Scheduling Schemes Based on Searching the Aggregated Graph of Operations Planning Sequence

Article Preview

Abstract:

The searching state space in scheduling of real manufacturing systems with discrete and multi-assortment production is discussed in this paper. The production load is represented by a directed and/or graph called “the aggregated graph of operations planning of the set of orders”. It determines the order of operations, according to which they will be inserted into a schedule. This order must always comply with all assumed precedence and resource constraints and also with given scheduling strategy of a production order. In the elaborated representation the complex products structures and alternative routes of their realization are also considered. The most important issues related to searching this space are discussed in this paper. These include: a general method for searching the graph, sequencing of parallel processes and operations using schedule generation schemes and selection of routes variants.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

1462-1467

Citation:

Online since:

November 2015

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2015 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] A. Mital, S. Anand, Handbook of Expert Systems Applications in Manufacturing Structures and Rules, Chapman & Hall, London, (1994).

Google Scholar

[2] F. Xhafa, A. Abraham, Metaheuristics for Scheduling in Industrial and Manufacturing Applications (Studies in Computational Intelligence), Springer, Berlin Heidelberg, (2008).

DOI: 10.1007/978-3-540-78985-7

Google Scholar

[3] D. Krenczyk, B. Skołud, Production Preparation and Order Verification Systems Integration Using Method Based on Data Transformation and Data Mapping, Lecture Notes in Artificial Intelligence, Hybrid Artificial Intelligent Systems. 6679 (2011).

DOI: 10.1007/978-3-642-21222-2_48

Google Scholar

[4] K. Kalinowski, C. Grabowik, W. Kempa, I. Paprocka, The procedure of reaction to unexpected events in scheduling of manufacturing systems with discrete production flow, Advanced Materials Research. 1036 (2014) 840-845.

DOI: 10.4028/www.scientific.net/amr.1036.840

Google Scholar

[5] P. Lopez, F. Roubellat, Production Scheduling, John Wiley & Sons, Hoboken, (2010).

Google Scholar

[6] E. Pinson, C. Prins, F. Rullier, Using Tabu Search for Solving the Resource-Constrained Project Scheduling Problem, Proceedings of the 4th International Workshop on Project Management and Scheduling, Leuven, (1994).

Google Scholar

[7] R. Kolisch, Serial and parallel resource-constrained project scheduling methods revisited: Theory and computation, European Journal of Operational Research. 90 (1996) 320-333.

DOI: 10.1016/0377-2217(95)00357-6

Google Scholar

[8] S. Hartmann, Project Scheduling Under Limited Resources: Models, Methods, and Applications, Springer, Berlin, (1999).

Google Scholar

[9] Ch. Artigues, P. Lopez, P.D. Ayache, Schedule generation schemes for the job-shop problem with sequence-dependent setup times: dominance properties and computational analysis, Annals of Operations Research. 138 (2005) 21-52.

DOI: 10.1007/s10479-005-2443-4

Google Scholar

[10] J. Kim, R. Ellis, Comparing Schedule Generation Schemes in Resource-Constrained Project Scheduling Using Elitist Genetic Algorithm, Journal of Construction Engineering and Management. 136 (2010) 160-169.

DOI: 10.1061/(asce)0733-9364(2010)136:2(160)

Google Scholar

[11] G. Ćwikła, The methodology of development of the Manufacturing Information Acquisition System (MIAS) for production management, Applied Mechanics and Materials. 474 (2014) 27-32.

DOI: 10.4028/www.scientific.net/amm.474.27

Google Scholar

[12] A. Dymarek, T. Dzitkowski, Passive reduction of system vibrations to the desired amplitude value, Journal of Vibroengineering. 15, 3 (2013) 1254-1264.

Google Scholar

[13] S. Zolkiewski, Numerical Application for Dynamical Analysis of Rod and Beam Systems in Transportation, Solid State Phenomena. 164 (2010) 343-348.

DOI: 10.4028/www.scientific.net/ssp.164.343

Google Scholar

[14] K. Kalinowski, C. Grabowik, I. Paprocka, W. Kempa, Interaction of the decision maker in the process of production scheduling, Advanced Materials Research. 1036 (2014) 830-833.

DOI: 10.4028/www.scientific.net/amr.1036.830

Google Scholar

[15] N. J. Nilsson, Artificial Intelligence: A New Synthesis, Morgan Kaufmann, (1998).

Google Scholar

[16] M. Hetmańczyk, The multilevel prognosis system based on matrices and digraphs methods, in: Mechatronic Systems and Materials, Solid State Phenomena. 199 (2013) 79-84.

DOI: 10.4028/www.scientific.net/ssp.199.79

Google Scholar

[17] R. Marinescu, R. Dechter, AND/OR Tree Search for Constraint Optimization, Proceedings of the 6th International Workshop on Preferences and Soft Constraints, Toronto, (2004).

Google Scholar

[18] W. Imrich, S. Klavzar, D.F. Rall, Topics in Graph Theory: Graphs and Their Cartesian Product, AK PETERS LTD. Wellesley, Massachusetts, (2008).

Google Scholar

[19] M.L. Pinedo, Scheduling Theory, Algorithms and Systems. Springer, New York, (2012).

Google Scholar