Hua Ke

Dual-channel closed-loop supply chain with government consumption-subsidy

The government has been acting as an important role in the formation and operation of closed-loop supply chain. This paper focuses on how consumption-subsidy influences dual-channel closed-loop supply chain. After introducing government consumption-subsidy program and dual-channel closed-loop supply chain, the paper analyzes the channel members’ decisions before and after the government-funded program performance, respectively. Finally, influence of consumption-subsidy has been considered from the consumers, the scale of closed-loop supply chain and the enterprises perspectives, which provides an important basis for our propositions. The key propositions of the paper are listed as follows: All the consumers that purchase the new products are beneficiaries of the government consumption-subsidy in varying degrees; the consumption-subsidy is conducive to the expansion of closed-loop supply chain; both the manufacturer and the retailer are beneficiaries of the consumption-subsidy, while the e-tailer benefits or not is uncertain.

Project scheduling problem with stochastic activity duration times

Project scheduling problem is to determine the schedule of allocating resources so as to balance the total cost and the completion time. This paper considers project scheduling problem with stochastic activity duration times, which has the objective of minimizing the total cost under some completion time limits. Three types of stochastic models will be built to solve the problem according to different management requirements. Moreover, stochastic simulation and genetic algorithm will be integrated to design a hybrid intelligent algorithm to solve the above models. Finally, some numerical examples are illustrated to show the effectiveness of the algorithm.

Uncertain random multilevel programming with application to production control problem

For modeling decentralized decision-making problems with uncertain random parameters, an uncertain random multilevel programming is proposed. For some special case, an equivalent crisp mathematical programming to the established uncertain random programming is presented. A searching method by integrating uncertain random simulations, neural network, and genetic algorithm is produced to search the quasi-optimal solution under some decision-making criterion. Finally, the proposed uncertain random multilevel programming is applied to a production control problem.

Optimization models and a GA-based algorithm for stochastic time-cost trade-off problem

In real-life projects, both the trade-off between the project cost and the project completion time, and the uncertainty of the environment are considerable aspects for decision-makers. However, the research on the time-cost trade-off problem seldom concerns stochastic environments. Besides, optimizing the expected value of the objective is the exclusive decision-making criterion in the existing models for the stochastic time-cost trade-off problem. In this paper, two newly developed alternative stochastic time-cost trade-off models are proposed, in which the philosophies of chance-constrained programming and dependent-chance programming are adopted for decision-making. In addition, a hybrid intelligent algorithm integrating stochastic simulations and genetic algorithm is designed to search the quasi-optimal schedules under different decision-making criteria. The goal of the paper is to reveal how to obtain the optimal balance of the project completion time and the project cost in stochastic environments.

Stability in mean for uncertain differential equation

Canonical process is an uncertain process with stationary and independent normal increments, and the uncertain differential equation is a differential equation driven by canonical process. So far, the concept of stability in measure for uncertain differential equations has been proposed. This paper presents a concept of stability in mean for uncertain differential equations, and it gives a sufficient condition for an uncertain differential equation being stable in mean. In addition, it discusses the relationship between stability in mean and stability in measure.

Almost sure stability for uncertain differential equation

Uncertain differential equation is a type of differential equation driven by Liu process. So far, concepts of stability and stability in mean for uncertain differential equations have been proposed. This paper aims at providing a concept of almost sure stability for uncertain differential equation. A sufficient condition is given for an uncertain differential equation being almost surely stable, and some examples are given to illustrate the effectiveness of the sufficient condition.

Pricing decision problem for substitutable products based on uncertainty theory

Increasing studies in marketing and distribution channels have shown that the power of manufacturers and retailers is reversing. In this paper, we consider a pricing decision problem in which two different manufacturers compete to distribute differentiated but substitutable products through a common retailer under different power structures. The manufacturing costs, sales costs and demands are characterized by uncertain variables. Meanwhile, uncertainty theory and game-theory-based modeling approaches are employed to formulate the pricing decision problem with three different power structures under uncertain environment. How to make the optimal pricing decisions on wholesale prices and retailer markups under three possible scenarios is derived. Numerical experiments are also given to examine the effects of power structures on the equilibrium prices and profits in uncertain environment. It is found that if the sales cost is high, consumers can enjoy lower prices when facing a powerful retailer and the super retailer can also make the supply chain more efficient.

New fuzzy models for time-cost trade-off problem

The time-cost trade-off problem is a specific type of the project scheduling problem which studies how to modify project activities so as to achieve the trade-off between the completion time and the project cost. In real projects, the trade-off between the project cost and the completion time, and the uncertainty of the environment are both considerable aspects for managers. In this paper, three new fuzzy time-cost trade-off models are proposed, in which credibility theory is applied to describe the uncertainty of activity duration times. A searching method by integrating fuzzy simulation and genetic algorithm is produced to search the quasi-optimal schedules under some decision-making criteria. The purpose of the paper is to reveal how to obtain the optimal balance of the completion time and the project cost in fuzzy environments.

An Uncertain Random Programming Model for Project Scheduling Problem

Project scheduling problem (PSP) is to determine the resource allocation schedule for the trade‐off between the project cost and the completion time. In this paper, PSP in the environment with uncertainty and randomness simultaneously is considered. In detail, the concepts of uncertain variable and uncertain random variable are introduced. Based on some concepts and theorems of chance theory, an uncertain random project scheduling model is built. For some special case, the proposed uncertain random programming model is transformed to a crisp mathematical programming model. Besides, uncertain random simulation techniques and genetic algorithm are integrated into a hybrid intelligent algorithm for searching the quasi‐optimal schedule.

A genetic algorithm-based optimizing approach for project time-cost trade-off with uncertain measure

Both the trade-off between the project cost and the project completion time and the indeterminacy of the environment are important issues for real-life project managers. In this paper, an uncertain time-cost trade-off problem, where activity cost functions are assumed to be linear and the objective function to be minimized is the project direct cost, is described based on uncertainty theory. Two uncertain time-cost trade-off models are built to satisfy different management requirements. To solve the proposed models, two equivalent crisp mathematical programming models are given, and genetic algorithm is introduced to search for quasi-optimal schedules. For future research, resource constraints or more types of indeterminacy can be included.