Bex George Thomas

Automated Bed Assignments in a Complex and Dynamic Hospital Environment

Bed management, an important function of any hospital, has a major impact on patient care, patient flow, patient and staff satisfaction, and ultimately on the hospitals operating margin. A key challenge in bed management is optimizing the bed-assignment process in a complex and dynamic operating environment. Efficient bed assignment requires the merging of clinical information, hospital operations information, interdependencies between units, and real-time information on patients, resources, and workflows. We have developed analytical decision support tools with embedded mathematical models to periodically recommend bed-patient assignments. Using an innovative mixed-integer goal-programming modeling approach, we are able to accommodate the multiple goals and complex operating rules of different hospitals. We implemented and hosted our prototype bed-assignment solution as a cloud-based application for Mount Sinai Medical Center in New York.

General Electric Uses an Integrated Framework for Product Costing, Demand Forecasting, and Capacity Planning of New Photovoltaic Technology Products

General Electric (GE) Energys nascent solar business has revenues of over

SYSTEMS AND METHODS FOR MULTI-RESOURCE SCHEDULING

100 million, expects those revenues to grow to over

Method and system for demand response management in a network

1 billion in the next three years, and has plans to rapidly grow the business beyond this period. GE Global Research (GEGR), in partnership with GE Energys solar platform team, is pursuing a number of technological alternatives to bring new low-cost solar products to the market. However, the GE solar business is facing a challenge---making optimal investment decisions to realize its growth objectives in the presence of major uncertainties in technology, costs, demands, and energy policy. We have developed analytical decision support tools with embedded mathematical models to estimate product costs and demands, and to support capacity planning decisions under cost and demand uncertainties. In this paper, we outline our algorithmic approach and system implementation, which help to support strategic decisions at GE.