Containership routing with time deadlines and simultaneous deliveries and pick-ups

In this paper we seek to determine optimal routes for a containership fleet performing pick-ups and deliveries between a hub and several spoke ports. A capacitated vehicle routing problem with pick-ups, deliveries and time deadlines is formulated and solved using a hybrid genetic algorithm for establishing routes for a dedicated containership fleet. Results on the performance of the algorithm and the feasibility of the approach show that a relatively small fleet of containerships could provide efficient services within deadlines. Moreover, through sensitivity analysis we discuss performance robustness and consistency of the developed algorithm under a variety of problem settings and parameters values.     

Foreign Debt and Economic Development

This paper characterizes the optimal path of foreign public debt that can support politically unanimous (Pareto welfare improving) economic growth under uncertainty. The feasibility of the plan depends on whether the maintenance of political consensus in the debtor country requires additional loans that are high relative to the country's outstanding debt. If a program is feasible, then the creditors will have an incentive to preserve political support for the plan, in case of adverse developments, by offering debt relief. Unlike the certainty case, where only total debt matters, uncertainty makes also the timing of debt repayments critical for economic development.     

Investigating the accuracy of cross-learning time series forecasting methods

The M4 competition identified innovative forecasting methods, advancing the theory and practice of forecasting. One of the most promising innovations of M4 was the utilization of cross-learning approaches that allow models to learn from multiple series how to accurately predict individual ones. In this paper, we investigate the potential of cross-learning by developing various neural network models that adopt such an approach, and we compare their accuracy to that of traditional models that are trained in a series-by-series fashion. Our empirical evaluation, which is based on the M4 monthly data, confirms that cross-learning is a promising alternative to traditional forecasting, at least when appropriate strategies for extracting information from large, diverse time series data sets are considered. Ways of combining traditional with cross-learning methods are also examined in order to initiate further research in the field.     

THE RESPONSE OF FORWARD EXCHANGE RATES TO INTEREST RATE FORECASTING ERRORS

This paper examines the effects of interest rate news on changes in forward foreign exchange rates. Virtually none of the errors in forecasting forward exchange rates are explained by interest rate forecasting errors. The results are consistent with a conjecture that the forward exchange rate is not an estimate of the expected spot exchange rate.