Energy-Based Scheduling Optimization To Minimize The Total Energy Consumption And The Total Tardiness In A Single Machine Manufacturing System With The Sequence-Dependent Setup Times
Yıl 2024,
Cilt: 27 Sayı: 1, 169 - 183, 29.02.2024
Elif Tarakçı
,
Abdül Halim Zaim
,
Oğuzhan Öztaş
Öz
Nowadays, reducing energy consumption is an important target for energy-intensive manufacturing systems due to many reasons such as global warming, legal obligations and lowering company expenses. Therefore, this paper focuses on energy-based scheduling problem in manufacturing systems. A mixed-integer nonlinear programming (MINLP) model is developed for a single machine scheduling problem with the sequence-dependent setup times and different arrival times in order to minimize the total energy consumption and the total tardiness. An energy-based genetic optimization (EGOP) method is proposed by adopting the genetic algorithm (GA) approach, which is a heuristic method to solve the problem. The objective values and the computation times are compared with the analytical solution and the General Algebraic Modeling System (GAMS) solution so as to evaluate the performance of the proposed method. As a result, it is seen that the proposed EGOP method provides effective results.
Kaynakça
- [1] Mikhaylidi Y., Naseraldin H. and Yedidsion L., “Operations scheduling under electricity time-varying prices”, International Journal of Production Research, 53(23):7136-7157, (2015).
- [2] Shrouf F., Gong B. and Ordieres-Mere J., “Multi-level awareness of energy used in production processes”, Journal of Cleaner Production, 142(4): 2570-2585, (2017).
- [3] Neugebauer R., Wabner M., Rentzsch H. and Ihlenfeldt S., “Structure principles of energy efficient machine tools”, CIRP Journal of Manufacturing Science and Technology, 4(2): 136-147, (2011).
- [4] Biel K. and Glock C. H., “Systematic literature review of decision support models for energy efficient production planning”, Computers & Industrial Engineering, 101: 243-259, (2016).
- [5] Choi Y. C., “Dispatching rule-based scheduling algorithms in a single machine with sequence-dependent setup times and energy requirements”, 48th CIRP Conference on Manufacturing Systems, 24-26 June 2015, Italy, 135-140, (2016).
- [6] Mouzon G. and Yildirim M. B., “A framework to minimise total energy consumption and total tardiness on a single machine”, International Journal of Sustainable Engineering, 1(2): 105-116, (2008).
- [7] Liu C. Y. and Chang S. C., “Scheduling flexible flow shops with sequence-dependent setup effects”, IEEE Transactions on Robotics and Automation, 16(4): 408-419, (2000).
- [8] Mouzon G., Yildirim M. B. and Twomey J., “Operational methods for minimization of energy consumption of manufacturing equipment”, International Journal of Production Research, 45(18-19): 4247-4271, (2007).
- [9] Fang K., Uhan N., Zhao F. and Sutherland J. W., “A new approach to scheduling in manufacturing for power consumption and carbon footprint reduction”, Journal of Manufacturing Systems, 30(4): 234-240, (2011).
- [10] Dai M., Tang D., Giret A., Salido M. A. and Li W. D., “Energy-efficient scheduling for a flexible flow shop using an improved genetic-simulated annealing algorithm”, Robotics and Computer-Integrated Manufacturing, 29(5): 418-429, (2013).
- [11] Bruzzone A. A. G., Anghinolfi D., Paolucci M. and Tonelli F., “Energy-aware scheduling for improving manufacturing process sustainability: a mathematical model for flexible flow shops”, CIRP Annals - Manufacturing Technology, 61(1): 459-462, (2012).
- [12] Shrouf F., Ordieres-Mere J., García-Sanchez A. and Ortega-Mier M., “Optimizing the production scheduling of a single machine to minimize total energy consumption costs”, Journal of Cleaner Production, 67: 197-207, (2014).
- [13] Fang K., Uhan N. A., Zhao F. and Sutherland J. W., “Scheduling on a single machine under time-of-use electricity tariffs”, Annals of Operations Research, 238(1-2): 199-227, (2016).
- [14] Lee S., Chung B. D., Jeon H. W. and Chang J., “A dynamic control approach for energy-efficient production scheduling on a single machine under time-varying electricity pricing”, Journal of Cleaner Production, 165: 552-563, (2017).
- [15] Li Y., Huang W., Wu R. and Guo K., “An improved artificial bee colony algorithm for solving multi-objective low-carbon flexible job shop scheduling problem”, Applied Soft Computing Journal, 95. https://doi.org/10.1016/j.asoc.2020.106544, (2020).
- [16] Zhou S., Jin M. and Du N., “Energy-efficient scheduling of a single batch processing machine with dynamic job arrival times”, Energy 209, https://doi.org/10.1016/j.energy.2020.118420, (2020).
- [17] Nailwal K. K., Gupta D. and Sharma S., “A dual-criteria flow shop scheduling with sequence-dependent setup times”, Journal of Information and Optimization Sciences, 36(5): 485-500, (2015).
- [18] Varmazyar M. and Salmasi N., “Sequence-dependent flow shop scheduling problem minimising the number of tardy jobs”, International Journal of Production Research, 50(20): 5843-5858, (2012).
- [19] Velez-Gallego M. C., Maya J. and Montoya-Torres J. R., “A beam search heuristic for scheduling a single machine with release dates and sequence dependent setup times to minimize the makespan”, Computers & Operations Research, 73: 132-140, (2016).
- [20] Li J., Sang H., Han Y., Wang C. and Gao K., “Efficient multi-objective optimization algorithm for hybrid flow shop scheduling problems with setup energy consumptions”, Journal of Cleaner Production, 181: 584-598, (2018).
- [21] Lu C., Gao L., Li X., Pan Q. and Wang Q., “Energy-efficient permutation flow shop scheduling problem using a hybrid multi-objective backtracking search algorithm”, Journal of Cleaner Production, 144: 228-238, (2017).
- [22] Liu C. G., Yang J., Lian J., Li W. J., Evans S. and Yin Y., “Sustainable performance oriented operational decision-making of single machine systems with deterministic product arrival time”, Journal of Cleaner Production, 85: 318-330, (2014).
- [23] Goldberg D. E., “Genetic algorithms in search, optimization and machine learning”, Addison Wesley Longman, Inc, USA, (1989).
- [24] Al-Tabtabai H. and Alex A. P., “Using genetic algorithms to solve optimization problems in construction”, Engineering, Construction and Architectural Management, 6(2): 121-132, (1999).
- [25] Elmas Ç., “Yapay zekâ uygulamaları”, Seçkin Yayıncılık San. ve Tic. A.Ş., Ankara, (2016).
- [26] Yildirim M. Y. and Mouzon G., “Single-machine sustainable production planning to minimize total energy consumption and total completion time using a multiple objective genetic algorithm”, IEEE Transactions on Engineering Management, 59(4): 585-597, (2012).
- [27] Zhang R. and Chiong R., “Solving the energy-efficient job shop scheduling problem: a multi-objective genetic algorithm with enhanced local search for minimizing the total weighted tardiness and total energy consumption”, Journal of Cleaner Production, 112 (4): 3361-3375, (2016).
- [28] Rajkumar R. and Shahabudeen P., “Bi-criteria improved genetic algorithm for scheduling in flowshops to minimise makespan and total flowtime of jobs”, International Journal of Computer Integrated Manufacturing, 22(10): 987-998, (2009).
- [29] Kurose J. F. and Ross E. W., “Computer networking”, Pearson Education Limited, England, (2013).
Sıra Bağımlı Hazırlık Süreli Tek Makineli Üretim Sisteminde Toplam Enerji Tüketimini Ve Toplam Teslim Gecikme Süresini Minimize Etmek İçin Enerji Odaklı Çizelgeleme Optimizasyonu
Yıl 2024,
Cilt: 27 Sayı: 1, 169 - 183, 29.02.2024
Elif Tarakçı
,
Abdül Halim Zaim
,
Oğuzhan Öztaş
Öz
Günümüzde küresel ısınma, yasal zorunluluklar ve şirket giderlerinin düşürülmesi gibi birçok nedenden dolayı enerji yoğun üretim sistemleri için enerji tüketimini azaltmak önemli bir hedef haline gelmiştir. Bu nedenle, bu makalede üretim sistemlerinde enerji odaklı çizelgeleme problemine odaklanılmıştır. Sıra bağımlı hazırlık süreli (SBHS) tek makineli bir üretim sisteminde farklı geliş zamanlarına sahip işlerin toplam enerji tüketimini ve toplam teslim gecikme süresini minimize etmeyi sağlayan bir karma tamsayılı doğrusal olmayan programlama (MINLP) modeli geliştirilmiştir. Problemi çözmek için sezgisel bir yöntem olan genetik algoritma (GA) tabanlı enerji odaklı genetik optimizasyon (EGOP) yöntemi önerilmiştir. Önerilen yöntemin performansını değerlendirmek için amaç değerleri ve hesaplama süreleri analitik çözüm ve General Algebraic Modeling System (GAMS) çözüm ile karşılaştırılmıştır. Sonuç olarak, önerilen EGOP yönteminin etkili sonuçlar verdiği görülmüştür.
Kaynakça
- [1] Mikhaylidi Y., Naseraldin H. and Yedidsion L., “Operations scheduling under electricity time-varying prices”, International Journal of Production Research, 53(23):7136-7157, (2015).
- [2] Shrouf F., Gong B. and Ordieres-Mere J., “Multi-level awareness of energy used in production processes”, Journal of Cleaner Production, 142(4): 2570-2585, (2017).
- [3] Neugebauer R., Wabner M., Rentzsch H. and Ihlenfeldt S., “Structure principles of energy efficient machine tools”, CIRP Journal of Manufacturing Science and Technology, 4(2): 136-147, (2011).
- [4] Biel K. and Glock C. H., “Systematic literature review of decision support models for energy efficient production planning”, Computers & Industrial Engineering, 101: 243-259, (2016).
- [5] Choi Y. C., “Dispatching rule-based scheduling algorithms in a single machine with sequence-dependent setup times and energy requirements”, 48th CIRP Conference on Manufacturing Systems, 24-26 June 2015, Italy, 135-140, (2016).
- [6] Mouzon G. and Yildirim M. B., “A framework to minimise total energy consumption and total tardiness on a single machine”, International Journal of Sustainable Engineering, 1(2): 105-116, (2008).
- [7] Liu C. Y. and Chang S. C., “Scheduling flexible flow shops with sequence-dependent setup effects”, IEEE Transactions on Robotics and Automation, 16(4): 408-419, (2000).
- [8] Mouzon G., Yildirim M. B. and Twomey J., “Operational methods for minimization of energy consumption of manufacturing equipment”, International Journal of Production Research, 45(18-19): 4247-4271, (2007).
- [9] Fang K., Uhan N., Zhao F. and Sutherland J. W., “A new approach to scheduling in manufacturing for power consumption and carbon footprint reduction”, Journal of Manufacturing Systems, 30(4): 234-240, (2011).
- [10] Dai M., Tang D., Giret A., Salido M. A. and Li W. D., “Energy-efficient scheduling for a flexible flow shop using an improved genetic-simulated annealing algorithm”, Robotics and Computer-Integrated Manufacturing, 29(5): 418-429, (2013).
- [11] Bruzzone A. A. G., Anghinolfi D., Paolucci M. and Tonelli F., “Energy-aware scheduling for improving manufacturing process sustainability: a mathematical model for flexible flow shops”, CIRP Annals - Manufacturing Technology, 61(1): 459-462, (2012).
- [12] Shrouf F., Ordieres-Mere J., García-Sanchez A. and Ortega-Mier M., “Optimizing the production scheduling of a single machine to minimize total energy consumption costs”, Journal of Cleaner Production, 67: 197-207, (2014).
- [13] Fang K., Uhan N. A., Zhao F. and Sutherland J. W., “Scheduling on a single machine under time-of-use electricity tariffs”, Annals of Operations Research, 238(1-2): 199-227, (2016).
- [14] Lee S., Chung B. D., Jeon H. W. and Chang J., “A dynamic control approach for energy-efficient production scheduling on a single machine under time-varying electricity pricing”, Journal of Cleaner Production, 165: 552-563, (2017).
- [15] Li Y., Huang W., Wu R. and Guo K., “An improved artificial bee colony algorithm for solving multi-objective low-carbon flexible job shop scheduling problem”, Applied Soft Computing Journal, 95. https://doi.org/10.1016/j.asoc.2020.106544, (2020).
- [16] Zhou S., Jin M. and Du N., “Energy-efficient scheduling of a single batch processing machine with dynamic job arrival times”, Energy 209, https://doi.org/10.1016/j.energy.2020.118420, (2020).
- [17] Nailwal K. K., Gupta D. and Sharma S., “A dual-criteria flow shop scheduling with sequence-dependent setup times”, Journal of Information and Optimization Sciences, 36(5): 485-500, (2015).
- [18] Varmazyar M. and Salmasi N., “Sequence-dependent flow shop scheduling problem minimising the number of tardy jobs”, International Journal of Production Research, 50(20): 5843-5858, (2012).
- [19] Velez-Gallego M. C., Maya J. and Montoya-Torres J. R., “A beam search heuristic for scheduling a single machine with release dates and sequence dependent setup times to minimize the makespan”, Computers & Operations Research, 73: 132-140, (2016).
- [20] Li J., Sang H., Han Y., Wang C. and Gao K., “Efficient multi-objective optimization algorithm for hybrid flow shop scheduling problems with setup energy consumptions”, Journal of Cleaner Production, 181: 584-598, (2018).
- [21] Lu C., Gao L., Li X., Pan Q. and Wang Q., “Energy-efficient permutation flow shop scheduling problem using a hybrid multi-objective backtracking search algorithm”, Journal of Cleaner Production, 144: 228-238, (2017).
- [22] Liu C. G., Yang J., Lian J., Li W. J., Evans S. and Yin Y., “Sustainable performance oriented operational decision-making of single machine systems with deterministic product arrival time”, Journal of Cleaner Production, 85: 318-330, (2014).
- [23] Goldberg D. E., “Genetic algorithms in search, optimization and machine learning”, Addison Wesley Longman, Inc, USA, (1989).
- [24] Al-Tabtabai H. and Alex A. P., “Using genetic algorithms to solve optimization problems in construction”, Engineering, Construction and Architectural Management, 6(2): 121-132, (1999).
- [25] Elmas Ç., “Yapay zekâ uygulamaları”, Seçkin Yayıncılık San. ve Tic. A.Ş., Ankara, (2016).
- [26] Yildirim M. Y. and Mouzon G., “Single-machine sustainable production planning to minimize total energy consumption and total completion time using a multiple objective genetic algorithm”, IEEE Transactions on Engineering Management, 59(4): 585-597, (2012).
- [27] Zhang R. and Chiong R., “Solving the energy-efficient job shop scheduling problem: a multi-objective genetic algorithm with enhanced local search for minimizing the total weighted tardiness and total energy consumption”, Journal of Cleaner Production, 112 (4): 3361-3375, (2016).
- [28] Rajkumar R. and Shahabudeen P., “Bi-criteria improved genetic algorithm for scheduling in flowshops to minimise makespan and total flowtime of jobs”, International Journal of Computer Integrated Manufacturing, 22(10): 987-998, (2009).
- [29] Kurose J. F. and Ross E. W., “Computer networking”, Pearson Education Limited, England, (2013).