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, truck item packing, workshop layout optimization, etc. The Renault OR team has been using Hexaly Optimizer to solve many of these problems since 2016. In this case study, we present Renault’s packaging logistics optimization problem.
Renault’s packaging logistics optimization problem
The French carmaker uses packaging to transport its parts from suppliers to plants. The suppliers need enough packaging to ship the parts. Therefore, Renault must ensure enough packaging in circulation in its supply chain. The shipment of parts between suppliers and factories is supervised by Renault’s Material Requirements Planning (MRP) systems, like SAP.
Renault also reuses most of its packaging, which plants must send back to suppliers after receiving the parts. The transportation of parts and empty packaging generates complex packaging flows inside Renault Group’s supply network, as 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 that must be cleaned or repaired;
- Gray arrows represent the transport of the packaging to be discarded.
Efficiently handling empty packaging is crucial for Renault. Indeed, empty packaging management represents several million euros per year in terms of transportation and renewal costs. To reduce these costs, Renault’s OR team sought Hexaly’s help. As a result, return flows of empty packaging are now operated by the Packaging Management System (PMS), a custom software solution developed by Renault and based on Hexaly as an optimization engine.
Mathematical model and results
The mathematical model to be solved by Hexaly is characterized by the following decisions, constraints, and objectives:
- 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:
- Number of pallets transported 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 packaging shortages (immediate and in stock)
- Minimize the number of shipments
- Minimize the total distance traveled
This model enables Renault’s OR team to quickly solve even the largest instances of their packaging optimization problem. Indeed, a typical instance of this problem comprises
- 45,000 integer variables with large domain ranges, typically between 0 and 10,000
- 100,000 constraints
- 4 ordered objectives
and is solved to near-optimality within minutes. Indeed, Hexaly reaches an average optimality gap below 1.5% with a time limit of 5 minutes.
After a benchmark against IBM ILOG CPLEX, Renault‘s Operations Research team chose Hexaly to solve this packaging logistics optimization problem. The simple and powerful modeling language offered on top of Hexaly, LSP, allowed the team to develop the mathematical model quickly. Then, the innovative primal heuristics inside Hexaly, 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 Hexaly experts was also instrumental in moving the project forward quickly.
We use Hexaly Optimizer 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 Hexaly formalism. Besides, exchanging with the Hexaly team is always fruitful and pleasant.
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