Fengmei Yang

Coordination of Supply Chain with a Dominant Retailer under Demand Disruptions

We develop a coordination model of a one-manufacturer multi-retailers supply chain with a dominant retailer. We consider the impact of a dominant retailer on the market retail price and his sales promotion opportunity and examine how the manufacturer can coordinate such a supply chain by revenue-sharing contract after demand disruptions. We address the following important research questions in this paper. (i) How do we design an appropriate revenue-sharing contract to coordinate the supply chain with a dominant retailer without demand disruptions? (ii) When demand is disrupted with variations in market scale and price sensitive coefficient, can the original contract still be valid? (iii) How do the demand disruptions affect the coordination mechanism under different disruption scenarios and how should the new contract change? Finally, we generate important insights by both analytical and numerical examples.

ANALYSES OF LOCATION-PRICE GAME ON NETWORKS WITH STOCHASTIC CUSTOMER BEHAVIOR AND ITS HEURISTIC ALGORITHM ∗

In this paper, a two-stage model is developed to investigate the location strategy and the commodity pricing strategy for a retail firm that wants to enter a spatial market with multiple competitive facilities, where a competitor firm is already operating as a monopoly with several outlets. Expected market shares are calculated based on the stochastic customer behavior on networks. The authors provide a sufficient condition for the existence of equilibrium prices in the price game for the first time. The existence and uniqueness of the pure strategy Nash equilibrium price with a specified utility function are proved in the subgame. A metaheuristic based on tabu search is proposed to search the optimal location-price solution of the model. In addition, the authors provide two numerical examples to illustrate how to obtain the optimal solution and conduct sensitivity analysis. The analysis shows that the best location decision is robust for the follower firm, price game is more intense when incomes of consumers are lower or there are more substitution products, and neither chain retail gains from the price competition.

Forecasting time series with genetic programming based on least square method

Although time series are frequently nonlinear in reality, people tend to use linear models to fit them under some assumptions unnecessarily in accordance with the truth, which unsurprisingly leads to unsatisfactory performance. This paper proposes a forecast method: Genetic programming based on least square method (GP-LSM). Inheriting the advantages of genetic algorithm (GA), without relying on the particular distribution of the data, this method can improve the prediction accuracy because of its ability of fitting nonlinear models, and raise the convergence speed benefitting from the least square method (LSM). In order to verify the validity of this method, the authors compare this method with seasonal auto regression integrated moving average (SARIMA) and back propagation artificial neural networks (BP-ANN). The results of empirical analysis show that forecast accuracy and direction prediction accuracy of GP-LSM are obviously better than those of the others.

A Robust VRPHTW Model with Travel Time Uncertainty

Abstract Vehicle routing problem with hard time window (VRPHTW) is extremely strict in travel time. However, the travel time is usually uncertain due to some stochastic factors such as weather and other road conditions. It is an important issue to take travel time uncertainty into consideration in VRPHTW. This paper develops a robust VRPHTW model to cope with time uncertainty. We use robustness method of Bertismas to consider the maximum change of uncertain travel time in the degree of robustness set by decision maker. The probability that the optimal solution violates constraints is derived. The violated probability shows that the robustness of VRPHTW model can reach a satisfactory level. Finally, one modified max-min ant system algorithm is proposed to solve this problem and one numerical example is conducted to illustrate the model and the algorithm. Both theory analysis and numerical example show the effectiveness of the proposed robust model.

Pricing credit spread option with Longstaff-Schwartz and GARCH models in Chinese bond market

This paper investigates the mean-reversion and volatile of credit spread time series by using regression and time series analysis in Chinese bond market. Then the Longstaff-Schwartz model and GARCH model are applied to price credit spread put option. The authors compare the features of these two models by employing daily bond prices of government bonds and corporate bonds for the period 2010–2012 in Chinese bond market. The proposed results show that the higher the credit ratings of the corporate bonds are, the lower the prices of the credit spread options are.

Application of User-Based Collaborative Filtering Recommendation Technology on Logistics Platform

With the rapid development of logistics industry, there are lots of logistics service and tons of data emerging on logistics platform. To match service and customers better, personalized recommendations logistics services is needed. In this paper, we introduce a collaborative filtering technology of e-commerce personalized recommendation system into the logistics platform. We present the application of the technology in the logistics platform step by step. Finally, we conduct a numerical example to illustrate the use of the technology proposed in this paper.

The Research on Evolutionary Game of Remanufacturing Closed-Loop Supply Chain Under Asymmetric Situation

Remanufacturing is an effective means to realize energy saving and emission reduction. The remanufacturing industry makes important contribution to achieve the goal of low-carbon economy. This paper develops an evolutionary game model with a two-echelon closed-loop supply chain to study evolutionary stable strategies (ESS) of manufacturers and retailers. Through analyzing evolutionary path of the game, we find that there are two possible evolutionary results affected by the profits of manufacturers.

Public or Private? An Analysis of Supply Chain Choice of E-Markets

This paper develops some models to explore the choice of supply chain between public e-markets and private e-markets. We derive the expected profits of the retailer and the supplier and obtain the corresponding optimal strategies in private e-markets, i.e., the supply chain member owns the e-markets. We also analyze the profits of the supply chain in public e-markets, i.e., the e-markets is run by a third party. In addition, we compare the cost of operating supply chains private e-markets with that of public e-markets. Finally, we indentify the conditions under which the supply chain operate their own e-markets.

Electronic Markets Selection in Supply Chain with Uncertain Demand and Uncertain Price

In recent years, more and more companies start online operation. Electronic market becomes a key component of some companies’ strategy. Supply chain management is another key component of the strategy as being adopted by an increasing number of companies. There are many interactions between electronic market and supply chain. One of the key questions is to select one type of electronic market from the view of supply chain. This paper develops some models to explore the issue of selection between public electronic market and private electronic market in three scenarios where electronic market is used for buying, for selling, and for both selling and buying, respectively. In a public electronic market, neither the supplier nor the retailer is the owner of the electronic market. However, in a private electronic market, there is an owner that is either the supplier or the retailer. Besides demand uncertainty, we take into account the price uncertainty in electronic market. We explore the conditions under which the agent of supply chain selects one certain type of electronic market by comparing expected profits of supply chain members in different scenarios. Some sensitivity analyses are conducted to explore the impact of the customer demand, electronic market retail price, and e-market use fee on the selection of electronic market. Finally, some interesting managerial and academic insights are obtained.

Prediction model on Chinese annual live hog supply and its application

In this paper, a prediction model on Chinese annual live hog supply was established. With cointegration test, backward and forward stochastic selection and other methods, four main factors (hog price, prices of inputs in hog production, the level of hog inventory, as well as emergency and government policy) were chosen from 16 relevant factors to establish the model and make improvement. Applied the improved model, annual live hog supply in China from 2013 to 2016 was predicted in three scenarios. The predicted results showed that if there were no major emergencies from 2013 to 2016, there would be an upward trend in Chinese live hog supply year by year. The supply of live hogs in China in 2013 would be about 707.663 million head, in 2014 would be between 715.935 and 742.969 million head, in 2015 between 734.458 and 779.413 million head, and in 2016 between 750.923 and 809.450 million head.