Negotiating and Sharing Capacities of Large Additive Manufacturing Networks
This paper focuses on dynamics of productive and demanding nodes for Scattered Manufacturing Networks within 3D Printings contexts. The various nodes issue orders or sell production slots in order to achieve their own aims. An orchestrator coordinates the dynamics along the network according to principles of sustainability, equated shared resources and transparency by managing communication activities among nodes. In particular, suitable tradeoffs occur by a unique framework that, with the aim of optimizing the overall costs, suggests either logistics paths along the network or negotiation policies among nodes in order to reallocate resources. Numerical examples present the proposed approach.
Keywords: Industry 4.0, Additive Manufacturing, Sharing Capacities, Operation Models, Optimization of networks
JEL Codes: C02; O21 and P40
Applegate DL, Bixby RE, Chvátal, V, and Cook WJ 2006, The Traveling Salesman Problem: A Computational Study, Princeton University Press.
Ariss, S, Raghunathan, TS, and Kunnathar, A 2000, Factors affecting the adoption of advanced manufacturing technology in small firms. S.A.M. Adv. Manag. J, Vol. 65, No. 2.
Brettel, M, Friederichsen, N, Keller, and Rosenberg, M 2014, How virtualization, decentralization and network building change the manufacturing landscape: an Industry 4.0 perspective, Int J Mech Aerospace. Ind. Mechatronics, Vol. 8, No. 1, pp. 37–44.
Chen, F, Deng, P, Wan, J, Zhang, D, Vasilakos, AV and Rong, X 2015, Data mining for the internet of things: literature review and challenges. International Journal of Distributed Sensor Networks, Vol. 2015, Article ID 431047, 14 pages.
De Falco, M, Gaeta, M, Loia, V, Rarità, L, and Tomasiello S 2016, Differential quadrature-based numerical solutions of a fluid dynamic model for supply chains. Commun. Math. Sci., Vol. 14, No. 5, pp. 1467–1476.
Durão, LFCS, Christ, A, Zancul, E, Anderl, R, and Schützer, K 2017, Additive manufacturing scenarios for distributed production of spare parts, Int. J. Adv. Manuf. Technol., pp. 1-12.
Gutin, G. and Punnen AP (eds.) 2007, The Traveling Salesman Problem and its Variations, Kluwer, 2002 and Springer-Verlag.
Gutin, G, Yeo, A, and Zverovitch, A 2002, Traveling salesman should not be greedy: domination analysis of greedy-type heuristics for the TSP, Discrete Appl. Math., Vol. 117, pp. 81–86.
Gutin, G, and Yeo, A 2007, The Greedy Algorithm for the Symmetric TSP. Algorithmic Oper. Res., Vol. 2, pp. 33–36.
Hauder, VA, Beham, A, Wagner, S and Affenzeller, M 2017 'Optimization networks for real-world production and logistics problems', Proc. Genet. Evol. Comput. Conf. Companion - GECCO ’17, pp. 1411–1414.
Jonsson, P 2000, An empirical taxonomy of advanced manufacturing technology. Int. J. Oper. Prod. Manag, Vol. 20, No. 12, pp. 1446 –1474.
Karp, RM 1979, A patching algorithm for the non-symmetric traveling salesman problem, SIAM J. Comput., Vol. 8, No. 4, pp. 561–573, 1979.
Khajavi, SH, Partanen, J, and Holmström, J 2014, Additive manufacturing in the spare parts supply chain. Comput. Ind., Vol. 65, No. 1, pp. 50–63.
Leitão, P 2009, Agent-based distributed manufacturing control: a state-of the-art survey. Eng. Appl. Artif. Intell., Vol. 22, No. 7, pp. 979–991.
MacDougall, W 2014. Industrie 4.0: Smart Manufacturing for the Future. Germany Trade & Invest.
Oliff H, and Liu, Y 2017, Towards Industry 4.0 Utilizing Data-Mining Techniques: A Case Study on Quality Improvement, Procedia CIRP, Vol. 63, pp. 167–172.
Preux, P, Delepoulle, S, and Darcheville, JC 2004, A generic architecture for adaptive agents based on reinforcement learning. Inform. Sci., Vol. 161, No. 1, pp. 37–55.
Scheuermann, C, Verclas, S, and Bruegge, B 2015, Agile Factory-An Example of an Industry 4.0 Manufacturing Process, Proc. - 3rd IEEE Int. Conf. Cyber-Physical Syst. Networks, Appl. CPSNA 2015, pp. 43–47.
Schroder, R, Sohal, AS 1999, Organizational characteristics associated with AMT adoption: towards a contingency framework. Int. J. Oper. Prod. Manag, Vol. 19, No. 12, pp. 1270 –1291.
Tomasiello, S, Macías-Díaz, JE 2017, Note on a picard-like method for caputo fuzzy fractional differential equations. Appl. Math. Inform. Sci., Vol. 11, No.1, pp. 281–287.
Wan, J, Minglun, Y, Li, D, Zhang, C, Wang, S, and Zhou, K 2016, Mobile services for customization manufacturing systems: an example of industry 4.0, IEEE Access, Vol. 4, pp. 8977–8986.
Wang, S, Wan, J, Zhang, D, Li, D, and Zhang, C 2016, Towards smart factory for industry 4.0: a self-organized multi-agent system with big data based feedback and coordination. Comput. Netw., Vol. 101, pp. 158–168.
Weiser, M 1993, Some computer science issues in ubiquitous computing, Communications of the ACM - Special issue on computer augmented environments: back to the real world, Vol. 36, No. 7, pp. 75–84.