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Two-sided assembly line balancing using teaching-learning based optimization algorithm

Abstract

Assembly line balancing plays a crucial role in modern manufacturing companies in terms of the growth in productivity and reduction in costs. The problem of assigning tasks to consecutive stations in such a way that one or more objectives are optimized subject to the required tasks, processing times and some specific constraints is called the Assembly Line Balancing Problem (ALBP). Depending on production tactics and distinguishing working conditions in practice, assembly line systems show a large diversity. Although, a growing number of researchers addressed ALBP over the past fifty years, real-world assembly systems which require practical extensions to be considered simultaneously have not been adequately handled. This study deals with an industrial assembly system belonging to the class of two-sided line with additional assignment restrictions which are often encountered in practice. Teaching-Learning Based Optimization (TLBO), which is a recently developed nature-inspired search method, is employed to solve the line balancing problem. Computational results are compared with the current situation in terms of the line efficiency, and the solution structure with workload assigned to the stations is presented.

Keywords

  • Two-sided assembly lines;
  • Line balancing;
  • Teaching-learning based optimization;
  • Application, manufacturing

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