012)
The absolute deviation and the
squared deviation (Pesant and R´egin,
2005; Smet et al., 2012)
The sum of squared (Martin et al.,
2013),
Jain’s index (Martin et al., 2013).
(BUTO)
Review
Neglect most of the
individual penalties
Tends to level all individual penalties
Positive trade-off is
not always preferred
Tends to increase individual penalties
Integrated Manpower Planning & Personnel Rostering
Nurse
Score
...
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Personnel rostering (Chapter 6)
Motivation and Contribution
Contributions
The State-of-The-Art:
Approach
The maximum and the range (Stolletz
and Brunner, 2012; Smet et al., 2012)
The absolute deviation and the
squared deviation (Pesant and R´egin,
2005; Smet et al., 2012)
The sum of squared (Martin et al.,
2013),
Jain’s index (Martin et al., 2013).
Review
Neglect most of the
individual penalties
Tends to level all individual penalties
Positive trade-off is
not always preferred
Tends to increase individual penalties
Nurse
Score
...
Our Contribution is the lexicographic objective function:
Minimize all individual penalties
maximize fairness by allowing positive trade-off.
(BUTO)
Integrated Manpower Planning & Personnel Rostering
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Personnel rostering (Chapter 6)
Motivation and Contribution
Improving fairness
Minimizing the lexicographic objective function
P Lexi is defined as a permutation of all individual penalties, sorted in a
non-increasing order.
0
0
0
0
0
0
P Lexi = (PH,1
, PH,2
, .., PH,n
) s.t. PH,1
≥ PH,2
≥ ... ≥ PH,n
(BUTO)
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Personnel rostering (Chapter 6)
Motivation and Contribution
Experiments for lexicographic objective function
TP-P Lexi generally produces vectors P Lexi that are lexicographically smaller
than the ones produced by other approaches
(BUTO)
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Conclusions & Future Work
Conclusions & Future Work
Conclusions
The models and algorithms presented are able to improve the
personnel planning process
The models and algorithms are beneficial in improving the
personnel motivation by:
Providing sufficient personnel with the right personnel-mix
(Integrated model)
Ensuring the career progression expectation (manpower
planning model)
Fair working schedule (personnel rostering model)
Future research
multi-level and multi-scale integrated personnel planning
global optimization approach
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Conclusions & Future Work
Thank you for your attention!
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Bibliography
References I
William J. Abernathy, Nicholas Baloff, John C. Hershey, and Sten Wandel. A
Three-Stage Manpower Planning and Scheduling Model–A Service-Sector
Example. Operations Research, 21(3):693–711, 1973.
Jonathan F. Bard and Hadi W. Purnomo. Incremental changes in the workforce to
accommodate changes in demand. Health Care Management Science, 9(1):
71–85, February 2006.
Jeroen Beli¨en, Brecht Cardoen, and Erik Demeulemeester. Improving workforce
scheduling of aircraft line maintenance at Sabena Technics. Interfaces, 42(4):
352–364, 2012.
Burak Bilgin, Patrick De Causmaecker, Benoˆıt Rossie, and Greet Vanden Berghe.
Local search neighbourhoods for dealing with a novel nurse rostering model.
Annals of Operations Research, 194(1):33–57, 2012.
Albert Corominas and Rafael Pastor. Replanning working time under annualised
working hours. International Journal of Production Research, 48(5):1493–1515,
March 2010. ISSN 0020-7543. doi: 10.1080/00207540802582227. URL
http://www.tandfonline.com/doi/abs/10.1080/00207540802582227.
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Integrated Manpower Planning & Personnel Rostering
February 19, 2015
31 / 34
Bibliography
References II
G. S. Davies. Control of grade sizes in partially stochastic markov manpower
model. Journal of Applied Probability, 19:439–443, 1982.
Tim De Feyter and Marie-Anne Guerry. Evaluating Recruitment Strategies Using
Fuzzy Set Theory in Stochastic Manpower Planning. Stochastic Analysis and
Applications, 27(6):1148–1162, 2009.
Marie-Anne Guerry. The probability of attaining a structure in a partially
stochastic model. Advances in applied probability, 25(4):818–824, 1993.
Marie-Anne Guerry. Using Fuzzy Sets in Manpower Planning. Journal of Applied
Probability, 36(1):155–162, 1999.
Stefaan Haspeslagh. Negotiation at the short and mid term level supported by
analysis of nurse rostering problems. PhD thesis, Computer Science, Kulak,
2012.
Broos Maenhout and Mario Vanhoucke. An integrated nurse staffing and
scheduling analysis for longer-term nursing staff allocation problems. Omega,
41(2):485–499, 2013.
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Integrated Manpower Planning & Personnel Rostering
February 19, 2015
32 / 34
Bibliography
References III
Simon Martin, Djamila Ouelhadj, Pieter Smet, Greet Vanden Berghe, and Ender
¨
Ozcan.
Cooperative search for fair nurse rosters. Expert Systems with
Applications, 40:6674–6683, 2013.
Martin Mundschenk and Andreas Drexl. Workforce planning in the printing
industry. International Journal of Production Research, 45(20):4849–4872,
October 2007.
K. Nilakantan, J.K. Sankaran, and BG Raghavendra. A proportionality model of
Markov manpower systems. Journal of Modelling in Management, 6(1):
100–122, 2011.
Yasar A. Ozcan. Quantitative methods in health care management: techniques
and applications. Jossey-bass, San Fransisco, second edition, 2009.
Gilles Pesant and Jean-Charles R´egin. Spread: A balancing constraint based on
statistics. In Peter van Beek, editor, Principles and Practice of Constraint
Programming - CP 2005, volume 3709 of Lecture Notes in Computer Science,
pages 460–474. Springer Berlin Heidelberg, 2005.
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Integrated Manpower Planning & Personnel Rostering
February 19, 2015
33 / 34
Bibliography
References IV
¨
Pieter Smet, Simon Martin, Djamila Ouelhadj, Ender Ozcan,
and Greet Vanden
Berghe. Investigation of fairness measures for nurse rostering. In the
International Conference on the Practice and Theory of Timetabling (PATAT
2012), pages 369–372, 2012.
Haiqing Song and Huei-Chuen Huang. A successive convex approximation method
for multistage workforce capacity planning problem with turnover. European
Journal of Operational Research, 188(1):29–48, 2008.
Raik Stolletz and Jens O. Brunner. Fair optimization of fortnightly physician
schedules with flexible shifts. European Journal of Operational Research, 219
(3):622–629, 2012.
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Integrated Manpower Planning & Personnel Rostering
February 19, 2015
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