Narrow Results

This paper studies a problem of dynamic pricing faced by a retailer with limited inventory, uncertain about the demand rate model, aiming to maximize expected discounted revenue over an infinite time horizon. The retailer doubts his demand model which is generated by historical data and views it as an approximation. Uncertainty in the demand rate model is represented by a notion of generalized relative entropy process, and the robust pricing problem is formulated as a two-player zero-sum stochastic differential game. The pricing policy is obtained through the Hamilton-Jacobi-Isaacs (HJI) equation. The existence and uniqueness of the solution of the HJI equation is shown and a verification theorem is proved to show that the solution of the HJI equation is indeed the value function of the pricing problem. The results are illustrated by an example with exponential nominal demand rate.

The return of used products (cores) is the beginning of remanufacturing. Although an appropriate pricing policy can effectively manage the returns, a static pricing policy cannot match the returns and demands because of the high uncertainties in both sides, which in turn results in high inventory cost or lost-sale cost. In this paper, we apply a dynamic pricing policy commonly used in retail setting to the core acquisition management in remanufacturing and study the pricing problem for used products with the objective of minimizing average cost over an infinite horizon. We formulate the pricing problem as a continuous-time Markov decision process and characterize the structural properties of the optimal policy. We also conduct a numerical study to investigate the benefit of dynamic pricing.

Based on China’s green energy development strategy, this paper constructs a basic model of recycling and a channel expansion model for the circular economy foundation of Zaozhuang, Shandong Province, China. Through numerical simulation, it is found that each member of the supply chain should control the rate of price adjustment, otherwise it will cause market disruption. The model is controlled based on a chaos control method. Then, based on the fuzzy comprehensive evaluation method, an early warning system for the circular economy of Zaozhuang City is constructed. It is found that the economic development of Zaozhuang is a serious warning, resources are moderate warning, and the environment is not in an alarm state. In addition to paying attention to energy conservation and emission reduction of enterprises, the government should pay attention to creating awareness of energy conservation and emission reduction in society, and strengthen the technological investment in reducing pollutant emissions. This paper provides a strategic reference for the circular economy model in Zaozhuang, Shandong, China.

We consider a manufacturer's dual distributions channels consisting on the one hand of a virtual (online) channel operated directly by a manufacturer and on the other hand of a real (offline) channel operated by an intermediate retailer. Customers are assumed heterogeneous in their virtual acceptance, deriving a surplus according to the channel they shop at. Assuming that customers' derived benefits are random with a known probability distribution, we obtain a probabilistic model, which is used to construct an inter-temporal model for shopping online. In addition, we suppose that the retailer uses a markup pricing strategy and has a strategic role. This results in a Stackleberg differential game where the manufacturer is leader and the retailer is a follower. The optimal policy shows that the manufacturer charges the same price across both channels. This finding is consistent with classical results in economics. However, our research goes beyond this observation and indicates that the online price, the retailer's markup and the probability to buy are affected by consumers' heterogeneity in a specific manner. Moreover, we show that while the retailer sets a price equal to the product value, the online price is lower and is equal to the product value less the guarantee provided by the manufacturer for the risk the customer take to buy online. This guarantee is not discriminating and is set to the risk of the customer with the lowest virtual acceptance. Finally, we show that the introduction of the online store is a win-win strategy; both the customers and the manufacturer are better off.

E-commerce over the Internet has become an attractive means of conducting business in today's world. However, the principles of classical economics demand a fresh insight before they can be adapted to the market structure presented by the Internet. Here, we investigate markets for goods that are characterized by an experience-limited supply curve. We propose an algorithm that maximizes the welfare in the e-market by maximizing the combined profit of the buyers and sellers. For this, the buyers and sellers must reveal their value and cost curves to a trusted intermediary who can determine the transaction that maximizes their joint welfare. We show that accurate revelation of hidden profits offers better incentives, both to the buyers and the sellers, than inaccurate or incomplete revelation.

In a declining market for goods, we optimize the net profit in business when inventory management allows change in the selling prices n times over time horizon. We are computing optimal number of changes in prices, respective optimal prices, and optimal profit in each of the cycle for a deteriorating product. This paper theoretically proves that for any business setup there exists an optimal number of price settings for obtaining maximum profit. Theoretical results are supported by numerical examples for different setups (data set) and it is found that for every setup the dynamic pricing policy outperforms the static pricing policy. In our model, the deterioration factor has been taken into consideration. The deteriorated units are determined by the recurrence method. Also we studied the effect of different parameters on optimal policy with simulation. For managerial purposes, we have provided some "suggested intervals" for choosing parameters depending upon initial demand, which help to predict the best prices and arrival of customers (demand).