QUALITY IMPROVEMENT AND PRICING WITH REFERENCE QUALITY EFFECT

This article studies a problem of joint pricing and dynamic product quality investment with consumers＇ reference quality effect under the existence of quality inflation. Optimal control models are constructed to maximize the total profit with a limited quality investment capacity, where the demand is sensitive to historical product quality level. The optimal quality investment strategies for i＇mite and infmite planning horizon are given respectively by solving these optimal control models on the basis of Pontryagin＇s maximum principle, which enables the exact trajectory of the optimal quality investment with the reference quality effect over time to be depicted. In addition, an effective algorithm is designed to generate the optimal joint pricing and dynamic quality investment policy for the system. The main difference between the strategy of finite planning horizon and that of infmite planning horizon is that the latter is a constant. Our study indicates that it is never optimal for firm to increase quality investment all the way throughout the planning horizon. The level of quality investment is higher when taking into account the impact of reference quality. Moreover, numerical example is given to illustrate the validness of the theoretical results. Also, sensitivity analysis is carried out to show how system parameters affect the optimal policies, and some managerial suggestions are presented.

DYNAMIC PRICING UNDER TEMPERATURE CONTROL FOR PERISHABLE FOODS

Consumers pay more and more attention to the quality of perishable roods, which is mainly affected by storage temperature. This paper presents a dynamic pricing model for perishable foods under temperature control. To maximize the total profit, the optimal price and storage temperature are obtained using Pontryagin＇s maximum principle. A static pricing model is provided to compare with the dynamic one. It is shown by a numerical example that the dynamic policy can make more revenue than the static one. Moreover, the managerial implications are analyzed and the effectiveness of the proposed method is demonstrated.

DYNAMICAL ANALYSIS AND CONTROL IN A DELAYED DIFFERENTIAL-ALGEBRAIC BIO-ECONOMIC MODEL WITH STAGE STRUCTURE AND DIFFUSION

Nowadays, biological resource in prey-predator ecosystem is commercially harvested and sold with aim of achieving economic interest. Furthermore, harvest effort is usually influ- enced by variation of economic interest of harvesting and spatial heterogeneous environ- ment. In this paper, a delayed differential-algebraic bio-economic model is proposed, which is utilized to investigate interaction and coexistence mechanism of biological pop- ulation in the harvested ecosystem due to the variation of economic interest of harvesting as well as the change of population spatial diffusion and gestation delay. Local stability analysis of the proposed model without gestation delay and diffusion reveals that there is a phenomenon of singularity induced bifurcation due to the variation of economic interest of harvesting, and state feedback controllers are designed to stabilize the pro- posed model at the interior equilibrium. Furthermore, local stability of the proposed model with gestation delay and diffusion is studied, which reveals that the interior equi- librium loses its stability at some critical values of gestation delay and corresponding cycle occurs. It is also shown that population spatial diffusion and harvesting have a stabilizing effect on population dynamics. Finally, numerical simulations are carried out to show consistency with theoretical analysis obtained in this paper.

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.