Machinery
Machinery
Production scheduling optimization
This project was carried out by a company operating in the field of design and construction of metal processing plants. It started with a number of objectives, including the improvement of production processes, the reduction of machine downtime, the optimization of production times and the reduction of delivery delays.
- Description and benefits
The clients’ productions are characterized by a series of batches that are processed within the available fleet of machines. This is a complex process that must take into account several factors such as the consecutive elaboration of batches on different machines, long set up times, deliveries and priorities that change daily.
All these variables can cause delays. We have formalized the various aspects in mathematical terms and have created a scheduling software that, thanks to optimization algorithms, allows a quick and effective planning of the processes, respecting the desired criteria. This way, the scheduler operator has an optimized solution generated by the algorithm on which to intervene for minor changes.
Decreased machine downtime
Thanks to the result of the optimizer, the machines are used at their best, reducing the waiting time caused by the sequence of operations on different machines
Reduced planning time
It is possible to optimally plan the activity in a few minutes, compared to the hours previously required
Increased competitiveness
The software is integrated in the processing machines, representing an important competitive advantage for companies
Deepening case study
Scheduling software based on a mathematical model
Our mathematical model describes the overall plant, composed of all the machines available to the company and the set of operations that must be carried out on the batches. We have translated the production logic into mathematical constraints that help to determine, very quickly, whether a certain schedule is feasible or not. In order to solve the optimization problem associated with scheduling, we have also implemented an optimization algorithm, developed ad hoc and based on genetic algorithms. Our solution returns the optimal schedule, with respect to variables of interest such as completion time, machine workload, and total delivery delay.
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