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Bulanık Öğrenme Etkili Akış Tipi Çizelgeleme Problemlerinin Paralel Kanguru Algoritması İle Çözümü

Yıl 2017, Cilt: 1 Sayı: 1, 31 - 48, 30.06.2017

Ahmet Sezer Küpeli Orhan Engin Batuhan Eren Engin

Öz

Öğrenme etkili çizelgeleme
problemlerinin daha gerçekçi sonuçlar verdiği bilinmektedir. Bu çalışmada,
insan faktöründen kaynaklanan öğrenme etkisini göz önüne alan akış tipi
çizelgeleme problemleri ele alınmıştır. Belirsiz işlem sürelerine çizelgeleme
problemlerinde sıklıkla karşılaşılır. Bu nedenle işlem süreleri
bulanıklaştırılmıştır. Bulanık mantık literatürde bu problemlerin çözümü için
sıkça kullanılan bir yöntemdir. Paralel Kanguru Algoritması çözüm metodu olarak
kullanılmıştır. Çalışma diğer çalışmalardaki sonuçlarla kıyaslanarak
sonuçlandırılmıştır.

Anahtar Kelimeler

Akış tipi çizelgeleme Bulanık mantık Öğrenme etkisi Paralel Kanguru Algoritması

Kaynakça

  • Abdullah, S. ve Abdolrazzagh-Nezhad, M., 2014, Fuzzy job-shop scheduling problems: A review, Information Sciences, 278, 380-407.
  • Agarwal, A., Colak, S. ve Eryarsoy, E., 2006, Improvement heuristic for the flow-shop scheduling problem: An adaptive-learning approach, European Journal of Operational Research, 169 (3), 801-815.
  • Ahmadizar, F. ve Hosseini, L., 2012, Minimizing makespan in a single-machine scheduling problem with a learning effect and fuzzy processing times, The International Journal of Advanced Manufacturing Technology, 65 (1-4), 581-587.
  • Ambika, G. ve Uthra, G., 2014, Branch and Bound Technique in Flow Shop Scheduling Using Fuzzy Processing Times, Annals of Pure and Applied Mathematics, Vol. 8 ( No. 2), 37-42
  • Amirian, H. ve Sahraeian, R., 2015, Augmented ε-constraint method in multi-objective flowshop problem with past sequence set-up times and a modified learning effect, International Journal of Production Research, 53 (19), 5962-5976.
  • Asadi, H., 2017, Apply Fuzzy Learning Effect with Fuzzy Processing Times for Single Machine Scheduling Problems, Journal of Manufacturing Systems, 42, 244-261.
  • Bachman, A. ve Janiak, A., 2004, Scheduling jobs with position-dependent processing times, Journal of the Operational Research Society, 55, 257-263.
  • Balin, S., 2011, Parallel machine scheduling with fuzzy processing times using a robust genetic algorithm and simulation, Information Sciences, 181 (17), 3551-3569.
  • Baysal, M. E., Durmaz, T., Sarucan, A. ve Engin, O., 2012, Açık atölye tipi çizelgeleme problemlerinin paralel kanguru algoritması ile çözümü, Journal of the Faculty of Engineering and Architecture of Gazi University, 27, 855-864.
  • Behnamian, J. ve Fatemi Ghomi, S. M. T., 2014, Multi-objective fuzzy multiprocessor flowshop scheduling, Applied Soft Computing, 21, 139-148.
  • Biskup, D., 1999, Single-machine scheduling with learning considerations, European Journal of Operational Research, 115, 173–178.
  • Biskup, D., 2008, A state-of-the-art review on scheduling with learning effects, European Journal of Operational Research, 188 (2), 315-329.
  • Cheng, T., Wu, C. ve Lee, W., 2008, Some scheduling problems with sum-of-processing-times-based and job-position-based learning effects, Information Sciences, 178 (11), 2476-2487.
  • De Jong, J. R., 1957, THE EFFECTS OF INCREASING SKILL ON CYCLE TIME AND ITS CONSEQUENCES FOR TIME STANDARDS, Ergonomics, 1 (1), 51-60.
  • Dong, Y., 2003, One Machine Fuzzy Scheduling to Minimize Total Weighted Tardiness, Earliness, and Recourse Cost, International Journal of Smart Engineering System Design, 5 (3), 135-147.
  • Durmaz, T., 2011, Açık Atölye Çizelgeleme Problemlerinin Paralel Kanguru Algoritması ile Çözümü, Selçuk Üniversitesi, Fen Bilimleri Enstitüsü (Yüksek Lisans Tezi).
  • Engin, O., Kahraman, C. ve Yılmaz, M. K., 2009, A Scatter Search Method for Multi Objective Fuzzy Permutation Flow Shop Scheduling Problem A Real World Application, U.K. Chakraborty (Ed.): Comput. Intel. in Flow Shop and Job Shop Sched., SCI 230, 169-189.
  • Engin, O., Yılmaz, M. K., Kahraman, C., Baysal, M. E. ve Sarucan, A., 2011, A Scatter Search Method for Fuzzy Job Shop Scheduling Problem with Availability Constraints, Proceedings of the World Congress on Engineering, Vol II.
  • Erdem, Y. ve Keskintürk, T., 2011, Paralel Kanguru Algoritması ve Gezgin Satıcı Problemine Uygulanması, İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi, 19, 51-63.
  • Gao, K. Z., Suganthan, P. N., Pan, Q. K., Chua, T. J., Chong, C. S. ve Cai, T. X., 2016a, An improved artificial bee colony algorithm for flexible job-shop scheduling problem with fuzzy processing time, Expert Systems with Applications, 65, 52-67.
  • Gao, K. Z., Suganthan, P. N., Pan, Q. K., Tasgetiren, M. F. ve Sadollah, A., 2016b, Artificial bee colony algorithm for scheduling and rescheduling fuzzy flexible job shop problem with new job insertion, Knowledge-Based Systems, 109, 1-16.
  • He, H., 2016, Minimization of maximum lateness in an m -machine permutation flow shop with a general exponential learning effect, Computers & Industrial Engineering, 97, 73-83.
  • Huang, W., Oh, S.-K. ve Pedrycz, W., 2013, A fuzzy time-dependent project scheduling problem, Information Sciences, 246, 100-114.
  • Janiak, A. ve Rudek, R., 2010, A note on a makespan minimization problem with a multi-ability learning effect, Omega, 38 (3-4), 213-217.
  • Jellouli, O. ve Chatelet, E., 2001, Monte Carlo simulation and stochastic algorithms for optimising supply chain management in an uncertain environment, 2001 IEEE International Conference on Systems, Man and Cybernetics. e-Systems and e-Man for Cybernetics in Cyberspace (Cat.No.01CH37236), 1840-1844 vol.1843.
  • Ji, M., Tang, X., Zhang, X. ve Cheng, T. C. E., 2015, Machine scheduling with deteriorating jobs and DeJong’s learning effect, Computers & Industrial Engineering, 91, 42-47.
  • Kahraman, C., Engin, O. ve Yilmaz, M. K., 2009, A New Artificial Immune System Algorithm for Multiobjective Fuzzy Flow Shop, International Journal of Computational Intelligence Systems, 2 (3), 236-247.
  • Kökçam, A. H. ve Engin, O., 2010, Solving the fuzzy project scheduling problems with metaheuristic methods, Mühendislik ve Fen Bilimleri Dergisi (Sigma), 28, 86-101.
  • Lai, P.-J. ve Lee, W.-C., 2011, Single-machine scheduling with general sum-of-processing-time-based and position-based learning effects, Omega, 39 (5), 467-471.
  • Lee, W.-C. ve Chung, Y.-H., 2013, Permutation flowshop scheduling to minimize the total tardiness with learning effects, International Journal of Production Economics, 141 (1), 327-334.
  • Lei, D. ve Guo, X., 2012, Swarm-based neighbourhood search algorithm for fuzzy flexible job shop scheduling, International Journal of Production Research, 50 (6), 1639-1649.
  • Liao, T. W. ve Su, P., 2017, Parallel machine scheduling in fuzzy environment with hybrid ant colony optimization including a comparison of fuzzy number ranking methods in consideration of spread of fuzziness, Applied Soft Computing, 56, 65-81.
  • Lin, J., 2015, A hybrid biogeography-based optimization for the fuzzy flexible job-shop scheduling problem, Knowledge-Based Systems, 78, 59-74.
  • Liu, B., Fan, Y. ve Liu, Y., 2015, A fast estimation of distribution algorithm for dynamic fuzzy flexible job-shop scheduling problem, Computers & Industrial Engineering, 87, 193-201.
  • Liu, G.-S., Zhou, Y. ve 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.
  • Lowe, C. ve Tedford, J. D., 1997, Fuzzy Production Scheduling for JIT Manufacturing, Intelligent Automation & Soft Computing, 3 (4), 319-329.
  • Lu, Y.-Y., 2015, Research on no-idle permutation flowshop scheduling with time-dependent learning effect and deteriorating jobs, Applied Mathematical Modelling.
  • McCahon, C. S. ve Lee, E. S., 1990, Job sequencing with fuzzy processing times, Computers & Mathematics with Applications, 19 (7), 31-41.
  • Minzu, V. ve Beldiman, L., 2007, Some aspects concerning the implementation of a parallel hybrid metaheuristic, Engineering Applications of Artificial Intelligence, 20 (7), 993-999.
  • Mitsuru Kuroda, Z. W., 1996, Fuzzy Job Scheduling, Int. J. Production Economics, 44, 45-51. Mosheiov, G., 2001, Scheduling problems with a learning effect, European Journal of Operational Research, 132, 687-693.
  • Noori-Darvish, S., Mahdavi, I. ve Mahdavi-Amiri, N., 2012, A bi-objective possibilistic programming model for open shop scheduling problems with sequence-dependent setup times, fuzzy processing times, and fuzzy due dates, Applied Soft Computing, 12 (4), 1399-1416.
  • Palacios, J. J., González, M. A., Vela, C. R., González-Rodríguez, I. ve Puente, J., 2015, Genetic tabu search for the fuzzy flexible job shop problem, Computers & Operations Research, 54, 74-89.
  • Peng, J. ve Iwamura, K., 2003, Three types of models for stochastic scheduling with fuzzy information, Journal of Statistics and Management Systems, 6 (3), 493-504.
  • Petrovic, S., Fayad, C. ve Petrovic, D., 2008, Sensitivity analysis of a fuzzy multiobjective scheduling problem, International Journal of Production Research, 46 (12), 3327-3344.
  • Pollard, J. M., 2000, Kangaroos, Monopoly and Discrete Logarithms, Journal of Cryptology, 13 (4), 437-447.
  • Rostami, M., Pilerood, A. E. ve Mazdeh, M. M., 2015, Multi-objective parallel machine scheduling problem with job deterioration and learning effect under fuzzy environment, Computers & Industrial Engineering, 85, 206-215.
  • Sathish, S. ve Ganesan, K., 2012, Flow Shop Scheduling Problem to minimize the Rental Cost under Fuzzy Environment, Journal of Natural Sciences Research, Vol.2, No.10.
  • Serbencu, A., Minzu, V. ve Serbencu, A., 2007, An Ant Colony System Based Metaheuristic for Solving Single Machine Scheduling Problem, The Annals of “Dunarea de Jos” University of Galati Fascicle III.
  • Soltani, R., Jolai, F. ve Zandieh, M., 2010, Two robust meta-heuristics for scheduling multiple job classes on a single machine with multiple criteria, Expert Systems with Applications, 37 (8), 5951-5959.
  • Stein, A. ve Teske, E., 2002, The parallelized Pollard kangaroo method in real Quadratic function fields, Math. Comput., 71 (238), 793-814.
  • Teske, E., 2003, Computing discrete logarithms with the parallelized kangaroo method, Discrete Applied Mathematics, 130 (1), 61-82.
  • Torabi, S. A., Sahebjamnia, N., Mansouri, S. A. ve Bajestani, M. A., 2013, A particle swarm optimization for a fuzzy multi-objective unrelated parallel machines scheduling problem, Applied Soft Computing, 13 (12), 4750-4762.
  • Vahedi-Nouri, B., Fattahi, P., Tavakkoli-Moghaddam, R. ve Ramezanian, R., 2014, A general flow shop scheduling problem with consideration of position-based learning effect and multiple availability constraints, The International Journal of Advanced Manufacturing Technology, 73 (5-8), 601-611.
  • Vahedi Nouri, B., Fattahi, P. ve Ramezanian, R., 2013, Hybrid firefly-simulated annealing algorithm for the flow shop problem with learning effects and flexible maintenance activities, International Journal of Production Research, 51 (12), 3501-3515.
  • Wang, J. B., Ng, C. T., Cheng, T. C. E. ve Liu, L. L., 2008, Single-machine scheduling with a time-dependent learning effect, International Journal of Production Economics, 111 (2), 802-811.
  • Wang, L., Zhou, G., Xu, Y. ve Liu, M., 2013a, A hybrid artificial bee colony algorithm for the fuzzy flexible job-shop scheduling problem, International Journal of Production Research, 51 (12), 3593-3608.
  • Wang, X.-Y., Zhou, Z., Zhang, X., Ji, P. ve Wang, J.-B., 2013b, Several flow shop scheduling problems with truncated position-based learning effect, Computers & Operations Research, 40 (12), 2906-2929.
  • Xu, Y., Wang, L., Wang, S.-y. ve Liu, M., 2015, An effective teaching–learning-based optimization algorithm for the flexible job-shop scheduling problem with fuzzy processing time, Neurocomputing, 148, 260-268.
  • Yeh, W.-C., Lai, P.-J., Lee, W.-C. ve Chuang, M.-C., 2014, Parallel-machine scheduling to minimize makespan with fuzzy processing times and learning effects, Information Sciences, 269, 142-158.
  • Yimer, A. D. ve Demirli, K., 2009, Fuzzy scheduling of job orders in a two-stage flowshop with batch-processing machines, International Journal of Approximate Reasoning, 50 (1), 117-137.
  • Yin, Y., Xu, D., Sun, K. ve Li, H., 2009, Some scheduling problems with general position-dependent and time-dependent learning effects, Information Sciences, 179 (14), 2416-2425.
  • Zhang, X., Yan, G., Huang, W. ve Tang, G., 2012, A note on machine scheduling with sum-of-logarithm-processing-time-based and position-based learning effects, Information Sciences, 187, 298-304.

USING PARALLEL KANGAROO ALGORITHM TO SOLVE FLOW SHOP SCHEDULING PROBLEMS WITH FUZZY LEARNING EFFECT

Yıl 2017, Cilt: 1 Sayı: 1, 31 - 48, 30.06.2017

Ahmet Sezer Küpeli Orhan Engin Batuhan Eren Engin

Öz

Knowing that the scheduling
problems considering learning ability can give more realistic results, this
paper deals with flow shop scheduling problem considering learning effects due
to the human factor. Uncertain processing time is frequently encountered in
scheduling problems; therefore, the processing times are fuzzed. Fuzzy logic is
a frequently used method to solve this problem in the literature. Parallel
Kangaroo Algorithm is used as a solution method. The study is concluded with
comparing the results found in other studies.

Anahtar Kelimeler

Flow shop scheduling Fuzzy logic Learning effects Parallel Kangaroo Algorithm

Kaynakça

  • Abdullah, S. ve Abdolrazzagh-Nezhad, M., 2014, Fuzzy job-shop scheduling problems: A review, Information Sciences, 278, 380-407.
  • Agarwal, A., Colak, S. ve Eryarsoy, E., 2006, Improvement heuristic for the flow-shop scheduling problem: An adaptive-learning approach, European Journal of Operational Research, 169 (3), 801-815.
  • Ahmadizar, F. ve Hosseini, L., 2012, Minimizing makespan in a single-machine scheduling problem with a learning effect and fuzzy processing times, The International Journal of Advanced Manufacturing Technology, 65 (1-4), 581-587.
  • Ambika, G. ve Uthra, G., 2014, Branch and Bound Technique in Flow Shop Scheduling Using Fuzzy Processing Times, Annals of Pure and Applied Mathematics, Vol. 8 ( No. 2), 37-42
  • Amirian, H. ve Sahraeian, R., 2015, Augmented ε-constraint method in multi-objective flowshop problem with past sequence set-up times and a modified learning effect, International Journal of Production Research, 53 (19), 5962-5976.
  • Asadi, H., 2017, Apply Fuzzy Learning Effect with Fuzzy Processing Times for Single Machine Scheduling Problems, Journal of Manufacturing Systems, 42, 244-261.
  • Bachman, A. ve Janiak, A., 2004, Scheduling jobs with position-dependent processing times, Journal of the Operational Research Society, 55, 257-263.
  • Balin, S., 2011, Parallel machine scheduling with fuzzy processing times using a robust genetic algorithm and simulation, Information Sciences, 181 (17), 3551-3569.
  • Baysal, M. E., Durmaz, T., Sarucan, A. ve Engin, O., 2012, Açık atölye tipi çizelgeleme problemlerinin paralel kanguru algoritması ile çözümü, Journal of the Faculty of Engineering and Architecture of Gazi University, 27, 855-864.
  • Behnamian, J. ve Fatemi Ghomi, S. M. T., 2014, Multi-objective fuzzy multiprocessor flowshop scheduling, Applied Soft Computing, 21, 139-148.
  • Biskup, D., 1999, Single-machine scheduling with learning considerations, European Journal of Operational Research, 115, 173–178.
  • Biskup, D., 2008, A state-of-the-art review on scheduling with learning effects, European Journal of Operational Research, 188 (2), 315-329.
  • Cheng, T., Wu, C. ve Lee, W., 2008, Some scheduling problems with sum-of-processing-times-based and job-position-based learning effects, Information Sciences, 178 (11), 2476-2487.
  • De Jong, J. R., 1957, THE EFFECTS OF INCREASING SKILL ON CYCLE TIME AND ITS CONSEQUENCES FOR TIME STANDARDS, Ergonomics, 1 (1), 51-60.
  • Dong, Y., 2003, One Machine Fuzzy Scheduling to Minimize Total Weighted Tardiness, Earliness, and Recourse Cost, International Journal of Smart Engineering System Design, 5 (3), 135-147.
  • Durmaz, T., 2011, Açık Atölye Çizelgeleme Problemlerinin Paralel Kanguru Algoritması ile Çözümü, Selçuk Üniversitesi, Fen Bilimleri Enstitüsü (Yüksek Lisans Tezi).
  • Engin, O., Kahraman, C. ve Yılmaz, M. K., 2009, A Scatter Search Method for Multi Objective Fuzzy Permutation Flow Shop Scheduling Problem A Real World Application, U.K. Chakraborty (Ed.): Comput. Intel. in Flow Shop and Job Shop Sched., SCI 230, 169-189.
  • Engin, O., Yılmaz, M. K., Kahraman, C., Baysal, M. E. ve Sarucan, A., 2011, A Scatter Search Method for Fuzzy Job Shop Scheduling Problem with Availability Constraints, Proceedings of the World Congress on Engineering, Vol II.
  • Erdem, Y. ve Keskintürk, T., 2011, Paralel Kanguru Algoritması ve Gezgin Satıcı Problemine Uygulanması, İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi, 19, 51-63.
  • Gao, K. Z., Suganthan, P. N., Pan, Q. K., Chua, T. J., Chong, C. S. ve Cai, T. X., 2016a, An improved artificial bee colony algorithm for flexible job-shop scheduling problem with fuzzy processing time, Expert Systems with Applications, 65, 52-67.
  • Gao, K. Z., Suganthan, P. N., Pan, Q. K., Tasgetiren, M. F. ve Sadollah, A., 2016b, Artificial bee colony algorithm for scheduling and rescheduling fuzzy flexible job shop problem with new job insertion, Knowledge-Based Systems, 109, 1-16.
  • He, H., 2016, Minimization of maximum lateness in an m -machine permutation flow shop with a general exponential learning effect, Computers & Industrial Engineering, 97, 73-83.
  • Huang, W., Oh, S.-K. ve Pedrycz, W., 2013, A fuzzy time-dependent project scheduling problem, Information Sciences, 246, 100-114.
  • Janiak, A. ve Rudek, R., 2010, A note on a makespan minimization problem with a multi-ability learning effect, Omega, 38 (3-4), 213-217.
  • Jellouli, O. ve Chatelet, E., 2001, Monte Carlo simulation and stochastic algorithms for optimising supply chain management in an uncertain environment, 2001 IEEE International Conference on Systems, Man and Cybernetics. e-Systems and e-Man for Cybernetics in Cyberspace (Cat.No.01CH37236), 1840-1844 vol.1843.
  • Ji, M., Tang, X., Zhang, X. ve Cheng, T. C. E., 2015, Machine scheduling with deteriorating jobs and DeJong’s learning effect, Computers & Industrial Engineering, 91, 42-47.
  • Kahraman, C., Engin, O. ve Yilmaz, M. K., 2009, A New Artificial Immune System Algorithm for Multiobjective Fuzzy Flow Shop, International Journal of Computational Intelligence Systems, 2 (3), 236-247.
  • Kökçam, A. H. ve Engin, O., 2010, Solving the fuzzy project scheduling problems with metaheuristic methods, Mühendislik ve Fen Bilimleri Dergisi (Sigma), 28, 86-101.
  • Lai, P.-J. ve Lee, W.-C., 2011, Single-machine scheduling with general sum-of-processing-time-based and position-based learning effects, Omega, 39 (5), 467-471.
  • Lee, W.-C. ve Chung, Y.-H., 2013, Permutation flowshop scheduling to minimize the total tardiness with learning effects, International Journal of Production Economics, 141 (1), 327-334.
  • Lei, D. ve Guo, X., 2012, Swarm-based neighbourhood search algorithm for fuzzy flexible job shop scheduling, International Journal of Production Research, 50 (6), 1639-1649.
  • Liao, T. W. ve Su, P., 2017, Parallel machine scheduling in fuzzy environment with hybrid ant colony optimization including a comparison of fuzzy number ranking methods in consideration of spread of fuzziness, Applied Soft Computing, 56, 65-81.
  • Lin, J., 2015, A hybrid biogeography-based optimization for the fuzzy flexible job-shop scheduling problem, Knowledge-Based Systems, 78, 59-74.
  • Liu, B., Fan, Y. ve Liu, Y., 2015, A fast estimation of distribution algorithm for dynamic fuzzy flexible job-shop scheduling problem, Computers & Industrial Engineering, 87, 193-201.
  • Liu, G.-S., Zhou, Y. ve 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.
  • Lowe, C. ve Tedford, J. D., 1997, Fuzzy Production Scheduling for JIT Manufacturing, Intelligent Automation & Soft Computing, 3 (4), 319-329.
  • Lu, Y.-Y., 2015, Research on no-idle permutation flowshop scheduling with time-dependent learning effect and deteriorating jobs, Applied Mathematical Modelling.
  • McCahon, C. S. ve Lee, E. S., 1990, Job sequencing with fuzzy processing times, Computers & Mathematics with Applications, 19 (7), 31-41.
  • Minzu, V. ve Beldiman, L., 2007, Some aspects concerning the implementation of a parallel hybrid metaheuristic, Engineering Applications of Artificial Intelligence, 20 (7), 993-999.
  • Mitsuru Kuroda, Z. W., 1996, Fuzzy Job Scheduling, Int. J. Production Economics, 44, 45-51. Mosheiov, G., 2001, Scheduling problems with a learning effect, European Journal of Operational Research, 132, 687-693.
  • Noori-Darvish, S., Mahdavi, I. ve Mahdavi-Amiri, N., 2012, A bi-objective possibilistic programming model for open shop scheduling problems with sequence-dependent setup times, fuzzy processing times, and fuzzy due dates, Applied Soft Computing, 12 (4), 1399-1416.
  • Palacios, J. J., González, M. A., Vela, C. R., González-Rodríguez, I. ve Puente, J., 2015, Genetic tabu search for the fuzzy flexible job shop problem, Computers & Operations Research, 54, 74-89.
  • Peng, J. ve Iwamura, K., 2003, Three types of models for stochastic scheduling with fuzzy information, Journal of Statistics and Management Systems, 6 (3), 493-504.
  • Petrovic, S., Fayad, C. ve Petrovic, D., 2008, Sensitivity analysis of a fuzzy multiobjective scheduling problem, International Journal of Production Research, 46 (12), 3327-3344.
  • Pollard, J. M., 2000, Kangaroos, Monopoly and Discrete Logarithms, Journal of Cryptology, 13 (4), 437-447.
  • Rostami, M., Pilerood, A. E. ve Mazdeh, M. M., 2015, Multi-objective parallel machine scheduling problem with job deterioration and learning effect under fuzzy environment, Computers & Industrial Engineering, 85, 206-215.
  • Sathish, S. ve Ganesan, K., 2012, Flow Shop Scheduling Problem to minimize the Rental Cost under Fuzzy Environment, Journal of Natural Sciences Research, Vol.2, No.10.
  • Serbencu, A., Minzu, V. ve Serbencu, A., 2007, An Ant Colony System Based Metaheuristic for Solving Single Machine Scheduling Problem, The Annals of “Dunarea de Jos” University of Galati Fascicle III.
  • Soltani, R., Jolai, F. ve Zandieh, M., 2010, Two robust meta-heuristics for scheduling multiple job classes on a single machine with multiple criteria, Expert Systems with Applications, 37 (8), 5951-5959.
  • Stein, A. ve Teske, E., 2002, The parallelized Pollard kangaroo method in real Quadratic function fields, Math. Comput., 71 (238), 793-814.
  • Teske, E., 2003, Computing discrete logarithms with the parallelized kangaroo method, Discrete Applied Mathematics, 130 (1), 61-82.
  • Torabi, S. A., Sahebjamnia, N., Mansouri, S. A. ve Bajestani, M. A., 2013, A particle swarm optimization for a fuzzy multi-objective unrelated parallel machines scheduling problem, Applied Soft Computing, 13 (12), 4750-4762.
  • Vahedi-Nouri, B., Fattahi, P., Tavakkoli-Moghaddam, R. ve Ramezanian, R., 2014, A general flow shop scheduling problem with consideration of position-based learning effect and multiple availability constraints, The International Journal of Advanced Manufacturing Technology, 73 (5-8), 601-611.
  • Vahedi Nouri, B., Fattahi, P. ve Ramezanian, R., 2013, Hybrid firefly-simulated annealing algorithm for the flow shop problem with learning effects and flexible maintenance activities, International Journal of Production Research, 51 (12), 3501-3515.
  • Wang, J. B., Ng, C. T., Cheng, T. C. E. ve Liu, L. L., 2008, Single-machine scheduling with a time-dependent learning effect, International Journal of Production Economics, 111 (2), 802-811.
  • Wang, L., Zhou, G., Xu, Y. ve Liu, M., 2013a, A hybrid artificial bee colony algorithm for the fuzzy flexible job-shop scheduling problem, International Journal of Production Research, 51 (12), 3593-3608.
  • Wang, X.-Y., Zhou, Z., Zhang, X., Ji, P. ve Wang, J.-B., 2013b, Several flow shop scheduling problems with truncated position-based learning effect, Computers & Operations Research, 40 (12), 2906-2929.
  • Xu, Y., Wang, L., Wang, S.-y. ve Liu, M., 2015, An effective teaching–learning-based optimization algorithm for the flexible job-shop scheduling problem with fuzzy processing time, Neurocomputing, 148, 260-268.
  • Yeh, W.-C., Lai, P.-J., Lee, W.-C. ve Chuang, M.-C., 2014, Parallel-machine scheduling to minimize makespan with fuzzy processing times and learning effects, Information Sciences, 269, 142-158.
  • Yimer, A. D. ve Demirli, K., 2009, Fuzzy scheduling of job orders in a two-stage flowshop with batch-processing machines, International Journal of Approximate Reasoning, 50 (1), 117-137.
  • Yin, Y., Xu, D., Sun, K. ve Li, H., 2009, Some scheduling problems with general position-dependent and time-dependent learning effects, Information Sciences, 179 (14), 2416-2425.
  • Zhang, X., Yan, G., Huang, W. ve Tang, G., 2012, A note on machine scheduling with sum-of-logarithm-processing-time-based and position-based learning effects, Information Sciences, 187, 298-304.
Toplam 62 adet kaynakça vardır.

Ayrıntılar

Bölüm olgu sunumları
Yazarlar

Ahmet Sezer Küpeli

Orhan Engin

Batuhan Eren Engin

Yayımlanma Tarihi 30 Haziran 2017
Gönderilme Tarihi 27 Ekim 2017
Yayımlandığı Sayı Yıl 2017 Cilt: 1 Sayı: 1

Kaynak Göster

APA Küpeli, A. S., Engin, O., & Engin, B. E. (2017). Bulanık Öğrenme Etkili Akış Tipi Çizelgeleme Problemlerinin Paralel Kanguru Algoritması İle Çözümü. Kapadokya Akademik Bakış, 1(1), 31-48.
 Kapak Resmi İndir