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HAZIR GİYİM SANAYİNDE ÜRETİM HATLARINDA MAKİNE DEĞİŞİM SÜRESİNİN EN AZA İNDİRİLMESİ

Year 2013, Volume: 23 Issue: 2, 159 - 167, 01.12.2013

Abstract

Bu çalışmada bir hazır giyim firması için makine değişimlerinin düzenleyen çizelgeleme sorunu ele alınmıştır ve sipariş edilen ürünlerin üretimi için gerekli makine değişimleri dikkate alarak, toplam kurulum süresini en aza indirmek için bir sezgisel metot kullanılmıştır. En kısa işlem süresi ve en erken teslim tarihi gibi sık kullanılan sezgisel metotlar ile geçerliliği olan bir çizelgeleme için kullanılabilir, ancak genellikle bu durumda en iyi sonuca yakın çizelgeler üretilemez. Bu çalışmada sorunu iki alt problem bölerek bir çözüm yöntemi geliştirdik. İlk problemin çözüm sonuçları ikincisinin giriş verileri olarak kullanılmıştır. Bu süreçte, birinci problemin çözümünde yeni geliştirilmiş bir sezgisel metot kullanıldı. İkinci problem açık ve asimetrik bir seyyar satıcı problem olarak formüle edildi. İkinci problem Genetik (GA) ve Benzetilmiş Tavlama (BT) algoritmaları kullanılarak çözüldü. Deney sonuçları önerilen algoritmanın makine değişimlerini göz önüne alan çizelgeleme sorununun çözümünde etkin olduğunu göstermiştir

References

  • 1. Manne, A.S.,1960, “On the job shop scheduling problem”, Operations Research, 8(2), pp. 219-223.
  • 2. Wagner, H., 1959, “An integer linear-programming model for machine scheduling”, Naval Research Logistics Quarterly, 6(2), pp. 131-140.
  • 3. Candido, M.A.B., Khator, S.K., Barcia, R.M., 1998, “A genetic algorithm based procedure for more realistic job shop scheduling problems”, International Journal of Production Research, 36(12), pp. 3437-3457.
  • 4. Ovacik, I.M., Uzsoy, R., 1994, “Exploiting shop floor status information to schedule complex job shops”, Journal of Manufacturing Systems, 13(2), pp. 73–84.
  • 5. Vinod, V., Sridharan, R., 2009, “Simulation-based metamodels for scheduling a dynamic job shop with sequence-dependent setup times”, International Journal of Production Research, 47(6), pp. 1425-1447.
  • 6. Zhu, X., Wilhelm, W.E., 2006, “Scheduling and lot sizing with sequence-dependent setup: A literature review”, IIE Transactions, 38(11), pp. 987-1007
  • 7. Allahverdi, A., Ng, C.T., Cheng, T.C.E., Kovalyovc, M.Y., 2008, “A survey of scheduling problems with setup times or costs”, European Journal of Operational Research, 187(3), pp. 985-1032.
  • 8. Diaz-Santillan, E., Malave, C.O., 2004, “Simulated annealing for parallel machine scheduling with split jobs and sequence-dependent set-ups”, International Journal of Industrial Engineering – Theory Applications and Practice, 11(1), pp. 43-53.
  • 9. Lin, S.W., Ying, K.C., 2009, “Applying a hybrid simulated annealing and tabu search approach to non-permutation flowshop scheduling problems”, International Journal of Production Research, 47(5), pp. 1411-1424.
  • 10. Azizi, N., Zolfaghari, S., Liang, M., 2010, “Hybrid simulated annealing with memory: an evolution-based diversification approach”, International Journal of Production Research, 48(18), pp. 5455-5480.
  • 11. Hasan, S.M.K., Sarker, R., Essam, D., 2011, “Genetic algorithm for job-shop scheduling with machine unavailability and breakdowns”, International Journal of Production Research, 49(16), pp. 4999-5015.
  • 12. Jia, Z., Lu, X., Yang, J., Jia, D., 2011, “Research on job-shop scheduling problem based on genetic algorithm”, International Journal of Production Research, 49(12), pp. 3585-3604.
  • 13. Ali, M.B., Sassi, M., Gossa, M., Harrath, Y., 2011, “Simultaneous scheduling of production and maintenance tasks in the job shop”, International Journal of Production Research 49(13), pp. 3891-3918.
  • 14. Chan, F.T.S., Wong, T.C., Chan, L.Y., 2006, “Flexible job-shop scheduling problem under resource constraints”, International Journal of Production Research, 44(11), pp. 2071-2089.
  • 15. Bowers, M.R., Agarwal, A., Knoxville, T.N., 1993, “Hierarchical production planning: scheduling in the apparel industry”, International Journal of Clothing Science and Technology, 5(3/4), pp. 36-43.
  • 16. Kwong, C.K., Mok, P.Y., Wong, W.K., 2006, “Scheduling flexible manufacturing systems for apparel production”, International Journal of Production Research, 44(21), pp. 4465-4490.
  • 17. Chan, C.C., Hui, C.L., Yeung, K.W., Ng, S.F., 1997, “Handling the assembly line balancing problem in the clothing industry using a genetic algorithm”, International Journal of Clothing Science and Technology, 10(1), pp. 21-37.
  • 18. Tucci, M., Rinaldi, R., 1999, “From theory to application: tabu search in textile production scheduling”, Production Planning & Control, 10(4), pp. 365-374.
  • 19. Wong, W.K., Mok, P.Y., Leung, S.Y.S., “Developing a genetic optimization approach to balance an apparel assembly line”, International Journal of Advanced Manufacturing Technology, 28(3-4), pp. 387-394.
  • 20. Guo, Z.X., Wong, W.K., Leung, S.Y.S., Fan, J.T., Chan, S.F., “Mathematical model and genetic optimization for the job shop scheduling problem in a mixed- and multi-product assembly environment: A case study based on the apparel industry”, Computers & Industrial Engineering, 50(3), pp. 202-219.
  • 21. Karabuk, S., “Production planning under uncertainty in textile manufacturing”, Journal of the Operational Research Society, 59, pp. 510-520.
  • 22. Alkaya, A.F., 2009, “Optimizing the Throughput of a Textile Manufacturing Company”, YA\EM 09 Tam Bildiri Metinleri, Ankara, Turkiye.
  • 23. Chen, W.J., 2009, “Scheduling with dependent setups and maintenance in a textile company”, Computers & Industrial Engineering, 57(3), pp. 867-873.
  • 24. Hsu, H.-M., Hsiung, Y., Chen, Y.-Z., Wu, M.-C., “A GA methodology for the scheduling of yarn-dyed textile production”, Expert Systems with Applications, 36(10), pp. 12095–12103.
  • 25. Pinedo, M, 2002, Scheduling: Theory, Algorithms and Systems, Prentice Hall, Englewood Cliffs, NJ.
  • 26. Gutin, G., Punnen, A., 2002, The Traveling Salesman Problem and its Variants, Kluwer Academic Publishers.
  • 27. Holland, H., 1990, "Escaping brittleness: the possibilities of general purpose learning algorithms applied to parallel rule-based systems”, in Machine Learning: An Artificial Intelligence Approach, Volume II, R. S. Michalski et al., Eds. Los Altos, CA, Morgan Kaufmann, pp. 593-623.
  • 28. Beasley, D., Bull, D.R., Martin, R.R., 1993, “An overview of genetic algorithms: Part I, Fundamentals”, University Computing, 15, pp. 58-69.
  • 29. Michalewicz, Z., 1994, Genetic Algorithms+Data Structures= Evolution Programs, Springer, New York, USA.
  • 30. Lancaster, J., Ozbayrak, M., “Evolutionary algorithms applied to project scheduling problems-a survey of the state-of-the-art”, International Journal of Production Research, 45(2), pp. 425-450.
  • 31. Aytug, H., Khouja, M., Vergara, F.E., “Use of genetic algorithms to solve production and operations management problems: A review”, International Journal of Production Research, 41(17), pp. 3955-4009
  • 32. Blum, C., Roli, A., “Metaheuristics in combinatorial optimization: Overview and conceptual comparison”, ACM Computing Surveys, 35, pp. 268-308.

MINIMIZING MACHINE CHANGEOVER TIME IN PRODUCT LINE IN AN APPAREL INDUSTRY

Year 2013, Volume: 23 Issue: 2, 159 - 167, 01.12.2013

Abstract

This study deals with a scheduling problem with machine changeovers in a apparel company and presents a heuristic to minimize the total setup time subject to machine changeovers in models ordered. Commonly used heuristics such as shortest processing time and earliest due date can be used to calculate a feasible schedule quickly, but usually do not produce schedules that are close to optimal in these environments. A solution approach for the problem is developed by dividing it into two subproblems. Solution of the first problem is given as an input for the second problem. In this process, an originally developed heuristic is applied for the first problem and the second problem is formulated as an open and asymmetric traveling salesman problem. The second problem is solved by Genetic (GA) and Simulated Annealing (SA) algorithms. The experimental results demonstrate the effectiveness of the proposed algorithm to solve the scheduling problem with machine changeovers

References

  • 1. Manne, A.S.,1960, “On the job shop scheduling problem”, Operations Research, 8(2), pp. 219-223.
  • 2. Wagner, H., 1959, “An integer linear-programming model for machine scheduling”, Naval Research Logistics Quarterly, 6(2), pp. 131-140.
  • 3. Candido, M.A.B., Khator, S.K., Barcia, R.M., 1998, “A genetic algorithm based procedure for more realistic job shop scheduling problems”, International Journal of Production Research, 36(12), pp. 3437-3457.
  • 4. Ovacik, I.M., Uzsoy, R., 1994, “Exploiting shop floor status information to schedule complex job shops”, Journal of Manufacturing Systems, 13(2), pp. 73–84.
  • 5. Vinod, V., Sridharan, R., 2009, “Simulation-based metamodels for scheduling a dynamic job shop with sequence-dependent setup times”, International Journal of Production Research, 47(6), pp. 1425-1447.
  • 6. Zhu, X., Wilhelm, W.E., 2006, “Scheduling and lot sizing with sequence-dependent setup: A literature review”, IIE Transactions, 38(11), pp. 987-1007
  • 7. Allahverdi, A., Ng, C.T., Cheng, T.C.E., Kovalyovc, M.Y., 2008, “A survey of scheduling problems with setup times or costs”, European Journal of Operational Research, 187(3), pp. 985-1032.
  • 8. Diaz-Santillan, E., Malave, C.O., 2004, “Simulated annealing for parallel machine scheduling with split jobs and sequence-dependent set-ups”, International Journal of Industrial Engineering – Theory Applications and Practice, 11(1), pp. 43-53.
  • 9. Lin, S.W., Ying, K.C., 2009, “Applying a hybrid simulated annealing and tabu search approach to non-permutation flowshop scheduling problems”, International Journal of Production Research, 47(5), pp. 1411-1424.
  • 10. Azizi, N., Zolfaghari, S., Liang, M., 2010, “Hybrid simulated annealing with memory: an evolution-based diversification approach”, International Journal of Production Research, 48(18), pp. 5455-5480.
  • 11. Hasan, S.M.K., Sarker, R., Essam, D., 2011, “Genetic algorithm for job-shop scheduling with machine unavailability and breakdowns”, International Journal of Production Research, 49(16), pp. 4999-5015.
  • 12. Jia, Z., Lu, X., Yang, J., Jia, D., 2011, “Research on job-shop scheduling problem based on genetic algorithm”, International Journal of Production Research, 49(12), pp. 3585-3604.
  • 13. Ali, M.B., Sassi, M., Gossa, M., Harrath, Y., 2011, “Simultaneous scheduling of production and maintenance tasks in the job shop”, International Journal of Production Research 49(13), pp. 3891-3918.
  • 14. Chan, F.T.S., Wong, T.C., Chan, L.Y., 2006, “Flexible job-shop scheduling problem under resource constraints”, International Journal of Production Research, 44(11), pp. 2071-2089.
  • 15. Bowers, M.R., Agarwal, A., Knoxville, T.N., 1993, “Hierarchical production planning: scheduling in the apparel industry”, International Journal of Clothing Science and Technology, 5(3/4), pp. 36-43.
  • 16. Kwong, C.K., Mok, P.Y., Wong, W.K., 2006, “Scheduling flexible manufacturing systems for apparel production”, International Journal of Production Research, 44(21), pp. 4465-4490.
  • 17. Chan, C.C., Hui, C.L., Yeung, K.W., Ng, S.F., 1997, “Handling the assembly line balancing problem in the clothing industry using a genetic algorithm”, International Journal of Clothing Science and Technology, 10(1), pp. 21-37.
  • 18. Tucci, M., Rinaldi, R., 1999, “From theory to application: tabu search in textile production scheduling”, Production Planning & Control, 10(4), pp. 365-374.
  • 19. Wong, W.K., Mok, P.Y., Leung, S.Y.S., “Developing a genetic optimization approach to balance an apparel assembly line”, International Journal of Advanced Manufacturing Technology, 28(3-4), pp. 387-394.
  • 20. Guo, Z.X., Wong, W.K., Leung, S.Y.S., Fan, J.T., Chan, S.F., “Mathematical model and genetic optimization for the job shop scheduling problem in a mixed- and multi-product assembly environment: A case study based on the apparel industry”, Computers & Industrial Engineering, 50(3), pp. 202-219.
  • 21. Karabuk, S., “Production planning under uncertainty in textile manufacturing”, Journal of the Operational Research Society, 59, pp. 510-520.
  • 22. Alkaya, A.F., 2009, “Optimizing the Throughput of a Textile Manufacturing Company”, YA\EM 09 Tam Bildiri Metinleri, Ankara, Turkiye.
  • 23. Chen, W.J., 2009, “Scheduling with dependent setups and maintenance in a textile company”, Computers & Industrial Engineering, 57(3), pp. 867-873.
  • 24. Hsu, H.-M., Hsiung, Y., Chen, Y.-Z., Wu, M.-C., “A GA methodology for the scheduling of yarn-dyed textile production”, Expert Systems with Applications, 36(10), pp. 12095–12103.
  • 25. Pinedo, M, 2002, Scheduling: Theory, Algorithms and Systems, Prentice Hall, Englewood Cliffs, NJ.
  • 26. Gutin, G., Punnen, A., 2002, The Traveling Salesman Problem and its Variants, Kluwer Academic Publishers.
  • 27. Holland, H., 1990, "Escaping brittleness: the possibilities of general purpose learning algorithms applied to parallel rule-based systems”, in Machine Learning: An Artificial Intelligence Approach, Volume II, R. S. Michalski et al., Eds. Los Altos, CA, Morgan Kaufmann, pp. 593-623.
  • 28. Beasley, D., Bull, D.R., Martin, R.R., 1993, “An overview of genetic algorithms: Part I, Fundamentals”, University Computing, 15, pp. 58-69.
  • 29. Michalewicz, Z., 1994, Genetic Algorithms+Data Structures= Evolution Programs, Springer, New York, USA.
  • 30. Lancaster, J., Ozbayrak, M., “Evolutionary algorithms applied to project scheduling problems-a survey of the state-of-the-art”, International Journal of Production Research, 45(2), pp. 425-450.
  • 31. Aytug, H., Khouja, M., Vergara, F.E., “Use of genetic algorithms to solve production and operations management problems: A review”, International Journal of Production Research, 41(17), pp. 3955-4009
  • 32. Blum, C., Roli, A., “Metaheuristics in combinatorial optimization: Overview and conceptual comparison”, ACM Computing Surveys, 35, pp. 268-308.
There are 32 citations in total.

Details

Other ID JA88PS48ZH
Journal Section Articles
Authors

Aykut Kentli This is me

Vedat Dal This is me

Ali Fuat Alkaya This is me

Publication Date December 1, 2013
Submission Date December 1, 2013
Published in Issue Year 2013 Volume: 23 Issue: 2

Cite

APA Kentli, A., Dal, V., & Alkaya, A. F. (2013). MINIMIZING MACHINE CHANGEOVER TIME IN PRODUCT LINE IN AN APPAREL INDUSTRY. Textile and Apparel, 23(2), 159-167.

No part of this journal may be reproduced, stored, transmitted or disseminated in any forms or by any means without prior written permission of the Editorial Board. The views and opinions expressed here in the articles are those of the authors and are not the views of Tekstil ve Konfeksiyon and Textile and Apparel Research-Application Center.