Optimizing Assembly Line Operations: A Study on Dispatching Rule Performance

Abstract:

Due to rising environmental awareness, many individuals are moving away from fossil-fuel vehicles like gasoline and diesel cars. In contrast, electric vehicles improve air quality and reduce carbon emissions, leading to a surge in their popularity. This growth presents challenges for the motor industry, including a gap between product availability and demand, causing longer delivery times. Efficiently assigning orders to machines, adapting production, and ensuring timely delivery are critical research areas. Dispatching rules are vital in managing production processes, determining how goods are handled as they enter the system. Conventional dispatching systems, which rely on dispatcher expertise and on-site information, often result in incomplete orders being delayed, causing financial losses. This study aims to evaluate various dispatching rules using system simulation to identify the optimal approach for improving order fulfillment. Using the DC motor production line for electric vehicles as a case study, this research develops a production model through FlexSim, used primarily for visualizing production processes. The dispatching rules evaluated include First In, First Out (FIFO), Shortest Processing Time (SPT), and Earliest Due Date (EDD). Results highlight ways the company can improve its processes by comparing fulfillment rates and delay times.

• Periodical: Engineering Headway (Volume 18)
• Pages: 141-152
• DOI: https://doi.org/10.4028/p-0VMQoy
• Online since: February 2025
• Authors: Ronald Sukwadi, Yung Tsan Jou, Vicky Pratama Putra, Ferdian Aditya Pratama, Riana Magdalena Silitonga*, M.M. Wahyuni Inderawati, Nguyen Thi Bich Thu
• Keywords: Dispatching Rules, FlexSim, System Simulation

References

[1] Eghtesadifard M, Khalifeh M, Khorram M. A systematic review of research themes and hot topics in assembly line balancing through the web of science within 1990–2017. Comput Ind Eng. 2020; 139:106182.

DOI: 10.1016/j.cie.2019.106182

[2] Pinedo M L. Scheduling - Theory, Algorithms, and Systems. 6th ed. New York: Springer; 2012.

DOI: 10.1007/978-3-031-05921-6

[3] Brown A, Dimitrov S, Barlatt AY. Routing distributions and their impact on dispatch rules. Comput Ind Eng. 2015;88:293-306.

DOI: 10.1016/j.cie.2015.07.014

[4] Dominic PDD, Kaliyamoorthy S, Saravana Kumar M. Efficient dispatching rules for dynamic job shop scheduling. Int J Adv Manuf Technol. 2004;24(1):70-75. https://doi.org/10.1007/ s00170-002-1534-5

[5] Ho N, Tay J, Lai E. An effective architecture for learning and evolving flexible job-shop schedules. Eur J Oper Res. 2007;179(2):316-333

DOI: 10.1016/j.ejor.2006.04.007

[6] Sels V, Gheysen N, Vanhoucke M. A comparison of priority rules for the job shop scheduling problem under different flow time- and tardiness-related objective functions. Int J Prod Res. 2012;50(15):4255-4270.

DOI: 10.1080/00207543.2011.611539

[7] Ouelhadj D, Petrovic S. A survey of dynamic scheduling in manufacturing systems. J Sched. 2009;12(4):417-432.

DOI: 10.1007/s10951-008-0090-8

[8] Ferreira C, Figueira G, Amorim P. Effective and interpretable dispatching rules for dynamic job shops via guided empirical learning. Omega. 2022;111:102643.

DOI: 10.1016/j.omega.2022.102643

[9] Huang X, Yang L. A hybrid genetic algorithm for multi-objective flexible job shop scheduling problem considering transportation time. Int J Intell Comput Cybern. 2019;12(2):154-174.

DOI: 10.1108/IJICC-10-2018-0136

[10] Law AM, Kelton WD. Simulation modeling and analysis. 3rd ed. McGraw-Hill; 2000.

[11] Power D, Sharda R. Model-driven decision support systems: Concepts and research directions. Decision Support Systems. 2007;43(3):1044–1061.

DOI: 10.1016/j.dss.2005.05.030

[12] Banks J, ed. Discrete-event system simulation. 5th ed. Pearson; 2010.

[13] Cassandras CG, Lafortune S. Introduction to Discrete Event Systems. 3rd ed. Springer; 2021.

[14] Krenczyk D, Kempa WM, Kalinowski K, Grabowik C, Paprocka I. Production planning and scheduling with material handling using modelling and simulation. MATEC Web Conf. 2017; 112:9015.

DOI: 10.1051/matecconf/201711209015

[15] Yijian L. Simulation of manufacturing systems. Taipei City: Gao Li Book Co., Ltd.; 2005.