Post on 20-Jan-2016
Centre National de la Recherche Scientifique Institut Polytechnique de Grenoble Université Joseph Fourier
Laboratoire G-SCOP46, av Félix Viallet38031 Grenoble Cedexwww.g-scop.fr
INTEGRATING TRUCK SCHEDULING
AND EMPLOYEE ROSTERING IN A
CROSS-DOCKING PLATFORM – AN
ITERATIVE APPROACHAnne-Laure Ladier, Gülgün Alpan
2
CROSS-DOCKING OPERATIONSLess than 24h
of temporary
storageDocking
Unloading
Control
Transfer
Loading
1 color = 1 client
Context
Truck scheduling
Integrated pb ConclusionEmpl rostering
GENERAL IDEA
3
How to schedule the trucks and employees together?
Van Belle et al. (2012)
Ladier et Alpan (2014)
Günther et Nissen (2014)
Ladier et al. (2014)
« The scheduling of the trucks heavily influences the
workload for the internal resources »
Van Belle et al. (2012)
Context
Truck scheduling
Integrated pb ConclusionEmpl rostering
CROSS-DOCK TRUCK SCHEDULING
Ladier et Alpan (2014)
5
TRUCK SCHEDULING PROBLEM
Reservation system
Minimize Quantity put in storage
Dissatisfaction of the transportation providers
10am-12am
6am-8am
9am-12am
6am-7am
6am-9am
6am-9am
11am-12am
7am-10am
Context
Truck scheduling
Integrated pb ConclusionEmpl rostering
6
DECISIONS VARIABLES
Number of units moving at each time period: from each inbound truck to each outbound truck
from each inbound truck to storage
from storage to each outbound truck
Time windows chosen for the trucks
Context
Truck scheduling
Integrated pb ConclusionEmpl rostering
INTEGER PROGRAMMING MODEL
(IP*)min ( a0 × penalty on the inbound time window chosen
+ b0 × penalty on the outbound time window chosen + g0 × number of pallets put in storage)
# trucks present ≤ # doors
Pallets move from the present trucks only
Flow conservation (for each destination)
Outbound truck leave when fully loaded
Each truck is assigned to exactly 1 time window
Stock conservation rule
7
Context
Truck scheduling
Integrated pb ConclusionEmpl rostering
EMPLOYEE ROSTERING
Ladier et al. (2014)
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EMPLOYEE ROSTERING
Manpower: 1st cost center for logistic providers
Context
Truck scheduling
Integrated pb ConclusionEmpl rostering
10
SEQUENTIAL SOLVING
Detailed task
allocation
Starting/ending
time per
employees1 or 2
weeks
¼ hour
Weekly
tim
eta
blin
g
Daily
rost
eri
ng
Nb temporary
workers
Total nb hours
workedExact times
Day
Hour and shift
Ben works 8
hours on Friday
Ben works from
9h to 17h on
Friday
Ben unloads
from 9h to
11h15, controls
from 11h15 to
12h …
MILP1
MILP2
MILP3
Context
Truck scheduling
Integrated pb ConclusionEmpl rostering
INTEGRATED PROBLEM
How to solve both problems in an integrated manner?
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12
SEQUENTIAL APPROACHIntuitive approach:
Manage external
matters first, then
internal
Input data
IP H2or
MILP1
MILP2MILP3
Workload Workload
Context
Truck scheduling
Integrated pb ConclusionEmpl rostering
13
ITERATIVE APPROACH: IDEAS
Aircraft routing and crew scheduling (Weide et al. 2010)
Crew scheduling
Aircraft routing
The objective function of
each module integrates
information from the problem
solved previously
Context
Truck scheduling
Integrated pb ConclusionEmpl rostering
Linking
constraints
ITERATIVE APPROACH:
PRINCIPLE
14
Workload
Capacity contraints
Employe
es
first
Trucks
first
Context
Truck scheduling
Integrated pb ConclusionEmpl rostering
15
EMPLOYEES FIRSTInput data
IP H2ou
MILP1
MILP2
MILP3
Workload
Capacity
constraints
Announced timetable
Context
Truck scheduling
Integrated pb ConclusionEmpl rostering
TRUCK FIRST
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Input data
IP* H2or
MILP1
MILP2
MILP3
IP H2orWorkload
Capacity
constraints
Announced
timetable
WorkloadWorkload
Context
Truck scheduling
Integrated pb ConclusionEmpl rostering
17
RESULTS
Stock
Employees
Truck ponctuality
Linking constraints
0.0 20.0 40.0 60.0 80.0
Trucks-first Employees-first
Average value of the related objective function elements
Context
Truck scheduling
Integrated pb ConclusionEmpl rostering
18
CONCLUSION
IP H2
MILP
1MILP
2
MILP
3
Context
Truck scheduling
Integrated pb ConclusionEmpl rostering
19
PERSPECTIVES
Optimal solution for the integrated problem?
Þ Adapt an idea from Guyon et al. (2010) Integrated production scheduling and employee
timetabling
Logic-based Benders decomposition
Slave problem = maximum flow problem
Context
Truck scheduling
Integrated pb ConclusionEmpl rostering
Centre National de la Recherche Scientifique Institut Polytechnique de Grenoble Université Joseph Fourier
Laboratoire G-SCOP46, av Félix Viallet38031 Grenoble Cedexwww.g-scop.fr
THANK YOU FOR YOUR
ATTENTIONwww.anne-laure-ladier.fr
anne-laure.ladier@insa-lyon.fr
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BIBLIOGRAPHY Günther, M., & Nissen, V. (2014). A comparison of three heuristics on a practical
case of sub-daily staff scheduling. Annals of Operations Research, 218(1), 201–
219.
Guyon, O., Lemaire, P., Pinson, É., & Rivreau, D. (2010). Cut generation for an
integrated employee timetabling and production scheduling problem. European
Journal of Operational Research, 201(2), 557–567. doi:10.1016/j.ejor.2009.03.013
Ladier, A.-L., Alpan, G., & Penz, B. (2014). Joint employee weekly timetabling and
daily rostering: A decision-support tool for a logistics platform. European Journal of
Operational Research, 234(1), 278–291.
Ladier, A.-L., & Alpan, G. (2014). Crossdock truck scheduling with time windows −
Earliness, tardiness and storage policies. Journal of Intelligent Manufacturing.
doi:10.1007/s10845-014-1014-4
Van Belle, J., Valckenaers, P., & Cattrysse, D. (2012). Cross-docking: State of the
art. Omega, 40(6), 827–846.
Weide, O., Ryan, D., & Ehrgott, M. (2010). An iterative approach to robust and
integrated aircraft routing and crew scheduling. Computers & Operations
Research, 37(5), 833–844.