Navy Training Scheduling - Euro 2021

Navy Training Scheduling
—
Eray Cakici, Qiannan Gao, Hanadi Wali, Gianmaria Leo
IBM Data Science & AI Elite
EURO 2021 / July 12, 2021 / © 2021 IBM Corporation

Agenda
1. Introduction
2. Use Case & Business Challenges
3. Our Approach
4. Conclusions
2

BS, Industrial Engineering, Baskent University
MS, PhD, Industrial Engineering, University of Arkansas
15 + years of industry experience
(Transplace, Zero Gap Analytics, IBM)
Adjunct Faculty at Bogazici, Koc, Baskent Universities
Author and Referee of many academic articles
Introduction
3

Data Science & AI Elite team
helps client to take the AI journey and win
What is DSE?
An international team of
Data Science and AI
experts
What do you get?
The Expertise to
transform your business
with AI; working side by
side with your client’s
team!
What do we offer?
Guidance, skills, tools,
and an agile approach
to kick start your AI
journey
IBM Investment
4

… Navy Training Scheduling Team
Gian
CP approach
Eray
MIP approach
Qiannan
Squad Leader,
CP approach
Hanadi
MIP approach
5

Agenda
1. Introduction
3. Our Approach
4. Conclusions
6

7
USE CASE
Optimize business-
critical operations
scheduling to meet
demand, under
multiple operational
restrictions and
business rules.
IBM Data Science & AI Elite and
Capita Consulting work together
to effectively solve challenging
Resource-Constrained
Scheduling Optimization
Problems
CASE STUDY
EXPECTED BENEFIT
Deliver optimized
recommendations.
Boost computational time
and problem scale with
IBM CPLEX.
Measure and control the
business impact of the
scheduling.
UNIQUE CHALLENGE
Many resource conflicts
induced by availability and
capacity of resources,
jobs’ release date and
demand due date.
Multiple objectives.
Various use-case
scenarios to be targeted.
LOGO
“Working with the team
and using CPLEX we were
able to reduce the time to
find a feasible solution for
this highly complex
problem from hours to
seconds.”
Vince Powell
Partner, Solutions Lead

Optimizing Recruit Training of a European Navy
• The Royal Navy has planned missions to be executed
• Every mission has a release date to kick off operations
A navy mission requires:
• Equipment
(aircraft carriers, vessels, submarines)
• Specialized personnel with specific certifications
(engineers, nurses, navy specialists)
The recruit training offers:
• Designation and specialization certification programs
(CIS eng., medical assistant, submarine eng. technician)
• Courses to get one or more specific skills
(radio ops, drowning treatment, microminiature repair)
IBM Cloud / April 2021 / © 2021 IBM Corporation

How does the training work?
Royal Navy organizes courses that have:
• Instructors (civilian and military)
• Facilities (equipment dedicated to the trainings only)
• Limited capacity (max number of allowed trainees)
Before jumping on any mission, recruits must get certified
for their designation (or specialization):
• A certification is awarded when a given set of skills is
acquired by the recruit
• A skill is acknowledged if any course covering the skill
has been completed by the recruit (no chance to fail!)
• Some course cannot be taken until all its preparatory
courses have been completed (no chance to escape!)
Ready to join the mission?
IBM Cloud / April 2021 / © 2021 IBM Corporation

Navy Training Scheduling (NTS)
• NTS can be formulated as an Optimization Problem, belonging to the class of Resource-Constrained
Scheduling (RCS)
• As many other real-life problems, scheduling problem involves multiple objectives i.e. maximize
number of recruits ready on-time and minimize average delays
• RCS is often very challenging to solve (precedences, auxiliary resources, release and due dates etc.)
finding feasible non-optimal solutions often requires unacceptable computational time, even for
instances of small-medium size!
• Our client used a commercial solver to solve the problem…
But they could provide a feasible (non-optimal) solution after hours of computation…
Ø That was not acceptable given the business needs of evaluating effectively and quickly the scheduling
against plans and changes
IBM Cloud / April 2021 / © 2021 IBM Corporation 10

Agenda
1. Introduction
3. Our Approach
4. Conclusions
11

Motivations & Ideas behind
the Solution Approaches
Target maximum complexity of the problem:
• No simplifying data assumptions
• No simplifying problem assumptions or
heuristic decompositions
Formulate 2 different approaches, based on:
• CPLEX Constraint Programming solver (CP)
• CPLEX Mixed Integer Programming solver (MIP)
Develop a flexible cloud application:
• Agnostic respect to the architecture
• Open range of deployment options
o Gain knowledge about how the solver responds
to the complexity of the problem
o Build a preliminary evidence whether the solver
can scale over the size of the problem and how
o CPLEX CP is powerful to model and solve
complex or even large RCS problems
o CPLEX MIP allows sophisticated math
programming methods tailored on the problem
o Leverage technology to empower the solution
o Open to multiple possibilities about hybrid /
public / private / (or on-prem) architectures
12

From the Business Problem
to the Math
Business
problem
Mathematical
Model
Solver
(CPLEX)
Mathematical
Solution
Business
decisions
Enterprise
Data
Code
(Python)
Code
(Python)
Enterprise
Data
13

… and then?
Qiannan + Gian:
– Freestyle formulation: “critical” to validate
assumptions, ask the right questions, gather all the
requirement, digest the client goal
– CP “translation” and implementation
Hanadi + Eray:
– Review freestyle formulation
– MIP “translation” and implementation
Settlement:
– We cooperated on the same source code (github),
developed in Python and solved on the docloud
– With the new version of the DO Deployments in WS
this is achievable very effectively
– We compared the results and cross-checked the
correctness of our formulations
• Our results: we solved the problem with CPLEX
in seconds with CP and in a couple minutes
with MIP (same HW specs)
Ø Experimented formulations with MIP and CP
Ø Technology: DO CPLEX on Cloud, now
migrated to WML
Ø Multiple deployment options
“Working with the team and using
CPLEX we were able to reduce the time
to find a feasible solution for this highly
complex problem from hours to
seconds.”
Vince Powell
Partner, Solutions Lead (Capita)
14

Results – Base Scenario
Boat B Boat A
KPIs
Boat A: 86%
Boat B: 14%
Avg-Delay: 0.88
Objective
Maximize # of recruits
ready on-time
&
minimize average
delays

Results – Scenario 1: Change of Recruitment Plan
Boat B Boat A
KPIs
Boat A: 100%
Boat B: 14%
Avg-Delay: 0.90
Description
All recruits ready
from week 1

Results – Scenario 2: Change of Facility Availability
Boat B Boat A
KPIs
Boat A: 86%
Boat B: 14%
Avg-Delay: 1.94
Description
Facility 3 not
available during
weeks 11 – 13

Results – Scenario 3: Change of Platform Schedule
Boat B
Boat A
KPIs
Boat A: 100%
Boat B: 100%
Avg-Delay: 1.35
Description
Extended
deadlines for
Boat A and B
resp. to weeks
13 and 15

Design of the Prototype
Language: Python 3 (OOP)
Solver Engines: IBM ILOG CPLEX CP & MIP (docplex APIs)
Open-source deps: networkx
Data Handler Optimizer Solution Handler
Read data from
(CSV) files
Model and solve with
CPLEX CP
Model and solve with
CPLEX MIP
Write solution to
(CSV) files
Get data from
REST API
Expose solution through
REST API
IBM ILOG CPLEX on Cloud / On-Prem / Local
Capita’s Application Environment

Agenda
1. Introduction
3. Our Approach
4. Conclusions
20

21
Conclusions | CP vs MIP
• IBM algorithms outperforms classical MIP approaches drastically in both solution
time and quality
• Large and hard problems which cannot be solved by MIPs are now solvable
• Difficult business rules can also be modeled easily
• i.e. Certain lots/activities must observe a maximum time between 2 steps (consecutive or not)
• Certain steps of oven-type (diffusion step) and batching with size constraints
[Reference 1] Ham. A and Cakici. E.,2016, Flexible job shop scheduling problem with parallel batch processing machine: MIP
and CP approaches, Computers & Industrial Engineering, 102,160-165
• Comparison of CP Optimizer vs different MIP formulations on a set of 20 instances from literature

22
Conclusions | CP vs Heuristics
• There is again significant performance difference between IBM CP Optimizer’s
unique algorithms and widely used heuristic approaches
• Better solutions can be achieved much more quickly
• Heuristics’ development and maintenance is not easy
• Solution quality not guaranteed as data and few parameters change
• Can require a lot of coding
[Reference 2] Ham. A, Fowler. J.W., Cakici. E., 2017, Constraint programming approach for scheduling jobs with different job
release times and incompatible families on parallel batching machines, IEEE Transactions on Semiconductor Manufacturing
• Comparison of CP Optimizer vs different heuristics & MIP models proposed by Cakici et. al.,2013 on a set of 2560
instances

23
Conclusions | Industrial Scheduling Problems
The classical job-shop scheduling problem
• Resource/machines are over-simplified
• In reality: setup-times, production modes, activities incompatibilities, batching, cumulative resources,
inventories (reservoirs), execution conditions (e.g. resource safety levels, auxiliary resources),...
• All operations are performed in a unique way
• In reality: resource allocation, optional operations, alternative recipes, hierarchical decomposition
• The single objective function is completely unrealistic
• In reality: combination of earliness/tardiness costs, nonexecution cost, resource related costs,
constraint violation, job/customer priorities…
• Real problems are often much larger than the size of current benchmarks
adapted from https://www.slideshare.net/PhilippeLaborie/planningscheduling-with-cp-optimizer - Philippe Laborie, Principal Scientist at IBM

24
Future Research | CP & MIP & Heuristics together
A Hybrid Approach for Machine Scheduling
Job assignment to machines with MIP or Heuristics (Master Problem)
+
Job sequencing & scheduling with CP (Subproblem)

25
Future Research | Application Areas
• Workforce Scheduling
• Vehicle Routing
• Advertisement Scheduling
• Sports League Scheduling
• Bridge Building
• Gate Allocation

26

Navy Training Scheduling - Euro 2021

Recommended

Recommended

More Related Content

Similar to Navy Training Scheduling - Euro 2021

Similar to Navy Training Scheduling - Euro 2021 (20)

Recently uploaded

Recently uploaded (20)

Navy Training Scheduling - Euro 2021