A survey of exact algorithms for the simple assembly line balancing problem
Management Science
An efficient heuristic for solving stochastic assembly line balancing problems
Computers and Industrial Engineering
Eureka: a hybrid system for assembly line balancing
Management Science
Two-sided assembly line balancing to maximize work relatedness and slackness
Computers and Industrial Engineering
A new heuristic method for mixed model assembly line balancing problem
Computers and Industrial Engineering
Neural Networks for Combinatorial Optimization: a Review of More Than a Decade of Research
INFORMS Journal on Computing
A portable and scalable algorithm for a class of constrained combinatorial optimization problems
Computers and Operations Research
A tabu search algorithm for the routing and capacity assignment problem in computer networks
Computers and Operations Research
Combining heuristic procedures and simulation models for balancing a PC camera assembly line
Computers and Industrial Engineering
Expert Systems with Applications: An International Journal
Assembly line balancing in garment industry
Expert Systems with Applications: An International Journal
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This paper addresses an optimization model for assembly line-balancing problem in order to improve the line balance of a production line under a human-centric and dynamic apparel assembly process. As the variance of operator efficiency is vital to line imbalance in labor intensive industry, an approach is proposed to balance production line through optimal operator allocation with the consideration of operator efficiency. Two recursive algorithms are developed to generate all feasible solutions for operator allocation. Three objectives, namely, the lowest standard deviation of operation efficiency, the highest production line efficiency and the least total operation efficiency waste, are devised to find out the optimal solution of operator allocation. The method in this paper improves the flexibility of the operator allocation on different sizes of data set of operations and operators, and enhances the efficiency of searching for the optimal solution of big size data set. The results of experiments are reported. The performance comparison demonstrates that the proposed optimization method outperforms the industry practice.