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 maxi...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.展开更多
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...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.展开更多
This paper considers the economic production quantity (EPQ) problem with backorderin which the setup cost,the holding cost and the backorder cost are characterized as fuzzy variables,respectively.Following expected va...This paper considers the economic production quantity (EPQ) problem with backorderin 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 expectedvalue model (EVM) and a chance constrained programming (CCP) model are constructed.Then fuzzysimulations are employed to estimate the expected value of fuzzy variable and α-level minimal averagecost.In order to solve the CCP model,a particle swarm optimization (PSO) algorithm based on thefuzzy simulation is designed.Finally,the effectiveness of PSO algorithm based on the fuzzy simulationis illustrated by a numerical example.展开更多
文摘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.
基金supported by the National Natural Science Foundation of China No.71371133,No.61004015,No.61473204the Program for New Century Excellent Talents in Universities of China(NCET11-0377)
文摘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.
基金supported by the National Natural Science Foundation of China under Grant No. 70471049
文摘This paper considers the economic production quantity (EPQ) problem with backorderin 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 expectedvalue model (EVM) and a chance constrained programming (CCP) model are constructed.Then fuzzysimulations are employed to estimate the expected value of fuzzy variable and α-level minimal averagecost.In order to solve the CCP model,a particle swarm optimization (PSO) algorithm based on thefuzzy simulation is designed.Finally,the effectiveness of PSO algorithm based on the fuzzy simulationis illustrated by a numerical example.
