ROSTUDEL demos & samples

Thanks to our new JAVA remote platform, you will find soon here some interactive demos of ROSTUDEL applications. Meanwhile, we provide here some code samples or screenshots that show the power of modeling using cutting-edge platforms we intensively use at ROSTUDEL.
Adress sparsity in OPL

OPL and sparsity : a common pitfall in modeling is to get stucked in tedious data structures. Imagine you have some people in an organization that can work everyday but some prefixed vacations and you would like to set up a constraint only on those working days. Furthermore, let's assume you would also like to count only those worked hours that correspond to "odd" days (Monday, Wednesday, Friday and Sunday) and exclude the first person of your employees from this set of person.

Well, coding corresponding data structures would be tedious in a usual object-oriented language such as C++ or JAVA. Meanwhile, it is almost straightforward as you could see in the code sample. Note the beauty of collection filtering, 
Comprehension arrays in OptimJ

OPTIMJ and comprehension arrays: in this screenshot, you can see the eclipse environment with a file with an .optimj extension. The code illustrates the powerful comprehension notation in OPTIMJ that allows to build arrays or maps with advanced filters. The developpers benefits from a very high level of abstraction similar to HQL in the ORM world. Note the bottom panel that shows the log including lp-solve logs. The left panel shows the benefit of including directly .optimj files within your eclipse project at no additional cost.
Scheduling Sample

Scheduling in OPL6: Combinatorial problems involving tasks to schedule under precedence constraints and resource availability are one of the hardest problems to solve. They include for instance PERT, Job-Shop, Open-Shop... OPL6 introduces high level constraint programming concepts addressing this issue, including interval variables and cumulative functions. The sample shows a practical scheduling model minimizing the makespan for a set of tasks linked by precedence constraints, and consuming two kind of resources : a unary resource and a capacity one. Thanks to new default search heuristics, OPL6 runs this model to optimality for 3000 tasks, and achieves the same quality for a number of other benchmarks.
ODM sample

What-if analysis with ODM: Analysts and customers are very demanding in scenarios analysis : what if I increase my inventory areas, what if prices rise in two months, should I buy for stock now or just when the order falls ? Thanks to the ODM platform, scenarios can be created on the fly from an OPL model, duplicated for data changes, analysed on basis of several kpi's, compared to a base scenario. On this screenshot, we compare the inventory level of a given item subject to nominal purchasing prices (lower curve) with an “inflation scenario” where the prices increase from period 5. We clearly notice the order anticipation at period 4 that reflects a “purchase-to-stock” policy rather than buying at higher prices.