Optimization of part type selection and loading problem with alternative production plans in flexible manufacturing system using hybrid genetic algorithms - part 1: Modelling and representation

Two NP-hard and strongly related problems in flexible manufacturing system (FMS), part type selection problem and loading problem, are addressed in this paper. Various flexibilities including alternative production plans are considered. This effort will further exploit the flexibility of the FMS and improve system productivity. Real coded genetic algorithms (RCGA) which uses an array of real numbers as chromosome representation is proposed to solve these problems. Hybridizing the RCGA with variable neighborhood search (VNS) is performed to obtain better results. A strategy to maintain population diversity and avoid a premature convergence is also implemented. This first part of the paper addresses a modeling of the problems and discusses how the chromosome representation of the RCGA can handle various flexibilities of operations in the FMS. The second part of the paper will discuss the effectiveness of this hybrid approach to solve several test bed problems.