Minimizing Total Tardiness in Parallel Machines with Simulated Annealing using Python
Production scheduling is important for the electronic component manufacturing industry made from silicone rubber. Product completion time is one of the problems faced by the company. It often happens that products completion incapable of meeting due dates. This is caused by the inability of the molding workstation to complete the production process in the expected time. The production process requires 6 workstations with a hybrid flow-shop process, and one of them is a molding workstation. The problem encountered was to schedule 37 types of products with different order quantities and due dates. The lot size for each batch is different for each type of product, therefore each type of product with different order quantity will have different number of batches. The aim of the research is to minimize the total tardiness in batch completion time, using the metaheuristic approach Simulated Annealing (SA) with Python programming. The case study in this paper is to arrange the schedule of 37 types of products consisting of 574 batches at a molding workstation with 45 parallel machines that are independent. The results of data processing showed that total tardiness in molding machines was 13700 minutes.