Optimization of Constrained Multi-Item Fuzzy Inventory Problems using Genetic Algorithm

This paper proposes the strategy of optimizing constrained multi-item inventory problems under fuzzy environment using a Genetic Algorithm (GA). The GA is used in the sense that it is computationally simple yet powerful in its search for improvement. The typical inventory analysis is sensitive to reasonable errors in the measurement of relevant inventory costs. Therefore the inventory costs are assumed to be vague and imprecise in this paper. The objective of minimizing the total inventory cost and the constraints’ goals are also imprecise in nature. The impreciseness in these variables has been represented by fuzzy linear membership functions. Numerical examples have been worked out to highlight the method, and the results are compared with those of corresponding crisp models. Sensitivity analysis has also been presented for one of the examples.