Optimization of In-Plant Production Supply with Black Hole Algorithm

Abstract:

Logistic process is a basic factor in the success of manufacturing plants’ operation and has a direct impact on its efficiency, flexibility and reliability. Today’s successful operation of manufacturing processes views logistics as a high priority to ensure maximum utilization of resources. The material supply of manufacturing processes in the automotive industry is usually based on supermarkets and milk runs. This paper proposes an integrated supply model of manufacturing processes, which includes facility location and assignment. After a careful literature review, this paper introduces a mathematical model to formulate the problem of supermarkets and milk run based supply of machines. The model seeks the optimal location of buffers as well as the optimal assignment of buffers and machines so as to minimize the material handling costs while taking into account order limits of machines and capacities of resources. Next, we demonstrate an enhanced black hole algorithm dealing with multi-objective supply chain model to find the optimal structure of the system. Numerical results demonstrate how the proposed model supports the efficiency, flexibility and reliability of the manufacturing process.

• Periodical: Solid State Phenomena (Volume 261)
• Pages: 503-508
• DOI: https://doi.org/10.4028/www.scientific.net/SSP.261.503
• Online since: August 2017
• Authors: Péter Veres, Tamás Bányai*, Béla Illés
• Keywords: Black Hole Optimization, In-Plant Supply, Manufacturing, Milkrun, Optimization

References

[1] J. Demetrovics, H. N. Son, Á. Gubán, A formal representation for structured data, Acta Polytech. Hung. 13(2) (2016) 59-76.

[2] C. S. Kumar, R. Panneerselvam, Literaure review of JIT-KANBAN system., Int. J. Adv. Manuf. Tech. 32(3-4) (2007) 393-408.

DOI: 10.1007/s00170-005-0340-2

[3] H. Luo, K. Wang, X. T. R. Kon, . Lu, T. Qu, Synchronized production and logistics via ubiquitous computing technology, Robot. CIM-Int. Manuf. 45 (2017) 99-115.

DOI: 10.1016/j.rcim.2016.01.008

[4] J, M. Garcia, S. Lozano, D. Canca, Coordinated scheduling of production and delivery from multiple plants, , Robot. CIM-Int. Manuf. 20 (2004) 191-198.

DOI: 10.1016/j.rcim.2003.10.004

[5] H. S. Kilic, M. B. Durmusoglu, M. Baskak, Classification and modeling for in-plant milkrun distribution systems, Int. J. Adv. Manuf. Tech. 62(9-12) (2012) 1135-1146.

DOI: 10.1007/s00170-011-3875-4

[6] H. S. Hwang, Heuristic transporter routing model for manufacturing facility design, Comput. Ind. Eng. 46(2) (2004) 243-251.

DOI: 10.1016/j.cie.2003.12.021

[7] D. Chhajed, B. Montreuil, T. J. Lowe, Flow network design for manufacturing systems layout, Eur. J. Oper. Res. 57(2) (1992) 145-161.

DOI: 10.1016/0377-2217(92)90039-c

[8] S. I. Satoglu, I. E. Sahin, Design of a just-in-time periodic material supply system for the assembly lines and an application in electronics industry, Int. J. Adv. Manuf. Tech. 65(1-4) (2013) 319-332.

DOI: 10.1007/s00170-012-4171-7

[9] G. Nagy, S. Salhi, Heuristic algorithms for single and multiple depot vehicle routing problems with pickups and deliveries, Eur. J. Oper. Res. 162(1) (2005) 126-141.

DOI: 10.1016/j.ejor.2002.11.003

[10] S. J. Sadjadi, M. Jafari, T. Amini, A new mathematical modeling and a genetic algorithm search for milk run problem (an auto industry supply chain case study), Int. J. Adv. Manuf. Tech. 44(1-2) (2009) 194-200.

DOI: 10.1007/s00170-008-1648-5

[11] T. Du, F. K. Wang, P. Y. Lu, A real-time vehicle-dispatching system for consolidating milkruns, Transport. Res. E-log. 43(5) (2007) 565-577.

[12] J. Ou, X. Qi, C. Y. Lee, Parallel machine scheduling with multiple unloading servers, J. Sched. 13(3) (2010) 213-226.

DOI: 10.1007/s10951-009-0104-1