OPaPi: Optimized Parts Pick-up routing for efficient manufacturing

Abstract

Manufacturing environments require changes in work procedures and settings based on changes in product demand affecting the types of products for production. Resource re-organization and time needed for worker adaptation to such frequent changes can be expensive. For example, for each change, managers in a factory may be required to manually create a list of inventory items to be picked up by workers. Uncertainty in predicting the appropriate pick-up time due to differences in worker-determined routes may make it difficult for managers to generate a fixed schedule for delivery to the assembly line. To address these problems, we propose OPaPi, a human-centric system that improves the efficiency of manufacturing by optimizing parts pick-up routes and schedules. OPaPi leverages frequent pattern mining and the traveling salesman problem solver to suggest rack placement for more efficient routes. The system further employs interactive visualization to incorporate an expert’s domain knowledge and
different manufacturing constraints for real-time adaptive decision making.