Showing papers by "Kin Keung Lai published in 2018"

Pricing Decisions of CSR Closed-Loop Supply Chains with Carbon Emission Constraints

Abstract: Global warming has become a growing concern for countries around the world. Currently, the direct way to solve this issue is to curb carbon emissions. Governments and enterprises should assume the social responsibility to conserve the environment. Under the background of carbon emission constraint, this article investigates the optimal decisions of closed-loop supply chains in the context of social responsibility, explores the impacts of constraints of carbon emissions and corporate social responsibility on recycling and remanufacturing decisions, and introduces the model of maximizing social welfare for further comparison and analysis. The results show that the coefficient of remanufacturing and emission reduction and the coefficient of government reward and punishment are inversely proportional to recycling rates and the total carbon emissions. Governments should formulate rational carbon emission caps for enterprises with different coefficients of remanufacturing and emission reduction. Additionally, corporate social responsibility has a positive effect on recycling rates, and a rise in its strength can lead to a fall in carbon emissions per unit product. In terms of product recycling and profit sources, the model of maximizing social welfare is superior to that of maximizing the manufacturer’s total profits, which provides new managerial insights for decision-makers.

Channel competition and coordination of a dual-channel supply chain with demand and cost disruptions

Abstract: The impacts of simultaneous disruption of demand and cost on pricing, production and coordination of a dual-channel supply chain with one manufacturer and one retailer are examined. First, coordina...

A novel two-sided matching decision method for technological knowledge supplier and demander considering the network collaboration effect

Abstract: To obtain competitive advantage, the key target for the two-sided matching between technological knowledge supplier and demander is maximizing the individual exchange satisfaction. The structure of the supplier and demander relationship networks means that the two-sided matching approach not only gives point-to-point matching but also gives network-to-network matching. In this paper, supply and demand network characteristics are embedded into a two-phase decision analysis method. First, to select the matching pairs, a matching satisfaction matrix is constructed based on the supply and demand network characteristics, after which a multi-objective optimal model is built to determine the best optimization matching results and the overall improvements illustrated through comparison. Finally, a numerical example is given to show the practicality and validity of the proposed approach.

Trade-Old-for-Remanufactured Closed-Loop Supply Chains with Carbon Tax and Government Subsidies

Abstract: The constantly increasing CO2 emissions are threatening the environment tremendously. Facing the pressure of environmental activists and public opinion, businesses and governments are taking action to reduce carbon emissions. Among these endeavors, carbon tax and subsidy policies proposed by governments are widely adopted. Remanufacturing is believed to save manufacturing costs and reduce carbon emissions from the process of enterprise operation, and it is increasingly being accepted by enterprises. However, different consumers’ willingness to pay for remanufactured products and the durability of new products will also affect consumers’ willingness to buy remanufactured products. Therefore, considering the discrepancy between consumer willingness to pay and product durability, we established the trade-old-for-remanufactured (TOR) model for a scenario of carbon tax and government subsidies. Through the analysis of the model, we obtained the optimal pricing and production decisions of manufacturers (remanufacturers) in the case of carbon tax and government subsidies. Our results show that, when there is no carbon tax constraint, the increase in consumer willingness to pay and the adjustment in product durability can stimulate consumers to participate in TOR projects and augment enterprises’ profits. However, it can also lead to a carbon rebound that increases corporate carbon emissions. When there is a carbon tax constraint, the introduction of carbon tax contributes to a reduction in carbon emissions, while enterprises tend to lose profits. In order to achieve a “win-win” between corporate profits and carbon emissions, we considered government subsidy policies. Our numerical examples illustrate that appropriate carbon tax and government subsidies can curb carbon emissions and also increase profits for enterprises.

Using Ranked Weights and Acceptability Analysis to Construct Composite Indicators: A Case Study of Regional Sustainable Society Index

Abstract: A variety of published composite indicators, i.e., Energy Trilemma Index and Sustainable Society Index, are commonly aggregated with equal weights. However, this plausible scheme is criticized as eclecticism and ignores the discriminating power of the different indicators. Differing from the traditional methods that assign weights to each indicator for the purpose of aggregation, this paper proposes a new mechanism to construct composite indicators using ranked weights and stochastic multicriteria acceptability analysis. More specifically, this study comprehensively consider all possible preferences among the indicators. Under each preference, we develop a sophisticated mathematical transformation to calculate the least and most favorable scores of each entity, which formulates the lower and upper bounds of the intervals. Then an interval decision matrix, alternatively described as a stochastic decision problem, is formulated to construct the composite indicators. Holistic acceptability indices are generated and regarded as a new composite indicator, which is capable of providing a comprehensive and robust composite indicator with more discriminating power. We apply the proposed method to modify the regional sustainable society index and present the obtained results and comparisons.

Analysis of product return rate and price competition in two supply chains

Abstract: This article compares a normal and a reverse supply chain in the Betrand Competition. Each supply chain consists of a retailer and an exclusive supplier with stable partnership. The two chains compete with each other in three competition structures: the Centralized Competition Game, the Hybrid Competition Game (including two cases), and the Decentralized Competition Game. In different competition structures, we examine how the degree of competition intensity between the two chains and product return rate of the reverse chain influence the equilibrium decision of market price, profits of two chains and the choice of centralization. The article differs from the study of the traditional supply chain model as follows. Firstly, we analyze the normal and the reverse chain related to the same product in the Betrand competition. Secondly, the data show that the market price decreases with the rising of the product return rate and the falling of the competition intensity. Thirdly, it is found that the total profit of the normal chain decreases with the rising of the product return rate, while the total profit of the reverse chain increases with rising of the product return rate. Profits of two chains increase with the rise of the competition intensity. Finally, this article shows that centralization is an optimal strategy for one chain whereas centralization may be the best for the other chain.

Resource Allocation in Public Healthcare: A Team-DEA Model

Abstract: This paper combines the theory of teams and data envelopment analysis (DEA) to design a mechanism to optimally allocate resources in public healthcare. A statutory authority and the public hospitals under its governance are interpreted as a team, the members of which seek to operate efficiently under the shared institutional constraint that public healthcare is a public good. The individual public hospital exploits DEA to maximize own-payoff, subject to the team-condition that the payoff of each other public hospital does not fall and thereby subtract from the external effects created by the public supply of healthcare. The resulting team-DEA solution, which is shown to be both an individually-efficient and team-satisficing equilibrium and to be computable in terms of a convergent algorithm, can then be applied by the authority to determine the optimal allocation of resources in public healthcare. A case based on Chinese data is presented to illustrate the team-DEA model’s ready operationalization and computation.

Remanufacturing Decisions with WTP Discrepancy and Uncertain Quality of Product Returns

Abstract: In a remanufacturing system, the uncertain quality of the product returns tends to impact the manufacturer’s price of returns and remanufacturing. This article introduces the quality coefficient of waste products on the basis of the analysis of the structure of the remanufacturing cost. It explores the remanufacturing cost function, the recycling price function, and the recycling rate function. In the marketing process, new products compete with remanufactured products. Different consumers show different levels of degrees of acceptance of remanufactured products, contributing to the uncertainty of the willingness to pay for remanufactured products. The price of remanufactured products is always lower than that of new products, allowing price-sensitive consumers to turn to remanufactured products. This shows that product pricing has an impact on the market demand. Considering the difference between consumer willingness to pay and the quality of product returns, this article aims to construct a game model consisting of manufacturers, retailers, and consumers, with the manufacturers as leaders. The optimal pricing decisions of the production in supply chain members have been solved, and sensitivity of the model is analyzed through examples.

A Weighted Least-Square Dissimilarity Approach for Multiple Criteria ABC Inventory Classification

Abstract: The present paper develops a weighted least-square dissimilarity approach to address the Multiple Criteria ABC inventory classification problem, when the different criteria ranking makes impossible...

A Nonlinear Interval Portfolio Selection Model and Its Application in Banks

Abstract: In classical Markowitz’s Mean-Variance model, parameters such as the mean and covariance of the underlying assets’ future return are assumed to be known exactly. However, this is not always the case. The parameters often correspond to quantities that fall within a range, or can be known ambiguously at the time when investment decision must be made. In such situations, investors determine returns on investment and risks etc. and make portfolio decisions based on experience and economic wisdom. This paper tries to use the concept of interval numbers in the fuzzy set theory to extend the classical mean-variance portfolio selection model to a mean-downside semi-variance model with consideration of liquidity requirements of a bank. The semi-variance constraint is employed to control the downside risk, filling in the existing interval portfolio optimization model based on the linear semi-absolute deviation to depict the downside risk. Simulation results show that the model behaves robustly for risky assets with highest or lowest mean historical rate of return and the optimal investment proportions have good stability. This suggests that for these kinds of assets the model can reduce the risk of high deviation caused by the deviation in the decision maker’s experience and economic wisdom.

The Conductive and Predictive Effect of Oil Price Fluctuations on China’s Industry Development Based on Mixed-Frequency Data

Abstract: Presently, the total supply of crude oil is sufficient, but short-term supply and demand imbalances and regional imbalances still exist. The effect of crude oil supply security and price impact cannot be ignored. As the world’s largest oil importer, China is highly dependent on foreign oil. Therefore, the fluctuation of international oil prices may impact the development of China’s various industries in a significant and differential way. However, because the available data have different frequencies, much of the recent research that addresses the effect of oil prices on industry development need to replace, split, or merge the original data, resulting in loss of the information from the original data. Using the mixed data sampling model (MIDAS(m,K,h)-AR(1)) with the first-order lag autoregressive terms of the interpreted variables, this study builds a mixed data model to investigate the effect of oil price volatility on the output of China's industries. This study expands the extant research by financial market fluctuations and macroeconomic analysis, and at the same time makes short-term predictions on the output of China’s seven main industries. The analysis results show that the mixed data regression model brings the original information contained in different frequency data into the model analysis, and utilizes the latest high frequency data of the explanatory variables to perform real-time short-term prediction of low-frequency interpreted variables. This method improves the timeliness of forecasting macroeconomic indicators and the accuracy of short-term forecasts. The empirical results show that the spot price of international crude oil has a significant and differential impact on the outputs of the seven industries in China. Among them, oil price fluctuation has the greatest impact on the output of China’s financial industry.

Retailers’ Order Strategies in Transshipments in Disruption Risks of Supply Chains

Abstract: This article investigates the order problem in a two-stage supply chain consisting of retailers, primary suppliers and backup suppliers. From retailers’ perspectives, the optimal offering strategies in unidirectional transshipments are analyzed on the basis of disruption risks in supply chains. With a focus on retailers’ profits, it develops the decision model of retailers’ orders in the dual sourcing mode and in the mode of capacity options, to maximize the retailers’ expected profits. The simulation shows that retailers’ optimal order decisions are not correlated with the joint disruption probability in the two order modes without service constraints; retailers’ optimal decisions are influenced by the joint disruption probability in the two order modes with service constraints, and the impact of the joint disruption probability of the primary suppliers’ optimal order from retailers differs in the two modes. Retailers’ order decisions in respect of backup suppliers are more sensitive to the changing volumes of transshipments. The contribution of this article is the impact of main parameters on retailers’ decisions, providing some guidance for enterprises to make order decisions.

A Hybrid Approach for Studying the Lead-Lag Relationships Between China’s Onshore and Offshore Exchange Rates Considering the Impact of Extreme Events

Abstract: Understanding the characteristics of the dynamic relationship between the onshore Renminbi (CNY) and the offshore Renminbi (CNH) exchange rates considering the impact of some extreme events is very important and it has wide implications in several areas such as hedging. For better estimating the dynamic relationship between CNY and CNH, the Granger-causality test and Bry-Boschan Business Cycle Dating Algorithm were employed in this paper. Due to the intrinsic complexity of the lead-lag relationships between CNY and CNH, the empirical mode decomposition (EMD) algorithm is used to decompose those time series data into several intrinsic mode function (IMF) components and a residual sequence, from high to low frequency. Based on the frequencies, the IMFs and a residual sequence are combined into three components, identified as short-term composition caused by some market activities, medium-term composition caused by some extreme events and the long-term trend. The empirical results indicate that when it only matters the short-term market activities, CNH always leads CNY; while the medium-term impact caused by those extreme events may alternate the lead-lag relationships between CNY and CNH.

Topic generation for Chinese stocks: a cognitively motivated topic modelingmethod using social media data

Abstract: With the explosive growth of user-generated data in social media websites such as Twitter and Weibo, a lot of research has been conducted on exploring the prediction power of social media data in financial market and discussing the correlation between the public mood in social media and the stock market price movement. Our previous research has demonstrated that the topic-based public mood from Weibo can be used to predict the stock price movement in China. However, one of the most challenging problems in topic-based sentiment analysis is how to get the relevant topics about a stock. The relevant topics are also considered as concepts about a stock which can be used to build the ontology of stock market for semantic computing and behavioral finance research. In this paper, motivated by the basic level concept in cognitive psychology, we present a novel method using Latent Dirichlet Allocation (LDA) to generate topics about a stock based on the social media data. The experimental results show that the proposed method is e ective and better than other topic modeling methods. The topics generated by our method are more interpretable and could be used for topic-based sentiment analysis.

Impact of healthcare insurance on medical expense in China: new evidence from meta-analysis

Abstract: This paper is aimed to make sense of the real effect of implement of social healthcare insurance on one’s medical expense in China. Due to previous studies drew various and inconsistent conclusions on this issue, this works intend to apply meta-analysis to the problem. For 31 related studies, we first implement an advanced conditional Dirichlet-based Bayesian semi-parametric model specific to meta-analysis, and come to a primary conclusion that healthcare possesses little probability reducing one’s medical expense in China. Further, the authors conduct random effects meta-regression and find that heterogeneity exists among the observed effect sizes. Mixed effects model shows that the age variation may is actually the heterogeneity source. The coefficients for Non-old and Old are respectively 0.29 and 0.54, implying that when researching on the medical expense for the elderly, it is more likely to conclude the medical insurance could increase medical spending. The coefficients for IV and OLS are both remarkably negative at 90% confidence level. This suggests when directly using Instrument Variable (IV) approach and OLS method to assess the implementation effect for the healthcare insurance, it is inclined to result in the reduced impact on medical expense. We deduce this is because this two methods can’t solve the sample-selection bias when compared with the Two-part model and difference-in-difference (DID) model. Based on the results and discussion, we finally propose suggests for the government.

A Mathematical Programming Model to Determine Objective Weights for the Interval Extension of TOPSIS

Abstract: Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) method has been extended in previous literature to consider the situation with interval input data. However, the weights associated with criteria are still subjectively assigned by decision makers. This paper develops a mathematical programming model to determine objective weights for the implementation of interval extension of TOPSIS. Our method not only takes into account the optimization of interval-valued Multiple Criteria Decision Making (MCDM) problems, but also determines the weights only based upon the data set itself. An illustrative example is performed to compare our results with that of existing literature.

Supply Chain Risk Management in the Apparel Industry

Abstract: Apparel is one of the oldest and largest export industries in the world. It is also one of the most global industries because most nations produce for the international textile and apparel market. The changing global landscape drives cost volatility, regulatory risk and change in consumer preference. In today’s retail landscape, media and advocacy groups have focussed attention on social and environmental issues, as well as new regulatory requirements and stricter legislations. Understanding and managing any risk within the supply chain, particularly ethical and responsible sourcing, has become increasingly critical. This book first gives a systematic introduction to the evolution of SCRM through literature review and discusses the importance of SCRM in the apparel industry. Second, it describes the life cycle of the apparel supply chain and defines the different roles of the value chain in the apparel industry. Thirdly, it identifies the risk factors in the Apparel Life Cycle and analyses the risk sources and consequences and finally, extends the importance of selection of the suppliers and develops a supplier selection model and SCRM strategies solution by data analysis and case studies.

Identification and prediction of currency crisis: markov switching-based approach

Abstract: This paper investigates ways of identifying and predicting currency crises in world-wide markets, with special focus on 1997 and 2008 currency crises. A novel Markov switching method is proposed fo...