Solving machine loading problems in a flexible manufacturing system using a genetic algorithm based heuristic approach

The machine-loading problem of a flexible manufacturing system (FMS) has be en recognized as one of the most important planning problems. In this resea rch, a Genetic Algorithm (GA) based heuristic is proposed to solve the mach ine loading problem of a random type FMS. The objective of the loading prob lems is to minimize the system unbalance and maximize the throughput, satis fying the technological constraints such as availability of machining time, and tool slots. The proposed GA-based heuristic determines the part type s equence and the operation-machine allocation that guarantee the optimal sol ution to the problem, rather than using fixed predetermined part sequencing rules. The efficiency of the proposed heuristic has been tested on ten sam ple problems and the results obtained have been compared with those of exis ting methods.