A presentation I conducted at EARL 2014 in London.

1. Wit Jakuczun, PhD – CEO at WLOG Solutions
wit.jakuczun@wlogsolutions.pl
Optimization with R
Bringing the Power of LocalSolver to R: a
Real-Life Case-Study

2. Wit Jakuczun, PhD – CEO at WLOG Solutions
wit.jakuczun@wlogsolutions.pl
Diversity challenges at WLOG Solutions
• finance, logistics, production, telecoms, publicIndustries:
• on-site, near shore, off shoreDelivery models:
• consulting, solution implementation, trainingContract types:
• data fusion, prediction, visualization, simulation,
optimizationAnalytical problems:

3. Wit Jakuczun, PhD – CEO at WLOG Solutions
wit.jakuczun@wlogsolutions.pl

4. Wit Jakuczun, PhD – CEO at WLOG Solutions
wit.jakuczun@wlogsolutions.pl
One size does
not fit all
Software ecosystems:
R, SAS, Python,
JavaScript, PHP,
Java, C++, …
Optimization:
LocalSolver, Gurobi, IBM (ILOG),
Sicstus, ECLiPSe, COIN-OR, GLPK

5. Wit Jakuczun, PhD – CEO at WLOG Solutions
wit.jakuczun@wlogsolutions.pl
Optimization tool heaven for R
• Seamless to wrap into R processing workflow
• Separation of model and data specification
• High level definition of optimization task
• Swiss army knife (we would love to get a free
lunch )
• Reliable support and continued development
• Free and open-source

6. Wit Jakuczun, PhD – CEO at WLOG Solutions
wit.jakuczun@wlogsolutions.pl
LOCALSOLVER PACKAGE

7. Wit Jakuczun, PhD – CEO at WLOG Solutions
wit.jakuczun@wlogsolutions.pl
localsolver package architecture
Solution presentation
GNU R (e.g. shiny app)
Solving
LocalSolver engine
Model building
LSP language
Data preparation
GNU R

8. Wit Jakuczun, PhD – CEO at WLOG Solutions
wit.jakuczun@wlogsolutions.pl
LocalSolver engine
Innovative math modeling language New generation hybrid solver

9. Wit Jakuczun, PhD – CEO at WLOG Solutions
wit.jakuczun@wlogsolutions.pl
Why localsolver package?
Current optimization
packages
• Many tools for one task
• Low level API
• Low performance
• Restricted modelling
approach
localsolver package
• One tool for many tasks
• High-level API
• High performance
• Wide range of
applications
• What we got:
• Shorter projects
• Simpler to debug
• Lower delivery costs

10. Wit Jakuczun, PhD – CEO at WLOG Solutions
wit.jakuczun@wlogsolutions.pl
Solving k-medoids: a comparison
Rglpk
• 45 LOC
• 1 hour including „stupid”
bugs
• Need to populate
constraint matrix from R’s
data structures
• No high-level math
modelling language
localsolver
• 18 LOC
• 15 minutes spent mostly
inventing model
• Staying with R’s data
structures
• Flexible high-level math
modelling language
http://rsnippets.blogspot.com/2014/07/comparing-localsolver-with-rglpk-on-k.html

11. Wit Jakuczun, PhD – CEO at WLOG Solutions
wit.jakuczun@wlogsolutions.pl
LOGISTIC NETWORK PLANNING
FOR POULTRY MEAT PRODUCER

12. Wit Jakuczun, PhD – CEO at WLOG Solutions
wit.jakuczun@wlogsolutions.pl
Poultry meat logistic network planning
problem
Factories
3 locations
Warehouses
18 locations
3 different sizes
Customers
3000 locations
Products
2 types (fresh,
processed)
thousands of SKUs
Choose optimal locations and
capacities of warehouses

13. Wit Jakuczun, PhD – CEO at WLOG Solutions
wit.jakuczun@wlogsolutions.pl
THANK YOU!