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Real-time decision support for mixed-model assembly operations
Real- time decision support for mixed- model
assembly operations
Helen Xie, Joseph Neelamkavil and Weiming Shen I ntegrated Manufacturing Technologies I nstitute
5 June 2007
Outline
Background
Measures of manufacturing performance
Technical features
Benefits
Background
Challenges in mass customization manufacturing
Large product variety
Uncertainty of demand on both the number of products and
product mix
Short lead time
Dynamic operating conditions
Parts unavailability
Machine failure and repair
Background
Mixed-model assembly lines
A variety of similar product
models
Assembly time varies for
every model in every station
Feeder line to mixed-model
assembly lines
Feeder line requires setup
times and prefers to batch
operations
B3 B2 Assembly Station Buf f er Up Stream Down Stream Line B Line A Line C Line D B1 Buf f er Buffer Paint line Subassembly lineMain assembly line
Measures of
manufacturing
performance
Production throughputs
Rate of products assembled
Buffer inventories
Number of work-in-process products held in buffers
Resource utilization
Summary of technical
features ( 1)
Sequence optimization
Objective: improve throughput by leveling work load among each station
Objective: minimizing cost of setup times and inventory holding for a feeder line
Simulation in advanced planning stage
What-if scenario analysis
Processing time variation
Machine failure, repair, and preventive maintenance
Missing parts
Summary of technical
features ( 2)
Simulation in real-time production
Synchronization
Dynamic resource reallocation
Reconfiguration of production line
Simulation toolkit for modeling product sources, work stations, assembly stations, and buffers
Assembly line sequence
Start incompletion Job A: 1 min Job A: 1 min Job A: 1 min Job B: 3 min Job B: 3 min Job B: 3 minOccupancy time = 4 min Cycle time = 2 min
Start Job A: 1 min Job B: 3 min Job A: 1 min Job B: 3 min Job A: 1 min Job B: 3 min
Occupancy time = 4 min Cycle time = 2 min
end
Level work load on each station
Even distribution of the same model products into the sequence
Sequence analysis
Assembly/ subassembly line sequence Objective: to balance the work load among the various resources to produce the mix of products ordered
Result: even distribution of the same model products
Batch operated feeder line sequence Objective: Reduce setup time
Result: Large color block
Sequences for a feeder line and assembly line are not identical
Buffer inventories between a feeder line and assembly line
B3 B2 Assembly Station Buf f er Up Stream Down Stream Line B Line A Line C Line D B1 Buf f er Buffer Paint line Subassembly line
Main assembly line
Feeder line sequence
Production throughputs
Reducing setup times = > increasing throughputs
Work-in-process
Reducing setup times = > increasing buffer inventories
Algorithms developed for the optimized sequence:
Optimization on both setup cost and inventory cost
Optimization modelling
Objective function: Minimize the setup cost and inventory cost minimize c = ws cs+ wb cb
where
ci = 0, when CLi = CLi+ 1
ci = 1, when CLi
≠
CLi+ 1ws, wb– weight factor of setup cost, and inventory cost p n i i tj m j n i i s cc cc c
∑∑
∑
− = = − = + = 1 1 1 1 1n
i
p
p
c
b=
max{|
i−
i'|},
=
1
,...,
Numerical results
120 jobs, 7 colours
blue, black, green, orange, gray, black, green, yellow, red, gray, …
setup weight factor 0.75
I nventory weight factor 0.25Multiple runs of optimization
0 10 20 30 40 50 60 70 80 90 100 110 120 1 4 7 10 13 16 optimization instance fitness setup cost inventory cost
Simulation in advanced
planning stage
What-if scenario analysis Throughputs
Resource utilization
Buffer inventories
Simulation variables Processing time variation
Machine failure, repair, and preventive
maintenance
Missing parts
Operator absence
Simulation in real- time
Uncertainty in real-time production environment
Operator absence
Machine broken down
Part unavailable
Bottleneck
Decision support for quick response
Optimized actions to correct current deficiency
Limit the disturbance as few as possible
Synchronization between real-time operations and a simulation model
I nput via user interface
Real- time decision support
for resource reallocation
Operator absence scenario
Unutilized capacity: find out which stations with the most
unutilized capacity
Matched skills: find out which stations with matched skills
Simulation of throughputs with different control strategies
• Among the stations with matched skills, find a station with the most unutilized capacity
• Find a station with the most unutilized capacity, if matched skills not available, move operators
Simulation toolkit
Reconfigurable building blocks Product source Station Assembly station Buffer Connector Product Source # SZ/CAP ACC Q conveyor SZ/CAP AV SZ # # # queue # ARR REM # source output sourceCount AssemblySt at ion SZ/CAP AV SZ # # # queue # combine st at ion input1 input2 output BufferUp sequenceQueue # SZ/CAP ACC Q conveyor input output size current Counter St at ion SZ/CAP AV SZ # # # queue1 # SZ/CAP ACC Q conveyor SZ/CAP AV SZ # # # queue SZ/CAP # LAST VAL UTIL delay input output
Benefits
Optimized sequencing in minutes
Enhance productivity by throughputs improvement
Reduce investment by cutting inventories
Optimized buffer size for saving production line space
Optimized use of resources
Assist managers for quick response in real-time production
Summary and future w ork
Mixed-model assembly line performance measures:
Production throughputs
Buffer inventories
Resource utilization
