Effects of scale economies and production seasonality on optimal hub locations: the case of regional fresh produce aggregation

Interest in supporting local and regional food systems is rising and food hubs have attracted considerable attention among Federal, State, and local policymakers. This study explores the problem of endogenous hub location in fresh produce value chains in the Northeastern United States. To overcome limitations in the literature, we incorporate the effects of economies of scale and production seasonality into our models. Three experimental models are designed to examine the effects of alternatively applying yearly, quarterly, and monthly data on model solutions. We explicitly assess how interactions of scale economies and seasonality influence the structure and spatial attributes of an optimal regional produce aggregation hub system. The three models generate marketed different solutions and in many respects they lead to different conclusions about developing local/regional supply chains. The monthly model allows for production seasonality and actual hub operation cycle frequency and thus leads to more efficient hub solution with rich policy implications.     

Quantile regression for the qualifying match of GEFCom2017 probabilistic load forecasting

We present a simple quantile regression-based forecasting method that was applied in the probabilistic load forecasting framework of the Global Energy Forecasting Competition 2017 (GEFCom2017). The hourly load data are log transformed and split into a long-term trend component and a remainder term. The key forecasting element is the quantile regression approach for the remainder term, which takes into account both weekly and annual seasonalities, such as their interactions. Temperature information is used only for stabilizing the forecast of the long-term trend component. Information on public holidays is ignored. However, the forecasting method still placed second in the open data track and fourth in the definite data track, which is remarkable given the simplicity of the model. The method also outperforms the Vanilla benchmark consistently.     

Probabilistic wind forecasting up to three months ahead using ensemble predictions for geopotential height

We develop a method for forecasting the distribution of the daily surface wind speed at timescales from 15-days to 3-months in France. On such long-term timescales, ensemble predictions of the surface wind speed have poor performance, however, the wind speed distribution may be related to the large-scale circulation of the atmosphere, for which the ensemble forecasts have better skill. The information from the large-scale circulation, represented by the 500 hPa geopotential height, is summarized into a single index by first running a PCA and then a polynomial regression. We estimate, over 20 years of daily data, the conditional probability density of the wind speed at a specific location given the index. We then use the ECMWF seasonal forecast ensemble to predict the index for horizons from 15-days to 3-months. These predictions are plugged into the conditional density to obtain a distributional forecast of surface wind. These probabilistic forecasts remain sharper than the climatology up to 1-month forecast horizon. Using a statistical postprocessing method to recalibrate the ensemble leads to further improvement of our probabilistic forecast, which then remains calibrated and sharper than the climatology up to 3-months horizon, particularly in the north of France in winter and fall.     

Sell in May and Go Away: Evidence from China

Using the Chinese stock market data from 1997 to 2013, this paper examines the “Sell in May and Go Away” puzzle first identified by Bouman and Jacobsen (2002). We find strong existence of the Sell in May effect, robust to different regression assumptions, industries, and after controlling for the January or February effect. However, part of the puzzle is subsumed by the seasonal affective disorder effect. We then construct a trading strategy based on this puzzle, and find that it outperforms the buy-and-hold strategy and could resist the market downside risk during large recession periods.     

An adaptive large-neighborhood search heuristic for a multi-period vehicle routing problem

This problem involves optimizing product collection and redistribution from production locations to a set of processing plants over a planning horizon. This horizon consists of several days, and the collection-redistribution is performed on a repeating daily basis. A single routing plan must be prepared for the whole horizon, taking into account the seasonal variations in the supply. We model the problem using a sequence of periods, each corresponding to a season. We propose an adaptive large-neighborhood search with several specifically designed operators and features. The results show the excellent performance of the algorithm in terms of solution quality and computational efficiency.     

Seasonal concentration of tourism demand: Decomposition analysis and marketing implications

This paper analyzes seasonality in the United Kingdom, specifically the English regions in relation to tourists' place of origin and main travel motivation. The method used is a decomposition of the Gini index, which provides relative marginal effects that facilitate the identification of market segments open to counter-seasonal marketing efforts. This method has been combined with a graphical multivariate technique (biplot), which groups segments according to their seasonality characteristics. Seasonal patterns associated with particular segments differ significantly when studied on a disaggregated basis. Therefore, an adequate level of disaggregation is essential in the design of counter-seasonal strategies. Although this study focuses on British destinations, this methodology could be used as a control and monitoring measure in the regional analysis of any destination, facilitating regular adjustment of regional tourism marketing campaigns to minimize seasonality effects, specifically by targeting the types of tourists less prone to seasonality.     

The Effects of Year-Round Schools on the Hospitality Industry's Seasonal Labor Force in the State of Tennessee

Abstract

The hospitality industry in Tennessee employs 181,300 people as seasonal workers; fifty-three percent of those workers come from the school system. The loss of seasonal workers with the implementation of year-round schools will drastically increase the current labor shortage. An alternative labor pool needs to be identified and the issue of year-round schools must be addressed.     

Forecasting China's Monthly Inbound Travel Demand

Summary

China is currently expecting a growth in inbound travel demand as the result of China's “open door policy,” participation in World Trade Organization (WTO), success in hosting the Olympics in Beijing in the year 2008 and political stability. This paper focused on two issues: (1) forecasting China's monthly inbound travel demand and (2) seasonally and seasonal ARIMA model selection for monthly tourism time-series. In this paper following seasonal ARIMA models were considered: the seasonal ARIMA model with first differences and 11 seasonal dummy variables, the conventional seasonal ARIMA model with first and the fourth differences. In order to select the best forecasting model, finally both seasonal ARIMA models were compared with the AR model with fourth differences, the basic structural model (BSM) and the naive “No Change” model. In the one-step ahead forecasting comparison, the conventional seasonal ARIMA model with first and the fourth differences becomes the best forecasting model for both inbound foreign visitor demand and total visitor demand. This may be due to the nature of monthly seasonal variations in visitor arrivals, which is less marked. Our forecasts indicate that China foreign visitor arrivals and total visitor arrivals are expected to grow by 14% and 27% respectively from 2002 to 2005.     

U. S. winter migrants' park community attributes: An importance–performance analysis

North American seasonal migrants, who are usually retired, typically live in recreational vehicle/mobile home park communities in the southern U. S. during the winter months. This study examines the importance of specific park characteristics in the park selection process and uses the case of winter migrants in south Texas to demonstrate how parks may use a modified approach of importance–performance analysis and the promoter–detractor methodology to assess how well the parks perform on selected attributes. The analysis of survey responses from 1429 winter Texans categorized attributes based on the attributes' impact range and asymmetrical impact on park recommendation likelihood. Overall, seasonal migrants based their choice of location for their winter home largely on physical appearance (e.g., cleanliness) and affect (i.e., friendliness and caring management). Wireless Internet access and low rates were also of high importance. But park recommendation likelihood varies depending on the migrants' satisfaction with important attributes' performance.     

Seasonality patterns in dry bulk shipping spot and time charter freight rates

This paper investigates the nature of seasonality (deterministic and/or stochastic) in dry bulk freight rates, and measures and compares it across freight rates of different vessel sizes (Capesize, Panamax and Handysize), contract duration (spot, 1-year and 3-year time charters) and market conditions (peaks and troughs). Although, there is no evidence of stochastic seasonality, deterministic seasonality in freight rates is found to be varying from −18.2% to 15.3% in individual months within a year. Spot rates for larger vessels exhibit higher seasonal fluctuations compared to smaller vessels, although differences in seasonal fluctuations between sectors are eliminated as the contract duration increases. Also, for each vessel size, the seasonality declines as the contract duration rises. Asymmetries in seasonal fluctuations in freight rates over different market conditions are attributed to the high and low elasticities of supply expected under the respective market conditions. The results have implications for tactical shipping operations such as timing of dry-docking, chartering strategies and switching between freight markets.