Multi-item fuzzy economic order quantity models using genetic algorithms

A soft computing approach is proposed to solve non-linear programming problems under fuzzy objective goal and resources with/without fuzzy parameters in the objective function. It uses genetic algorithms (GAs) with mutation and whole arithmetic crossover. Here, mutation is carried out along the weighted gradient direction using the random step lengths based on Erlang and Chi-square distributions. These methodologies have been applied to solve multi-item fuzzy EOQ models under fuzzy objective goal of cost minimization and imprecise constraints on warehouse space and number of production runs with crisp/imprecise inventory costs. The fuzzy inventory models have been formulated as fuzzy non-linear decision making problems and solved by both GAs and fuzzy non-linear programming method based on Zimmermann's approach. The models are illustrated numerically and the results from different methods are compared.