Location - Based Allocation Strategy Based On Throughput Under Uncertainty Of Demand

With the rapid development of China’s logistics industry, companies are increasingly concerned about their own logistics costs. From the related data that, in logistics costs, warehousing costs around 30% cost of the logistics system; While in storage operating system cost, order-picking system costs around 55% of the warehousing operation systems costs. Through the two sets of data, it is not difficult to find order sorting system operating costs directly related to the company’s cost of logistics systems and logistics systems costs also played a decisive role. Therefore, in order to reduce logistics costs, warehousing systems cost becomes the focus of studies by relevant experts and scholars in the field. However, in practice in logistics and warehousing enterprises, there are many factors that affect order picking efficiency, including:storage system layout structure, location-allocation policy, location, storage, picking a routing policy, blocks of storage capacity, zoning regulations, personnel structure, as well as warehouse management system. Location-allocation policy in particular, picking a routing policy, and storage capacity of the block set, these three factors impact on the storage system the most, and almost a warehousing system is a direct result of operational performance.By reviewing the relevant literature and found academic studies generally focus on the storage system in the order handling system and order-picking systems. Orders sorting system and orders points goods system in function Shang also is has difference of, specific,:orders sorting system main contains has four a aspects of main function, that respectively for accept arrival, and points goods, and picked goods and packaging; and orders points goods system main including has three a aspects of main function, that respectively for to Member points goods, and packaging and select is chose of goods. Because both are greatly different in function, leading to location-allocation policy concerns effectiveness varies. For the former, the main concern is picking efficiency, while for the latter is primarily focused on storage performance issues. So far, the vast majority of literature mainly focuses on the order-picking system, which is characterized by the complete information systems, or a static information system. Because of this, storage allocation policies based on throughput to be acted on.Based on the needs of logistics and warehousing systems with uncertainties as background, to study the logistics warehouse dynamic location-allocation policy, with recent space allocation policies and location-allocation policy based on throughput, identify optimal locations to optimize storage performance allocation policy. According to the study, based on the throughput of the storage allocation policies more applicable uncertainty factors of logistics warehousing system. However, found in corporate practice with no practical value. In order to solve this problem, this paper builds customer demand forecasting model predict customer demand forecasting model for effective customer transaction volume in order to know in advance the effective customer demand and assigned positions, based on throughput storage allocation policies so as to effectively address the problems encountered. This article will use the Kunming International Flower Auction trading center (KIFA) historical transaction data to simulate the performance of its warehousing operations, final validation based on the throughput of the storage allocation policy is feasible.This paper will mainly do the following, as follows:1, The paper refer to related literature and sorting systems for domestic and foreign orders and cargo system, location-allocation policies and requirements in advance of current research are reviewed. On the basis of relevant theories, according to the characteristics and status quo of order-picking system, find the link of enterprise logistics system operating inefficiencies, through process analysis of links, clear the problem, and the problems of enterprise logistics order picking systems and provides solutions for improvement.2, The paper will build to customer demand forecasting models and buyers involved in auction transactions every day are forecast. According to historical data of buyers trading, predicted the total sample for each demand of buyers in the coming days, order picking system for KIFA to throughput storage allocation policies to lay preconditions. According to calculated buyers in the coming days after the demand, storage allocation policies according to different locations to work out the Act of distribution, transfer distances and distances, combined with buyer’s routing policy theory, performance analysis of storage allocation policies at different locations, respectively.3, The paper proposed customer demand forecasting model, effectively solve the throughput prediction problems during the implementation process, added a genetic algorithm, sensitivity analysis, solve the customer’s trading volumes there is a big problem. Through examples, location-allocation policy based on throughput forecast uncertainties in the warehouse picking system has a certain practicability and feasibility.