An investigation of inventory optimization by using geometric programming technique with decagonal fuzzy number representation.

Inventory optimization focuses on stabilizing the stockpile management in the company with the expenditure involved in a successful volatile system. The major challenge is to meet the customer's demand in a well-organized way for profitable management. The nonlinear programming problem (NLP) from fixed order inventory systems, was solved by using the geometric programming method with fuzzification executed by decagonal fuzzy numbers and graded-mean representation as a defuzzification method. In this paper, we had minimized the total cost for an inventory model to obtain the optimal quantity and compared it with the fuzzy inventory model and illustrated with a numerically. [ABSTRACT FROM AUTHOR]