Liang Liang

Bio: Liang Liang is an academic researcher from China University of Science and Technology. The author has contributed to research in topics: Game theory & Stackelberg competition. The author has an hindex of 1, co-authored 1 publications receiving 2 citations.

Stackelberg game in VMI system with sufficient capacity considering wholesale and retail prices

Abstract: This work discusses a VMI (vendor managed inventory) supply chain where one manufacturer produces and supplies a single product at a wholesale price to multiple retailers, maybe its agents, who then sell the product in dispersed and independent markets at retail prices. The manufacturer determines wholesale price, inventory replenishment cycle, and backorder quantity by maximizing its own profit with sufficient capacity. The retailers in turn take the manufacturer's decision results as given inputs to determine the optimal retail prices to maximize their own profits. This problem is modeled as a Stackelberg game where the manufacturer is the leader and retailers are followers. The analysis of the equilibrium of the Stackelberg game and its corresponding algorithm are given. A numerical study is conducted to understand the influence of some parameters.

Pricing and ordering decisions in a supply chain with imperfect quality items and inspection under buyback of defective items

Abstract: In this article, the economic production and inventory model in a three-layer supply chain including one distributor, one manufacturer and one retailer for a single-product and general demand functions under three scenarios is developed. We assume that during the production process, both healthy and defective items are generated. As the first scenario, we develop the first model, in which the defective items are not reworked and all considered as scrape, while in the second model, we assume that the defective items are reworked and are sold as perfect item. In the second scenario, we assume that defective item can be sold with lower price than the selling price. Moreover, raw materials with imperfect quality are sent back from a distributor to outside supplier under a lower price. Determining the order quantity of the distributor and the selling prices of the distributor and the manufacturer as well as the retailer was the goal of this article such that the total profit of each member is maximised. In ord...

Neural networks based vendor-managed forecasting: a case study

Abstract: Vendor-managed inventory (VMI) is a collaborative supply chain management practice adopted by many organisations. For making inventory-related decisions an accurate forecast is needed. Traditional forecasting models provide close but not accurate forecasts. In the recent years, decision support tools, like neural networks, are used for making an accurate forecast. This paper presents a case study of a small enterprise where a vendor-managed inventory pact was in force between enterprise and a retailer. In the study, various neural networks were used for demand forecasting. The results of neural network based forecasts are found and compared on various fronts. Multi-criteria decision-making tools are adopted for comparing and verifying the results. Study shows that even small enterprise could adopt the simple VMI system by using properly trained neural network and obtain substantial saving in inventory and costs.