Using fuzzy expectation-based programming for inventory management

Abstract

Background: Order allocation planning and inventory management are two important problems in manufacturing industries that must be solved optimally to gain maximal profit. Commonly, there are several unknown parameters in those problems such as future price, future demand, etc., and this means decision-making support that can handle this uncertainty is needed to calculate an optimal decision.

Objectives: This study aimed to propose a newly developed joint decision-making support to solve order allocation planning and inventory optimisation of raw materials in a production system comprising multiple suppliers, products and review times with fuzzy parameters.

Method: The model was formulated as a fuzzy expectation-based quadratic programming with the uncertain parameters approached as fuzzy numbers. This was used to handle the fuzzy parameters involved in the problem. A classical optimisation algorithm, the generalised reduced gradient combined with branch-and-bound embedded in LINGO 18.0 was applied to calculate the optimal decision. Numerical experiments were conducted using some randomly generated data with four suppliers, four raw materials and six review times.

Results: Results provided the optimal decision for the given problem, that is, the number of raw materials to be ordered from each supplier at each review time, as well as the corresponding number to be stored in the warehouse.

Conclusion: The proposed model successfully solved the given problems and thus can be used by decision-makers to solve their order allocation planning and inventory problems.