مقاله مروری بر مسائل زمان بندی جریان کارگاهی تحت عدم قطعیت

توضیحات

مقاله مروری بر مسائل زمان بندی جریان کارگاهی تحت عدم قطعیت

این مقاله به بررسی جامع و سیستماتیک ادبیات موضوعی مسائل زمان بندی جریان کارگاهی تحت شرایط عدم قطعیت می پردازد. در میان وظایف مختلف در تدارکات تولیدی، زمان بندی کارها، یکی از مهم ترین مسائل در سطح تصمیم گیری های عملیاتی برای قادر ساختن سازمان ها در رسیدن به رضایت بخشی، می باشد. مسائل زمان بندی جریان کارگاهی (FS) شامل توالی عملیات در محیط هایی است که در آن فعالیت ها یا عملیات ها در یک جریان پیوسته انجام می شوند. این نوع پیکربندی شامل خطوط مونتاژ، و صنایع شیمیایی، الکترونیکی، مواد غذایی، متالورژی و غیره است. تا کنون زمان بندی تقریبا برای موارد قطعی بررسی شده است، که در آن تمامی پارامترها در طول زمان تغییر نمی کنند و مشخص هستند. اما در دنیای واقعی رخدادها اغلب در شرایط عدم قطعیت اتفاق می افتند که می تواند بر روی فرآیند های تصمیم گیری اثر گذار باشد. بنابراین، مطالعه زمان بندی و توالی عملیات تحت شرایط عدم قطعیت اهمیت بالایی دارد چرا که این شرایط می تواند باعث بی نظمی ها و اختلالات شود. هدف از این مقاله مرور بر ادبیات موضوعی مسائل زمان بندی جریان کارگاهی در شرایط عدم قطعیت است. برای این منظور، تعداد 40 مقاله در مورد مسائل جریان کارگاهی و همچنین زمان بندی جریان کارگاهی انعطاف پذیر که از سال 2014 تا اکتبر 2017 منتشر شده اند، مورد تجزیه و تحلیل و طبقه بندی قرار گرفته است. در نهایت پس از نتیجه گیری از مطالعات انجام شده برخی از فرصت های تحقیقاتی در این زمینه ارائه شده است.

کلمات کلیدی: جریان کارگاهی، جریان کارگاهی انعطاف پذیر، عدم قطعیت، تدارکات تولید، مرور ادبیات.

…

فهرست مطالب مقاله مروری بر مسائل زمان بندی جریان کارگاهی تحت عدم قطعیت

• مقدمه
• مرور ادبیات
• نتیجه گیری و پیشنهادات آتی
• منابع

منابع مقاله مروری بر مسائل زمان بندی جریان کارگاهی تحت عدم قطعیت

• Adler, L., Fraiman, N., Kobacker, E., Pinedo, M., Plotnicoff, J. C., & Wu, T. P. (1993). BPSS: A Scheduling Support System for the Packaging Industry. Operations Research, 41(4), 641–648. https://doi.org/10.1287/ opre.41.4.641
• Adressi, a., Tasouji Hassanpour, S., & Azizi, V. (2016). Solving group scheduling problem in no-wait flexible flowshop with random machine breakdown. Decision Science Letters, 5(JANUARY), 157– 168. https://doi.org/10.5267/j.dsl.2015.7.001
• Aggarwal, S., Gupta, D., & Sharma, S. (2017). A fuzzy logic-based approach for multistage flowshop scheduling with arbitrary lags and transportation time. International Journal of Operational Research, 29(1), 87-100.
• Almeder, C., & Hartl, R. F. (2013). A metaheuristic optimization approach for a real-world stochastic flexible flow shop problem with limited buffer. International Journal of Production Economics, 145(1), 88–95. https://doi.org/10.1016/j.ijpe.2012.09.014
• Aydilek, A., Aydilek, H., & Allahverdi, A. (2013). Increasing the profitability and competitiveness in aproduction environment with random and bounded setup times. International Journal of Production Research, 51(1), 106–117.https://doi.org/10.1080/00207543.2011.652263
• Aydilek, A., Aydilek, H., & Allahverdi, A. (2015). Production in a two-machine flowshop scheduling environment with uncertain processing and setup times to minimize makespan. International Journal of Production Research, 53(9), 2803–2819. https://doi.org/10.1080/00207543.2014.997403
• Badger, D., Nursten, J., Williams, P., & Woodward, M. (2000). Should All Literature Reviews be Systematic? Evaluation & Research in Education, 14(3–4), 220–230. https://doi.org/10.1080/09500790008666974
• Behnamian, J. (2016). Survey on fuzzy shop scheduling. Fuzzy Optimization and Decision Making, 15(3), 331–366. https://doi.org/10.1007/s10700-015-9225-5
• Behnamian, J., & Fatemi Ghomi, S. M. T. (2014). Multi-objective fuzzy multiprocessor flowshop scheduling. Applied Soft Computing, 21, 139–148. https://doi.org/10.1016/j.asoc.2014.03.031
• Bożejko, W., Gniewkowski, Ł., & Wodecki, M. (2017, June). Blocks for the flow shop scheduling problem with uncertain parameters. In Polish Control Conference(pp. 703-711). Springer, Cham.
• Chang, Y.-C., & Huang, W.-T. (2014). An enhanced model for SDBR in a random reentrant flow shop International Journal of Production Research, 52(6), 1808–1826. https://doi.org/10.1080/00207543.2013.848491
• Cui, Z., & Gu, X. (2014). A Discrete Group Search Optimizer for Hybrid Flowshop Scheduling Problem with Random Breakdown. Mathematical Problems in Engineering, 2014, 1–11. https://doi.org/10.1155/2014/621393
• Ćwik, M., & Józefczyk, J. (2015). Evolutionary Algorithm for Minmax Regret Flow-Shop Problem. Management and Production Engineering Review, 6(3), 3–9. https://doi.org/10.1515/mper-2015- 0021
• Du, J., & Leung, J. Y.-T. (1990). Minimizing Total Tardiness on One Machine Is NP-Hard. Mathematics of Operations Research, 15(3), 483–495. https://doi.org/10.2307/3689992
• Ebrahimi, M., Fatemi Ghomi, S. M. T. M. T., & Karimi, B. (2014). Hybrid flow shop scheduling with sequence dependent family setup time and uncertain due dates. Applied Mathematical Modelling, 38(9–10), 2490–2504. https://doi.org/10.1016/j.apm.2013.10.061
• Elyasi, A., & Salmasi, N. (2013a). Stochastic flow-shop scheduling with minimizing the expected number of tardy jobs. International Journal of Advanced Manufacturing Technology, 66(1–4), 337– 346. https://doi.org/10.1007/s00170-012-4328-4
• Elyasi, A., & Salmasi, N. (2013b). Stochastic scheduling with minimizing the number of tardy jobs using chance constrained programming. Mathematical and Computer Modelling, 57(5–6), 1154–1164. https://doi.org/10.1016/j.mcm.2012.10.017
• Farahani, R. Z., Rezapour, S., Drezner, T., & Fallah, S. (2014). Competitive supply chain network design: An overview of classifications, models, solution techniques and applications. Omega, 45, 92–118. https://doi.org/10.1016/j.omega.2013.08.006
• Fazayeli, M., Aleagha, M.-R., Bashirzadeh, R., & Shafaei, R. (2016). A hybrid meta-heuristic algorithm for flowshop robust scheduling under machine breakdown uncertainty. International Journal of Computer Integrated Manufacturing, 29(7), 709–719. https://doi.org/10.1080/0951192X.2015.1067907
• Feng, X., Zheng, F., & Xu, Y. (2016). Robust scheduling of a two-stage hybrid flow shop with uncertain interval processing times. International Journal of Production Research, 54(12), 3706–3717. https://doi.org/10.1080/00207543.2016.1162341
• Fink, A. (1998). Conducting Research Literature Reviews: From Paper to the Internet. (A. Fink, Ed.), BMS: Bulletin of Sociological Methodology / Bulletin de Méthodologie Sociologique (Vol. 60). Thousand Oaks, CA, USA: Sage Publications, Ltd. https://doi.org/10.2307/24359723
• Framinan, J. M., & Perez-Gonzalez, P. (2015). On heuristic solutions for the stochastic flowshop scheduling problem. European Journal of Operational Research, 246(2), 413–420. https://doi.org/10.1016/j.ejor.2015.05.006
• Geng, J.-C., Cui, Z., & Gu, X.-S. (2016). Scatter search based particle swarm optimization algorithm for earliness/tardiness flowshop scheduling with uncertainty. International Journal of Automation and Computing, 13(3), 285–295. https://doi.org/10.1007/s11633-016-0964-8
• Gholami-Zanjani, S. M., Hakimifar, M., Nazemi, N., & Jolai, F. (2016). Robust and Fuzzy Optimisation Models for a Flow shop Scheduling Problem with Sequence Dependent Setup Times: A real case study on a PCB assembly company. International Journal of Computer Integrated Manufacturing, 1-12 https://doi.org/10.1080/0951192X.2016.1187293
• Gourgand, M., Grangeon, N., & Norre, S. (2003). A contribution to the stochastic flow shop scheduling problem. European Journal of Operational Research, 151(2), 415–433. https://doi.org/10.1016/S0377-2217(02)00835-4
• Gourgand, M., Grangeon, N., & Norre, S. (2005). Markovian analysis for performance evaluation and scheduling in m machine stochastic flow-shop with buffers of any capacity. European Journal of Operational Research, 161(1), 126–147. https://doi.org/10.1016/j.ejor.2003.08.032
• Graham, R. L., Lawler, E. L., Lenstra, J. K., & Kan, A. H. G. R. (1979). Optimization and Approximation in Deterministic Sequencing and Scheduling: a Survey. In Annals of Discrete Mathematics (Vol. 5, pp. 287–326). Elsevier. https://doi.org/10.1016/S0167-5060(08)70356-X
• Han, Y., Gong, D., Jin, Y., & Pan, Q. (2016). Evolutionary multi-objective blocking lot-streaming flow shop scheduling with interval processing time. Applied Soft Computing, 42, 229–245. https://doi.org/10.1016/j.asoc.2016.01.033
• Holthaus, O. (1999). Scheduling in job shops with machine breakdowns: An experimental study. Computers and Industrial Engineering, 36(1), 137–162. https://doi.org/10.1016/S0360-8352(99)00006-6
• Holthaus, O. (1999). Scheduling in job shops with machine breakdowns: An experimental study. Computers and Industrial Engineering, 36(1), 137–162. https://doi.org/10.1016/S0360- 8352(99)00006-6
• Jeet, K., Sharma, S., & Nailwal, K. K. (2017). Two-Machine Fuzzy Flow Shop Scheduling Using Black Hole Algorithm. Global Journal of Pure and Applied Mathematics, 13(6), 1935-1946.
• Jiao, B., & Yan, S. (2014). A cooperative co-evolutionary particle swarm optimiser based on a niche sharing scheme for the flow shop scheduling problem under uncertainty. Mathematical Structures in Computer Science, 24(5), e240502. https://doi.org/10.1017/S0960129512000461
• Juan, A. A., Barrios, B. B., Vallada, E., Riera, D., & Jorba, J. (2014). A simheuristic algorithm for solving the permutation flow shop problem with stochastic processing times. Simulation Modelling Practice and Theory, 46, 101–117. https://doi.org/10.1016/j.simpat.2014.02.005
• Lee, C.-Y., Cheng, T. C. E., & Lin, B. M. T. (1993). Minimizing the Makespan in the 3-Machine Assembly-Type Flowshop Scheduling Problem. Management Science, 39(5), 616–625. https://doi.org/10.1287/mnsc.39.5.616
• Lin, J. T., & Chen, C.-M. (2015). Simulation optimization approach for hybrid flow shop scheduling problem in semiconductor back-end manufacturing. Simulation Modelling Practice and Theory, 51, 100–114. https://doi.org/10.1016/j.simpat.2014.10.008
• Liu, G. S., Zhou, Y., & Yang, H. D. (2017). Minimizing energy consumption and tardiness penalty for fuzzy flow shop scheduling with state-dependent setup time. Journal of Cleaner Production, 147, 470-484.
• Montoya-Torres, J. R. (2016). Stochastic flexible flow shop scheduling problem under quantitative and qualitative decision criteria. Computers & Industrial Engineering, 101, 128–144. https://doi.org/10.1016/j.cie.2016.08.026
• Moore, R. E., & Bierbaum, F. (1979). Methods and applications of interval analysis (Vol. 2). Philadelphia: Siam.
• Mou, J., Gao, L., Li, X., Pan, Q., & Mu, J. (2017). Multi-objective inverse scheduling optimization of single-machine shop system with uncertain due-dates and processing times. Cluster Computing, 20(1), 371-390.
• Mou, J., Li, X., Gao, L., & Yi, W. (2015). An effective L-MONG algorithm for solving multi-objective flow-shop inverse scheduling problems. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-015-1129-2
• Nagasawa, K., Ikeda, Y., & Irohara, T. (2015). Robust flow shop scheduling with random processing times for reduction of peak power consumption. Simulation Modelling Practice and Theory. https://doi.org/10.1016/j.simpat.2015.08.001
• Noroozi, A., & Mokhtari, H. (2015). Scheduling of printed circuit board (PCB) assembly systems with heterogeneous processors using simulation-based intelligent optimization methods. Neural Computing and Applications, 26(4), 857–873. https://doi.org/10.1007/s00521-014-1765-z
• Pinedo, M. (2008). Scheduling: Theory, Algorithms, and Systems. 3rd. Ed. Springer, New York.
• Qin, W., Zhang, · J, Song, · D, Zhang, J., & Song, D. (2015). An improved ant colony algorithm for dynamic hybrid flow shop scheduling with uncertain processing time. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-015-1144-3
• Rahmani, D. (2017). A new proactive-reactive approach to hedge against uncertain processing times and unexpected machine failures in the two-machine flow shop scheduling problems. SCIENTIA IRANICA, 24(3), 1571-1584.
• Rahmani, D., & Heydari, M. (2014). Robust and stable flow shop scheduling with unexpected arrivals of new jobs and uncertain processing times. Journal of Manufacturing Systems, 33(1), 84–92. https://doi.org/10.1016/j.jmsy.2013.03.004
• Rahmani, D., Ramezanian, R., & Mehrabad, M. S. (2014). Multi-objective flow shop scheduling problem with stochastic parameters: fuzzy goal programming approach. International Journal of Operational Research, 21(3), 322–340. https://doi.org/10.1504/IJOR.2014.065411
• Rajba, P., & Wodecki, M. (2017, June). Sampling Method for the Flow Shop with Uncertain Parameters. In IFIP International Conference on Computer Information Systems and Industrial Management(pp. 580-591). Springer, Cham.
• Ruiz, R., & Vázquez-Rodríguez, J. A. (2010). The hybrid flow shop scheduling problem. European Journal of Operational Research, 205(1), 1–18. https://doi.org/10.1016/j.ejor.2009.09.024
• Shahnaghi, K., Shahmoradi-Moghadam, H., Noroozi, A., & Mokhtari, H. (2016). A robust modelling and optimisation framework for a batch processing flow shop production system in the presence of uncertainties. International Journal of Computer Integrated Manufacturing, 29(1), 92–106. https://doi.org/10.1080/0951192X.2014.1002814
• Sharma, S. (2017). Two Machines Flow Shop Scheduling with Single Transport Facility and Job-Block Criteria under Fuzzy Environment. Global Journal of Pure and Applied Mathematics, 13(6), 1903-1916.
• Shen, J., & Zhu, Y. (2017). Uncertain flexible flow shop scheduling problem subject to breakdowns. Journal of Intelligent & Fuzzy Systems, 32(1), 207-214.
• Thomé, A. M. T., Scavarda, L. F., & Scavarda, A. J. (2016). Conducting systematic literature review in operations management. Production Planning & Control, 27(5), 408–420. https://doi.org/10.1080/09537287.2015.1129464
• Wang, K., & Choi, S. H. (2014). A holonic approach to flexible flow shop scheduling under stochastic processing times. Computers & Operations Research, 43(1), 157–168. https://doi.org/10.1016/j.cor.2013.09.013
• Wang, K., Choi, S. H., & Qin, H. (2014). An estimation of distribution algorithm for hybrid flow shop scheduling under stochastic processing times. International Journal of Production Research, 52(24), 7360–7376. https://doi.org/10.1080/00207543.2014.930535
• Wang, K., Huang, Y., & Qin, H. (2016). A fuzzy logic-based hybrid estimation of distribution algorithm for distributed permutation flowshop scheduling problems under machine breakdown. Journal of the Operational Research Society, 67(1), 68–82. https://doi.org/10.1057/jors.2015.50
• Wang, S., Wang, L., Liu, M., & Xu, Y. (2015). An order-based estimation of distribution algorithm for stochastic hybrid flow-shop scheduling problem. International Journal of Computer Integrated Manufacturing, 28(3), 307–320. https://doi.org/10.1080/0951192X.2014.880803
• Xiao, S., Sun, S., & Jin, J. J. (2017). Surrogate Measures for the Robust Scheduling of Stochastic Job Shop Scheduling Problems. Energies, 10(4), 543.
• Ying, K. (2015). Scheduling the two-machine flowshop to hedge against processing time uncertainty. Journal of the Operational Research Society, 66(9), 1413–1425. https://doi.org/10.1057/jors.2014.100
• Zandieh, M., & Hashemi, A. (2015). Group scheduling in hybrid flexible flowshop with sequencedependent setup times and random breakdowns via integrating genetic algorithm and simulation. International Journal of Industrial and Systems Engineering, 21(3), 377. https://doi.org/10.1504/IJISE.2015.072273
• González-Neira, E. M., García-Cáceres, R. G., Caballero-Villalobos, J. P., Molina-Sánchez, L. P., &Montoya-Torres, J. R. (2016). Stochastic flexible flow shop scheduling problem under quantitativeand qualitative decision criteria. Computers & Industrial Engineering, 101, 128–144 . https://doi.org/10.1016/j.cie.2016.08.026

توجه:

مقاله مروری بر مسائل زمان بندی جریان کارگاهی تحت عدم قطعیت مختلف شامل یک فایل ورد 20 صفحه ای می باشد.

لینک دانلود فایل بلافاصله پس از خرید بصورت اتوماتیک برای شما ایمیل می گردد.