bringing the power of localsolver to r: a real-life case-study

Wit Jakuczun, PhD – CEO at WLOG [email protected]

Optimization with R

Bringing the Power of LocalSolver to R: aReal-Life Case-Study


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, optimization

Analytical problems:


One size doesnot fit allSoftware ecosystems:R, SAS, Python,JavaScript, PHP,Java, C++, …

Optimization:LocalSolver, Gurobi, IBM (ILOG), Sicstus, ECLiPSe, COIN-OR, GLPK


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


LOCALSOLVER PACKAGE


localsolver package architecture

Solution presentationGNU R (e.g. shiny app)

SolvingLocalSolver engine

Model buildingLSP language

Data preparationGNU R


LocalSolver engine

Innovative math modeling language New generation hybrid solver


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


Solving k-medoids: a comparisonRglpk• 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


LOGISTIC NETWORK PLANNING FOR POULTRY MEAT PRODUCER


Poultry meat logistic network planning problem

Factories

3 locations

Warehouses18 locations3 different sizes

Customers

3000 locations

Products 2 types (fresh, processed)thousands of SKUs

Choose optimal locations and capacities of warehouses


THANK YOU!

bringing the power of localsolver to r: a real-life case-study

Data & Analytics