Renault Group is a French multinational automobile manufacturer, and one of the largest automakers in the world. Its Operations Research department is in charge of solving the various optimization problems arising in the company. The most frequently encountered issues are related to Supply Chain Management: production scheduling, transportation optimization, network design, item packing in trucks, layout optimization of workshops, etc. Since 2016, the OR team uses LocalSolver to solve many of these problems. In this case, the Renault OR team sought to solve its packaging logistics optimization problem.
Renault’s packaging logistics optimization problem
The French carmaker uses packaging to transport its parts from suppliers to the plants. The suppliers need enough packaging to ship the parts. Hence, Renault must ensure that there is enough packaging in circulation in its supply chain at any time. In the end, empty packaging management represents several million euros per year in terms of transportation and renewal costs. Here we will overview how Renault optimizes packaging logistics in Europe.
The packaging flows inside Renault Group’s supply network are illustrated below:
- Orange arrows represent the parts’ shipping between the suppliers and the plants;
- Red arrows represent the return of empty packaging to the suppliers;
- Blue arrows represent the transport of empty packaging which must be cleaned or repaired;
- Gray arrows represent the transport of the packaging to be discarded.
The shipment of parts between suppliers and factories is driven by Renault’s Material Requirements Planning (MRP) systems, like SAP. Return flows of empty packaging are now operated by the Packaging Management System (PMS), a custom software solution developed by Renault and based on LocalSolver as an optimization engine.
Here is an overview of the optimization model to be solved by Renault:
- 1,400 suppliers in Europe
- 40 plants and cross docks
- 30 types of standard packaging
For each type of empty packaging, each day, each supplier, and each plant, one has to decide the number of pallets that will travel between the supplier and the plant
- Consistency between stocks and shipments
- Minimum quantities to allow shipping
- Maximum inflows and outflows per factory
- Stock balancing of transfer stations to avoid activity peaks
- Minimize the lack of packaging (immediate and in stock)
- Minimize the number of shippings
- Minimize the total distance traveled
Mathematical model and results
Here is the size of the instances to be solved once modeled using LocalSolver, and the key performance figures of the resolution:
- 45,000 integer variables with large ranges, typically between 0 and 10,000
- 100,000 constraints
- 4 ordered objectives
- Time limit: 5 minutes
- Optimality gap < 1.5%
After a benchmark against CPLEX, Renault’s Operations Research team finally chose LocalSolver to solve this packaging logistics optimization problem. The simple and powerful modeling language offered on the top of LocalSolver, namely LSP, allowed the team to develop the mathematical model quickly. Then, the innovative primal heuristics inside LocalSolver, combined with state-of-the-art exact methods, delivered proven near-optimal solutions in minutes of running times. The dedicated and responsive support provided by LocalSolver experts was also instrumental in moving the project fastly forward.
We use LocalSolver for several optimization problems, including scheduling door manufacturing in car factories and optimizing empty packaging return flows. What we appreciate most is the incomparable ease of modeling provided by LocalSolver mathematical formalism. Besides, exchanging with the LocalSolver team is always extremely fruitful and highly pleasant.