###### 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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