Mass Customized Projects Portfolio Scheduling - Imprecise Operations Time Approach

Abstract:

Declarative framework enabling to determine conditions employed in a decision support systems aimed at small and medium size enterprises involved in a unique, multi project-like and mass customized oriented production is discussed. The unique production orders grouped into the set of portfolio orders is considered. To each production order treated as an activity network of common shared resources, known in advance, however by nature imprecise operation times are allotted. The problem concerns of scheduling of a newly inserted projects portfolio taking into account imprecise operations imposed by a multi–project environment. The answer sought is: Whether a given portfolio can be completed within assumed time period in a manufacturing system in hand The goal is to provide a declarative model enabling to state a constraint satisfaction problem aimed at multi project-like and mass customized oriented production scheduling. The attached calculation example illustrates the computational efficiency of the proposed solution.

• Periodical: Applied Mechanics and Materials (Volume 791)
• Pages: 70-80
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.791.70
• Online since: September 2015
• Authors: Krzysztof Bzdyra*, Grzegorz Bocewicz, Zbigniew Banaszak
• Keywords: Declarative Modeling, Imprecise Operation Time, Projects Portfolio, Scheduling

References

[1] Q. -V. Dang, I. Nielsen, K. Steger-Jensen, O. Madsen, Scheduling a single mobile robot for part-feeding tasks of production lines, Journal of Intelligent Manufacturing, 25 (2014) 1-17.

DOI: 10.1007/s10845-013-0729-y

[2] M. P. Groover, Automation, Production Systems and Computer-Integrated Manufacturing, 3nd edition, Prentice Hall, NJ (2007).

[3] A. Gola, A. Świć, Computer-Aided Machine Tool Selection for Focused Flexibility Manufacturing Systems Using Economical Criteria, Actual Problems of Economics, 124, 10 (2011) 383-389.

[4] Z. Banaszak, CP-based decision support for project driven manufacturing. In: Perspectives in Modern Project Scheduling. International Series in Operations Research and Management Science, Springer, 92 (2006) 409-437.

DOI: 10.1007/978-0-387-33768-5_16

[5] K. Bzdyra, Z. Banaszak, G. Bocewicz, Multiple project portfolio scheduling subject to mass customized service, Advances in Intelligent Systems and Computing, 350 (2015) 11-21.

DOI: 10.1007/978-3-319-15796-2_2

[6] G. E. Khayat, A. Langevin, D. Riope, Integrated Production and Material Handling Scheduling Using Mathematical Programming and Constraint Programming, European Journal of Operational Research, 175, 3 (2006) 1818-1832.

DOI: 10.1016/j.ejor.2005.02.077

[7] D. Krenczyk, K. Kalinowski, C. Grabowik, Integration Production Planning and Scheduling Systems for Determination of Transitional Phases in Repetitive Production. Hybrid Artificial Intelligent Systems, Corchado E., et al. (eds. ), 7209 (2012).

DOI: 10.1007/978-3-642-28931-6_27

[8] M. Badell, J. Romero, R. Huertas, L. Puigjaner, Planning, scheduling and budgeting value-added chains, Computers and Chemical Engineering, 28 (2004) 45-61.

DOI: 10.1016/s0098-1354(03)00163-7

[9] I. Bach, G. Bocewicz, Z. Banaszak, W. Muszyński, Knowledge based and CP-driven approach applied to multi product small-size production flow, Control and Cybernetics, 39, 1 (2010) 69-95.

DOI: 10.3182/20081205-2-cl-4009.00008

[10] M. Relich, M. Jakabova, A decision support tool for project portfolio management with imprecise data, Proceedings of the 10th International Conference on Strategic Management and its Support by Information Systems, (2013) 164–172.

[11] I. Nielsen, G. Bocewicz, Q. -V. Dung, Production and Resource Scheduling in Mass Customization with Dependent Setup Consideration. Proceedings of the 7th World Conference on Mass Customization, Personalization, and Co-Creation (MCPC 2014), Lecture Notes in Production Engineering, (2014).

DOI: 10.1007/978-3-319-04271-8_39

[12] P. Sitek, J. Wikarek, Hybrid Solution Framework for Supply Chain Problems. Distributed Computing and Artificial Intelligence (DCAI 2014), Book Series: Advances in Intelligent Systems and Computing, 290 (2014) 11-18.

DOI: 10.1007/978-3-319-07593-8_2

[13] G. Bocewicz, Z. Banaszak, Declarative approach to cyclic steady state space refinement: periodic process scheduling, International Journal of Advanced Manufacturing Technology, 67 (2013) 137–155.

DOI: 10.1007/s00170-013-4760-0

[14] K. Tanaka, T. Niimura, An Introduction to Fuzzy Logic for Practical Applications, Springer (1996).