Open AccessPENJADWALAN PESANAN MENGGUNAKAN ALGORITMA GENETIKA UNTUK TIPE PRODUKSI HYBRID AND FLEXIBLE FLOWSHOP PADA INDUSTRI KEMASAN KARTON
Author(s) -
Nora Azmi,
Irawadi Jamaran,
Yandra Arkeman,
Djumali Mangunwidjaja
Publication year - 2012
Publication title -
jurnal teknik industri
Language(s) - English
Resource type - Journals
eISSN - 2622-5131
pISSN - 1411-6340
DOI - 10.25105/jti.v2i2.7028
Subject(s) - computer science , job shop scheduling , scheduling (production processes) , schedule , job scheduler , flow shop scheduling , job shop , process (computing) , heuristic , genetic algorithm , mathematical optimization , artificial intelligence , mathematics , operating system , machine learning , cloud computing
This research was intended to produce an order (job) scheduling model at Carton Packaging Industries (CPI) that is useful for giving information about the time delivery to customers. The proposed model is quite complicated because of the characteristic of Make to Order (MTO) varies production process greatly between each order. The job’s schedule for CPI is prepared for production process that consists of 5 stages where in each stage uses different type of machinery. Not all jobs can be processed by all machines at a given production stage. Every job flow through 5 stage in the same order, but not all stages have to visited by all jobs. Stages may be skipped for a particular job. This condition makes CPI is classified as Hybrid and flexible flowshop for machine eligibility (HFFME). HFFME is complicated and is difficult to calculate by using conventional heuristic model. This research used genetic algorithm for solving the complex problem of HFFME and the resulting model called the Genetic Algorithm for hybrid and flexible flowshop with machine eligibility (GA-HFFME). This model is developed to minimized makespan, the objective of scheduling. The experiment was conducted towards 11 orders and it was found that the GA-HFFME is effective to solve that problem.