Solving mixed-model assembly line sequencing problem using adaptive genetic algorithms

The focus of this M.Sc study is to introduce adaptive Genetic Algorithm (GA) based approaches for single- and multi-objective mixed-model assembly line sequencing problems (MMALSP), which deal with the determination of production launching orders so that the variations in part consumption rates (VPC) are minimized. In addition to this objective, minimization of total utility work (UW) and cost for sequence-dependent setups (SC) are also considered in multi-objective version of the MMALSP.In order to solve single-objective MMALSPs, an adaptive GA based approach which incorporates adaptive parameter control techniques into a pure GA is proposed. The proposed approach, integrates an adaptive elitist strategy and a scheme for varying probability of mutation according to the feedback taken from the algorithm. Using this approach, the MMALSP is solved under the objective of minimizing VPC in a four level assembly environment, i.e. product, subassembly, component and raw material.Later, by modifying the adaptive parameter control techniques and integrating them into a Pareto Stratum ? Niche Cubicle GA, a multi-objective MMALSP with three objective functions (i.e., minimization of VPC, UW and SC) is solved. Finally, to evaluate the performance of the proposed approach, various sets of experiments have been carried out.