Method and system for generating optimized workshop schedules

A system for generating optimized workshop schedules, comprising:

• an acquiring module configured to collect production data and routing information from an Enterprise Resource Planning (ERP) system or an Enterprise Data Warehouse (EDW) system;
• an optimization module configured to operatively run an optimization model to generate a workshop production schedule based on the collected production data and the collected routing information;
• a fine-tuning module configured to tune the workshop production schedule to create an enriched schedule comprising at least alternative routing information, wherein the fine-tuning of the workshop production schedule includes at least one of:
• balancing workloads among one or more resources in a workshop;
• and
• adjusting the workshop production schedule based on real-time information in the workshop;
• and
• a Machine Learning (ML) module configured to operatively run a supervised ML model to learn data constraints and one or more operational targets for generating an optimized workshop schedule based on the created enriched schedule, wherein the ML module being further configured to generate a final schedule for use in the workshop based on the learned data constraints and the learned operational targets.

A non-transitory computer-readable medium having stored thereon computer-readable instructions that, when executed by a processor, cause the processor to execute a method for generating optimized workshop schedules, comprising:

• collecting production data and routing information from an Enterprise Resource Planning (ERP) system or an Enterprise Data Warehouse (EDW) system;
• generating, by an optimization model, a workshop production schedule based on the collected production data and the collected routing information;
• fine-tuning the workshop production schedule to create an enriched schedule comprising at least alternative routing information, wherein the fine-tuning of the workshop production schedule includes at least one of:
• balancing workloads among one or more resources in a workshop;
• and
• adjusting the workshop production schedule based on real-time information in the workshop;
• training a supervised Machine Learning (ML) model to learn data constraints and one or more operational targets for generating an optimized workshop schedule based on the created enriched schedule;
• and
• deploying the trained ML model to generate a final schedule for use in the workshop based on the learned data constraints and the learned operational targets.