Background:STELIA (formerly Composites Atlantic) builds composite airplane parts mostly from carbon fiber. These parts contain a resin that under high pressure and heat binds the different layers. This high pressure and heat are applied to the parts with the use of autoclaves in the curing centre (See Figure 1). Once the parts are cured, they proceed to demoulding where the part is separated from the mould and then continue on to other downstream processes like trimming, drilling, painting, and assembly.

The curing centre is the bottleneck of the entire operation and contains very complicated processes. Although it currently operates well, there are significant opportunities for improvement through optimization and standardization of these processes. The first process to be improves it the building of cure-sets, which are a set of parts that can go together into the autoclave (Bin Packing Problem). The second process is the scheduling of these cure-sets across the planning horizon (Scheduling Problem).

Pains:● Pain 1: Manually building cure-sets

○ Time consuming○ Sub-optimal (but “pretty good”)

● Pain 2: Manually scheduling cure-sets○ Time consuming○ Complex constraints

Pains are exacerbated by a growing company. These problems grow exponentially and thus become even harder to solve.

Solution:Solve the Bin Packing Problem first, then the Scheduling Problem with the results from the BPP. Doing so on Amazon Web Services it was possible to use Gurobi Cloud, thus allowing a per hour fee instead of an expensive license purchase.

Bin Packing Problem (BPP)- How to optimally pack parts into an autoclave to respect the following constraints:

● Recipe constraints (heat+pressure profile)

● Thermocouple constraint● Volume constraints● Length constraints● Tool constraints● Customer-specific constraints

Objectives: 1. Minimize cost

a. Number of curesb. Use preferred autoclave for parts

2. Level out load for autoclaves

BPP Solution- Use a modified version of the hill-climbing algorithm (Lewis, 2009). Implemented with use of Python, solved on Amazon Web Services server.

Scheduling Problem- How to best schedule bin packings to minimize cost, maintain schedule flexibility, and meet the following constraints:

● Meet demand● Bin packings can only go on certain

autoclaves● Autoclave utilization constraints● Tool recycling time constraints● Blackout period constraints● Layup room manpower constraints

Objectives:1. Minimize cost

a. Overtimeb. Peak Power

2. Maintain schedule flexibility3. Maintain schedule usability for upstream

processes

Scheduling Solution- Use of Mixed-Integer-Programming to create a mathematical model. Model is outlined below and solved using Gurobi Optimization on the cloud (see Figure 3).

Expected Implementation: June 2015Expected Results:

Tools Used:● Amazon Web Services

○ API● Pyomo● Gurobi Cloud● SSH● Python

Autoclave Packing and SchedulingBy: Andres Collart | andrescollart@gmail.com | www.andrescollart.com

Kit Cutting Layup Curing Centre Demolding Downstream Processes

Figure 1: General Workflow of Plant Processes

Mold clean-upMold Mold

LocalMachine

AP

I Cal

l +

files

1. Get Inputs for week. Encode

2. Start Linux and send problem

3. Solve BPP with Heuristic

4. Create Scheduling MIP problem

5. Start Gurobi + send problem

AP

I Cal

l +

prob

lem

6. Solve + send back

Sol

utio

n

“I’m

don

e”

7. Download solution, decode, display

Sol

utio

n

Gurobi Cloud Server(AWS)

Linux Server(AWS)

How do you fit parts in the autoclave most

efficiently?

UNMOLD

carbon fiber airplane parts

layered parts cured in autoclave (big oven)

PRO

CES

S

How do you optimize scheduling of cures for

the autoclaves?

Decreased Peak Power

DecreasedPower

Consumption

Increased Schedule Flexibility

Decreased WeekendOvertime

Long term capacity planning

Figure 2: Large autoclave at STELIA (STELIA, 2015)

PAIN

SB

IN

PAC

KIN

GPR

OB

LEM

RESULTSEfficient Bin Packing

Optimized scheduling

SOLU

TIO

N

SCH

EDU

LIN

GPR

OB

LEM

lower cost png

Reduce Cost

Level out Autoclave Load

DecreasedPower

Consumption

Objectives:

Constraints:

Heat Profile(Recipe)

Volume +

Length

Mold(Tool)

Other Customer Specific

Reduce Cost

Objectives:

Constraints:

Schedule Flexibility

Balance Input Process

Preferences

Part-Autoclave compatibility

Mold Recycle

Time

Scheduled Downtime

Resource Capacity

(Autoclaves)

Other

Bin Packing Problem: ● Modified Hill-Climbing Heuristic

Scheduling Problem: ● Discrete-Time MIP with Rolling Time Horizon

=

Pains:●

●

Pains are exacerbated by a growing company. These problems grow exponentially and thus become even harder to solve.

Manually building cure-sets○ Time consuming○ Sub-optimal use of

autoclaves

Manually scheduling cure-sets○ Time consuming○ Complex constraints○ High energy cost

Thermos