• Automotive
• Gurobi
• Inbound Transports
• Lamborghini
• Optimization

For several years now, Industry 4.0 has represented a concept of organizing production and maintaining supply chains that many industries worldwide strive to implement. The automotive sector is at the forefront of this new "industrial revolution," with Automobili Lamborghini, a renowned Italian company, significantly enhancing its expertise since the launch of its super SUV.
A big player in the digitalization of the supply chain is the Transport Management System, an online platform, where each shipment is monitored starting from the order placement for the transport service and all the way to the unloading process in the warehouse.
In this framework, being able to automatically and seamlessly re-balance the inbound transports unloading schedule would have a significant positive impact on productivity and cost reduction.
For this purpose, an unloading schedule balancing model is formulated and later solved, highlighting its benefits when compared with the original scheduling. In particular, re-distributing the unloading agenda, the maximum workload associated to each day dropped on average by 36%.

For several years now, Industry 4.0 has represented a concept of organizing production and maintaining supply chains that many industries worldwide strive to implement. The automotive sector is at the forefront of this new "industrial revolution," with Automobili Lamborghini, a renowned Italian company, significantly enhancing its expertise since the launch of its super SUV. A big player in the digitalization of the supply chain is the Transport Management System, an online platform, where each shipment is monitored starting from the order placement for the transport service and all the way to the unloading process in the warehouse. In this framework, being able to automatically and seamlessly re-balance the inbound transports unloading schedule would have a significant positive impact on productivity and cost reduction. For this purpose, an unloading schedule balancing model is formulated and later solved, highlighting its benefits when compared with the original scheduling. In particular, re-distributing the unloading agenda, the maximum workload associated to each day dropped on average by 36%.