Genetic-Based Optimization of the Manufacturing Process of a Robotic Arm under Fuzziness

Abstract

Fuzziness is a key concern in modern industry and, thus, its implementation in manufacturing process modeling is of high practical importance for a wide industrial audience. The scientific contribution of the present attempt lies on the fact that the assembly line balancing problem of type 2 (SALBP-2) is approached for a real manufacturing process by introducing fuzzy processing times. The main scope of this work is the solution of the SALBP-2, which is an NP-hard problem, for a real manufacturing process considering fuzziness in the processing times. Since the data obtained from realistic situations are imprecise and uncertain, the consideration of fuzziness for the solution of SALBP-2 is of great interest. Thus, real data values for the processing times are gathered and estimated with uncertainty. Then, fuzzy processing times are used for finding the optimum cycle time. The optimization tool for the solution of the fuzzy SALBP-2 is a Genetic Algorithm (GA). The validity of the proposed approach is tested on the construction process of a metallic robotic arm. The experimental results demonstrate the effectiveness and efficiency of the proposed GA in determining the optimum sequence of the tasks assigned to workstations which provides the optimum fuzzy cycle time.