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Cybernetix personalisation system

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									Extending the smart-card personalisation system by the graphical treatment
Angelika Mader University of Twente

Cybernetix – smart card personalisation system
personalisation printer printer stations unloader flip over flip over laser engraver loader

conveyer belt

Cybernetix- smart card personalisation system
personalisation printer printer stations unloader flip over flip over 1 1 1 1 1 1 1 1 1 1 1 1 1 laser engraver loader 1

conveyer belt

way of a single card through the system

1

1 1

1

Cybernetix - smart card personalisation system
Research question: can we use model checking tools to find an optimal schedule?
optimal means optimal throughput; because the belt is not moving with constant speed, it is not the number of gaps that we optimize.

First approach: isolation of the personalisation part

Isolation of the personalisation part
personalisation printer printer stations unloader flip over flip over laser engraver loader

conveyer belt

processing times:
dominates: Personalisation 10-50 Unloader, Loader, Flip-Over 2 Printer 3 Laser Engraver 4 Belt movement 1

smart card personalisation super single mode
unloader personalisation stations
9 5 1 10 6 2 3 2 12 11 10 9 8 7 6 5 4 1 2 2 11 10 10 9 8 7 6 5 4 3 1 5 8 7 6 4 3 1 37 34 6 5 8 7 3 2 1 48 41 5 8 3 2 4 3 2 1

loader

belt

etc.

First results
• problem difficult for model checking: does not scale up to great numbers of cards • for a periodic schedule we need some sort of cycle detection

More first results
By elementary combinatorial argumentation: the throughput of the super-single mode is
k max{4k+3,p+2}
Cycle length

k: even number of stations p: personalisation time

The super-single mode meets the theoretical upper bound for throughput, if p >= 4k+1
(i.e. personalisation time is long enough w.r.t. number of stations)

Alternative Architecture
12 8 4 11 7 3 10 6 2 9 5 1 9 4 2 5 1 3 5 4 3 2 1 10 10 10 10 10 10 5 1212 9 7 9 3 9 12 8 4 3 4 6 4 11 4 7 9 3 2 3 8 11 11 11 6 4 2 9 9 8 4 7 9 6

Why: • allows for an easier schedule and easier analysis

• argumentation transfer to the more complicated super-single mode
• different composition properties: good for comparison

Even more first results
By elementary combinatorial argumentation: the throughput of the alternative architecture is
k max{4k,p+2}
Cycle length

k: number of stations p: personalisation time

The alternative schedule meets the theoretical upper bound for throughput, if p >= 4k-2
(i.e. personalisation time is long enough w.r.t. number of stations)

Personalisation & graphical treatment
personalisation printer printer stations unloader flip over flip over
laser engraver loader

conveyer belt

processing times:
Personalisation 10-50 Unloader, Loader, Flip-Over 2 Printer 3

• cards are processed on the belt • cards do not overtake each other • graphical treatment only adds delays

Laser Engraver 4 Belt movement 1

Personalisation & graphical treatment
• cards leave the personalisation part with a certain delay pattern (rythm) that depends on the schedule • the time pattern interferes with the delays by the graphical treatment:
extreme cases are that the graphical treatment delays synchronize completely with the time pattern of the personalisation part and have no negative effect at all, or contrary, that the delays are added completely to the production time this holds even for different optimal schedules (will be shown by experiments)

(the belt does not move with constant speed!)

• personalisation schedules interfere differently with graphical part;

• timing analysis of interference does not seem possible using elementary reasoning • use UPPAAL for throughput analysis of the composition of personalisation part and graphical part

Personalisation & graphical treatment

Timing analysis - idea: Add explicit scheduling process in the UPPAAL model, that enforces super-single mode or the alternative schedule for the personalisation part.

process Personalisation1{ clock pers_time; state PERSONALISING, IDLE; init IDLE; trans IDLE -> PERSONALISING{ guard card_id[0]==0, Belt[1]>0, moving==0; sync s_p1?; assign pers_time:=0, card_id[0]:=Belt[1], // load card in the pers. station Belt[1]:=0;}, PERSONALISING -> IDLE{ guard pers_time>=Personalise, Belt[1]==0, moving==0; sync s_p1?; assign Belt[1]:=-card_id[0], // put pers. card on the belt card_id[0]:=0;}; } // personalisation station is empty now // belt cell under station is empty // belt must stand still // position on the belt gets empty // no card in the personalisation station // there is an unpersonalised card on the belt // belt must stand still

process Scheduler{ state S1, S2, S3, S4, S5, S6, S7, S8, S9, S10, S11, S12, S13, S14, S15, S16, S17, S18, S19, S20, …, S95; init S1; trans S1 -> S2{sync s_ul!; }, S2 -> S3{sync s_m!;}, S3 -> S4{sync s_m!;}, S4 -> S5{sync s_ul!; }, S5 -> S6{sync s_m!;}, S6 -> S7{sync s_m!;}, … S21 -> S22{sync s_m!;}, S22 -> S23{sync s_ul!; }, S23 -> S24{sync s_m!;}, S24 -> S25{sync s_p1!;}, S25 -> S26{sync s_p2!;}, S26 -> S27{sync s_p3!;}, S27 -> S28{sync s_p4!;}, … ... // synchronizes with personalisation station 1 // synchronizes with personalisation station 2 … // synchonizes with unloader // synchronizes with belt

Personalisation & graphical treatment
Timing analysis – experiments: • super-single mode and alternative schedule • 4 and 8 personalisation stations • pick&drop times (1/2 time unit or zero) at personalisation stations • time measurements for 12,16,20,24 and 16,24,32,40 cards until cycle length is determined. • personalisation times 10,20,30,40,50 • cost-optimal UPPAAL

Timing analysis -experiments
4 stations & graphical treatment, super-single mode & alternative architecture number of cards personalisation time

12
113 140

16
137 171 153 148 179 199 168 201 249 291 231 177 161

20
202 210 234

10

20
30 40 50

129 167 207

198
247 228 299

241
351 281

284
334

Timing analysis -experiments
4 stations & graphical treatment, super-single mode & alternative architecture number of cards personalisation time

12
113 129 167 207 247 +24 140 +24 153 137

16
+24
+31
171

20
161 +31 177 202

10

20
30 40 50

148
+32 168 +42 198 +52 228 299 249 199

+24 +31 179 +32 +33 201 +42 +43 241 +52 +53 281

+31
231 +33 291 +43 351

210
234 284

+53

334

First new results
• decomposition helps to analyse more complex scheduling problems • results from the analysis of the first part go into a explicit scheduler of the larger system (model) • cycles could be detected (because we know batch size = cards per cycle) • with cycle length we also have throughput

Second version of graphical treatment
cards are printed on one side only: • Flip-overs (and laser engraver) are not in use • each card can be printed at first OR second printer • scheduling problem: what is the best schedule for the printers when the personalisation part is in super-single mode or the alternative schedule? • can be solved with cost-optimal UPPAAL
by similar approach as for the first version
laser engraver loader

personalisation printer printer stations unloader flip over flip over

Conclusions so far
• Personalisation part is dominant but not the only source of delay in the system • Different optimal schedules can interfere differently with graphical treatment part: optimality is not compositional

• Even if the whole problem is not (yet) solvable with model checking, model checking can be used for parts of solutions
• Decomposition method for complex schedules can help to find good schedules • Mixed strategies can help

What has to be done
• Schedule and timing analysis for faulty cards: performance models? • More experiments with different architectures (e.g. numbers of cells on the belt)


								
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