Minimizing the energy consumption and the total weighted tardiness for the flexible flowshop using NSGA-II and NRGA

Document Type: conference paper

Authors

1 University of Tehran

2 School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran

3 Department of Industrial Engineering, Alborz Campus, University of Tehran, Tehran, Iran

4 Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran

Abstract

This paper presents a bi-objective MIP model for the flexible flow shop scheduling  problem (FFSP) in which the total weighted tardiness and the energy consumption are minimized simultaneously. In addition to considering unrelated machines at each stage, the set-up times are supposed to be sequence- and machine-dependent, and it is assumed that jobs have different release times. Two Taguchi-based-tuned algorithms: (i) non-dominated sorting genetic algorithm II (NSGA-II), and (ii) non-dominated ranked genetic algorithm (NRGA) are applied to solve themodel. Six numerical examples with different sizes (small, medium, and large) are used to demonstrate the applicability and to exhibit the efficacy of the algorithms. The results show that the NRGA outperforms significantly the NSGA-II in the performance metrics for all six numerical examples.

Keywords

Main Subjects


Asif, M., & Muneer, T. (2007). Energy supply, its demand and security issues for developed and emerging economies. Renewable and Sustainable Energy Reviews11(7), 1388-1413.

 

Choi, S. W., Kim*, Y. D., & Lee, G. C. (2005). Minimizing total tardiness of orders with reentrant lots in a hybrid flowshop. International Journal of Production Research43(11), 2149-2167.

 

Dahmus, J. B., & Gutowski, T. G. (2004, January). An environmental analysis of machining. In ASME 2004 international mechanical engineering congress and exposition(pp. 643-652). American Society of Mechanical Engineers.

 

Dai, M., Tang, D., Giret, A., Salido, M. A., & Li, W. D. (2013). Energy-efficient scheduling for a flexible flow shop using an improved genetic-simulated annealing algorithm. Robotics and Computer-Integrated Manufacturing29(5), 418-429.

 

Davoudpour, H., & Ashrafi, M. (2009). Solving multi-objective SDST flexible flow shop using GRASP algorithm. The International Journal of Advanced Manufacturing Technology44(7-8), 737-747.

Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. A. M. T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation6(2), 182-197.

 

Drake, R., Yildirim, M. B., Twomey, J. M., Whitman, L. E., Ahmad, J. S., & Lodhia, P. (2006). Data collection framework on energy consumption in manufacturing.

 

Ebrahimi, M., Ghomi, S. F., & Karimi, B. (2014). Hybrid flow shop scheduling with sequence dependent family setup time and uncertain due dates. Applied Mathematical Modelling38(9-10), 2490-2504.

 

Fang, K., Uhan, N., Zhao, F., & Sutherland, J. W. (2011). A new approach to scheduling in manufacturing for power consumption and carbon footprint reduction. Journal of Manufacturing Systems30(4), 234-240.

 

Gupta, J. N., Krüger, K., Lauff, V., Werner, F., & Sotskov, Y. N. (2002). Heuristics for hybrid flow shops with controllable processing times and assignable due dates. Computers & Operations Research29(10), 1417-1439.

 

Gutowski, T., Murphy, C., Allen, D., Bauer, D., Bras, B., Piwonka, T., ... & Wolff, E. (2005). Environmentally benign manufacturing: observations from Japan, Europe and the United States. Journal of Cleaner Production13(1), 1-17.

 

JADAAN, O. A., RAJAMANI, L., & Rao, C. R. (2009). NON-DOMINATED RANKED GENETIC ALGORITHM FOR SOLVING CONSTRAINED MULTI-OBJECTIVE OPTIMIZATION PROBLEMS. Journal of Theoretical & Applied Information Technology5(5).

 

Jun, S., & Park, J. (2015). A hybrid genetic algorithm for the hybrid flow shop scheduling problem with nighttime work and simultaneous work constraints: A case study from the transformer industry. Expert Systems with Applications42(15-16), 6196-6204.

 

Kerzner, H., & Kerzner, H. R. (2017). Project management: a systems approach to planning, scheduling, and controlling. John Wiley & Sons.

 

Lee, G. C., & Kim*, Y. D. (2004). A branch-and-bound algorithm for a two-stage hybrid flowshop scheduling problem minimizing total tardiness. International Journal of Production Research42(22), 4731-4743.

 

Lee, G. C., Kim, Y. D., & Choi, S. W. (2004). Bottleneck-focused scheduling for a hybrid flowshop. International Journal of Production Research42(1), 165-181.

 

Li, D., Meng, X., Liang, Q., & Zhao, J. (2015). A heuristic-search genetic algorithm for multi-stage hybrid flow shop scheduling with single processing machines and batch processing machines. Journal of Intelligent Manufacturing26(5), 873-890.

 

Liang, P., Yang, H. D., Liu, G. S., & Guo, J. H. (2015). An ant optimization model for unrelated parallel machine scheduling with energy consumption and total tardiness. Mathematical Problems in Engineering2015.

 

LIN, Z. Y., & Zhang, W. (2014). Study on Multi-Objective Optimization of Hybrid Flow Shop Scheduling Problem. Advanced Materials Research1082.

 

Liu, Y., Dong, H., Lohse, N., & Petrovic, S. (2015). Reducing environmental impact of production during a rolling blackout policy–a multi-objective schedule optimisation approach. Journal of Cleaner Production102, 418-427.

 

Liu, Y., Dong, H., Lohse, N., Petrovic, S., & Gindy, N. (2014). An investigation into minimising total energy consumption and total weighted tardiness in job shops. Journal of Cleaner Production65, 87-96.

 

Luo, H., Du, B., Huang, G. Q., Chen, H., & Li, X. (2013). Hybrid flow shop scheduling considering machine electricity consumption cost. International Journal of Production Economics146(2), 423-439.

 

Naderi, B., Zandieh, M., & Roshanaei, V. (2009). Scheduling hybrid flowshops with sequence dependent setup times to minimize makespan and maximum tardiness. The International Journal of Advanced Manufacturing Technology41(11-12), 1186-1198.

 

Pechmann, A., & Schöler, I. (2011). Optimizing energy costs by intelligent production scheduling. In Glocalized Solutions for Sustainability in Manufacturing (pp. 293-298). Springer, Berlin, Heidelberg.

 

Pinedo, M., Zacharias, C., & Zhu, N. (2015). Scheduling in the service industries: An overview. Journal of Systems Science and Systems Engineering24(1), 1-48.

 

Rashidi, E., Jahandar, M., & Zandieh, M. (2010). An improved hybrid multi-objective parallel genetic algorithm for hybrid flow shop scheduling with unrelated parallel machines. The International Journal of Advanced Manufacturing Technology49(9-12), 1129-1139.

 

Ruiz, R., & Vázquez-Rodríguez, J. A. (2010). The hybrid flow shop scheduling problem. European journal of operational research205(1), 1-18.

 

Song, W., Zhang, C., Lin, W., & Shao, X. (2014). Flexible job-shop scheduling problem with maintenance activities considering energy consumption. Applied Mechanics & Materials, (521).

 

Tang, D., Dai, M., Salido, M. A., & Giret, A. (2016). Energy-efficient dynamic scheduling for a flexible flow shop using an improved particle swarm optimization. Computers in Industry81, 82-95.

 

Tavakkoli-Moghaddam, R., Javadian, N., Khorrami, A., & Gholipour-Kanani, Y. (2010). Design of a scatter search method for a novel multi-criteria group scheduling problem in a cellular manufacturing system. Expert Systems with Applications37(3), 2661-2669.

 

Yan, J., Li, L., Zhao, F., Zhang, F., & Zhao, Q. (2016). A multi-level optimization approach for energy-efficient flexible flow shop scheduling. Journal of Cleaner Production137, 1543-1552.

 

Yan, J. H., Zhang, F. Y., Li, X., Wang, Z. M., & Wang, W. (2014). Modeling and Multiobjective Optimization for Energy-Aware Hybrid Flow Shop Scheduling. In Proceedings of 2013 4th International Asia Conference on Industrial Engineering and Management Innovation (IEMI2013) (pp. 741-751). Springer, Berlin, Heidelberg.

 

Zitzler, E., & Thiele, L. (1998, September). Multiobjective optimization using evolutionary algorithms—a comparative case study. In International conference on parallel problem solving from nature (pp. 292-301). Springer, Berlin, Heidelberg.