Mixed model two sided assembly line balancing problem: an exact solution approach

Ashish Yadav (), Pawan Verma and Sunil Agrawal
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Ashish Yadav: PDPM Indian Institute of Information Technology Design and Manufacturing
Pawan Verma: PDPM Indian Institute of Information Technology Design and Manufacturing
Sunil Agrawal: PDPM Indian Institute of Information Technology Design and Manufacturing

International Journal of System Assurance Engineering and Management, 2020, vol. 11, issue 2, No 22, 335-348

Abstract: Abstract Mixed model two-sided assembly line balancing problem (MTALBP) is realized in plants delivering a high volume of big sizes products such as cars or trucks to increase space utilization. In MTALBP there is a procedure of introducing two single stations in each position left and right of the assembly line for the combined product model. In this paper, the proposed objective function is to maximize the workload at each station such that the number of stations is minimized. Since the problem is well known as NP hard, benchmark problems of MTALBP are solved using branch and bound algorithm on Lingo 16 solver. The proposed mathematical model is solved with benchmark test problems mentioned in research papers and applied to solve the case study problem of a turbocharger assembly line plant. The experimental results of the case study problem show that line efficiency is obtained 86.50% for model A and 80.75% for model B and the number of single and mated stations of the assembly line is close to the theoretical minimum number of stations. Results indicate that applying boundary conditions reduce the computational time to solve the case study problem as well as minimizes the number of stations, reduces idle time and reduces the length of the assembly line for the MTALBP.

Keywords: Assembly line balancing (ALB); Mixed model two sided assembly line balancing (MTALB); Mathematical model; Lingo 16 solver; Boundary conditions; Industrial case study problem (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s13198-020-00956-1

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