deploying gurobi models using the opalytics cloud …analytics (superset of mip). • define input...

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DEPLOYING GUROBI MODELS USING THE

OPALYTICS CLOUD PLATFORM

David Simchi-Levi Pete Cacioppi

Professor MIT Chief Scientist Opalytics

Chairman Opalytics

October 20th 2015

2 Confidential ©Copyright 2015 | Opalytics Inc.

Agenda

• Introduction

• Supply Chain Risk Optimization

• Opalytics Network Risk

• End to End Analytics Development

• Summary

• Q&A


Challenges • Need to solve Unique problems

– One size does not fit all

– New opportunities

• Reliable analytics and processes

– Data validation

– Tested analytics

• Ability to quickly deploy to users on a light platform

– Easy to use and train interface

– Data and Scenario management

• Provide Easy integration with other systems

– Extract corporate data

• Role-based access to information

– Ability to share results in different formats


Cloud based services Off-the-Shelf

ApplicationsCustom Solutions

Opalytics Cloud Platform (OCP)

• Simple to deploy• ETL services• File management• Dynamically allocate

resources(solvers, storage)

• Specialized analytics to specific problems

• Deploy various solutions in the same environment

• Network Risk• Multi Echelon

Inventory• Network Design• Segmentation

A Platform to Build Analytic Applications that Drive Business DecisionsA Platform to Build Analytic Applications that Drive Business DecisionsA Platform to Build Analytic Applications that Drive Business DecisionsA Platform to Build Analytic Applications that Drive Business Decisions


Opalytics Cloud Platform Features

ERP

BI

Excel

JSON

Excel

CSV

JSON

External

NoSQL

Database

Data

Validation

Geo-coding

Solver

Identification

File and

scenario

Management

User

Management

Roles and

Authentication

Solvers

Dynamic Solver

Deployment

Solution

Reports &

Visualization

External

Systems

Data Editing

and

Validation

Graphics

and

maps

SolverSolver

(i) Public Cloud; (ii) Private Cloud; (iii) Corporate (i) Public Cloud; (ii) Private Cloud; (iii) Corporate (i) Public Cloud; (ii) Private Cloud; (iii) Corporate (i) Public Cloud; (ii) Private Cloud; (iii) Corporate


Visualization


Agenda

• Introduction




• Summary

• Q&A


Managing Supply Chain Risk: Challenge

• Very difficult to predict many sources of risk,

especially the unknown-unknown

• Impact of disruption can be devastating

• Large investment in identifying every possible risk in

the supply chain

• Existing tools and techniques have been inadequate

– Mostly ad-hoc, intuition, gut feeling

– Exposure to risk may reside in unlikely places

– May lead to the wrong actions and waste resources

– No ability to prioritize mitigation investment

8


Illustrating Our Approach

Engine Plants

Contract

Manufacturers

Assembly

Suppliers

Steel Bar

Suppliers

Raw Chemical

Suppliers

Sheet Steel

Suppliers

• TimeTimeTimeTime----ToToToTo----Recover (TTR): The time it takes to recover to full functionality after a disruptionRecover (TTR): The time it takes to recover to full functionality after a disruptionRecover (TTR): The time it takes to recover to full functionality after a disruptionRecover (TTR): The time it takes to recover to full functionality after a disruption

Assembly

Plants

Stamping Plants

2015 New England Supply Chain Conference & Educational Exhibition



Engine Plants

Contract

Manufacturers

Assembly

Suppliers

Steel Bar

Suppliers

Raw Chemical

Suppliers

Sheet Steel

Suppliers


Assembly

Plants

Stamping Plants

TTR =2 Weeks




Engine Plants

Contract

Manufacturers

Assembly

Suppliers

Steel Bar

Suppliers

Raw Chemical

Suppliers

Sheet Steel

Suppliers


Assembly

Plants

Stamping Plants

2 Weeks

1 Week2 Weeks

2 Weeks

2 Weeks

TTR =2 Weeks

2 Weeks




• Performance Impact (PI): Impact of a disruption for the duration of TTR on a given performance measurePerformance Impact (PI): Impact of a disruption for the duration of TTR on a given performance measurePerformance Impact (PI): Impact of a disruption for the duration of TTR on a given performance measurePerformance Impact (PI): Impact of a disruption for the duration of TTR on a given performance measure


Engine Plants

Contract

Manufacturers

Assembly

Suppliers

Steel Bar

Suppliers

Raw Chemical

Suppliers

Sheet Steel

Suppliers

Assembly

Plants

Stamping Plants

2 Weeks

1 Week2 Weeks

2 Weeks

2 Weeks

TTR =2 Weeks

2 Weeks



2 Weeks$1.5B

1 Week$100M




Engine Plants

Contract

Manufacturers

Assembly

Suppliers

Steel Bar

Suppliers

Raw Chemical

Suppliers

Sheet Steel

Suppliers

Assembly

Plants

Stamping Plants

2 Weeks

2 Weeks

2 Weeks

TTR =2 Weeks

2 Weeks

2 Weeks$400M

2 Weeks$100M

2 Weeks$2.5B

TTR =2 WeeksPI = $400M

2 Weeks$300M



2 Weeks$1.5B



• Risk Exposure Index (REI): Normalizes the PI by the maximum PI over all disruption scenariosRisk Exposure Index (REI): Normalizes the PI by the maximum PI over all disruption scenariosRisk Exposure Index (REI): Normalizes the PI by the maximum PI over all disruption scenariosRisk Exposure Index (REI): Normalizes the PI by the maximum PI over all disruption scenarios


Engine Plants

Contract

Manufacturers

Assembly

Suppliers

Steel Bar

Suppliers

Raw Chemical

Suppliers

Sheet Steel

Suppliers

Assembly

Plants

Stamping Plants

2 Weeks

1 Week

2 Weeks

2 Weeks

TTR =2 Weeks

2 Weeks

2 Weeks$400M

1 Week$100M

2 Weeks$100M

2 Weeks$2.5B

TTR =2 WeeksPI = $400M

2 Weeks$300M



2 Weeks0.6



• Risk Exposure Index (REI): Normalizes the PI by the maximum PI over all disruption scenariosRisk Exposure Index (REI): Normalizes the PI by the maximum PI over all disruption scenariosRisk Exposure Index (REI): Normalizes the PI by the maximum PI over all disruption scenariosRisk Exposure Index (REI): Normalizes the PI by the maximum PI over all disruption scenarios


Engine Plants

Contract

Manufacturers

Assembly

Suppliers

Steel Bar

Suppliers

Raw Chemical

Suppliers

Sheet Steel

Suppliers

Assembly

Plants

Stamping Plants

2 Weeks

1 Week

2 Weeks

2 Weeks

TTR =2 Weeks

2 Weeks

2 Weeks0.16

1 Week0.04

2 Weeks0.04

2 Weeks1.0

TTR =2 WeeksREI = 0.16

2 Weeks0.12



Benefits of using the Risk Exposure Index

• It provides a $ measure of risk--it estimate the cost of risk

• It is based on the entire network rather

• It avoids the need to forecast the unknown-unknown;

• It forces a discussion to understand why TTR for similar

facilities or suppliers is different

• It forces a process to reduce TTR in various stages of the

supply chain;

• It makes sure you have a good understanding of supply chain

dependencies.

Visualizing a Simple ModelVisualizing a Simple ModelVisualizing a Simple ModelVisualizing a Simple Model

Plants

…

Parts

P1

P2

P3

…

P6

P7

P8

…

P3

P4

P5

Nodes (conceptual)

…

Transition (between

nodes)

P1

P2

P3

P6

P7

P4

P5P8

Bill of Materials Diagram

Model FormulationModel FormulationModel FormulationModel Formulation

Model Formulation:Model Formulation:Model Formulation:Model Formulation:

• Each optimization problem corresponds to a single disruption scenario

• The optimization problems are linear programs

� Important because a typical supply chain requires the analysis of tens of thousands of

possible disruption scenarios



Bill of Material ConstraintTotal production at node j (corresponding to a part at a particular facility) is bounded by the

volumes allocated from its upstream nodes



Parts Allocation ConstraintTotal allocation volume of node i is constrained by its production and its pipeline inventory



Disruption ConstraintProduction of node j is halted due to disruption



Demand loss constraintsLoss of production for vehicle j is lower bounded by the demand minus the production over

the TTR duration



Production capacity constraintsTotal production of all nodes at site/plant α is bounded by its capacity

Ford’s Supply Chain: The ChallengeFord’s Supply Chain: The ChallengeFord’s Supply Chain: The ChallengeFord’s Supply Chain: The Challenge

LLLLarge multiarge multiarge multiarge multi----tier supply chain networktier supply chain networktier supply chain networktier supply chain network

� Complex bill of materials and supply chain structure

� Over 50 manufacturing plants

� 10 tiers of suppliers

� 1400 tier 1 supplier companies with 4,400

manufacturing sites in over 60 countries

� 55,000 different parts

� 6 million vehicles produced annually

Performance Impact of Different Supplier’s SitesPerformance Impact of Different Supplier’s SitesPerformance Impact of Different Supplier’s SitesPerformance Impact of Different Supplier’s Sites

Number of Sites

Performance Impact

Another 2773 sites with No Impact

2773

805

142252

154

408

1

201

401

601

801

1001

1201

1401

1601

1801

No Impact Very Low Low Medium High Very High

Performance Impact and Total Spent at Supplier SitePerformance Impact and Total Spent at Supplier SitePerformance Impact and Total Spent at Supplier SitePerformance Impact and Total Spent at Supplier Site


Time-to-Recover and Time-to-Survive

Time-to-Recover (TTR): The time it takes the supply chain to return to 100% capacity after a disruption.

Time-to-Survive (TTS): Time duration where service is not interrupted under any disruption

TTR < TTS

Robust Supply Chain


Time-to-Survive across all Tier 1 suppliers

0

50

100

150

200

250

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9 1

1.1

1.2

1.3

1.4

1.5

1.6

1.7

1.8

1.9 2

10

20

30

40

50

>5

0

Nu

mb

er

of

Su

pp

liers

TTS (weeks)


Agenda

• Introduction




• Summary

• Q&A


User Management


Folder and Scenario Management


Model Tables


Data Editing & Validation


Solver Parameters


Solver Run


Results


Agenda

• Introduction




• Summary

• Q&A


Challenges in deploying MIP Solvers • Complex skills needed for simple MIP solves

– Learn a whole language for basic MIP?

– Separate data from solver to accommodate multiple stand alone

formats.

• Debug and test

– Failure to check data integrity beforehand leads to

big headaches afterwards.

• Deploy to end users– Apply rich GUI.

– Exploit cloud scalability and data sharing.

– MIPs have their own data mining challenges.


End to end Analytics development� Step 1: Develop Analytics

– Define Data: schema, keys, types, validation rules

– Develop Solver (ticdat, gurobipy, scikit-learn, many more)

– Create input files in order to test the schema

– Debug using standard development tools.

� Step 2: Deploy with Opalytics Cloud Platform

– Schema and data validation recognized by OCP.

– OCP provides off-the-shelf rich GUI.

� Step 3: Add Visualization

– Opalytics can efficiently customize your graphic needs.

– Plug into tools like Tableau.

� Step 4 : Go live!

– Integrate into systems of record.

– Don't forget to support it!


The Final End of End-to-End

Solver

Opalytics Cloud Platform


Opalytics Data Library (ticdat)

• ticdat is a Python library dedicated to Prescriptive

Analytics (superset of MIP).

• Define input and output schema and data integrity rules.

• Creates template for readable, consistent, Pythonic data

structures.

• Recognize data integrity problems.

• Test the engine locally with sample data.

– Excel, CSV, Access, SQLite, hard-coded and customized formats.

• Engines that use ticdat can be easily deployed via the OCP.


Learn 3 thing to write a basic MIP model

1. Superficial Python - dictionaries, tuples, attributes,

loops and functions.

2. ticdat for reading and writing tabular data. – Data object factory for Pythonic data structures with context

specific names.

– ticdat supports the most common stand alone data sources and is

easily extended

3. gurobipy for representing mathematical equations and

solving a MIP.– gurobipy.tuplelist meshes with ticDat’s usage of tuples as

dictionary keys.


Add 3 thing to create a stand-alone pilot

1. Check each data table for duplicate rows.

– Row duplication recognized via identifier fields (i.e.

primary keys)

2. Connect tables with foreign key relationships.

– Foreign keys simply capture “this is one of those”

relationship between tables.

– You have primary keys and foreign keys right now, but

you might not know what to call them.

3. Define data types for actual data fields.– Numerical or not, ranges (to include infinity), nullability, string

choices.


Remember everything to support your

solver

1. Python/ticdat skills support the entire developer process.

– Interactive analysis, proof-of-concept, application.

– The Python tools you use to build your solver can connect directly

to the OCP.

2. The syntax and tools you learn for one create a more efficient

thought process for all.

– Avoid the Tower-of-Babel, skip the cognitive-switch.

3. Python and ticdat connect you to both the cloud computing and

interactive programming movements.

– Jupyter, Google Cloud Datalab, matplotlib


Agenda

• Introduction




• Summary

• Q&A


Opalytics Supports You!

• Empowers Business Analytics developers

• Provides tools for Data Management and

Debugging of your Gurobi Model

• Fast deployment to users through Light IT


More Information

• For general information - www.opalytics.com

• For demo - [email protected]

deploying gurobi models using the opalytics cloud …analytics (superset of mip). • define input...

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