Journal
INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT
Volume 13, Issue 1, Pages 395-408Publisher
SPRINGER INDIA
DOI: 10.1007/s13198-021-01284-8
Keywords
Assembly line balancing; Robotic two sided assembly line balancing problem (RTALBP); Mathematical model; Production case study problem; Lingo 16 solver
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This paper investigates the Robotic two-sided assembly line balancing problem, developing a mathematical model to maximize workload at workstations and proposing an exact solution approach. By collecting and applying real production case study data, the study successfully reduces the number of robots, improves efficiency, and saves costs and space.
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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