Abstract
In this study, the production process of Karaman Textiles, for which production planning is done manually, was observed and analyzed. This study aims to minimize the unused machine capacity and idle time of the existing system by developing a mixed integer programming (MIP) model, developing scenarios, and obtaining the results with GAMS software. In addition, the results of the scenarios are compared and evaluated to reach the optimal result that meets and maximizes the efficiency of the production process. The company's usual production planning is done manually and determined according to annual demand. The study aims to minimize the machine working times and that complete planning of the production in the most optimal way. In this study, the company's production time and machine usage times were optimized, and under normal conditions, 15,000 fabric pants were produced for 8 hours a day, 5 days a week, and 4 weeks a month. The scenario analysis aimed to produce by adding 4,000 pieces of velvet pants in addition to 15,000 fabric pants in the first scenario. The second scenario aims to minimize the production time of 15,000 fabric pants by limiting the working times of the machines owned by the company.
Keywords
Assignment Problem Mixed-Integer-Linear Programming Production Line Textile Industry Production Planning
References
- Bevilacqua M., Ciarapica, F., Crosta, A., Mazzuto G., and Paciarotti, C., 2013. Designing an efficient production system: A case study of a clothing company, Int. J. Eng., vol. 5, pp. 36.
- Bongomin, O., Mwasiagi, J., Nganyi, E., and Nibikora, I., 2020. Simulation metamodeling approach to complex design of Garment Assembly Lines, PLOS ONE, vol. 15, no. 9, pp. 1-22.
- Boysen, N., Schulze, P., & Scholl, A., 2022. Assembly line balancing: What happened in the last fifteen years?, European Journal of Operational Research vol. 301, no. 3, pp. 797-814.
- Brooke, A., Kendrick, D., Meeraus, A., Raman, R., 1998. GAMS:A User's Guide. GAMS Development Co., Washington, DC.
- Deenen, P., Adan, J., and Akcay, A., 2020. Optimizing class-constrained wafer-to-order allocation in semiconductor back-end production, J. Manuf. Syst., vol. 57, pp. 72-81.
- Durakbasa, N.M., and Gençyılmaz, M.G., 2022a. Digitizing Production Systems, LNME, pp. 500–509.
- Durakbasa, N.M., and Gençyılmaz, M.G., 2022b. Digitizing Production Systems, LNME, pp. 510-518.
- Hsu, H.M., Hsiung, Y., Chen, Y.Z., and Wu, M.C., 2009. A GA methodology for the scheduling of yarn-dyed textile production, Exp. Syst. with Appl., vol. 36, pp. 12095-12103.
- Huynh, N., and Chien, C., 2018. A hybrid multi-subpopulation genetic algorithm for textile bach dyeing scheduling and an empirical study, Comp. & Ind. Eng., vol. 125, pp. 615-627
- Katiraee, N., Calzavara, M., Finco, S., and Battini, D., 2021. Consideration of workforce differences in assembly line balancing and worker assignment problem, IFAC-PapersOnLine, vol. 54, no. 1, pp. 13-18.
- Kim, J.G., Song, S., and Jeong, B.J., 2020. Minimising total tardiness for the identical parallel machine scheduling problem with splitting jobs and sequence-dependent setup times, Int. J. Prod. Res, vol. 58, no. 6, pp. 1628-1643.
- Lim, H.W., Cassidy, T., 2017. A comparative study of trouser pattern making methods, Text. Eng. & Fash. Tech., vol. 1, no. 5, pp. 189-196.
- Pilati, F., Lelli, G., Regattieri, A., & Ferrari, E., 2022. Assembly line balancing and activity scheduling for customised products manufacturing. The International Journal of Advanced Manufacturing Technology, vol. 120, no: 5, pp. 3925-3946.
- TÜİK (Turkish Statistical Institute), 2020, Available: https://data.tuik.gov.tr/Bulten/Index?p=Yillik-Sanayi-Urun-(PRODCOM)-Istatistikleri-2020-37494
- TÜİK, (Turkish Statistical Institute), 2021. Turkey Manufacturing Production. Available: https://data.tuik.gov.tr/
- Ünal, C., Yüksel, A.D., 2020. Cut order planning optimization in the apparel industry, Fibr. & Text. In East. Eur., vol. 139, no: 1, pp. 8-13.
- Wang, T.J., Peng, J.Y., Hung, Y.F., 2016. Modeling fabric cutting scheduling as mixed integer programming, in IEEE Int. Conf. Ind. on. Eng. & Eng. Mng., 2016, pp. 922-926.
- Yemane, A., Gebremicheal, G., Meraha, T., and Hailemicheal, M., 2020. Productivity improvement throught line balancing by using simulation modelling, J. Optim. Ind. Eng., vol. 13, no. 1, pp. 153-165.
- Zhang, G., Shang, X., Alawneh, F., Yang, Y., Nishi, T., 2021. Integrated production planning and warehouse storage assignment problem: An IoT assisted case, Int. J. of Prod. Econ., vol. 234, pp. 108058.
- Zhao, R., Zou, G., Su, Q., Zou, S., Deng, W., Yu, A., & Zhang, H., 2022. Digital Twins-Based Production Line Design and Simulation Optimization of Large-Scale Mobile Phone Assembly Workshop, Machines, 10(5), 367.
Abstract
Bu çalışmada, üretim planlaması elle yapılan Karaman Tekstil'in üretim süreci gözlemlenmiş ve analiz edilmiştir. Bu çalışma, bir karma tamsayılı programlama modeliyle, senaryolar geliştirerek ve sonuçları GAMS yazılımı ile elde ederek mevcut sistemin kullanılmayan makine kapasitesini ve boşta kalma süresini en aza indirmeyi amaçlamaktadır. Ayrıca, üretim sürecinin verimliliğini karşılayan ve maksimize eden optimal sonuca ulaşmak için senaryoların sonuçları karşılaştırılmakta ve değerlendirilmektedir. Şirketin olağan üretim planlaması elle yapılmakta ve yıllık talebe göre belirlenmektedir. Çalışma, makine çalışma sürelerinin en aza indirilmesini ve üretim planlamasının optimal şekilde tamamlanmasını amaçlamaktadır. Bu çalışmada, firmanın üretim süresi ve makine kullanım süreleri optimize edilmiş olup, normal şartlar altında, günde 8 saat, haftada 5 gün ve ayda 4 hafta olmak üzere 15.000 kumaş pantolon üretilmiştir. Senaryo analizi, ilk senaryoda 15.000 adet kumaş pantolona ek olarak 4.000 adet kadife pantolon eklenerek üretilmesi hedeflenmiştir. İkinci senaryoda ise şirkete ait makinelerin çalışma sürelerinin sınırlandırılarak 15.000 kumaş pantolon üretim süresinin en aza indirilmesi planlanmıştır.
Keywords
Atama Problemi Karışık Tam Sayılı Doğrusal Programlama Üretim Hattı Tekstil Endüstris Üretim Planlama
References
- Bevilacqua M., Ciarapica, F., Crosta, A., Mazzuto G., and Paciarotti, C., 2013. Designing an efficient production system: A case study of a clothing company, Int. J. Eng., vol. 5, pp. 36.
- Bongomin, O., Mwasiagi, J., Nganyi, E., and Nibikora, I., 2020. Simulation metamodeling approach to complex design of Garment Assembly Lines, PLOS ONE, vol. 15, no. 9, pp. 1-22.
- Boysen, N., Schulze, P., & Scholl, A., 2022. Assembly line balancing: What happened in the last fifteen years?, European Journal of Operational Research vol. 301, no. 3, pp. 797-814.
- Brooke, A., Kendrick, D., Meeraus, A., Raman, R., 1998. GAMS:A User's Guide. GAMS Development Co., Washington, DC.
- Deenen, P., Adan, J., and Akcay, A., 2020. Optimizing class-constrained wafer-to-order allocation in semiconductor back-end production, J. Manuf. Syst., vol. 57, pp. 72-81.
- Durakbasa, N.M., and Gençyılmaz, M.G., 2022a. Digitizing Production Systems, LNME, pp. 500–509.
- Durakbasa, N.M., and Gençyılmaz, M.G., 2022b. Digitizing Production Systems, LNME, pp. 510-518.
- Hsu, H.M., Hsiung, Y., Chen, Y.Z., and Wu, M.C., 2009. A GA methodology for the scheduling of yarn-dyed textile production, Exp. Syst. with Appl., vol. 36, pp. 12095-12103.
- Huynh, N., and Chien, C., 2018. A hybrid multi-subpopulation genetic algorithm for textile bach dyeing scheduling and an empirical study, Comp. & Ind. Eng., vol. 125, pp. 615-627
- Katiraee, N., Calzavara, M., Finco, S., and Battini, D., 2021. Consideration of workforce differences in assembly line balancing and worker assignment problem, IFAC-PapersOnLine, vol. 54, no. 1, pp. 13-18.
- Kim, J.G., Song, S., and Jeong, B.J., 2020. Minimising total tardiness for the identical parallel machine scheduling problem with splitting jobs and sequence-dependent setup times, Int. J. Prod. Res, vol. 58, no. 6, pp. 1628-1643.
- Lim, H.W., Cassidy, T., 2017. A comparative study of trouser pattern making methods, Text. Eng. & Fash. Tech., vol. 1, no. 5, pp. 189-196.
- Pilati, F., Lelli, G., Regattieri, A., & Ferrari, E., 2022. Assembly line balancing and activity scheduling for customised products manufacturing. The International Journal of Advanced Manufacturing Technology, vol. 120, no: 5, pp. 3925-3946.
- TÜİK (Turkish Statistical Institute), 2020, Available: https://data.tuik.gov.tr/Bulten/Index?p=Yillik-Sanayi-Urun-(PRODCOM)-Istatistikleri-2020-37494
- TÜİK, (Turkish Statistical Institute), 2021. Turkey Manufacturing Production. Available: https://data.tuik.gov.tr/
- Ünal, C., Yüksel, A.D., 2020. Cut order planning optimization in the apparel industry, Fibr. & Text. In East. Eur., vol. 139, no: 1, pp. 8-13.
- Wang, T.J., Peng, J.Y., Hung, Y.F., 2016. Modeling fabric cutting scheduling as mixed integer programming, in IEEE Int. Conf. Ind. on. Eng. & Eng. Mng., 2016, pp. 922-926.
- Yemane, A., Gebremicheal, G., Meraha, T., and Hailemicheal, M., 2020. Productivity improvement throught line balancing by using simulation modelling, J. Optim. Ind. Eng., vol. 13, no. 1, pp. 153-165.
- Zhang, G., Shang, X., Alawneh, F., Yang, Y., Nishi, T., 2021. Integrated production planning and warehouse storage assignment problem: An IoT assisted case, Int. J. of Prod. Econ., vol. 234, pp. 108058.
- Zhao, R., Zou, G., Su, Q., Zou, S., Deng, W., Yu, A., & Zhang, H., 2022. Digital Twins-Based Production Line Design and Simulation Optimization of Large-Scale Mobile Phone Assembly Workshop, Machines, 10(5), 367.
Details
| Primary Language | English |
|---|---|
| Subjects | Industrial Engineering |
| Journal Section | Research Articles |
| Authors | |
| Publication Date | March 27, 2023 |
| Submission Date | April 11, 2022 |
| Acceptance Date | December 12, 2022 |
| Published in Issue | Year 2023 Volume: 11 Issue: 1 |
