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The global tourism industry is struggling to recover from the COVID-19 pandemic. During the COVID-19 pandemic, daily tourism forecasting is more critical than ever before in supporting decisions and planning. Considering the changes in tourist psyche and behaviour caused by COVID-19, this study attempts to investigate whether the statistical modelling methods can work reliably under the new normal when travel restrictions are eased or lifted. To this end, we first compare the predictivity of daily tourism demand data before and during COVID-19, and observe heterogeneous impacts across different geographical scales. Then an improved multivariate & multiscale decomposition-ensemble framework is proposed to forecast daily tourism demand. The empirical study indicates the superiority and practicability of the proposed framework before and during COVID-19. Finally, we call for more research on the comparability of tourism demand forecasting. [ABSTRACT FROM AUTHOR]

To seek more business opportunities, cross-market retailers pursue horizontal joint promotion (HJP) to promote their products together. It is important to determine the factors that make cross-market retailers pursue HJP and how HJP affects the retailers' optimal decisions. We address such issues in the context of a shopping mall in which two cross-market retailers pursue HJP with the possible involvement of the shopping mall. We develop game-theoretic models to study whether the shopping mall should provide consumers with coupons that they can obtain from the retailer in the source market and consume at the retailer in the target market. We find that when coupons are offered to consumers, the retailer in the source market has a higher price and will pay more for HJP, but has a smaller demand and a lower profit than the retailer in the target market. Furthermore, when the effect of the coupon is relatively large, both retailers prefer the coupon as they can have higher prices, demands, and profits, and their promotional strategies change with the denomination and effect of the coupon. Conversely, both retailers would reduce investments in HJP because the coupon may bring them lower profits. [ABSTRACT FROM AUTHOR]

We examine the impact of upper (lower) market volatility and skewness risks on stock returns. Our results show that market volatility and skewness risks have no significant effect on future stock returns. However, when separately considering call and put options, we find a significantly negative relation between innovations in upper market volatility (skewness) extracted from call options and subsequent stock returns. Moreover, the results remain significant after controlling for other known factors included in the factor models or a battery of firm-level characteristics. Our research shows that call and put options contain different information, and are differently priced in the stock market. This study can help explain the inconsistent results of the recent papers on asset pricing about market volatility and skewness. [ABSTRACT FROM AUTHOR]

Tourism managers and practitioners rely on accurate demand forecasting and well-informed management guidance. Given the pandemic's consequences on tourism, future analysis in the during-epidemic era is urgently needed. This study aims to achieve three goals combining the utilization of decomposition algorithms and deep learning models: 1) to investigate the changes in tourism demand according to seasonal fluctuations of various frequencies, 2) to improve modelling accuracy in tourism demand forecasting during non-crisis periods and in the peri-COVID-19 era, and 3) to analyze tourism demand evolution in the peri-Covid-19 era. The volume of domestic tourism in Hawaii is used as sample data for demonstration and validation. The empirical findings demonstrate that the framework provided in this study has excellent interpretability and forecasting accuracy, surpasses all benchmark models in terms of error calculation and statistical tests and can provide further insights into peri-Covid-19 demand analysis and management. [ABSTRACT FROM AUTHOR]

This paper explores the optimization for the integrated vehicle routing of package collection and delivery in rural areas, based on the coverage idea and customer self-pickup strategy. The purpose is to optimize package collection and delivery in rural areas with the scattered and small-scale demand for the express service. An integrated vehicle routing model for rural package collection and delivery is established with regard to the radius of customer self-pickup, via taking the sum of operation cost, transportation cost, time penalty cost and carbon emission cost as the optimization goal. A two-stage algorithm based on the improved k-means clustering algorithm and genetic algorithm is designed to solve the model. Ziyuan county of Guangxi province is taken for typical example. The simulation results show that the optimization model can raise vehicle loading rate and service coverage rate, while reducing cost. A rise in the customer's self-pickup radius and the number of packages collected and delivered can decrease the unit collection and delivery cost, and it is found that carbon emissions have little effects on the optimization of vehicle routing from the perspective of cost. [ABSTRACT FROM AUTHOR]

Daily tourism demand forecasting can provide important implications for the tourism industry. However, there exist limitations in applying traditional methods to forecast daily tourism demand because of the complex seasonal patterns and holiday effects. In this study, we introduce FB Prophet and apply it to the forecasting of daily tourism demand in the Jiuzhai Valley National Park and Macao from Mainland China. The decomposition result of FB Prophet shows its ability to handle the influence of seasonal patterns and holiday effects. The forecasting results show that considering seasonal patterns, holiday effects, and other predictors can significantly improve the forecasting performance, and FB Prophet outperforms other methods. [ABSTRACT FROM AUTHOR]

Effective and timely forecasting of daily tourism volume is an important topic for tourism practitioners and researchers, which can reduce waste and promote the sustainable development of tourism. Several studies are based on the decomposition-ensemble model to forecast the time series of high volatility in tourism volume, but ignore different forecasting methods suitable for different subseries. This study provides an adaptive decomposition-ensemble hybrid forecasting approach. Firstly, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is used to effectively decompose the original time series into multiple relatively easy subseries, which reduces the complexity of the data. Secondly, sample entropy calculates the complexity of a sequence, and then adopts the elbow rule to adaptively divide them into different complex sets. Finally, multi-kernel extreme learning machine (KELM) models are used to forecast the components of different sets and integrate them. This hybrid approach makes full use of the advantages of different models, which enables effective use of data. The empirical results demonstrate that the approach can both produce results that are close to the actual values and be utilized as a strategy for forecasting daily tourism volume. [ABSTRACT FROM AUTHOR]

Practitioners and policy makers rely on accurate crude oil forecasting to avoid price risks and grasp investment opportunities, but the core of existing predictive models for such prices is based on point-valued inputs and outputs, which may suffer from informational loss of volatility. This paper addresses this issue by proposing a modified threshold autoregressive interval-valued models with interval-valued factors (MTARIX), as extended by Sun et al. [Threshold autoregressive models for interval-valued time series. J. Econom., 2018, 206, 414–446], to analyze and forecast interval-valued crude oil prices. In contrast to point-valued data methods, MTARIX models simultaneously capture nonlinear features in price trend and volatility, and this informational gain can produce more accurate forecasts. Several interval-valued factors and point-valued threshold variables are analyzed, including supply and demand, speculation, stock market, monetary market, technical factor, and search query data. Empirical results suggest that MTARIX models with appropriate threshold variables outperform other competing forecast models (ACIX, CR-SETARX, ARX, and VARX). The findings indicate that oil price range information is more valuable than oil price level information in forecasting crude oil prices. [ABSTRACT FROM AUTHOR]

Many researchers have examined the relationship between the original equipment manufacturer (OEM) and the third-party remanufacturer (TPR) and found that is mutually beneficial or competitive. This paper aims to study the competitive strategy and production strategy of the OEM and the TPR. The OEM's competitive strategy is to change their competitive intensity by investing in pricing power of new components. For this problem, two different competitive strategies are proposed: Investment strategy and Non-investment strategy. Two models are established based on competitive strategies, and their analytical solutions are obtained by Stackelberg game. Comparing the two competitive strategies found that only when the investment cost and cost of new components both are low, can the OEM adopt Investment strategy. The Investment strategy have a threefold effect on the OEM: increasing profit, reducing cannibalization, and blocking the TPR's entry. It is also found that as the cost of new components increases, the OEM may shift its competitive strategy from Investment to Non-investment. At the moment of competitive strategy shift, the production strategy may change discontinuously. Finally, comparing the environmental effect found that which competitive strategy is environmentally friendly only depend on the discount factor and the environmental advantages of the remanufactured product. [ABSTRACT FROM AUTHOR]

The anti-bullwhip effect, coined by Li et al. in 2005 based on the findings in a simulation study, is the contrary effect of the well-known bullwhip effect. Although there is ample empirical evidence that suggests both effects exist, current literature has not yet provided an integrated framework to address how the two effects are related with each other. By extending the classic work of Lee, Padmanabhan, and Whang of 1997 to a multi-stage supply chain, we derive closed-form formula to analytically describe how the two effects originate initially and then evolve over time and space in the supply chain. Our results show both the bullwhip effect and the anti-bullwhip effect can occur when facing different end-customer demands. However, the magnitude of these effects gradually decreases when moving upstream. We also show the impact of long lead-time on increasing the magnitude of information transformation at the next stage and simultaneously decreasing the magnitude at higher stages. These analytical results provide a theoretical explanation to most simulation findings of Li et al.'s 2005 work and can be used by researchers and practitioners to examine the empirical data and design innovate marketing strategies to convert the unwelcome bullwhip effect into the anti-bullwhip effect. [ABSTRACT FROM PUBLISHER]

Owing to the convergence of multiple cultures coupled with the unprecedented rapid development in the decades since the late 1990s, the value creation and innovation logic of Asia-Pacific business models (BMs) has been constantly altered by cultural heterogeneity and thus, have evolved into a more complex and diverse landscape relative to Western developed-economy BMs. Given that relevant issues remain under-researched, the main purpose of our collection is to fill this gap. We provide new insights into identifying, exploring and rationalizing the indigenous innovation ecosystems and unorthodox value-creating logic of BMs emerging from the Greater China territory. Moreover, whereas the COVID-19 crisis has fundamentally changed the way people do business along with the lockdowns followed by some forms of de-globalization; it is expected to see a new wave of BM evolution. We, thus, outline three promising and nascent pathways for future efforts departing from a cultural view to explore the value logic of BMs and BM innovation within the Asia-Pacific region in the post-COVID-19 era: (1) the role of culture in BM innovation; (2) new ways of creating the knowledge and managing knowledge iceberg phenomenon and (3) using the 'Bottom of Pyramid' approach to BM innovation for incorporating social goals into value creation logic. [ABSTRACT FROM AUTHOR]

Crude oil prices are of vital importance for market participants and governments to make energy policies and decisions. In this paper, we apply a newly proposed autoregressive conditional interval (ACI) model to forecast crude oil prices. Compared with the existing point-based forecasting models, the interval-based ACI model can capture the dynamics of oil prices in both level and range of variation in a unified framework. Rich information contained in interval-valued observations can be simultaneously utilized, thus enhancing parameter estimation efficiency and model forecasting accuracy. In forecasting the monthly West Texas Intermediate (WTI) crude oil prices, we document that the ACI models outperform the popular point-based time series models. In particular, ACI models deliver better forecasts than univariate ARMA models and the vector error correction model (VECM). The gain of ACI models is found in out-of-sample monthly price interval forecasts as well as forecasts for point-valued highs, lows, and ranges. Compared with GARCH and conditional autoregressive range (CARR) models, ACI models are also superior in volatility (conditional variance) forecasts of oil prices. A trading strategy that makes use of the monthly high and low forecasts is further developed. This trading strategy generally yields more profitable trading returns under the ACI models than the point-based VECM. [ABSTRACT FROM AUTHOR]

This paper proposes a new class of nonlinear interval models for interval-valued time series. By matching the interval model with interval observations, we develop a nonlinear minimum-distance estimation method for the proposed models, and establish the asymptotic theory for the proposed estimators. Superior to traditional point-based methods, the proposed interval modelling approach can assess the change in both the trend and volatility simultaneously. Within the proposed interval framework, this paper examines the impact of the 2016 US presidential election (henceforth Trump election) on the US stock market as a case study. Considering the validity of daily high-low range as a proxy of market efficiency, we employ an interval-valued return to jointly measure the fundamental value movement and market efficiency simultaneously. Empirical results suggest a strong evidence that the Trump election has increased the level/trend and lowered the volatility of the S&P 500 index in both ex ante and ex post analysis. Furthermore, a longer half-life period for the impact on fundamental value (62.4 days) than high-low range (15.9 days) has shown that the impact of Trump's victory on fundamental value is more persistent than its impact on market efficiency. [ABSTRACT FROM AUTHOR]

Subway delays would affect passengers' travels and cause panic, having massive impacts on urban transit systems. Analyzing causes and the duration of subway delays would necessarily assist operational departments to take effective measures to alleviate the negative impact. Using Hong Kong subway incident data from 2005 to 2009, a text-based analysis of Accelerated Failure Time (AFT) model is developed to explore significant variables affecting subway delays and predict time lost as a result of subway delays. The results of this study suggest that a proposed mixture model outperforms the classical AFT model, because it utilizes topic model to extract candidate variables from textual information. In this way, factors affecting delays can be found. Beside the fact, that the results illustrate that non-device related factors i.e., human related factors, accidents and foreign object invasion cause longer delays. In conclusion, this study helped to develop predictions and thereby can reduce delays, minimizing the negative impact on the subway service and the passengers. [ABSTRACT FROM AUTHOR]

In recent years, the body of literature on frequentist model averaging in econometrics has grown significantly. Most of this work focuses on models with different mean structures but leaves out the variance consideration. In this article, we consider a regression model with multiplicative heteroscedasticity and develop a model averaging method that combines maximum likelihood estimators of unknown parameters in both the mean and variance functions of the model. Our weight choice criterion is based on a minimization of a plug-in estimator of the model average estimator's squared prediction risk. We prove that the new estimator possesses an asymptotic optimality property. Our investigation of finite-sample performance by simulations demonstrates that the new estimator frequently exhibits very favorable properties compared with some existing heteroscedasticity-robust model average estimators. The model averaging method hedges against the selection of very bad models and serves as a remedy to variance function mis-specification, which often discourages practitioners from modeling heteroscedasticity altogether. The proposed model average estimator is applied to the analysis of two data sets on housing and economic growth. [ABSTRACT FROM AUTHOR]

This article presents a non-expected utility decision model which is nonlinear in the winning probabilities. The model not only explicitly expresses bidders' attitudes to risk, but also addresses their preference over the bidding criteria. To demonstrate how to apply the model in the practical auction design, the first- and second-price auctions with both commission rate and reserve price are examined, respectively. For nonrisk-neutral bidders, the equilibrium bidding strategies are characterized, in which the commission rate has a significant influence on the bidding strategy through the critical valuation. However, the existence of the optimal commission rate is uncertain, but once it exists, it depends on the information rent of the highest or second highest order valuation in terms of the inverse hazard rate. With risk-aversion bidders, the only difference to the optimal reserve price is a constant between the first- and second-price auctions. The revenue comparisons show that the classical Revenue Equivalence Theorem fails in practical auctions with the commission rate. This article extends the application of the decision-making model in the auction design in theory and provides some guidance for the auction house and the seller to make their decisions in reality. [ABSTRACT FROM AUTHOR]

We develop a production-inventory model for deteriorating items with demand disruption. This differs from the conventional inventory model which is affected only by either deterioration or disruption. In real-life production-inventory systems, deterioration of products and demand disruption are inevitable. The objective of this paper is to address these issues and to be able to derive the optimal production run time and replenishment policy for spot market purchases. We divide the problem into different scenarios according to disruption's time and magnitude. In each scenario the optimal production and inventory plans are provided so that the manufacturer can satisfy the new demand and decrease the loss. Then a numerical example is used to illustrate the model. [ABSTRACT FROM PUBLISHER]

A production system is designed with three elements: works, connections and pathways (WCP), which cover how people work, how people connect and how the production line is constructed, from the point of view of a flow unit. The design principle of highly specifying WCP leads to two operations pillars that support the Toyota production system (TPS): just-in-time (JIT) and autonomation. Some stochastic models are proposed to verify the logic of highly specifying work for JIT. The stochastic models show that works are highly specified to reduce the variability whereas highly specifying connections and pathways without resource pooling expose variability in works as low inventory exposes the hidden problems in a factory. To achieve JIT, highly specifying works in each workstation is fundamental; the key solution is to reduce the variability of works. The information entropy theory is used to verify the logic of highly specifying WCP for autonomation. Highly specifying WCP can reduce the information needed by workers to identify and solve problems in the flow unit. [ABSTRACT FROM AUTHOR]

It is normal for enterprises to engage in remanufacturing nowadays. Nevertheless, the concern of enterprises and researchers is whether remanufacturers should compete for leadership in supply chains. Considering the WTP for new and remanufactured products differs, this article the evolutionary game theory model in three structures of leadership in supply chains. It investigates the processes of market evolution and choices of strategies when manufacturers and retailers compete or do not compete for leadership in the market. It has been shown in the case of market evolution, according to the 12 conditions formed from different prices that manufacturer and retailer have to pay for competing for leadership, there are five types of ultimate evolutionary stability about the choices of strategies. Further analyses show that these five different evolutionary stability results include nine different evolutionary paths. Analyses of examples further show the evolutionary processes and results in distinct circumstances. This article extends the application of the evolutionary game in remanufacturing supply chains in theory and provides some guidance for enterprises to make decisions when they consider whether they compete for leaders in remanufacturing supply chains in reality. [ABSTRACT FROM AUTHOR]

In this paper we propose an estimation procedure which uses joint data on the underlying asset and option prices to extract market prices of return and volatility risks in the context of the G.A.R.C.H. diffusion model. The procedure is flexible and simple to implement. Firstly, a quasi-closed form pricing formula for European options in the G.A.R.C.H. diffusion model is derived. This result greatly eases the computational burden for computing option prices, and well suited for our model estimation. Then, based upon the joint data, we develop an efficient importance sampling-based maximum likelihood (E.I.S.-M.L.) estimation method for the objective and risk-neutral parameters of the G.A.R.C.H. diffusion model and a particle filter algorithm for latent state variable. Hence, this allows us to infer the market prices of risks that link the objective measure and the risk-neutral measure. Finally, we illustrate our approach using actual data on the Hang Seng Index (H.S.I.) and index warrant prices. The results show that both the return and volatility risks are priced by the market. Moreover, an option pricing study demonstrates that the market price of the volatility risk plays an important role in fitting option prices. [ABSTRACT FROM AUTHOR]

Supply chain disruption causes tremendous loss to supply chains and global businesses. Organizational structure and operational features of supply chain networks, therefore, constitute a major portion of research for coping with supply chain disruption risk. This article first discusses the theoretical foundation, analyses the strength theory of strong and weak ties and the collaborative theory of strong and weak ties. Also, research methods are explicated; the scale-free networks and the theory of strong ties are integrated; the features of supply chain networks are considered from single statistical parameters and comprehensive analyses. Next, we provide numeric simulation of the properties of supply chain networks, verifying the accuracy of parameter analyses of single statistics and comprehensive analyses. Ultimately, when coping with supply chain disruption risk, each node enterprise of supply chain network is supposed to deploy the characteristics of scale-free networks to systematically manage existing weak ties, weak ties newly introduced, existing strong ties and strong ties newly introduced. This study is conducive to node enterprises of supply chains to recognize and apply the scale-free networks and the strength theory of ties to analyze the properties of supply chain networks, and to improve the capacity to cope with disruption risk. [ABSTRACT FROM AUTHOR]

We propose an efficient numerical integration-based nonparametric entropy estimator for serial dependence and show that the new entropy estimator has a smaller asymptotic variance than Hong and White’s (2005) sample average-based estimator. This delivers an asymptotically more efficient test for serial dependence. In particular, the uniform kernel gives the smallest asymptotic variance for the numerical integration-based entropy estimator over a class of positive kernel functions. Moreover, the naive bootstrap can be used to obtain accurate inferences for our test, whereas it is not applicable to Hong and White’s (2005) sample averaging approach. A simulation study confirms the merits of our approach. [ABSTRACT FROM AUTHOR]

This paper proposes two types of dummy variables for an interval regression model to assess the impact of economic shocks/crises on an interval time series (ITS), e.g. daily intervals of energy prices. We present different economic interpretations of the two types of dummy variables for an interval regression model. Particularly, we discuss how they measure the direction and magnitudes of the change of an ITS caused by an economic crisis, and develop the corresponding hypothesis tests. A main advantage of the proposed ITS modelling approach over traditional point-based methods is that it can assess the change in both the trend and volatility of an asset price process simultaneously. This is due to the informational gain of an ITS sample over a point-valued sample, e.g. closing prices, since an interval observation contains both the trend and variation information of a price process in a given period. Using the proposed interval framework, we focus on the impact of the subprime mortgage crisis in the commodity market as a case study based on the ITS of monthly crude oil future price data. Empirical results suggest a strong evidence that the subprime crisis has lowered the level/trend and increased the volatility of crude oil prices. We also show that the trend of crude oil future prices moves towards an equilibrium state driven by the variation of the price process in last period, and the speculation index, as a proxy of crude oil market liquidity, is significant in explaining the dynamics of crude oil prices. Both findings provide quantitative evidence for theoretical results in the previous literature. [ABSTRACT FROM AUTHOR]

This article formulates a gaming model of the closed-loop supply chain with manufacturers (as the leader), sellers and consumers where consumers may or may not be willing to pay remunerative price for remanufactured products vis-à-vis new products. In the model, manufacturers produce new as well as remanufactured products, whereas sellers distribute them. In stable circumstances, this article presents the functional formula of the optimal manufacturing pricing decisions. The results show that when the cost of new products is significantly lower than that of remanufactured ones, manufacturers choose to produce new products only. When the difference between cost of new and remanufactured products is moderate, manufacturers tend to produce both new and remanufactured products, and in some regions, production of new and remanufactured products is proportional to each other. When the difference between cost of new and remanufactured products is enormous, their production and sale tend to stop. Also, this article analyses the effects of varying cost of new and remanufactured products and the recycling rate on pricing decisions of supply chain members. This article contributes to the management of manufacturers’ and sellers’ remanufacturing decisions and also provides advice on how governments can guide consumer preferences. [ABSTRACT FROM PUBLISHER]

With the rapid growth of seaborne commodity trades, port development and management has become a challenging issue to the government, enterprise and academia. To alleviate pressures on spatial demand and the environment, sustainable development and scientific management of a port is of crucial importance for its investment, construction and operation. In this article, a research path based on throughput estimation is proposed. The container port of Tianjin could expect to face immense, increasing pressure in the future several years. To meet future increasing capacity requirement, constructing new waterway and berths in a bigger contiguous area or new locations becomes a crucial strategy. Moreover, the strategy of accommodating peak seasonal traffic means existing container terminals have to attain higher output by redesigning their high-precision schedule, reconfiguring terminal topology, improving worker efficiency and employing more modern container-handling facilities. [ABSTRACT FROM AUTHOR]

This article explores the equilibrium behaviour of a basic supplier–retailer distribution channel under demand disruption via effort and revenue sharing contract. This differs from the traditional supply chain coordination model. Firstly, demand is simultaneously affected by retail price and nonprice marketing effort from manufacturers and retailers. Secondly, when the demand is disrupted, this article considers disruptions in the market scale and price sensitivity coefficient. Thirdly, the supply chain coordination model is proposed via effort and revenue sharing contract. In this way, the manufacturer reduces the wholesale price as an incentive for the retailer to share revenue. Finally, the total supply chain profit is greater with contract than no contract. This also constitutes another incentive for the players to follow the effort and revenue sharing contract. [ABSTRACT FROM PUBLISHER]

Shenzhen Port (SZP), once a negligible local port, has risen dramatically to a world-leading container port and an “equal” player with Hong Kong Port (HKP). Will this market share reallocation continue, or will equilibrium be eventually realized for HKP to prosper continuously? We examine the relationship between these two ports to answer this question. We propose a new transformation method to describe the growth of container cargo transport demand, define the quantitative measures of the competition relationship and port competitiveness, and present a rigorous analytical framework with econometric tests and models to understand the true relationship between HKP and SZP. Direct empirical tests suggest that SZP complements HKP; however, the two ports exhibit strong competition when the effect of demand growth is excluded. Considering transshipment separately, we find that the impact of SZP on HKP is negative in transshipment but complementary in direct shipment. We may conclude that HKP does not affect SZP, whereas SZP has competitive power over HKP. These results are consistent with the findings of some previous theoretical studies. [ABSTRACT FROM PUBLISHER]

The air transport industry crucially depends on traffic forecasting for supporting management decisions. In this study, a singular spectrum analysis (SSA)-based ensemble forecasting modeling approach is proposed. The original air passenger time series is first decomposed into three components: trend, seasonal oscillations, and irregular component. The trend is predicted by generalized regression neural network (GRNN), whereas seasonal oscillations are predicted by radial basis function networks (RNFNs). The empirical results of Hong Kong (HK) air passenger data show a significant improvement of the proposed ensemble method in comparison to other results of competing models. [ABSTRACT FROM AUTHOR]

This paper presents an integrated forecasting model based on the TEI@I methodology for forecasting demand for port logistics services – specifically, port container throughput. The model analyzes port logistics time series data and other information in several steps. In the first step, several econometric models are built to forecast the linear segment of port logistics time series. In the second step, a radial basis function neural network is developed to predict the nonlinear segment of the time series. In the third step, the event-study method and expert system techniques are applied to evaluate the effects of economic and other events that may impact demand for port logistics. In the final step, synthetic forecasting results are obtained, based on the integration of predictions from the above three steps. For an illustration, Hong Kong port's container throughput series is used as a case study. The empirical results show the effectiveness of the TEI@I integrated model for port logistics forecasting. [ABSTRACT FROM PUBLISHER]

Data communication service has an important influence on e-commerce. The key challenge for the users is, ultimately, to select a suitable provider. However, in this article, we do not focus on this aspect but the viewpoint and decision-making of providers for order allocation and pricing policy when orders exceed service capacity. It is a multiple criteria decision-making problem such as profit and cancellation ratio. Meanwhile, we know realistic situations in which much of the input information is uncertain. Thus, it becomes very complex in a real-life environment. In this situation, fuzzy sets theory is the best tool for solving this problem. Our fuzzy model is formulated in such a way as to simultaneously consider the imprecision of information, price sensitive demand, stochastic variables, cancellation fee and the general membership function. For solving the problem, a new fuzzy programming is developed. Finally, a numerical example is presented to illustrate the proposed method. The results show that it is effective for determining the suitable order set and pricing policy of provider in data communication service with different quality of service (QoS) levels. [ABSTRACT FROM PUBLISHER]

Bid criteria are of prime importance to bidders because they are the basis for the bidders to select bid price or bid mark-up. This article presents the alternative expressions of the bid criteria of the conditional negative and positive profit ratios proposed by Seydel and Olson (1990) and Lai et al. (2002), respectively, and interprets them in the traditional demand and supply theory. It is found that there is a 'frontier' bid mark-up if the bidders adopt the conditional profit ratio as their sole bid criterion. [ABSTRACT FROM AUTHOR]

We investigate a robust version of the portfolio selection problem under a risk measure based on the lower-partial moment (LPM), where uncertainty exists in the underlying distribution. We demonstrate that the problem formulations for robust portfolio selection based on the worst-case LPMs of degree 0, 1 and 2 under various structures of uncertainty can be cast as mathematically tractable optimization problems, such as linear programs, second-order cone programs or semidefinite programs. We perform extensive numerical studies using real market data to reveal important properties of several aspects of robust portfolio selection. We can conclude from our results that robustness does not necessarily imply a conservative policy and is indeed indispensable and valuable in portfolio selection. [ABSTRACT FROM AUTHOR]

Cuddington and World Bank-method are modified with trade credits and trade misinvoicing to estimate the short-term and total capital flight (CF) from China. Trade Misinvoicing are adjusted with the transit trade through Hong Kong. It is shown that the short-term is the main component of the CFs from China. Export under-invoicing is the main channel of CFs since 1994, and illegal capital inflow and CF exist simultaneously in China. [ABSTRACT FROM AUTHOR]

This paper addresses a topic of supply chain management from a new perspective: incorporating risk aversion in a supply contract model. A pay to delay capacity reservation contract is analyzed by using the concept of conditional value-at-risk (Cvar). First, we construct the manufacturer’s optimal ordering problem by using the dynamic programming approach. Then, we derive the manufacturer’s optimal ordering strategy and compare our results with those obtained by using expectation performance and mean-variance tradeoff. Finally, numerical results are shown to reveal the impact of risk aversion on the manufacturer’s optimal decisions. The analysis presented in this paper reveals advantages of using the Cvar approach over the mean-variance approach. [ABSTRACT FROM AUTHOR]

A stochastic linear goal programming model for multistage portfolio management is proposed. The model takes into account both the investment goal and risk control at each stage. A scenario generation method is proposed that acts as the basis of the portfolio management model. In particular, by matching the moments and fitting the descriptive features of the asset returns, a linear programming model is used to generate the single-stage scenarios. Scenarios for multistage portfolio management are generated by incorporating this single-stage method with the time-series model for the asset returns. Meanwhile, no arbitrage opportunity exists in the proposed method. A real case is solved via the goal programming model and the scenario generation approach which demonstrates the effectiveness of the model. We also comment on some practical issues of the approach. [ABSTRACT FROM AUTHOR]

The effects of Japanese yen's depreciation on Chinese exports in 2002 (only considering the exports of the Mainland of China) are analysed. An AR(2) valuation function for the Chinese exports is constructed. The results on sensitivity tests show that the depreciation of Japanese yen would not bring severe effects on the Chinese exports in 2002, though the increasing trend of Chinese exports would become slower. [ABSTRACT FROM AUTHOR]

Introduces a series of articles on applications of industrial engineering and operations research techniques in financing engineering.