Multi-factor integrated configuration model and three-layer hybrid optimization algorithm framework: turnkey project-oriented rapid manufacturing system configuration

doi:10.1007/s40436-023-00476-8

Abstract

Abstract: In the context of increasingly prominent product personalization and customization trends, intelligent manufacturing-oriented turnkey projects can provide manufacturers with fast and convenient turnkey services for manufacturing systems. Their key characteristic is the transformation of the traditional design process into a configuration process. However, the scope of configuration resources in existing research is limited; the cost and time required for manufacturing system construction are overlooked; and the integration of the system layout configuration is rarely considered, making it difficult to meet the manufacturing system configuration requirements of turnkey projects. In response, this study establishes a multi-factor integrated rapid configuration model and proposes a solution method for manufacturing systems based on the requirements of turnkey projects. The configuration model considers the system construction cost and duration and the product manufacturing cost and duration, as optimization objectives. The differences in product feature-dividing schemes and configuration of processes, equipment, tools, fixtures, and layouts were considered simultaneously. The proposed model-solving method is a three-layer hybrid optimization algorithm framework with two optimization algorithm modules and an intermediate algorithm module. Four hybrid configuration algorithms are established based on non-dominated sorting genetic algorithm-III (NSGAIII), non-dominated sorting genetic algorithm-II (NSGAII), multi-objective simulated annealing (MOSA), multi-objective neighborhood search (MONS), and tabu search (TS). These algorithms are compared and validated through a hydraulic valve block production case, and the TS and NSGAIII (TS-NSGAIII) hybrid algorithm exhibits the best performance. This case demonstrates the effectiveness of the proposed model and solution method.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-023-00476-8

Key words: Manufacturing systems, Configuration, Turnkey projects, Hybrid optimization algorithm, Multi-objective optimization

Shu-Lian Xie, Feng Xue, Wei-Min Zhang, Jia-Wei Zhu, Zi-Wei Jia. Multi-factor integrated configuration model and three-layer hybrid optimization algorithm framework: turnkey project-oriented rapid manufacturing system configuration[J]. Advances in Manufacturing, 2024, 12(4): 698-725.

TrendMD

References

[1] Sabioni RC, Daaboul J, Le Duigou J (2021) An integrated approach to optimize the configuration of mass-customized products and reconfigurable manufacturing systems. Int J Adv Manuf Technol 115:141-163
[2] Eynaud ABD, Klement N, Roucoules L et al (2022) Framework for the design and evaluation of a reconfigurable production system based on movable robot integration. Int J Adv Manuf Technol 118:2373-2389
[3] Mo F, Rehman HU, Monetti FM et al (2023) A framework for manufacturing system reconfiguration and optimisation utilising digital twins and modular artificial intelligence. Rob Comput Integr Manuf 82:102524. https://doi.org/10.1016/j.rcim.2022.102524
[4] Shivdas R, Sapkal S (2023) Proposed composite similarity metric method for part family formation in reconfigurable manufacturing system. Int J Adv Manuf Technol 125:2535-2548
[5] Koren Y, Heisel U, Jovane F et al (1999) Reconfigurable manufacturing systems. CIRP Ann 48:527-540
[6] Bennulf M, Danielsson F, Svensson B et al (2021) Goal-oriented process plans in a multiagent system for plug & produce. IEEE Trans Ind Inf 17:2411-2421
[7] Dahmani A, Benyoucef L, Mercantini JM (2022) Toward sustainable reconfigurable manufacturing systems (SRMS): past, present, and future. Procedia Comput Sci 200:1605-1614
[8] Nilsson A, Danielsson F, Svensson B (2023) Customization and flexible manufacturing capacity using a graphical method applied on a configurable multi-agent system. Rob Comput Integr Manuf 79:102450. https://doi.org/10.1016/j.rcim.2022.102450
[9] Cerqueus A, Delorme X (2023) Evaluating the scalability of reconfigurable manufacturing systems at the design phase. Int J Prod Res. https://doi.org/10.1080/00207543.2022.2164374
[10] Zhang Y, Yu X, Sun J et al (2022) Intelligent STEP-NC-compliant setup planning method. J Manuf Syst 62:62-75
[11] Xie S, Zhang W, Xue F et al (2022) Industry 4.0-oriented turnkey project: rapid configuration and intelligent operation of manufacturing systems. Machines 10:983. https://doi.org/10.3390/machines10110983
[12] Fleischer J, Albers A, Ovtcharova J et al (2022) Sino-german Industry 4.0 factory automation platform. https://publikationen.bibliothek.kit.edu/1000143693
[13] Gönnheimer P, Kimmig A, Mandel C et al (2019) Methodical approach for the development of a platform for the configuration and operation of turnkey production systems. Procedia CIRP 84:880-885
[14] Najid NM, Castagna P, Kouiss K (2020) System engineering-based methodology to design reconfigurable manufacturing systems. In: Benyoucef L (ed) Reconfigurable manufacturing systems: from design to implementation. Springer, Cham, pp 29-55. https://doi.org/10.1007/978-3-030-28782-5_3
[15] Sabioni RC, Daaboul J, Le Duigou J (2022) Concurrent optimisation of modular product and reconfigurable manufacturing system configuration: a customer-oriented offer for mass customisation. Int J Prod Res 60:2275-2291
[16] Hou SX, Gao J, Wang C (2022) Design for mass customisation, design for manufacturing, and design for supply chain: a review of the literature. IET Collab Intell Manuf 4:1-16
[17] Wang Y, Li X (2021) Mining product reviews for needs-based product configurator design: a transfer learning-based approach. IEEE Trans Ind Inf 17:6192-6199
[18] Dong L, Ren M, Xiang Z et al (2023) A novel smart product-service system configuration method for mass personalization based on knowledge graph. J Cleaner Prod 382:135270. https://doi.org/10.1016/j.jclepro.2022.135270
[19] Yoo JJW (2023) Computational modular system configuration with backward compatibility. Int J Adv Manuf Technol 125:3349-3362
[20] da Cunha C, Cardin O, Gallot G et al (2021) Designing the digital twins of reconfigurable manufacturing systems: application on a smart factory. IFAC PapersOnline 54(1):874-879
[21] Ameer M, Dahane M (2021) Reconfiguration effort based optimization for design problem of reconfigurable manufacturing system. Procedia Comput Sci 200:1264-1273
[22] Kutin A, Turkin M, Kliuev M (2021) Multivariate manufacturing process planning for aircraft airframe production based on weighted criteria analysis. Int J Adv Manuf Technol 117:2263-2268
[23] Gonnermann C, Hashemi-Petroodi SE, Thevenin S et al (2022) A skill- and feature-based approach to planning process monitoring in assembly planning. Int J Adv Manuf Technol 122:2645-2670
[24] Nwodu A, Pasha J, Jiang ZQ et al (2022) Co-optimization of supply chain reconfiguration and assembly process planning for factory-in-a-box manufacturing. J Manuf Sci E-T ASME 144:101006. https://doi.org/10.1115/1.4054519
[25] Mansour H, Afefy IH, Taha SM (2023) Simultaneous layout design optimization with the scalable reconfigurable manufacturing system. Prod Eng-Res Dev 17:565-573.
[26] Demir L, Koyuncuoğlu MU (2021) The impact of the optimal buffer configuration on production line efficiency: a VNS-based solution approach. Expert Syst Appl 172:114631. https://doi.org/10.1016/j.eswa.2021.114631
[27] Yazdani MA, Khezri A, Benyoucef L (2022) Process and production planning for sustainable reconfigurable manufacturing systems (SRMSs): multi-objective exact and heuristic-based approaches. Int J Adv Manuf Technol 119:4519-4540
[28] Cantini A, Peron M, De Carlo F et al (2022) A decision support system for configuring spare parts supply chains considering different manufacturing technologies. Int J Prod Res. https://doi.org/10.1080/00207543.2022.2041757
[29] Zhou B, Bao JS, Li J et al (2021) A novel knowledge graph-based optimization approach for resource allocation in discrete manufacturing workshops. Rob Comput Integr Manuf 71:102160. https://doi.org/10.1016/j.rcim.2021.102160
[30] Zhang CX, Dong SL, Chu HY et al (2021) Layout design of a mixed-flow production line based on processing energy consumption and buffer configuration. Adv Manuf 9:369-387
[31] Zeid IB, Doh HH, Shin JH et al (2021) Fast and meta heuristics for part selection in flexible manufacturing systems with controllable processing times. Proc Inst Mech Eng Part B-J Eng Manuf 235(4):650-662
[32] Soufi Z, David P, Yahouni Z (2021) A methodology for the selection of material handling equipment in manufacturing systems. In: The 17th IFAC symposium on information control problems in manufacturing (INCOM), Budapest, Hungary, 7-9 June. https://doi.org/10.1016/j.ifacol.2021.08.193
[33] Khettabi I, Benyoucef L, Boutiche MA (2021) Sustainable reconfigurable manufacturing system design using adapted multi-objective evolutionary-based approaches. Int J Adv Manuf Technol 115:3741-3759
[34] Gianessi P, Cerqueus A, Lamy D et al (2021) Using reconfigurable manufacturing systems to minimize energy cost: a two-phase algorithm. In: The 17th IFAC symposium on information control problems in manufacturing (INCOM), Budapest, Hungary, 7-9 June. https://doi.org/10.1016/j.ifacol.2021.08.042
[35] Battaia O, Dolgui A, Guschinsky N (2021) Design of reconfigurable machining lines: a novel comprehensive optimisation method. CIRP Ann Manuf Technol 70:393-398
[36] Naderi B, Azab A (2021) Production scheduling for reconfigurable assembly systems: mathematical modeling and algorithms. Comput Ind Eng 162:107741. https://doi.org/10.1016/j.cie.2021.107741
[37] Luo S, Zhang LX, Fan YS (2022) Real-time scheduling for dynamic partial-no-wait multiobjective flexible job shop by deep reinforcement learning. IEEE Trans Autom Sci Eng 19:3020-3038
[38] Han Y, Chen X, Xu M et al (2021) A multi-objective flexible job-shop cell scheduling problem with sequence-dependent family setup times and intercellular transportation by improved NSGA-II. Proc Inst Mech Eng Part B- J Eng Manuf 236:540-556
[39] Ren WB, Yan Y, Hu YG et al (2021) Joint optimisation for dynamic flexible job-shop scheduling problem with transportation time and resource constraints. Int J Prod Res. https://doi.org/10.1080/00207543.2021.1968526
[40] Dou J, Su C, Zhao X (2020) Mixed integer programming models for concurrent configuration design and scheduling in a reconfigurable manufacturing system. Concurrent Eng 28:32-46
[41] Dou J, Li J, Xia D et al (2020) A multi-objective particle swarm optimisation for integrated configuration design and scheduling in reconfigurable manufacturing system. Int J Prod Res 59:3975-3995
[42] Moussa M, ElMaraghy H (2021) Multiple platforms design and product family process planning for combined additive and subtractive manufacturing. J Manuf Syst 61:509-529
[43] Wikarek J, Sitek P (2021) Model of multidimensional resource configuration in production scheduling: proactive and reactive approach. In: The 17th IFAC symposium on information control problems in manufacturing (INCOM), Budapest, Hungary, 7-9 June. https://doi.org/10.1016/j.ifacol.2021.08.127
[44] Barkokebas B, Al-Hussein M, Hamzeh F (2023) Assessment of digital twins to reassign multiskilled workers in offsite construction based on lean thinking. J Constr Eng Manage 149:04022143. https://doi.org/10.1061/(asce)co.1943-7862.0002420
[45] Zhang Y, Tang DB, Zhu HH et al (2021) A flexible configuration method of distributed manufacturing resources in the context of social manufacturing. Comput Ind 132:103511. https://doi.org/10.1016/j.compind.2021.103511
[46] Zhou BH, Liao XM (2020) Particle filter and levy flight-based decomposed multi-objective evolution hybridized particle swarm for flexible job shop greening scheduling with crane transportation. Appl Soft Comput 91:106217. https://doi.org/10.1016/j.asoc.2020.106217
[47] Zhou BH, Shen CY (2018) Multi-objective optimization of material delivery for mixed model assembly lines with energy consideration. J Cleaner Prod 192:293-305
[48] While L, Hingston P, Barone L et al (2006) A faster algorithm for calculating hypervolume. IEEE Trans Evol Comput 10:29-38