A New Procedure of Production Orders Sequencing in Mixed-Model Production Systems

Abstract:

The paper presents the task scheduling issue, which main aim is to establish a proper sequence of tasks, that would maximize the utilization of companys production capacity. According to the literature sources, the presented sequencing problem, denoted as CSP (Car Sequencing Problem) belongs to the NP-hard class, as has been proven by simple reduction from Hamiltonians Path problem. Optimal method of solution has not yet been found, only approximate solutions have been offered, especially from the range of evolutionary algorithms. Regardless of specific production system, while considering reception of new tasks into the system, current review of the state of the system is required in order to decide whether and when a new order can be accepted for execution. In this paper, the problem of task scheduling is limited to the specific existing mixed-model production system. The main goal is to determine the effective method of creation of task sequence. Through the use of computational algorithms, and automatic analysis of the resulting sequence, rates of production are able to be checked in a real time, and so improvements can be proposed and implemented.

• Periodical: Advanced Materials Research (Volume 1036)
• Pages: 864-868
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.1036.864
• Online since: October 2014
• Authors: Marcin Zemczak*, Damian Krenczyk
• Keywords: Assembly, Decision-Making, Mixed-Model Production Systems, Scheduling, Sequencing

References

[1] M. Zemczak, Production orders scheduling in multi-version and multi-assortment production systems – PART I, Selected Engineering Problems Seminar, no. 2 (2011) 379-384.

[2] M. Zemczak, D. Krenczyk, Formulation of a sequencing problem in a mixed-model production system, Journal of Machine Engineering, vol. 12 no. 3 (2012) 45-51.

[3] B. Estellon, F. Gardi, Car sequencing is NP-hard: a short proof, Journal of the Operational Research Society no. 64 (2013) 1503–1504.

DOI: 10.1057/jors.2011.165

[4] D. Krenczyk, K. Kalinowski, C. Grabowik, Integration Production Planning and Scheduling Systems for Determination of Transitional Phases in Repetitive Production, Hybrid Artificial Intelligence Systems, Lecture Notes in Computer Science, vol. 7209 (2012).

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

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

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

[6] K. Kalinowski, D. Krenczyk, C. Grabowik, Predictive - Reactive Strategy for Real Time Scheduling of Manufacturing Systems, Applied Mechanics and Materials 307 (2013) 470-473.

DOI: 10.4028/www.scientific.net/amm.307.470

[7] D. Parello, W. C. Kabat, L. Wos, Job-shop scheduling using automated reasoning: a case study of the car sequencing problem, Journal of Automated Reasoning, no. 2 (1986) 1–42.

DOI: 10.1007/bf00246021

[8] C. Solnon, V. D. Cung, A. Nguyen, C. Artigues, The car sequencing problem: overview of state-of-the-art methods and industrial case-study of the ROADEF'2005 Challenge problem, European Journal of Operational Research 191(3) (2008) 912 – 927.

DOI: 10.1016/j.ejor.2007.04.033