PENGEMBANGAN SISTEM PENJADWALAN INTERAKTIF UNTUK PRODUKSI JOB SHOP DENGAN MENGGUNAKAN ALGORITMA GENETIKA PADA CV CIPTA SINERGI MANUFACTURING

• Main Author: Mazaya Awanis, Nurshadrina
• Format: Final Project
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/77525
• Institution: Institut Teknologi Bandung

CV Cipta Sinergi Manufacturing (CV CSM) is a manufacturing company that produces machinery products based on Make-To-Order (MTO) method. CV CSM has a monthly on time delivery target of 90%. However, this target has not yet been achieved in January until March 2023. The cause of production delay is the inaccuracy of production scheduling plans with the actual production condition due to scheduling process that is still done manually. An interactive scheduling system has been developed to solve this issue and currently can only accommodate scheduling using non delay algorithm and insertion algorithm that has focus only on rearranging job schedules within a single machine. The production case at CV CSM can be categorized as a job shop problem, as it involves allocating m machines to complete n different jobs that each consisting of multiple operations. This research proposes a genetic algorithm as a solution to solve the job shop problem since it can provides alternative machine allocation. CV CSM has six CNC milling machines, two CNC lathe machines, four conventional milling machines, two drilling machines, and two welding machines that can be used in parallel. Based on that, optimizing machine usage can be a solution to minimize makespan in order to reach on time delivery target. Integrating genetic algorithm into the interactive scheduling system can provide additional solution alternatives as a consideration for PPIC department to make decision on weekly production schedule. Based on research results, genetic algorithm is capable of generating better solutions for a 21 jobs in weekly production schedule, resulting in a production time reduction of 27,9%. This reduction happened because the allocation of machine usage is more balance. Better alternative solutions that is being generated by genetic algorithm can serve as considerations for PPIC departments in determining weekly production schedules.