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Mathematical model for robotic two-sided assembly line balancing problem with zoning constraints

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Abstract

In, the recent scenario, the utilization of robots in different areas such as packaging, loading/unloading, transportation, and especially assembly lines enhance the productivity of production systems. The increasing interest of customers in customized products and full fill customer demand within time is a challenge for the companies to balance and configure their robotic assembly line more efficient and effective than ever before. Robotic two-sided assembly line balancing problem (RTALBP) generally occurs in plants producing high volume, large-sized products, where there is a process of installing more than one robot on every single station of the assembly line for manufacturing the product. The main aim of this paper is to develop a new mathematical model with the objective of workload maximization on each workstation that directly minimizes the number of workstations when cycle time is fixed. Robotic two-sided assembly line balancing problem is well known NP-hard problem that’s why an exact solution approach is proposed to solve the problem. In this paper, real-life assembly line balancing based production case study data is collected, and a proposed mathematical model is applied to get a feasible solution. Benchmark problem and production case study problem is solved using a branch and bound algorithm on Lingo 16 solver. Computational results indicated proper allocation of robots and reduce the number of robots which indirectly increased efficiency and save cost and space for assembling the products.

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Acknowledgements

This research was partially supported by ABC production plant for providing help in the collection of data and valuable information.

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The author has not found any funds (Financial Support) for this work.

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Correspondence to Ashish Yadav.

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Yadav, A., Agrawal, S. Mathematical model for robotic two-sided assembly line balancing problem with zoning constraints. Int J Syst Assur Eng Manag 13, 395–408 (2022). https://doi.org/10.1007/s13198-021-01284-8

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  • DOI: https://doi.org/10.1007/s13198-021-01284-8

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