Gang Xie

Variable precision rough set for group decision-making: An application

This study uses the variable precision rough set (VPRS) model as a tool to support group decision-making (GDM) in credit risk management. We consider the case that the classification in decision tables consisting of risk exposure (RE) may be partially erroneous, and use a variable precision factor @bk to adjust the classification error. In this paper, we firstly combine VPRS and AHP to obtain the weight of condition attribute sets decided by each decision-maker (DM). Then, the integrated risk exposure (IRE) of attributes is obtained based on the three VPRS-based models. Subsequently, a new procedure of obtaining @bk-stable intervals for DMk is investigated. To verify the effectiveness of these proposed methods, an illustrative example is presented. The experimental results suggest that the VPRS-based IRE have advantages in recognizing important attributes.

Coordination of Supply Chains with Bidirectional Option Contracts

In this paper we develop a supply contract for a two-echelon manufacturer–retailer supply chain with a bidirectional option, which may be exercised as either a call option or a put option. Under the bidirectional option contract, we derive closed-form expressions for the retailer’s optimal order strategies, including the initial order strategy and the option purchasing strategy, with a general demand distribution. We also analytically examine the feedback effects of the bidirectional option on the retailer’s initial order strategy. In addition, taking a chain-wide perspective, we explore how the bidirectional option contract should be set to attain supply chain coordination.

Hybrid approaches based on LSSVR model for container throughput forecasting: A comparative study

In this study, three hybrid approaches based on least squares support vector regression (LSSVR) model for container throughput forecasting at ports are proposed. The proposed hybrid approaches are compared empirically with each other and with other benchmark methods in terms of measurement criteria on the forecasting performance. The results suggest that the proposed hybrid approaches can achieve better forecasting performance than individual approaches. It is implied that the description of the seasonal nature and nonlinear characteristics of container throughput series is important for good forecasting performance, which can be realized efficiently by decomposition and the divide and conquer principle.

A group decision-making model of risk evasion in software project bidding based on VPRS

This study develops a group decision-making model based on Variable Precision Rough Set, which can be used to adjust classification error in decision tables consisting of risk-evading level (REL) of software project bidding. In order to reflect experts ability, impartiality and carefulness roundly during the course of group decision-making, a weight is endowed with each expert. Integrated risk-evading level (IREL) of projects and risk indices are computed. Then, risk-evading measures, the rank of risk-evading strength and risk-evading methodology are discussed.

Dynamic Pricing Strategy of Provider with Different QoS Levels in Web Service

In order to improve the service provider profit, the pricing strategies in service network have been studied, but primarily in static pricing setting without considering different quality of service (QoS) levels. However, in real situation, providers usually dynamically adjust their prices and offer multiple class services to meet different customers. Moreover, because service provider will satisfy demands of customers on a specific future date, customers may cancel order. In this paper, we establish a new dynamic pricing model to consider order cancellation ration and different QoS levels for maximizing provider revenue. The analytical results from this new model reveal that the optimal capacity and prices are derived via closed-form solutions. Finally, a numerical example is presented to illustrate that the proposed method is effective for determining the optimal capacity and prices. In addition, sensitivity analysis of the optimal capacity and profit with respect to some important parameters are also conducted to illustrate the optimal decision characteristics.

Web-Based Risk Avoidance Group Decision Support System in Software Project Bidding

Web-based risk avoidance group decision support system (RAGDSS) provides a platform for expert group to make risk decisions for software project bidding. In this paper, we study the principle, process, framework and functions of RAGDSS. Finally, we introduce a prototype of RAGDSS based on variable precision rough set (VPRS) model

Quality Improvement Policies in a Supply Chain with Stackelberg Games

We first analyze quality and price decisions in a supply chain with two Stackelberg games: Manufacturer’s Stackelberg (MS) and Supplier’s Stackelberg (SS). Then, we investigate how equilibrium solutions are influenced by proposed quality improvement policies: coordination and manufacturer’s involvement. Also, we derive the conditions under which the policies can be implemented in both MS and SS strategies. Numerical experiments illustrate the problems and several related issues are discussed. The results suggest that proposed quality improvement policies can realize Pareto improvement for the supply chain performance.

Optimal βk-stable interval in VPRS-based group decision-making: A further application

Abstract This study extends the extant research on use of variable precision rough set (VPRS) for group decision-making (GDM), where the optimal βk-stable interval is derived for the best group consensus. Firstly, we introduce the basic concepts a VPRS model encompasses, and the approach of VPRS-based GDM. Next, using a mathematical programming approach, we derive the optimal βk-stable interval for DMk. Then, an application in petroleum project investment risk management, including risk-based project selection and risk ranking, is investigated. The results suggest that βk-stable intervals have significant impacts on risk management.

Determining Optimal Selling Price, Order Size and the Number of Price Changes with Weibull Distribution Deterioration

An inventory model for a deteriorating item with price sensitive demand is developed under deterioration rate, which is assumed to follow weibull distribution with known parameter. In addition, we allow for multiple price changes about an item, but the number of price changes isn’t given. The purpose of this paper is to find the optimal number of price settings, the optimal dynamic prices and the order quantity that maximizes profits for firm. A solution procedure is found to determine the optimal decisions.

VPRS-Based Group Decision-Making for Risk Response in Petroleum Investment

From the perspective of risk response in petroleum project investment, the authors use a group decision-making GDM approach based on a variable precision rough set VPRS model for risk knowledge discovery, where experts were invited to identify risk indices and evaluate risk exposure RE of individual projects. First, the approach of VPRS-based GDM is introduced. Next, while considering multiple risks in petroleum project investment, the authors use multi-objective programming to obtain the optimal selection of project portfolio with minimum RE, where the significance of risk indices is assigned to each of corresponding multi-objective functions as a weight. Then, a numerical example on a Chinese petroleum companys investments in overseas projects is presented to illustrate the proposed approach, and some important issues are analyzed. Finally, conclusions are drawn and some topics for future work are suggested.