OPTIGLASS: Application of Artificial Intelligence-based techniques for optimizing the continuous Glass Cutting Problem
Problem and technical solution
Float glass manufacturing is a continuous process whereby a ribbon of molten glass is produced in a furnace and then cooled on a bath of molten tin to ensure flatness. The stream of glass is pulled along the top of the molten tin by haul-off conveyors at the end of the float area which transport the glass into the annealing lehr. Then the sheet of glass is cut according to customer requirements, and it is offloaded for storage and distribution. This process involves many tasks that must be optimized to improve profitability. This industry is considered an energy intensive industry, so the main objective is to maximize the production of glass with the given energy consumption. The information reported by the inspection scanners to detect flaws in the glass is stored and used to determine the cutting process. The previous cutting process used a greedy algorithm that does not take into account all available information about the distribution of flaws in the layout. The main objective of the TTP OPTIGLASS is to design and transfer Artificial Intelligence-based technology to solve two interrelated scheduling problems involved in the cutting process of a glass manufacturing company. These problems are the customer order selection problem, to select, from the batch of all customer orders, the four more appropriate ones to be inserted into the system, and the glass cutting problem to generate the sequence of sheets of the input customer orders to minimize the layout scrap.
To this end, and given the information reported by the inspection scanners, our tool carries out a preprocessing step to obtain feasible sections of glass that can satisfy the customer requirement according to quality constraints. Thus, the constraint programming based algorithm determines the best glass sheet that minimizes the cullet. This algorithm must search for all sheet possibilities to decide which sheet must be selected to be cut. It is necessary that the algorithm finishes in a given time due to the continuous glass manufacturing process. The algorithm includes a heuristic search technique to obtain anytime optimized solutions in order to be adapted to all thickness ranges.
How did TETRACOM help
As part of the TETRACOM initiative, the OPTIGLASS project partially supports the development of a simulation tool that serves to analyze different scenarios with large sets of customer orders and layouts according to historic data. The experts of the company have a large log with data about the customer orders, and the glass waste obtained for each scheduling day. This has been very useful to develop the simulation tool to solve the customer order selection and the glass cutting problem.
In the TTP, the proposed algorithm has been compared with the existing algorithm (Greedy) to analyze the behavior of both techniques and the achieved improvement.
The profitability of applying intelligent techniques to optimize problems associated to this process is twofold: the company remains more competitive and also it is more sustainable.
The tool developed in this TTP has reduced the cullet of the company and increased the competitiveness of the company in the market. The company ensures that they save around 150,000€/year.