Assembly line balancing using genetic algorithms
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For the last few decades, the genetic algorithms (GAs) have been used as a kind of heuristic in many areas of manufacturing. Facility layout, scheduling, process planning, and assembly line balancing are some of the areas where GAs are already popular. GAs are more efficient than traditional heuristics and also more flexible as they allow substantial changes in the problem’s constraints and in the solution approach with small changes in the program. For this reason, GAs attract the attention of both the researchers and practitioners. Chromosome structure is one of the key components of a GA. Therefore, in this thesis, we focus on the special structure of the assembly line balancing px'oblem and design a chromosome structure that operates dynamically. We propose a new mechanism to work in parallel with GAs, namely dynamic partitioning. Different from many other GA researchers, we particularly compare different population re\asion mechanisms and the effect of elitism on these mechanisms. Elitism is revised by the simulated annealing idea and various levels of elitism are created and their effects are observed. The proposed GA is £ilso compared with the traditional heuristics.