Title: Infrastructure for model-based production scheduling
Author: Sanjay Jain, Lars Monch, Thomas Jahnig, Peter Lendermann
Department of Decision Sciences, George Washington University, 2201, G Street N.W., Washington, DC 20052, USA.
Department of Mathematics and Computer Science, Chair of Enterprise-wide Software Systems, University of Hagen, 58097 Hagen, Germany.
Qimonda AG, Koenigsbruecker Str. 62, 01099 Dresden, Germany.
D-SIMLAB Technologies Pte. Ltd., 9 Jurong Town Hall Road, #03-45 iHUB 609431, Singapore
Abstract: A large body of literature exists on algorithms and approaches for model-based production scheduling; however, very few of these developments have made it to the production shop floor. One of the major obstacles for implementation of model-based scheduling is the lack of required infrastructure. Very limited literature exists on the required infrastructure contributing to continued existence of the obstacle. This paper discusses the required infrastructure for supporting implementation of model-based production scheduling software. The focus of the paper is on tangible factors though the human factors are briefly discussed. Five major issues are identified and rank ordered based on their criticality. The relevance of each of the major issues is considered with respect to two major aspects of model-based production scheduling – schedule evaluation and periodic and real time schedule generation. Examples of real life implementation experience are provided in support of the identified issues.
Keywords: model-based scheduling; production scheduling; algorithms; infrastructures; scheduling software; schedule evaluation; real time schedule generation; periodic schedule generation; interoperability; data input; automotive components; semiconductor manufacturing; dicrete event simulation; execution integration; human factors.
Int. J. of Industrial and Systems Engineering, 2010 Vol.6, No.4, pp.441 - 462
Available online: 03 Oct 2010