Abstract: Asia-Pacific Journal of Operational Research, Volume 34, Issue 01, February 2017. A modeling methodology for blog recommendation and forecasting based on information entropy is presented. With the increasing popularity of smartphones and the rapid development of the mobile Internet, the amount of user-generated content such as blogs is increasing daily. Valuable information, such as bloggers’ opinions, feelings, and attitudes, is often part of this content. Particularly in the context of an emergency, this information should also be used to facilitate decision making. The current blog recommendation model examines primarily users’ interests or content similarity, whereas in this paper, the value of the blog is considered. The primary contribution of this paper is the proposal of an information-entropy-based blog recommendation model for finding valuable blogs to facilitate decision-making in an emergency context. A series of indicators for evaluating a blog in an emergency context are proposed. Using the method of information entropy, a blog recommendation model is developed. The model can also be used to forecast the value of emergency blogs in the future. The model has been tested and validated using crawled data from the Sina Blog, and the results have demonstrated that the proposed model can effectively determine the value of emergency-related blogs. Citation: Asia-Pacific Journal of Operational Research PubDate: 2017-02-24T05:35:52Z DOI: 10.1142/S0217595917400073

Abstract: Asia-Pacific Journal of Operational Research, Volume 34, Issue 01, February 2017. Online peer-to-peer (P2P) lending is an emerging financial mode that combines the Internet with private lending to provide unsecured lending among individuals. The interest rate and risk depend on online lenders and borrowers’ behavior choices and game in the context of P2P lending. In this paper, we propose an evolutionary behavior forecasting model for online participants based on the risk preference behavior of lenders and the credit choice of borrowers. We highlight four evolutionary equilibrium states of online lenders and borrowers’ behavior and their effects on the risk of online P2P lending platforms. We run a numeric experiment using the Paipaidai platform in China as a case and find that the evolutionary behavior of online lenders and borrowers is determined by the mutual effect of the interest rate, information gathering cost, borrowing cost, and yield rate. This paper uses evolutionary game methodology to analyze online P2P lending behavior in China and explores P2P fund success from the dual perspective of lenders and borrowers. Citation: Asia-Pacific Journal of Operational Research PubDate: 2017-02-24T05:35:48Z DOI: 10.1142/S0217595917400085

Abstract: Asia-Pacific Journal of Operational Research, Volume 34, Issue 01, February 2017. In this paper, we study a pricing and after-sales service contract design problem, where a retailer purchases products from a manufacturer and then sells to the consumers. The sales cost is the retailer’s private information and might be mined by the manufacturer via advanced learning algorithms and related big data techniques. We first develop a crisp equivalent model, based on which the optimal contracts and the supply chain parties’ profits under asymmetric information are derived. We show that, compared with the optimal wholesale price and after-sales service level with symmetric information, asymmetric cost information makes the wholesale price distorted upward when the retailer’s sales cost is low. When the retailer’s cost is high, the after-sales service level is distorted downward. We characterize the manufacturer’s loss and the retailer’s gains due to asymmetric sales cost information. This helps the manufacturer make the investment decision of big data techniques. Interestingly, we find that the retailer might voluntary disclose the sales cost information, which results in a win-win situation for the manufacturer and the retailer. This makes the manufacturer less favor big data techniques. Finally, we conduct extensive sensitivity analysis with respect to the retailer’s sales cost, the consumer’s sensitivity to the retailer’s after-sale service level, and the fraction of high-type retailers in the market. Citation: Asia-Pacific Journal of Operational Research PubDate: 2017-02-24T05:35:46Z DOI: 10.1142/S0217595917400024

Abstract: Asia-Pacific Journal of Operational Research, Volume 34, Issue 01, February 2017. In the “Post-Kyoto” Era, climate change has become a serious worldwide concern, though the international community has not yet identified a cooperative solution that satisfies all parties. The carbon tariffs, which proposed by some developed countries to address competitiveness concerns and carbon leakage from unilateral reduction measures, may impose significant hardships on developing countries. This paper tries to design a global cooperation scheme against the carbon tariffs. A differentiated carbon taxation scheme is introduced based on the principle of ability to pay (CTAP). An advanced forecasting system named the energy version of the global trade analysis model (GTAP-E) was used to compare the different impacts of carbon tariffs and the CTAP scheme. The results show that CTAP is better than carbon tariffs in terms of global GDP, welfare, and emissions reduction. The CTAP scheme could yield less welfare deterioration for developing regions than the carbon tariffs, and also lessens the competitive concerns of developed countries. The proposed CTAP scheme provides new ideas for international cooperative strategies to address climate change. Citation: Asia-Pacific Journal of Operational Research PubDate: 2017-02-24T05:35:45Z DOI: 10.1142/S0217595917400048

Abstract: Asia-Pacific Journal of Operational Research, Volume 34, Issue 01, February 2017. This paper discusses optimal order-taking strategies under competing trade credit policies with varying demands. This study examines three decision-making scenarios, namely, (i) a centralized supply chain, (ii) a decentralized supply chain, and (iii) a coordinated supply chain with a buyback contract. Optimal decisions are obtained for each scenario. We find that if the average forecast value of credit sensitivity is higher than the real value, then the supply chain is at risk of having an overestimated performance; if the average forecast value of credit sensitivity is lower than the actual value, then the supply chain is at risk of having an underestimated performance. As the sensitivity of the credit competition increases, the supply chain’s performance decreases. The supply chain can be coordinated by a buyback contract. Citation: Asia-Pacific Journal of Operational Research PubDate: 2017-02-24T05:35:42Z DOI: 10.1142/S0217595917400103

Abstract: Asia-Pacific Journal of Operational Research, Volume 34, Issue 01, February 2017. This study incorporates consumer’s low carbon awareness (CLA) and demand forecasting into supply chains that adopt the cap-and-trade system. Three demand forecasting scenarios are discussed, namely, information sharing, full information sharing, and retailer-only forecasting. Strategies for pricing and reduction of equilibrium of carbon emission are derived. We also compare the decisions and profits in the three cases and present numerical analysis. Citation: Asia-Pacific Journal of Operational Research PubDate: 2017-02-24T05:35:41Z DOI: 10.1142/S021759591740005X

Abstract: Asia-Pacific Journal of Operational Research, Volume 34, Issue 01, February 2017. In the background of big data era, the ability to accurately forecast the number of the Internet users has considerable implications for evaluating the growing trend of a newly-developed business. In this paper, we use four models, the Gompertz model, the Logistic model, the Bass model, and the Lotka–Volterra model, to forecast the Internet population in China with the historical data during 2007 to 2014. We compare the prediction accuracy of the four models using the criterions such as the mean absolute percentage error (MAPE), the mean absolute error (MAE) and the root mean square error (RMSE). We find that the Lotka–Volterra model has the highest prediction accuracy. Moreover, we use the Lotka–Volterra model to investigate the relationship between the rural Internet users and the urban Internet users in China. The estimation results show that the relationship is commensalism. Citation: Asia-Pacific Journal of Operational Research PubDate: 2017-02-24T05:35:40Z DOI: 10.1142/S0217595917400061

Abstract: Asia-Pacific Journal of Operational Research, Volume 34, Issue 01, February 2017. The constant elasticity of variance (CEV) model is widely studied and applied for volatility forecasting and optimal decision making in both areas of financial engineering and operational management, especially in option pricing, due to its good fitting effect for the volatility process of various assets such as stocks and commodities. However, it is extremely difficult to conduct parameter estimation for the CEV model in practice since the precise likelihood function cannot be derived. Motivated by the gap between theory and practice, this paper initiatively applies the Markov Chain-Monte Carlo (MCMC) method into parameter estimation for the CEV model. We first construct a theoretical structure on how to implement the MCMC method into the CEV model, and then execute an empirical analysis with big data of CSI 300 index collected from the Chinese stock market. The final empirical results reveal insights on two aspects: On one aspect, the simulated results of the convergence test are convergent, which demonstrates that the MCMC estimation method for the CEV model is effective; On the other aspect, by a comparison with other two most frequently used estimation methods, the maximum likelihood estimation (MLE) and the generalized moment estimation (GMM), our method is proved to be of high accuracy and has a simpler implementation and wider application. Citation: Asia-Pacific Journal of Operational Research PubDate: 2017-02-24T05:35:32Z DOI: 10.1142/S0217595917400097

Abstract: Asia-Pacific Journal of Operational Research, Volume 34, Issue 01, February 2017. Sharing forecast information helps supply chain parties to better match demand and supply. The extant literature has shown that sharing forecast information improves supply chain performance. In the big data era, supply chain managers have the ability to deal with a massive amount of data by big data technologies and analytics. Big data technologies and analytics provide more accurate forecast information and give an opportunity to transform business models. In this paper, a comprehensive review on forecast information sharing for managing supply chain in the big data era is conducted. The value and obstacles of sharing forecast information are discussed. Given the sufficient data, the appropriate approaches of analyzing and sharing forecast information are highlighted. Insights on the current state of knowledge in each respective area are discussed and some associated pertinent challenges are explored. Inspired by various timely and important issues, future research directions are suggested. Citation: Asia-Pacific Journal of Operational Research PubDate: 2017-02-24T05:35:30Z DOI: 10.1142/S0217595917400012

Abstract: Asia-Pacific Journal of Operational Research, Volume 34, Issue 01, February 2017. The last decade has witnessed an increase in the number of big-data-based businesses, in particular for those industries with complicated supply chain structure. This paper investigates the pricing and production strategies in a decentralized supply chain composed of a manufacturer, a key supplier, and a general supplier. We establish two different leader–follower structure models. One is the key supplier–leader game, in which the key supplier decides the prices for the other two components, and the manufacturer and general supplier determine the order and production quantities, respectively. The other model is the manufacturer–leader game, in which the manufacturer offers the prices for the other two components, and the suppliers determine the product quantities. We show that equilibrium price and production quantity under the key supplier–leader game are higher than those under the manufacturer–leader game. The key supplier–leader game is suitable for the channel. Moreover, we show that channel payoff has a non-monotonic relation with the production cost of suppliers; it initially increases and then decreases with production costs. Citation: Asia-Pacific Journal of Operational Research PubDate: 2017-02-24T05:35:29Z DOI: 10.1142/S0217595917400036

Abstract: Asia-Pacific Journal of Operational Research, Volume 34, Issue 01, February 2017. Emergency incidents can trigger heated discussions on microblogging platforms, and a great number of tweets related to emergency incidents are retweeted by users. Consequently, social media big data related to the emergency incidents is generated from various social media platforms, which can be used to predict users’ retweeting behavior. In this paper, the characteristics of individuals’ retweeting behaviors in emergency incidents are analyzed, and then 11 important characteristics are extracted from recipient characteristics, retweeter characteristics, tweet content characteristics, and external media coverage. A back propagation neural network (BPNN) model called PRBBP is used to predict retweeting behavior in such emergency incidents. Based on PRBBP, an algorithm called PRABP is proposed to predict the number of retweets in emergency incidents. The experiments are performed on a large-scale dataset crawled from Sina weibo. The simulation results show that both the PRBBP model and the PRABP algorithm proposed by this paper have excellent predictive performance. Citation: Asia-Pacific Journal of Operational Research PubDate: 2017-02-24T05:35:27Z DOI: 10.1142/S0217595917400115

Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print. With the rapid development of the Internet and big data technologies, a rich of online data (including news releases) can helpfully facilitate forecasting oil price trends. Accordingly, this study introduces sentiment analysis, a useful big data analysis tool, to understand the relevant information of online news articles and formulate an oil price trend prediction method with sentiment. Three main steps are included in the proposed method, i.e., sentiment analysis, relationship investigation and trend prediction. In sentiment analysis, the sentiment (or tone) is extracted based on a dictionary-based approach to capture the relevant online information concerning oil markets and the driving factors. In relationship investigation, the Granger causality analysis is conducted to explore whether and how the sentiment impacts oil price. In trend prediction, the sentiment is used as an important independent variable, and some popular forecasting models, e.g., logistic regression, support vector machine, decision tree and back propagation neural network, are performed. With crude oil futures prices of the West Texas Intermediate (WTI) and news articles of the Thomson Reuters as studying samples, the empirical results statistically support the powerful predictive power of sentiment for oil price trends and hence the effectiveness of the proposed method. Citation: Asia-Pacific Journal of Operational Research PubDate: 2017-04-04T07:18:15Z DOI: 10.1142/S021759591740019X

Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print. In this research, we discuss three different approaches to generate demand forecasting and pricing decision for mix of national brand and store brand products in the era of big data. We derive the equilibrium wholesale price and retail price for the national brand products, and the equilibrium retail price for the store brand products based on demand forecast under three different information scenarios, including Noninformation Sharing ([math]), Information Sharing ([math]), and Retailer Forecasting ([math]). We comprehensively discuss how information collection, information sharing, forecast accuracy under era of big data affect firms’ prices and profits. Our numerical experiments illustrate and verify our analytical findings and provide further managerial insights and interpretations. Citation: Asia-Pacific Journal of Operational Research PubDate: 2017-03-29T06:42:01Z DOI: 10.1142/S0217595917400188

Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print. Customized bundling is a pricing strategy that allows consumers to choose a certain quantity of products at a fixed price. In the reality, a customer usually has a specific rank on information goods based on their valuations, or information goods can be ranked into a list of products with decreasing valuations for a customer. Thus, we characterize customers in two dimensions for constructing the customized bundles of ranked information goods: (i) the valuation that a customer sets for his/her most favorite information good; and (ii) the total quantity of information goods with positive valuations that a customer requires. We derive the optimal customized bundling strategies in two typical scenarios and examine the impact of customer heterogeneity in terms of each dimension on the optimal pricing schemes of customized bundles. Analytical results indicate that the two features have similar effects on optimal bundle price, market penetration, and maximal profit, but impact differently on optimal bundle size. Larger customer heterogeneity leads to a lower or identical optimal bundle price, market penetration, and maximal profit. However, optimal bundle size shrinks or remains unchanged with increased customer heterogeneity on the total quantities of information goods with positive valuations, but it grows or stays the same when customers have larger heterogeneity on the valuations of their most favorite information goods. Our results provide explanations to the marketing practices of digital product firms, and also support the optimal decision of customized bundling of information goods for heterogeneous customers. Citation: Asia-Pacific Journal of Operational Research PubDate: 2017-03-17T08:02:13Z DOI: 10.1142/S0217595917500075

Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print. In this paper, we address a supply chain scheduling model with outsourcing and transportation. A job can be scheduled either on a single machine at a manufacturer’s plant or outsourced to a subcontractor. We assume that there are a sufficient number of vehicles at the manufacturer and the subcontractor such that each completed job can be transported to its customer immediately. For a given set of jobs, the decisions we need to make include the selection of jobs to be outsourced and the schedule of all the jobs. When the objective functions are to minimize the weighted sum of common scheduling measures and the total cost, we present their complexity analysis and a [math]-approximation algorithm for the second problem. Citation: Asia-Pacific Journal of Operational Research PubDate: 2017-03-17T08:02:11Z DOI: 10.1142/S0217595917500099

Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print. In this paper, location–allocation problem of a three-stage supply chain network, including suppliers, plants, distribution centers (DCs) and customers is investigated. With respect to the total cost, the aim is determining opened plants and DCs and designing transportation trees between the facilities. Considering the capacity of suppliers, plants and DCs are limited and there is a limitation on the maximum number of opened plants and DCs, a mixed-integer linear programming (MILP) model of the problem is presented. Since multi-stage supply chain networks have been recognized as NP-hard problems, applying priority-based encoding and a four-step backward decoding procedure, a meta-heuristic algorithm, namely GAIWO, based on the best features of genetic algorithm (GA) and invasive weed optimization (IWO) is designed to solve the problem. In small size problems, the efficiency of the GAIWO is checked by solutions of GAMS software. For larger size problems, the performance of the proposed approach is compared with four evolutionary algorithms in both aspects of the structure of the GAIWO and the efficiency of the proposed encoding–decoding procedure. Besides usual evaluation criteria, Wilcoxon test and a chess rating system are used for evaluating and ranking the algorithms. The results show higher efficiency of the proposed approach. Citation: Asia-Pacific Journal of Operational Research PubDate: 2017-03-17T08:02:10Z DOI: 10.1142/S0217595917500087

Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print. We study single-machine scheduling problems with job rejection and a deteriorating maintenance activity, where the impact of performing this activity is reflected in a reduction of the job processing times. The duration of the maintenance activity is a linear increasing function of its starting time. The aim is to determine the location of the maintenance activity and the job sequence of the accepted jobs so as to minimize scheduling cost of the accepted jobs plus total penalty of the rejected jobs. When the scheduling measures are the makespan, total completion time and combination of earliness, tardiness and due date cost, we provide polynomial time algorithms to solve these problems, respectively. When the scheduling measures are the maximum tardiness and total weighted completion time under the agreeable ratio assumption, we introduce pseudo-polynomial time algorithms to solve these [math]-hard problems, respectively. Citation: Asia-Pacific Journal of Operational Research PubDate: 2017-03-14T04:05:01Z DOI: 10.1142/S0217595917500105

Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print. In this paper, we address the scheduling issues in a class of maintenance, repair and overhaul systems. By considering all key characteristics such as disassembly, material recovery uncertainty, material matching requirements, stochastic routings and variable processing times, the scheduling problem is formulated into a simulation optimization problem. To solve this difficult problem, we developed two hybrid algorithms based on nested partitions method and optimal computing budged allocation technology. Asymptotic convergence of these two algorithms is proved and numerical results show that the proposed algorithms can generate high quality solutions which outperform the performance of many heuristics. Citation: Asia-Pacific Journal of Operational Research PubDate: 2017-03-08T06:37:13Z DOI: 10.1142/S0217595917500038

Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print. Public involvement in transportation planning and decision-making process is a key component for ensuring that decisions are made with consideration of public needs and preferences. In this paper, a weighted Euclidean distance based TOPSIS method (WEDTOPSIS) is developed for modeling such a public decision-making process. The Weber–Fechner psycho-physical law is adopted for behavioral modeling of human judgments. Distances to the positive-ideal and negative-ideal solutions of TOPSIS are converted to value measurement models using the Weber–Fechner law. The proposed method is applied on a case where public approval of two different types of public bus operation systems considering six criteria is sought. A numerical illustration is also provided to demonstrate the applicability of the approach. The method provides plausible results in terms of preferences, and shows a high agreement with the ordinary TOPSIS in terms of rankings. Another example showing disagreement on ranking is further analyzed to outline the discrepancies between the TOPSIS and WEDTOPSIS and to indicate the proposed model’s consistency with the behavioral theory. The results are also compared with the results of the additive multi-attribute value (MAVT) method for assessing the performance of the model. Based on the findings, using the proposed method as a decision support tool can be useful, particularly where public input is needed. Citation: Asia-Pacific Journal of Operational Research PubDate: 2017-03-07T03:51:06Z DOI: 10.1142/S021759591750004X

Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print. In this paper, we consider an online order scheduling problem with the same order size on two identical machines. The objective is to minimize the makespan. An order list [math] is given, where [math] is a positive integer. For the problem under study, we assume that [math] ([math]), that is to say, at least [math] orders arrive. A lower bound [math] for [math] is obtained. Also, we design two optimal algorithms [math] and [math] for [math] and [math] ([math]), respectively. Citation: Asia-Pacific Journal of Operational Research PubDate: 2017-02-28T03:31:19Z DOI: 10.1142/S0217595917500063

Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print. This paper investigates the impact of emergency order in a price-dependent newsvendor setting. To this end, we compare two ways handling the excess demand: the excess demand is lost and a penalty cost is incurred, or the excess demand can be satisfied by an emergency order. Which way is better depends on the emergency purchase cost [math] in emergency-order way and the price [math] plus penalty cost [math] in lost-sales way. For a risk-neutral newsvendor, our results indicate that, when [math] is not larger than [math], the emergency order way can lead to smaller order quantity and higher expected profit. We continue to discuss the impact of newsvendor’s risk aversion and demand uncertainty on the optimal decisions of the two ways. Theoretical analysis and numerical examples indicate that when the emergency purchase cost is not high, the differentials of the optimal order quantities and expected profits will be larger as the degree of risk aversion/demand uncertainty increases. What is more, we prove that there exists a threshold value of the emergency purchase cost so that the two ways handling excess demand can obtain the same expected profit, and this threshold value increases as the degree of risk aversion decreases. Citation: Asia-Pacific Journal of Operational Research PubDate: 2017-02-24T03:52:27Z DOI: 10.1142/S0217595917500014

Abstract: Asia-Pacific Journal of Operational Research, Ahead of Print. In this paper, we use the meta-frontier network DEA approach to evaluate the innovation efficiency of 30 provinces in China from 2009 to 2011. These provinces have been classified into two groups based on their levels of economic development. The first group comprises provinces in the Eastern region, while the second group comprises provinces in the Central and Western regions. First, we use the meta-frontier network DEA method to estimate the technology gaps of innovation efficiency between different operating types. Second, the quadrant analysis method explores the reasons for efficiency losses. Finally, we take the fixed effect model to examine whether industry–university–research cooperation influences technology efficiency. The empirical results indicate (i) the Eastern region has significantly higher innovation efficiency than the Central and Western regions. (ii) Some Eastern provinces have a high technology level, yet their resource allocation capabilities still need to be improved. (iii) Industry–university–research cooperation is an effective way to improve innovation performance. Citation: Asia-Pacific Journal of Operational Research PubDate: 2017-02-24T03:52:27Z DOI: 10.1142/S0217595917500051