The Model And Algorithm Of Supply Chain Scheduling Under Uncertain Environment

In current market economy conditions, the competition between the companies hasexpanded to supply chains. Enhancing supply chain’s overall competitive ability andimproving the whole service level have become crucial issue for many researchers. Numerousformer studies on supply chain management are based on strategic level, but few of them isbased on operational level. In addition, due to the existence of uncertain factors such asproduction equipment, staff member and market factor as well as the weather condition,supply chain usually operates in uncertain environment. Therefore, this article study thesupply chain management issue based on operational level, main in the scheduling problembased on supply chain operational level under uncertain environment. Optimizing supplychain scheduling can improve the service level of the supply chain and boost customersatisfaction.The study considers the two-stage supply chain scheduling problem. In the first stage,many manufacturers located in the same zone process products with different processingspeed and setup time in each period. If the quantity of the products assigned to a manufacturerexceeds its unit cycle production capacity, the manufacturer would process the left products inthe next period cycle. In the second stage, many vehicles deliver the products to a distributorwith different speed and different routes as well as loading capacity. If the quantity of theproducts assigned to a vehicle exceeds its loading capacity, the vehicle would return to themanufacture for the next batch.Based on the above-mentioned problem description, formulate the situation as thestochastic expected value model. The objective function is to minimize the expectation of allproducts production cost and delivery cost. Due to the complexity of the model, geneticalgorithm is proposed to solve this model. Moreover, two numerical examples are studied. Inthe first numerical example, the products number is30, each producer cost is1in per unittime, so as each vehicle. In the second numerical example, the quantity of the productsexpanded to50, the cost of each producer or vehicle is not always1. The optimal schedulecases can be obtained by using genetic algorithm. The production cost and delivery cost ofeach product, the number of production cycle and production time of each producer, thenumber of delivery batch and delivery time of each vehicle can be calculated by analyzing theoptimal scheduling case. In the same time, the two examples verify the feasibility andeffectiveness of the genetic algorithm. Through performing parameter analysis, when eachproducer or vehicle cost is1in per unit time, the number of the produers and the vehiclesshould be matched; the bottleneck should not exist in the supply chain. In addition, the overall cost of the products is mainly determined by the first stage. Reducing the workload of theeach product or improving each producer’s production rate can greatly lower the overall cost.