Two-sided assembly line balancing using teaching-learning based optimization algorithm and group assignment procedure

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 thesis deals with an industrial assembly system belonging to the class of two-sided line with several additional assignment restrictions which are often encountered in practice. First, we solved the two-sided ALBP by using a heuristic approach named Group Assignment Procedure. Then, we used Teaching-Learning Based Optimization (TLBO) Algorithm which is recently developed for the optimization of mechanical design problems, and then applied to various engineering problems. Computational results are compared in terms of the line efficiency, and the solution structure with workload assigned to the stations was presented.