Title: Manufacturing network configuration by optimal scheduling considering specific machines and alternative process plans

Authors: George-Christopher Vosniakos; Fotiοs Sakellariou; Xenofon V. Gogouvitis

Addresses: School of Mechanical Engineering, National Technical University of Athens, Manufacturing Division, Heroon Polytehniou 9, 15780 Athens, Greece ' School of Mechanical Engineering, National Technical University of Athens, Manufacturing Division, Heroon Polytehniou 9, 15780 Athens, Greece ' School of Mechanical Engineering, National Technical University of Athens, Manufacturing Division, Heroon Polytehniou 9, 15780 Athens, Greece

Abstract: Formation of a networked manufacturing system for particular multi-part product orders is tackled in the presence of alternative process plans for the parts. Each process plan can be transformed into a production schedule in different ways, depending on the machines that are available for each operation in various nodes of the network. Machine suitability is decided on technical criteria using an appropriate database. Optimum assignment of machines to operations is achieved by a genetic algorithm (GA), its chromosome representing all operations of the process plans with integer encoding. The GA evaluation function for each network configuration solution uses total order completion time, derived by automatic construction corresponding Gantt charts, subtracting bonuses to promote combined transportation of individual part batches and adding penalties for delays and use of specific highly loaded machines. An example is presented and a methodology for investigating GA performance enhancement is carried out. [Received 13 February 2013; Revised 23 September 2013; Accepted 13 October 2013]

Keywords: manufacturing networks; network configuration; machine assignment; genetic algorithms; alternative process plans; optimal scheduling; process planning.

DOI: 10.1504/IJMR.2014.062445

International Journal of Manufacturing Research, 2014 Vol.9 No.2, pp.223 - 244

Published online: 29 Jul 2014 *

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