Abstract:
This study presents operational decision support software to efficiently solve cell formation problems with multiple routes. The software includes two revised algorithms that assist decision makers in choosing the best cellular layout. These revised algorithms are the multiobjective genetic algorithm that uses two different scalarization methods such as conic scalarization and weighted sum scalarization and the fuzzy c-means algorithm for problems with multiple routes. From these algorithms, the multiobjective genetic algorithm with conic scalarization has important advantages over many multiobjective approaches in the literature for being able to reach all efficient solutions for linear and nonlinear multiobjective models. These revised algorithms and the decision support software were tested on some data sets collected from an engine manufacturing plant and the literature. The test results showed that, in many cases, the revised multiobjective genetic algorithm with conic scalarization results in better performance than the revised fuzzy c-means algorithm and the methods used in the test problems for problems with multiobjective and multiple routes. Practitioners and researchers can use this operational decision support software and the multiobjective genetic algorithm with conic scalarization to obtain higher-quality cellular layout solutions compared with other software applications in the literature.
