Integrated supply chain inventory model with quality improvement involving controllable lead time and backorder price discount

For the past four decades the integrated vendor and buyer supply chain inventory model has been an interesting topic, but quality improvement of defective items in the integrated inventory model with backorder price discount involving controllable lead time has been rarely discussed. The aim of this paper is to minimize the total related cost in the continuous review model by considering the order quantity, reorder point, lead time, process quality, backorder price discount and number of shipment as decision variables. Moreover, we assume that an investment function is used to improve the process quality. The lead time demand follows a normal distribution. In addition, the buyer offers backorder price discount to motivate the customers for possible backorders. There are some defective items in the arrival lot, so its treatment is also taken in account in this paper. We develop an iterative procedure for finding the optimal values of decision variables and numerical example is presented to illustrate the solution procedure. Additionally, sensitivity analysis with respect to major parameters is also carried out

Three-Echelon Inventory Model with Permissible Delay in Payments under Controllable Lead Time and Backorder Consideration

This paper proposes a three-echelon inventory model with permissible delay in payments under controllable lead time and backorder consideration to find out the suitable inventory policy to enhance profit of the supply chain. In today’s highly competitive market, the supply chain management has become a critical issue in both practice and academic and supply chain members have to cooperate with each other to bring more benefits. In addition, the inventory policy is a key factor to influence the performance of the supply chain. Therefore, in this paper, we develop a three-echelon inventory model with permissible delay in payments under controllable lead time and backorder consideration. Furthermore, the purpose of this paper is to maximize the joint expect total profit on inventory model and attempt to discuss the inventory policy under different conditions. Finally, with a numerical example provided here to illustrate the solution procedure, we may discover that decision-makers can control lead time and payment time to enhance the performance of the supply chain

The effectiveness of a simple policy for coordinating inventory control and pricing strategies

Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 51-54).We investigate the effectiveness of an (s, S, p) policy relative to an (s, S, A, p) policy in a single product, periodic review, finite horizon model with stochastic multiplicative demand and fixed ordering cost, in which an (s, S, A, p) policy is optimal. An extensive numerical study shows that empirically an (s, S, p) policy is highly effective relative to an (s, S, A, p) policy. We also formulate two alternative benchmark policies and find that the (s, S, p) policy is superior in terms of profit. In addition, we propose an efficient algorithm with simulated annealing and modified binary search to determine the (s, S, p) policy for the model.by Zhibo Sun.S.M

Dynamic pricing models for electronic business

Dynamic pricing is the dynamic adjustment of prices to consumers depending upon the value these customers attribute to a product or service. Today’s digital economy is ready for dynamic pricing; however recent research has shown that the prices will have to be adjusted in fairly sophisticated ways, based on sound mathematical models, to derive the benefits of dynamic pricing. This article attempts to survey different models that have been used in dynamic pricing. We first motivate dynamic pricing and present underlying concepts, with several examples, and explain conditions under which dynamic pricing is likely to succeed. We then bring out the role of models in computing dynamic prices. The models surveyed include inventory-based models, data-driven models, auctions, and machine learning. We present a detailed example of an e-business market to show the use of reinforcement learning in dynamic pricing