TY - JOUR
T1 - Incorporating physical demand criteria into assembly line balancing
AU - Carnahan, B. J.
AU - Norman, B. A.
AU - Redfern, M. S.
N1 - Funding Information:
Bryan A. Norman is an Assistant Professor of Industrial Engineering at the University of Pittsburgh. He received the Ph.D. degree in Industrial and Operations Engineering from the University of Michigan in 1995, where he was a National Science Foundation Fellowship holder, and has B.S.I.E. and M.S.I.E. degrees from the University of Oklahoma. His research interests primarily focus on the modeling of complex problems in manufacturing systems. His areas of application include scheduling, sequencing, job rotation, assembly line balancing and facility layout and material handling system design. His research has been funded by the National Science Foundation, the Ben Franklin Technology Center of Western Pennsylvania, the Society of Manufacturing Engineers and by local industry. He has published his research in IIE Transactions, Naval Research Logistics, Engineering Design and Automation, and Computers and Industrial Engineering. He is a member of IIE and INFORMS.
PY - 2001/10
Y1 - 2001/10
N2 - Many assembly line balancing algorithms consider only task precedence and duration when minimizing cycle time. However, disregarding the physical demands of these tasks may contribute to the development of work-related musculoskeletal disorders in the assembly line workers. Three line balancing heuristics that incorporate physical demand criteria were developed to solve the problem of finding assembly line balances that consider both the time and physical demands of the assembly tasks: a ranking heuristic, a combinatorial genetic algorithm, and a problem space genetic algorithm. Each heuristic was tested using 100 assembly line balancing problems. Incorporating physical demands using these algorithms does impact the assembly line configuration. Results indicated that the problem space genetic algorithm was the most adept at finding line balances that minimized cycle time and physical workload placed upon participants. Benefits of using this approach in manufacturing environments are discussed.
AB - Many assembly line balancing algorithms consider only task precedence and duration when minimizing cycle time. However, disregarding the physical demands of these tasks may contribute to the development of work-related musculoskeletal disorders in the assembly line workers. Three line balancing heuristics that incorporate physical demand criteria were developed to solve the problem of finding assembly line balances that consider both the time and physical demands of the assembly tasks: a ranking heuristic, a combinatorial genetic algorithm, and a problem space genetic algorithm. Each heuristic was tested using 100 assembly line balancing problems. Incorporating physical demands using these algorithms does impact the assembly line configuration. Results indicated that the problem space genetic algorithm was the most adept at finding line balances that minimized cycle time and physical workload placed upon participants. Benefits of using this approach in manufacturing environments are discussed.
UR - http://www.scopus.com/inward/record.url?scp=0035480374&partnerID=8YFLogxK
U2 - 10.1023/A:1010926722609
DO - 10.1023/A:1010926722609
M3 - Article
AN - SCOPUS:0035480374
SN - 0740-817X
VL - 33
SP - 875
EP - 887
JO - IIE Transactions (Institute of Industrial Engineers)
JF - IIE Transactions (Institute of Industrial Engineers)
IS - 10
ER -