Proposed Optimization Model for Two-Warehouse Stock using Genetic Algorithm

Abstract

Soft computing is a technique which uses the approximate model to provide the solution for real life complex problems like stock the items in two warehouses. Decay of items stocked in warehouse is natural phenomenon. The three-parameter Weibull distribution is an astounding speculation of exponential disintegration, which can be utilized for things with any underlying estimation of the pace of crumbling and for things which starts breaking down simply after a specific timeframe. It has been observed that for any new brand items launches in to the market, the interest rate increases with time up to a specific period and afterward at last balance out and gets steady. This sort of adjustment has been named as 'ramptype' demand rate. Here we have created two stockroom stock models with slope type request rate and three-boundary Weibull dispersion disintegration under inflationary conditions, where allowable postponement in installment is accessible for retailer if extraordinary sum is repaid inside the given credit time frame. Since, not all clients are eager to sit tight for backlogged during the lack time frame. in this investigation deficiencies are additionally permitted and somewhat accumulated. The proposed study is to calculate the retailer's best refilling policies in two warehouses and to minimize the total price to stock the items in inventory. A mathematical problem is solved to authenticate the discussed model using Genetic Algorithm (GA).