Abstract
Evaluating the role of logistics and supply chain management skills or applications is necessary for the success of any organization or business. As market competition becomes more aggressive, it is crucial to evaluate ways in which a business can maintain a strategic edge over competitors. The Handbook of Research on Information Management for Effective Logistics and Supply Chains highlights strategies, tools, and skills necessary for supply management within organizations and companies. Featuring best practices and empirical research within the field, this handbook is a critical reference source for scholars, practitioners, researchers, information systems and telecommunication specialists, and managers.

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Abstract

Abstract
Forecasting future sales is one of the most important issues that is beyond all strategic and planning decisions in effective operations of retail supply chains. For profitable retail businesses, accurate sales forecasting is crucial in organizing and planning purchasing, production, transportation and labor force. Retail sales series belong to a special type of time series that typically contain strong trend and seasonal patterns, presenting challenges in developing effective forecasting models. This paper compares the forecasting performance of state space models and ARIMA models. The forecasting performance is demonstrated through a case study of retail sales of five different categories of women footwear: Boots, Booties, Flats, Sandals and Shoes. An approach based on cross-validation is used to identify automatically appropriate state space and ARIMA models. The forecasting performance of these models is also compared by examining the out-of-sample forecasts. The results indicate that the overall out-of-sample forecasting performance of ARIMA models evaluated via RMSE, MAE and MAPE is better than state space models. The performance of both forecasting methodologies in producing forecast intervals was also evaluated and the results indicate that ARIMA produces slightly better coverage probabilities than state space models for the nominal 95% forecast intervals. For the nominal 80% forecast intervals the performance of state space models is slightly better.

Abstract
Personnel scheduling problems are widely studied both by the scientific community and by the human resource managers of the companies. The financial impact of the decisions, the welfare of the employees, or more subjective concepts like "fairness" and "balance", turn this case into much more than just a routine problem. When it comes to medical personnel scheduling, additional difficulties can be found, such as uninterrupted work (24 hours per day, 7 days per week), or the quality of service that has to be ensured. The work presented in this paper was based on the rules defined in INRCII - Second International Nurse Rostering Competition - but always with the vision of creating the necessary basis for the future development of an automatic and optimized generic personnel scheduling software.

Abstract
At a time when the need to reduce costs has become part of the day-to-day reality of all educational institutions, it is unthinkable to continue to manually perform those tasks (i.e., the creation of timetables) that can be automated and optimized. The automatic creation of timetables for educational institutions is one of the most studied problems by the scientific community. However, almost all studies have been based on very simplified models of reality that have no practical application. A realistic model of the problem, robust algorithms that are able to find valid solutions in highly restricted environments, and optimization methods that are able to quickly provide quality results are key factors to consider when attempting to solve this (real) problem faced by educational institutions. This paper presents a summary of the work performed by Bullet Solutions over the last few years, from the first stage of understanding and modelling the problem to the final analysis of the results obtained using the developed software under real conditions.