Parallel Behavior Composition for Manufacturing Paolo Felli1

Brian Logan 1 1 University 2 RMIT

Sebastian Sardina 2

of Nottingham, UK

University, Australia

IJCAI 2016 New York

Felli, Logan, Sardina

Parallel Behavior Composition for Manufacturing

IJCAI 2016

1

The problem

• manufacturers increasingly need to produce variable volumes of

highly customised products, rapidly and at low cost • one way to do this is to allow production control software greater

autonomy in determining how products will be manufactured • requires the automated synthesis of controllers that are able to

manufacture any instance of a given product type on a particular production or assembly line • problem: standard AI behavior composition, e.g., (De Giacomo et

al 2013), assumes a single sequential execution of the target behavior module

Felli, Logan, Sardina

Parallel Behavior Composition for Manufacturing

IJCAI 2016

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want to manufacture multiple instances of a product in parallel Felli, Logan, Sardina

Parallel Behavior Composition for Manufacturing

IJCAI 2016

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Key contributions

• we extend classical AI behavior composition to manufacturing

settings • we introduce a novel solution concept for manufacturing

composition, target production processes, that are able to manufacture multiple instances of a product simultaneously in a given production plant • we propose a technique for synthesizing the largest target

production process, together with an associated controller for the production plant

Felli, Logan, Sardina

Parallel Behavior Composition for Manufacturing

IJCAI 2016

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Production recipes & cycles • a production recipe is the (finite) sequence of steps required to

manufacture any instance (e.g., a blue shirt of size XL) of a particular product type (e.g., shirts)

R

α

A

X

β

Y

γ

Z

• the production cycle LR induced by a recipe R represents the

repeated execution of R

LR

Felli, Logan, Sardina

A

α β, γ

Parallel Behavior Composition for Manufacturing

X

IJCAI 2016

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Production plants & controllers • production recipes are enacted by a production plant, consisting

of production resources and their associated capabilities • the capabilities of each resource are modelled as an available

behavior — a (possibly nondeterministic) transition system

0

β α

β 1

0

B1

γ α

1

α

B2

• a production plant system S consisting of m resources is the

synchronous product of m available behaviors (plus “no-op” ‘−’ actions) • a plant controller is a function which delegates k ≤ m actions to

m available behaviors in the plant Felli, Logan, Sardina

Parallel Behavior Composition for Manufacturing

IJCAI 2016

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Target production process

• production cycle LR tells us how to make items, and the available

behaviors tell us what the production plant can do • but the target production process specifying how to make

multiple items of a product type simultaneously is not given • note that the target production process is not just the synchronous

execution of multiple copies of LR • the production plant may be able to make n instances of an item in

parallel, but in only one (or a small number of) way(s) • we need a new solution concept for manufacturing composition

Felli, Logan, Sardina

Parallel Behavior Composition for Manufacturing

IJCAI 2016

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TPP requirements

R1. a TPP should allow the manufacture of multiple product items simultaneously R2. for each product item in a TPP the complete product recipe should be available R3. the evolution of a TPP may depend on how (nondeterministic) behaviors in the plant happen to evolve R4. a TPP may “pause” certain items (using “no-op” actions) R5. a TPP should never allow the starvation of any item

Felli, Logan, Sardina

Parallel Behavior Composition for Manufacturing

IJCAI 2016

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Production controllers n-pcycle Ln the synchronous execution of n copies of the production cycle L achieves R1, but is too demanding: represents all possible ways in which n items can be manufactured n-product T fragment of a n-pcycle with memory & uncontrollable transitions captures that not every interleaving of a n-pcycle may be possible in the production plant n-TPP an n-product that is complete and fair w.r.t. the n-pcycle

Given a recipe R and production plant system S A production controller is a pair hT , Pi, such that T is an m-TTP for m-pcycle Lm R and P is a plant composition for T in S Felli, Logan, Sardina

Parallel Behavior Composition for Manufacturing

IJCAI 2016

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Largest TPP • we can compute a production controller hT , Pi where T is the

largest realizable production process, i.e., which can “mimic” all realizable production processes • define a special notion of “simulation” (lazy simulation) between

the production plant S and the m-pcycle Lm R , s.t.: • S “mimics” m copies of the production cycle LR

• S can delegate all action vectors of Lm R , but not necessarily in a

step-by-step fashion: product instances may be paused • compute the set encoding memory and the set of uncontrollable

transitions in the m-product • both can be extracted from the enacted system: joint execution

of m-pcycle Lm R with production plant S

Felli, Logan, Sardina

Parallel Behavior Composition for Manufacturing

IJCAI 2016

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Main result • we say that a state s of S lazily simulates a state t of Lm ,

denoted s ; t, iff there exists a lazy simulation relation σ+ of Lm by S with hs, ti ∈ σ+ • we say that S lazily simulates Lm , denoted S ; Lm , iff s0 ; t0 • if S ; Lm we can build a maximal controller generator (CG),

that returns, at each step, the set of all possible action vectors that can be delegated to behaviors

Theorem Let C be the maximal CG for Lm and S. Then (i) any P is a plant composition for T in S iff hT , Pi is generated by C and (ii) T is the largest realizable m-TPP for L in S.

Felli, Logan, Sardina

Parallel Behavior Composition for Manufacturing

IJCAI 2016

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Summary & future work

• we have defined a notion of “adequacy” for solutions in the form of

TPPs that respect requirements R1-R5 • in future work we plan to refine m-TPPs to give “efficient”

manufacturing processes, e.g., with respect to average throughput, machine utilization, load balancing, etc. • such optimizations can be done after the TPP has been built, or

possibly during synthesis by discriminating between controllers

For more details, please see poster ??

Felli, Logan, Sardina

Parallel Behavior Composition for Manufacturing

IJCAI 2016

12

Parallel Behavior Composition for Manufacturing

highly customised products, rapidly and at low cost. • one way to do this is to allow .... in future work we plan to refine m-TPPs to give “efficient” manufacturing ...

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