Different versions of an adaptive harmony search to solve a robotic assembly line balancing problem

This paper deals with an industrial application of a robotic assembly lines problem (RALB). It consists of seizing products on a moving conveyor and placing them on different deposit points. The goal is to optimize the efficiency of the line by assigning the suitable tasks and components to each robot. The performances evaluations of the system are done using a discret event simulation model. As in our industrial application we are bounded by the execution time, we suggest an adaptive harmony search (HS) to solve the above problem. Then, we propose to couple the algorithm with a guided local search (GLS), a fuzzy logic controller (FLC) and an artificial neural network (ANN) in order to enhance the efficiency of this method. All these methods allow avoiding local optimum solutions by improving the search ability and the parameters of the adaptive harmony search. After that, an exact method based on full enumeration is also developed to assess the quality of the developed methods. The experimental results show the advantages and the efficiency of the HS-GLS within a short computational time.