Optimization of part type selection and loading problem with alternative production plans in flexible manufacturing system using hybrid genetic algorithms - part 2: Genetic operators and results

This paper as the continuation of the first part addresses two NP-hard and strongly related problems in flexible manufacturing system (FMS), part type selection problem and loading problem. This first part of the paper detailed a modeling of the problems and discussed how the chromosome representation of the real coded genetic algorithms (RCGA) can handle various flexibilities of operations in the FMS. Hybridizing the RCGA with variable neighborhood search (VNS) and a strategy to maintain population diversity were implemented. This second part of the paper discusses the effectiveness of this hybrid approach to solve several test bed problems. This approach improves the FMS performance by considering two objectives, maximizing system throughput and maintaining the balance of the system (minimizing system unbalance). The resulted objective values are compared to the optimum values produced by branch-and-bound method. The experiments show that the proposed RCGA could produces promising results and the hybridization can improve the performance of the RCGA.