modeling issues when using simulation to test the performance of mathematical programming models...
TRANSCRIPT
Modeling issues when using simulation
to test the performance of mathematical programming
models under stochastic conditions
Anne-Laure Ladier Univ. Lyon - DISP - INSA LyonAllen G. Greenwood Poznan Univ. of TechnologyGülgün Alpan Univ. Grenoble Alpes
2
Outline
Context
Cases
description
Foundational
differences
Operation
al differences
Conclusion
Anne-Laure Ladier, Allen G. Greenwood, Gülgün Alpan | ESM'2015, Leicester
Modeling issues when using simulation to test the performance of mathematical programming models under stochastic conditions
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Simulation and optimization
Anne-Laure Ladier, Allen G. Greenwood, Gülgün Alpan | ESM'2015, Leicester
Simulation
model
Optimization
model
Optim
izatio
n
mod
el
Simulation
model
Simulation
model
Optimization
model
Simulation
model
Optimization
model
Gambardella et al. (1998)
Hauser (2002)Liu and Takakuwa (2009)
Wang and Regan (2008)
McWilliams (2005)Aickelin and Adewunmi (2006)
Context Cases description
Foundational differences
Operational differences Conclusion
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Summary of our approach
Optimization
model
Integer
programming
System
Simulation
model
FlexSim Sim
ula
tion
anal
yses
Context Cases description
Foundational differences
Operational differences Conclusion
Anne-Laure Ladier, Allen G. Greenwood, Gülgün Alpan | ESM'2015, Leicester
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Research questions
What are the modelling issues raised by this optimization → simulation relationship?
How can they be solved or circumvented?
Context Cases description
Foundational differences
Operational differences Conclusion
Anne-Laure Ladier, Allen G. Greenwood, Gülgün Alpan | ESM'2015, Leicester
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CASES DESCRIPTION
Context Cases description
Foundational differences
Operational differences Conclusion
Anne-Laure Ladier, Allen G. Greenwood, Gülgün Alpan | ESM'2015, Leicester
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Cross-docking
Less than 24h of
temporary storage
docking
unloading
scanning
transfer
loading
departing
Context Cases description
Foundational differences
Operational differences Conclusion
Anne-Laure Ladier, Allen G. Greenwood, Gülgün Alpan | ESM'2015, Leicester
1 color = 1 client
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Case 1 – General ideas
Simulation model
Optimization
model
Truck schedule
Truck arrival and departure timeAmount in storagePallet transfer
Comparison
Logic
Logic
Random
events
Context Cases description
Foundational differences
Operational differences Conclusion
Anne-Laure Ladier, Allen G. Greenwood, Gülgün Alpan | ESM'2015, Leicester
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Case 1 – Model demonstration
Simulation software: FlexSim
Context Cases description
Foundational differences
Operational differences Conclusion
Anne-Laure Ladier, Allen G. Greenwood, Gülgün Alpan | ESM'2015, Leicester
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Case 2 – General ideas
Simulation model
Optimization
model
Comparison
Random
events
Employee timetable
Context Cases description
Foundational differences
Operational differences Conclusion
Anne-Laure Ladier, Allen G. Greenwood, Gülgün Alpan | ESM'2015, Leicester
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Case 2 – Model overview
Unloading Doors
Workers
Temporary Storage
Inbound Docks
Outbound Docks
Simulation software: FlexSim
Context Cases description
Foundational differences
Operational differences Conclusion
Anne-Laure Ladier, Allen G. Greenwood, Gülgün Alpan | ESM'2015, Leicester
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FOUNDATIONAL DIFFERENCES
Two modeling approaches represent the system differently
Context Cases description
Foundational differences
Operational differences Conclusion
Anne-Laure Ladier, Allen G. Greenwood, Gülgün Alpan | ESM'2015, Leicester
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Time representationMathematical optimization
« Big buckets » time intervals, masked time
Discrete-event simulation
Events ocur at precise instances of time
Shorten time intervals?
Increase complexity
Measure performance in terms of intervals
Time
Inbound truck
Outbound truck
Context Cases description
Foundational differences
Operational differences Conclusion
Anne-Laure Ladier, Allen G. Greenwood, Gülgün Alpan | ESM'2015, Leicester
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Spatial representationMathematical optimization If spatial
considerations are not the core of the problem, ignore!
Process times = average rates
Discrete-event simulation
Add spatial considerations?
Increase complexity
speed
distance
Processor for precise control of travel time
availability
Context Cases description
Foundational differences
Operational differences Conclusion
Anne-Laure Ladier, Allen G. Greenwood, Gülgün Alpan | ESM'2015, Leicester
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Model structure and sizeMathematical optimization
Execution time exponential in the instance size
Discrete-event simulation
Execution time linear in the instance size
Specify size early in the project
Context Cases description
Foundational differences
Operational differences Conclusion
Anne-Laure Ladier, Allen G. Greenwood, Gülgün Alpan | ESM'2015, Leicester
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OPERATIONAL DIFFERENCESMake the operations match
Context Cases description
Foundational differences
Operational differences Conclusion
Anne-Laure Ladier, Allen G. Greenwood, Gülgün Alpan | ESM'2015, Leicester
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Task order, batch size, parallelismMathematical optimization
No precise task order unless it is a key consideration
Discrete-event simulation
3 ×10 pallets/hour≠
1 × 30 pallets/hourNumber of pallets at time h
Context Cases description
Foundational differences
Operational differences Conclusion
Anne-Laure Ladier, Allen G. Greenwood, Gülgün Alpan | ESM'2015, Leicester
Order and batch size do have important impacts
1 pallet= 2 min
1 pallet= 6 min
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Process logicMathematical optimization Low granularity: only
overall workload in time interval
Optimal decision-making
Discrete-event simulation High granularity:
specific pallets, doors, workers, etc
FIFO logic
Greedy decision making
Operational decisions when deviation from schedule occurs
e.g. wait for assigned operator or use available (capable)
Context Cases description
Foundational differences
Operational differences Conclusion
Anne-Laure Ladier, Allen G. Greenwood, Gülgün Alpan | ESM'2015, Leicester
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CONCLUSION
Context Cases description
Foundational differences
Operational differences Conclusion
Anne-Laure Ladier, Allen G. Greenwood, Gülgün Alpan | ESM'2015, Leicester
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After model validation…
The simulation models were used to assess the robustness of the schedules/timetables obtained by mathematical programming
A.-L. Ladier, G. Alpan, and A. G. Greenwood, “Robustness evaluation of an IP-based cross-docking schedule using discrete-event simulation,” in Industrial and Systems Engineering Research Conference, 2014.
A.-L. Ladier and G. Alpan, “Robust cross-dock scheduling with time windows,” European Journal of Operational Research. Under revision.
Context Cases description
Foundational differences
Operational differences Conclusion
Anne-Laure Ladier, Allen G. Greenwood, Gülgün Alpan | ESM'2015, Leicester
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Conclusion
Mathematical programming and optimization are complementary decision-support tools
Understand their inherent differences in modeling the same system
Encourage an increase in the use of discrete-event
simulation to assess the performance of optimization models
Modelers in sharing their modeling issues/solution to the community
Context Cases description
Foundational differences
Operational differences Conclusion
Anne-Laure Ladier, Allen G. Greenwood, Gülgün Alpan | ESM'2015, Leicester
Thank you for your attention!
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Pallets transfer