Alexander Stepanov

Bio: Alexander Stepanov is an academic researcher from University of Massachusetts Amherst. The author has contributed to research in topics: The Internet & Yen's algorithm. The author has an hindex of 4, co-authored 4 publications receiving 294 citations.

Reflections of an Online Geographic Information Systems Course Based on Open Source Software

Abstract: This article summarizes the experience of offering an online introductory course on geographic information systems (GIS) that utilizes available free/libre and open source software (FOSS). Two primary objectives are to (a) reach students in developing countries and (b) to help move forward the development of an open-content GIS curriculum as part of the Open Source Geospatial Foundation (OSGeo; OSGeo.org) educational effort. Course design, key software (QGIS, GRASS, PostgreSQL//PostGIS), and online delivery methods are described. Results and factors leading to a low course-completion rate are discussed. Contributing factors include (a) a for-credit versus no-credit decision and (b) technical issues. Recommendations for others considering online offerings and for the OSGeo educational effort are provided.

GRASS GIS: A multi-purpose open source GIS

Abstract: The GIS software sector has developed rapidly over the last ten years. Open Source GIS applications are gaining relevant market shares in academia, business, and public administration. In this paper, we illustrate the history and features of a key Open Source GIS, the Geographical Resources Analysis Support System (GRASS). GRASS has been under development for more than 28 years, has strong ties into academia, and its review mechanisms led to the integration of well tested and documented algorithms into a joint GIS suite which has been used regularly for environmental modelling. The development is community-based with developers distributed globally. Through the use of an online source code repository, mailing lists and a Wiki, users and developers communicate in order to review existing code and develop new methods. In this paper, we provide a functionality overview of the more than 400 modules available in the latest stable GRASS software release. This new release runs natively on common operating systems (MS-Windows, GNU/Linux, Mac OSX), giving basic and advanced functionality to casual and expert users. In the second part, we review selected publications with a focus on environmental modelling to illustrate the wealth of use cases for this open and free GIS.

A multi-objective robust stochastic programming model for disaster relief logistics under uncertainty

Abstract: Humanitarian relief logistics is one of the most important elements of a relief operation in disaster management. The present work develops a multi-objective robust stochastic programming approach for disaster relief logistics under uncertainty. In our approach, not only demands but also supplies and the cost of procurement and transportation are considered as the uncertain parameters. Furthermore, the model considers uncertainty for the locations where those demands might arise and the possibility that some of the pre-positioned supplies in the relief distribution center or supplier might be partially destroyed by the disaster. Our multi-objective model attempts to minimize the sum of the expected value and the variance of the total cost of the relief chain while penalizing the solution's infeasibility due to parameter uncertainty; at the same time the model aims to maximize the affected areas' satisfaction levels through minimizing the sum of the maximum shortages in the affected areas. Considering the global evaluation of two objectives, a compromise programming model is formulated and solved to obtain a non-dominating compromise solution. We present a case study of our robust stochastic optimization approach for disaster planning for earthquake scenarios in a region of Iran. Our findings show that the proposed model can help in making decisions on both facility location and resource allocation in cases of disaster relief efforts.

Pre-positioning and dynamic delivery planning for short-term response following a natural disaster

Abstract: Natural disasters often result in large numbers of evacuees being temporarily housed in schools, churches, and other shelters. The sudden influx of people seeking shelter creates demands for emergency supplies, which must be delivered quickly. A dynamic allocation model is constructed to optimize pre-event planning for meeting short-term demands (over approximately the first 72 h) for emergency supplies under uncertainty about what demands will have to be met and where those demands will occur. The model also includes requirements for reliability in the solutions – i.e., the solution must ensure that all demands are met in scenarios comprising at least 100 α % of all outcomes. A case study application using shelter locations in North Carolina and a set of hurricane threat scenarios is used to illustrate the model and how it supports an emergency relief strategy.