Energy-efficient buffer and service rate allocation in manufacturing systems using hybrid machine learning and evolutionary algorithms

Abstract

Currently, simultaneous buffer and service rate allocation is a topic of interest in the optimization of manufacturing systems. Simultaneous allocation problems have been solved previously to satisfy economic requirements; however, owing to the progress of green manufacturing, energy conservation and environmental protection have become increasingly crucial. Therefore, an energy-efficient approach is developed to maximize the throughput and minimize the energy consumption of manufacturing systems, subject to the total buffer capacity, total service rate, and predefined energy efficiency. The energy-efficient approach integrates the simulated annealing-non-dominated sorting genetic algorithm-II with the honey badger algorithm-histogram-based gradient boosting regression tree. The former algorithm searches for Pareto-optimal solutions of sufficient quality. The latter algorithm builds prediction models to rapidly calculate the throughput, energy consumption, and energy efficiency. Numerical examples show that the proposed hybrid approach can achieve a better solution quality compared with previously reported approaches. Furthermore, the prediction models can rapidly evaluate manufacturing systems with sufficient accuracy. This study benefits the multi-objective optimization of green manufacturing systems.

Keywords

Energy-efficient allocation / Multi-objective optimization / Energy efficiency / Energy consumption / Machine learning

References

[1.] Lin G, Hao B. Research on green manufacturing technology. J Phys Conf Ser, 2020, 1601: 042046.

[2.] Tekkaya AE. Energy saving by manufacturing technology. Procedia Manuf, 2018, 21: 392-396.

[3.] Cruz FRB, Kendall G, While L, et al. Throughput maximization of queueing networks with simultaneous minimization of service rates and buffers. Math Probl Eng, 2012, 2012: 1-19.

[4.] Gao S, Higashi T, Kobayashi T, et al. Buffer allocation via bottleneck-based variable neighbourhood search. Appl Sci-Basel, 2020, 10(23): 8569.

[5.] Frigerio N, Matta A. Analysis on energy efficients witching of machine tool with stochastic arrivals and buffer information. IEEE Trans Autom Sci Eng, 2016, 13: 238-246.

[6.] Wang J, Fei Z, Chang Q, et al. Multi-state decision of unreliable machines for energy-efficient production considering work-in-process inventory. Int J Adv Manuf Technol, 2019, 102: 1009-1021.

[7.] Alaouchiche Y, Ouazene Y, Yalaoui F. Energy-efficient buffer allocation problem in unreliable production lines. Int J Adv Manuf Technol, 2021, 114: 2871-2885.

[8.] Alaouchiche Y, Ouazene Y, Yalaoui F. Economic and energetic performance evaluation of unreliable production lines: an integrated analytical approach. IEEE Access, 2020, 8: 185330-185345.

[9.] Gao S. A bottleneck detection-based tabu search algorithm for the buffer allocation problem in manufacturing systems. IEEE Access, 2022, 10: 60507-60520.

[10.] Nahas N, Nourelfath M, Gendreau M. Selecting machines and buffers in unreliable assembly/disassembly manufacturing networks. Int J Prod Econ, 2014, 154: 113-126.

[11.] Smith JM. Simultaneous buffer and service rate allocation in open finite queueing networks. IISE Trans, 2018, 50(3): 203-216.

[12.] Ng AHC, Shaaban S, Bernedixen J. Studying unbalanced workload and buffer allocation of production systems using multi-objective optimisation. Int J Prod Res, 2017, 55(24): 7435-7451.

[13.] Xi S, Smith JM, Chen Q, et al. Simultaneous machine selection and buffer allocation in large unbalanced series-parallel production lines. Int J Prod Res, 2021, 60(7): 2103-2125.

[14.] Renna P, Materi S. A literature review of energy efficiency and sustainability in manufacturing systems. Appl Sci-Basel, 2021, 11: 7366.

[15.] Weiss S, Schwarz JA, Stolletz R. The buffer allocation problem in production lines: formulations, solution methods, and instances. IISE Trans, 2019, 51(5): 456-485.

[16.] Weiss S, Matta A, Stolletz R. Optimization of buffer allocations in flow lines with limited supply. IISE Trans, 2018, 50: 191-202.

[17.] Liberopoulos G. Comparison of optimal buffer allocation in flow lines under installation buffer, echelon buffer, and CONWIP policies. Flex Serv Manuf J, 2020, 32: 297-365.

[18.] Kose SY, Kilincci O. Hybrid approach for buffer allocation in open serial production lines. Comput Oper Res, 2015, 60: 67-78.

[19.] Koyuncuoğlu MU, Demir L. A comparison of combat genetic and big bang–big crunch algorithms for solving the buffer allocation problem. J Intell Manuf, 2021, 32: 1529-1546.

[20.] Cruz FRB. Optimizing the throughput, service rate, and buffer allocation in finite queueing networks. Electron Notes Discrete Math, 2009, 35: 163-168.

[21.] George Shanthikumar J, Xu SH. Asymptotically optimal routing and service rate allocation in a multiserver queueing system. Oper Res, 1997, 45(3): 464-469.

[22.] Song D, Xing W, Sun Y. Optimal service rate allocation policy of an unreliable manufacturing system with random demands. ContTheo Apps, 1998, 15(4): 621-626.

[23.] Hillier FS, So KC. On the simultaneous optimization of server and work allocations in production line systems with variable processing times. Oper Res, 1996, 44(3): 435-443.

[24.] Nahas N, Nourelfath M. Joint optimization of maintenance, buffers and machines in manufacturing lines. Eng Optimiz, 2018, 50: 37-54.

[25.] Nahas N. Buffer allocation and preventive maintenance optimization in unreliable production lines. J Intell Manuf, 2017, 28: 85-93.

[26.] Yegul MF, Erenay FS, Striepe S, et al. Improving configuration of complex production lines via simulation-based optimization. Comput Ind Eng, 2017, 109: 295-312.

[27.] Pedrielli G, Matta A, Alfieri A, et al. Design and control of manufacturing systems: a discrete event optimisation methodology. Int J Prod Res, 2018, 56: 543-564.

[28.] Balsamo S. Queueing networks with blocking: analysis, solution algorithms and properties, 2011, Berlin: Springer.

[29.] Gordon WJ, Newell GF. Cyclic queuing systems with restricted length queues. Oper Res, 1967, 15(2): 266-277.

[30.] Zhang M, Pastore E, Alfieri A, et al. Buffer allocation problem in production flow lines: a new Benders-decomposition-based exact solution approach. IISE Trans, 2021, 54(5): 421-434.

[31.] Gao S, Rubrico JIU, Higashi T, et al. Efficient throughput analysis of production lines based on modular queues. IEEE Access, 2019, 7: 95314-95326.

[32.] Gao S, Kobayashi T, Tajiri A, et al. Throughput analysis of conveyor systems involving multiple materials based on capability decomposition. Comput Ind, 2021, 132: 103526.

[33.] Yan FY, Wang JQ, Li Y, et al. An improved aggregation method for performance analysis of Bernoulli serial production Lines. IEEE Trans Autom Sci Eng, 2021, 18: 114-121.

[34.] Mohammadi M, Dauzère-pérès S, Yugma C, et al. A queue-based aggregation approach for performance evaluation of a production system with an AMHS. Comput Oper Res, 2020, 115: 104838.

[35.] Bai Y, Tu J, Yang M, et al. A new aggregation algorithm for performance metric calculation in serial production lines with exponential machines: design, accuracy and robustness. Int J Prod Res, 2021, 59: 4072-4089.

[36.] Florescu A, Barabas SA. Simulation tool for assessing the performance of a flexible manufacturing system. IOP Conf Ser Mater Sci Eng, 2018, 398: 012023.

[37.] Oljira DG, Abeya TG, Ofgera G, et al. Manufacturing system modeling and performance analysis of mineral water production line using ARENA simulation. Int J Eng Adv Technol, 2020, 9: 312-317.

[38.] Wang J, Xu C, Zhang J, et al. Big data analytics for intelligent manufacturing systems: a review. J Manuf Syst, 2021, 62: 738-752.

[39.] Tsadiras AK, Papadopoulos CT, O’Kelly MEJ. An artificial neural network based decision support system for solving the buffer allocation problem in reliable production lines. Comput Ind Eng, 2013, 66: 1150-1162.

[40.] Demir L, Tunali S, Eliiyi DT. The state of the art on buffer allocation problem: a comprehensive survey. J Intell Manuf, 2014, 25: 317-392.

[41.] Ke G, Meng Q, Finley T et al (2017) LightGBM: a highly efficient gradient boosting decision tree. In: The 31st conference on neural information processing systems, NIPS, California

[42.] Sklearn. https://scikit-learn.org/stable/. Accessed 7 August

[43.] Hashim FA, Houssein EH, Hussain K, et al. Honey badger algorithm: new metaheuristic algorithm for solving optimization problems. Math Comput Simul, 2022, 192: 84-110.

[44.] Chaudhari P, Thakur AK, Kumar R, et al. Comparison of NSGA-III with NSGA-II for multi objective optimization of adiabatic styrene reactor. Mater Today Proc, 2021, 57(4): 1509-1514.

[45.] Niyomubyeyi O, Sicuaio TE, Díaz González JI, et al. A comparativestudy of four metaheuristic algorithms, AMOSA, MOABC, MSPSO, and NSGA-II for evacuation planning. Algorithms, 2020, 13(1): 16.

[46.] Deb K, Pratap A, Agarwal S, et al. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE T Evolut Comput, 2002, 6: 182-197.

[47.] Spinellis DD, Papadopoulos CT. A simulated annealing approach for buffer allocation in reliable production lines. Ann Oper Res, 2000, 93: 373-384.

[48.] Yelkenci KS, Kilincci O. A multi-objective hybrid evolutionary approach for buffer allocation in open serial production lines. J Intel Manuf, 2020, 31: 33-51.

[49.] Cruz FRB, Duarte AR, Souza GL. Multi-objective performance improvements of general finite single-server queueing networks. J Heuristics, 2018, 24: 757-781.

[50.] Su C, Shi Y, Dou J. Multi-objective optimization of buffer allocation for remanufacturing system based on TS-NSGAII hybrid algorithm. J Clean Prod, 2017, 166: 756-770.

[51.] Zhang K, Shen C, Liu X, et al. Multiobjective evolution strategy for dynamic multiobjective optimization. IEEE T Evolut Comput, 2020, 24: 974-988.

[52.] Huang CL. The construction of production performance prediction system for semiconductor manufacturing with artificial neural networks. Int J Prod Res, 1999, 37: 1387-1402.

[53.] Mirjalili S, Mirjalili SM, Lewis A. Grey wolf optimizer. Adv Eng Softw, 2014, 69: 46-61.

[54.] Mirjalili S, Saremi S, Mirjalili SM, et al. Multi-objective grey wolf optimizer: a novel algorithm for multi-criterion optimization. Expert Syst Appl, 2016, 47: 106-119.

[55.] Li L, Qian Y, Yang YM, et al. A fast algorithm for buffer allocation problem. Int J Prod Res, 2016, 54: 3243-3255.

[56.] Demir L, Diamantidis AC, Eliiyi DT, et al. Optimal buffer allocation for serial production lines using heuristic search algorithms: a comparative study. Int J Ind Syst Eng, 2019, 33: 252.

[57.] Nahas N, Ait-Kadi D, Nourelfath M. A new approach for buffer allocation in unreliable production lines. Int J Prod Econ, 2006, 103: 873-881.

Funding

National Natural Science Foundation of China http://dx.doi.org/10.13039/501100001809(52072412); International Postdoctoral Exchange Fellowship Program (Talent-Introduction Program)(YJ20210201)