AI-Based Discrete-Event Simulations for Manufacturing Schedule Optimization

Abstract

Optimization of manufacturing schedules is of great theoretical and practical significance, especially when high-tech products are produced in large manufacturing networks with global supply chains. In this paper, a novel approach is described how discrete-event simulations shall be enhanced by Artificial Intelligence (AI) to optimize manufacturing schedules for complex job shop manufacturing networks in the high-tech energy industry.This work attempts to close the gap between the growing expectations on AI-based discrete-event simulations for manufacturing schedule optimization on the one hand and the current limitations of data quality and Information Technology (IT) capabilities in existing Enterprise Resource Planning (ERP) systems on the other hand. In order to deliver the expected benefits, business targets of schedule optimization need to be understood, the solution approach has to be defined and verified in a complex manufacturing environment. Performance and reliability of the implemented solution are validated with two real data sets derived from ongoing business planning activities.An actor-critic architecture is proposed which uses a multi-stage schedule compression algorithm for modifying the start dates of production orders according to defined business targets and a genetic algorithm which selects production orders for automated make or buy decisions. The first research result is the provision of a manufacturing schedule for 375 product types, more than 180 manufacturing resources, 367 process variants (routings), and a total of 1.293 production orders at about 80% average capacity utilization of bottleneck machinery in a 12 months planning horizon, which is optimized according to lead times of customer products. The second research result is the generation of a manufacturing schedule for 3.657 production orders in a 24 months planning horizon, where the optimal outsourcing portion is calculated according to defined extensions of standard lead times within the same supply and manufacturing network.