A vector space model for automatic indexing
Communications of the ACM
Information Systems Research
Distributed resource allocation via local choices: A case study of workforce allocation
International Journal of Knowledge-based and Intelligent Engineering Systems - Selected papers from the KES2004 conference
IBM Journal of Research and Development - Business optimization
Statistical methods for automated generation of service engagement staffing plans
IBM Journal of Research and Development - Business optimization
Foundations and Trends in Databases
Sequence Mining for Business Analytics: Building Project Taxonomies for Resource Demand Forecasting
Proceedings of the 2008 conference on Applications of Data Mining in E-Business and Finance
Effective Decision Support for Workforce Deployment Service Systems
SCC '09 Proceedings of the 2009 IEEE International Conference on Services Computing
Simulation-based evaluation of dispatching policies in service systems
Proceedings of the Winter Simulation Conference
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Efficient utilization of human resources is imperative for information technology (IT) service businesses that continually manage the assignment and movement of practitioners to or between projects. These organizations constantly strive to balance the many objectives of multiple stakeholders in order to minimize idle resources while aiming to increase revenue from new project opportunities, as well as to improve the quality of practitioners assigned to each project. However, these various objectives conflict, so we can only hope to achieve an aggregated form of these disparate objectives. This emphasizes the need for a well-designed decision support system. Such a system will facilitate optimal assignment of practitioners to projects while minimizing the overheads involved in a multiuser decision-making environment. Here, "optimal" refers to maximizing certain aggregated attributes of the various matches made with respect to the system. The term Buser[ refers to the decision makers who assign employees to various projects. The system comprises a text-matching module, a business logic module, an optimization module, and a user interface (UI) module. Key innovations include translating the business objectives into optimization criteria to be solved using integer programming, incorporating individual user preferences into the optimization, and encapsulating user experience into the matching logic. Additional innovations include those in text analytics to differentiate skill and nonskill terms and a UI design that helps users understand the relative global optimality of each recommendation in familiar terms while influencing decisions made independently by individual users in a way that collectively leads to organizationally optimal outcomes. We also explain the role of decision support in implementing job-rotation and cross-training programs for practitioners. Finally, we share our experiences in the development and deployment of this system in an IT service company and describe areas where further work is needed.