Iwan Halim Sahputra
Faculty of Industrial Technology, Petra Christian University

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FUZZY INFERENCE SYSTEM MODELING FOR BED ACTIVE CARBON RE-GENERATION PROCESS (CO2 GAS FACTORY CASE) S. Febriana; M.D. Kurniawati; Iwan Halim Sahputra; I Nyoman Sutapa
Jurnal Teknik Industri Vol. 7 No. 2 (2005): DECEMBER 2005
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (217.845 KB) | DOI: 10.9744/jti.7.2.pp. 119-126

Abstract

Bed active carbon is one of the most important materials that had great impact in determining level of impurities in production of CO2 gas. In this particular factory case, there is unavailability of standard duration time of heating and cooling and steam flow rate for the re-generation process of bed active carbon. The paper discusses the fuzzy inference system for modeling of re-generation process of bed active carbon to find the optimum setting parameter. The fuzzy inference system was build using real historical daily processing data. After validation process, surface plot analysis was performed to find the optimum setting. The result of re-generation parameter setting is 9-10 hours of heating process, 4.66-5.32 hours of cooling process, and 1500-2500 kg/hr of steam flow rate.
ROBUST-HYBRID GENETIC ALGORITHM FOR A FLOW-SHOP SCHEDULING PROBLEM (A Case Study at PT FSCM Manufacturing Indonesia) Johan Soewanda; Tanti Octavia; Iwan Halim Sahputra
Jurnal Teknik Industri Vol. 9 No. 2 (2007): DECEMBER 2007
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (234.804 KB) | DOI: 10.9744/jti.9.2.pp. 144-151

Abstract

This paper discusses the application of Robust Hybrid Genetic Algorithm to solve a flow-shop scheduling problem. The proposed algorithm attempted to reach minimum makespan. PT. FSCM Manufacturing Indonesia Plant 4's case was used as a test case to evaluate the performance of the proposed algorithm. The proposed algorithm was compared to Ant Colony, Genetic-Tabu, Hybrid Genetic Algorithm, and the company's algorithm. We found that Robust Hybrid Genetic produces statistically better result than the company's, but the same as Ant Colony, Genetic-Tabu, and Hybrid Genetic. In addition, Robust Hybrid Genetic Algorithm required less computational time than Hybrid Genetic Algorithm