A cloud based job sequencing with sequence-dependent setup for sheet metal manufacturing

This paper presents a prototype system of sheet metal processing machinery which collects production order data, passes current information to cloud based centralized job scheduling for setup time reduction and updates the production calendar accordingly. A centralized cloud service can collect and analyse production order data for machines and suggest optimized schedules. This paper explores the application of sequencing algorithms in the sheet metal forming industry, which faces sequence-dependent changeover times on single machine systems. We analyse the effectiveness of using such algorithms in the reduction of total setup times. We describe alternative models: Clustering, Nearest Neighbourhood and Travelling Salesman Problem, and then apply them to real data obtained from a manufacturing company, as well as to randomly generated data sets. Based on the prototype implementation clustering algorithm was proposed for actual implementation. Sequence-dependency increases the complexity of the scheduling problems; thus, effective approaches are required to solve them. The algorithms proposed in this paper provide efficient solutions to these types of sequencing problems.