Hybrid Technique for Optimizing Stock Allocation (HTFOSA)

Abstract

The quest for optimum distribution of scarce goods from manufacturers to satisfy consumers demand via wholesalers and retailers has caused reasonable drift from ordinary allocation of goods to developing a mathematical model that enhances steady and efficient allocation. Demand
is dynamic, hence the need to keep stock. The act of keeping stock has its associated costs, likewise the act of not keeping stock(stockout). This study investigated the different ways by which stock could be allocated. The Lagrangian interpolation polynomial is used to obtain polynomial functions of allocations then the Chebyshev polynomial approximation technique was used to design a hybrid technique that optimizes returns. The features of the hybrid model along with its corresponding algorithm were stated. Relevant theorems are stated with numerical examples. The hybrid model developed has a wide area of applicability as it provides solution to different conditions of stock allocation simultaneously which reduced computational time and overcomes the problem of cause of dimensionality. The technique was compared with the Dynamic programming model of stock allocation and results show a significant and appreciable improvement on stock allocation.