Material Flow Optimization for Make-to-Stock Production System

Under the condition of the market competition becom in g more and more drastic, the demands of market take on some new features such as individuation, diversification, small batch, unstableness and quick delivery et c. The Make-to-Stock mode is usually adopted by many enterprises to improve th e balance and stableness of production process. In such enterprises, order batch , production batch and sales batch are the important factors, which affect the s atisfaction of clients, efficiency and benefit of the enterprise. It takes purch ase, production and sales into account respectively when optimizing product batc h in traditional way. However, it ignored the influences of relations between ea ch links of whole system. It is assumed that the consumption and market demand a re continuous process whereas the factual demands are batched when economic batc h is determined. So there exist some deviations between the economic batch deter mined by traditional way and that by integral optimization. Through the integral analysis of Logistics in the production system, we know that from materials are purchased, then manufactured, finally sold, the material changed in appearance and value, it still exist in different links of production system. The amount of materials occupied varies just in different status, from stock status to produc tion status, then to waiting-be-sold status, there is not any substantial chan ge in quantity until they are sold. So we must comprehensively analyze the relat ions among each link based on integral production system, to optimize the materi al batch and cut short production cycle in order to optimize the whole system. In this paper, the production system is taken as a global entity, and in which m aterials variation law and their relations of each link are analyzed; To optimiz e the whole materials flow, a new model of multi-product systems’ economic orde r batch, economic production batch and optimal sale lot multi-product syste ms’ is developed which based on the limit of capitals and stock area.

Research on the Impact of the Centralized Drug Procurement Policy on China’s Pharmaceutical Enterprises

Objective To analyze the impact of the centralized drug procurement policy on pharmaceutical enterprises,and put forward some suggestions for improving China’s centralized drug procurement policy.Methods Through literature analysis and comparative research,the relevant policies and implementation effects from the“4+7”drug procurement pilot to the seventh batch of volume procurement were sorted out,and the impact of the procurement policy on pharmaceutical enterprises was analyzed.Results and Conclusion The centralized drug procurement policy has continuously improved the enthusiasm of enterprises to participate in bidding,and the consistency evaluation work has been carried out well.However,there are also some problems,such as the reduction of enterprise profits,the difficulty of enterprise operation,the decline in the quality of winning drugs,and the insufficient innovation ability and the supply capacity of enterprises.In this regard,enterprises should reasonably control the cost of drugs to ensure the stable supply of winning drugs.Besides,enterprises should create brand effect for their timely transformation and development.While strengthening pharmaceutical innovation,they can change from low-level generic drug production to R&D innovation.The government should pay close attention to the impact of centralized procurement on the quality and efficiency of enterprise drugs.For instance,the government should improve the pricing rules for drug procurement and increase supervision of drug quality and supply.At the same time,it should give certain policy and financial support to enterprises’technological innovation and talent introduction.

FUZZY EPQ INVENTORY MODELS WITH BACKORDER

This paper considers the economic production quantity （EPQ） problem with backorder in which the setup cost, the holding cost and the backorder cost are characterized as fuzzy variables, respectively. Following expected value criterion and chance constrained criterion, a fuzzy expected value model （EVM） and a chance constrained programming （CCP） model are constructed. Then fuzzy simulations are employed to estimate the expected value of fuzzy variable and c^-level minimal average cost. In order to solve the CCP model, a particle swarm optimization （PSO） algorithm based on the fuzzy simulation is designed. Finally, the effectiveness of PSO algorithm based on the fuzzy simulation is illustrated by a numerical example.