Machine Learning Applications for Self-Organized Wireless Networks
In this paper, we present a practical line balancing problem: multiple-operator assembly line balancing problem II. The formulation of the proposed problem is novel in terms of investigating the operator assignment from a different perspective. In order to solve this problem, we develop a simulated annealing (SA)-based two-stage solution procedure, where a new neighborhood generation and a search method are designed and demonstrated to be more efficient than the traditional neighborhood search method. In theory, we prove the original neighborhood generation method is not efficient than the proposed one. Computational experiments on some benchmark cases have been conducted to validate the efficiency of the proposed algorithm compared to the traditional SA approach. Our work also has some practical merits. In the managerial situation, a factory may have already recruited some workers that cannot be dismissed. Our problem describes that the production manager allocates different number of workers to the workstations to minimize the cycle time.
This is a preview of subscription content, log in to check access.
Funding was provided by National Natural Science Funds of China (Grant No. 71704007).
Battaïa O, Dolgui A (2013) A taxonomy of line balancing problems and their solution approaches. Int J Prod Econ 142(2):259–277CrossRefGoogle Scholar
Bryton B (1954) Balancing of a continuous production line. M.S. Thesis, Northwestern University, Evanston, ILGoogle Scholar
De Vicente J, Lanchares J, Hermida R (2003) Placement by thermodynamic simulated annealing. Phys Lett A 317(5):415–423CrossRefzbMATHGoogle Scholar
Dolgui A, Kovalev S, Kovalyov MY, Malyutin S, Soukhal A (2018) Optimal workforce assignment to operations of a paced assembly line. Eur J Oper Res 264(1):200–211MathSciNetCrossRefzbMATHGoogle Scholar
Dong J, Zhang L, Xiao T, Mao H (2014) Balancing and sequencing of stochastic mixedmodel assembly U-lines to minimise the expectation of work overload time. Int J Prod Res 52(24):7529–7548CrossRefGoogle Scholar
Dou J, Dai X, Meng Z (2011) A GA-based approach for optimizing single-part flow-line configurations of RMS. J Intell Manuf 22(2):301–317CrossRefGoogle Scholar
Essafi M, Delorme X, Dolgui A (2012) A reactive GRASP and path relinking for balancing reconfigurable transfer lines. Int J Prod Res 50(18):5213–5238CrossRefGoogle Scholar
Hamzadayi A, Yildiz G (2012) A genetic algorithm based approach for simultaneously balancing and sequencing of mixed-model U-lines with parallel workstations and zoning constraints. Comput Ind Eng 62(1):206–215CrossRefGoogle Scholar
Hamzadayi A, Yildiz G (2013) A simulated annealing algorithm based approach for balancing and sequencing of mixed-model U-lines. Comput Ind Eng 66(4):1070–1084CrossRefGoogle Scholar
Kellegöz T, Toklu B (2012) An efficient branch and bound algorithm for assembly line balancing problems with parallel multi-manned workstations. Comput Oper Res 39(12):3344–3360CrossRefzbMATHGoogle Scholar
Kellegöz T, Toklu B (2015) A priority rule-based constructive heuristic and an improvement method for balancing assembly lines with parallel multi-manned workstations. Int J Prod Res 53(3):736–756CrossRefGoogle Scholar
Sepahi A, Naini SGJ (2016) Two-sided assembly line balancing problem with parallel performance capacity. Appl Math Model 40(13):6280–6292MathSciNetCrossRefGoogle Scholar
Seyed-Alagheband SA, Ghomi SF, Zandieh M (2011) A simulated annealing algorithm for balancing the assembly line type II problem with sequence-dependent setup times between tasks. Int J Prod Res 49(3):805–825CrossRefGoogle Scholar
Suresh G, Sahu S (1994) Stochastic assembly line balancing using simulated annealing. Int J Prod Res 32(8):1801–1810CrossRefzbMATHGoogle Scholar