原文传递
Development of DCMC Site Selection Model
| 题名： | Development of DCMC Site Selection Model |
| 作者： | Hulsingh, Jeffrey L.;Zimmerman, Randy; |
| 关键词： | COST EFFECTIVENESS, TRAVEL, SITE SELECTION, ALGORITHMS, SYMPOSIA, MANAGEMENT PLANNING AND CONTROL, DECISION MAKING, JOB TRAINING. |
| 摘要： | A recurrent reason for DCMC employee travel is to attend professional conferences or development training. The location of the conference or training site is often an arbitrary decision with little regard for travel cost. We developed a methodology to evaluate relevant travel costs and return the least expensive conference site given over 260 cities in the U.S. with Government contracted airfares. This schema involves a new use of Dykstra's algorithm modified for non-capacitated shortest path network optimization as well as the computation of inter-airport distances using latitude and longitude data. This paper introduces a program called OffSite which optimizes the selection of collective training events or conferences where people need to come together from geographically dispersed locations. Offsite requires meeting or conference planners to input a list of origin cities for conference participants. It then determines the least cost location for hosting the conference. Additionally, OffSite is flexible enough to allow planners to choose a preferred destination. This system can also restrict the search for meeting venues to origin locations should an organizer wish to have one of the meeting's participants host the conference. |
| 总页数： | 178 |
| 报告类型： | 科技报告 |
相关文献
- Evaluation of Logistics Led Economic Development, Summary
- Life-Cycle Cost/Benefit Assessment of Expedite Departure Path (EDP).
- Evaluation of Logistics Led Economic Development, Summary.
- Household Location Choices: The Case of Homebuyers and Apartment Dwellers in Austin, Texas.
- Evaluation of Logistics Led Economic Development.
- Siting Considerations for the Canister Shipping Facility at the Defense Waste Processing Facility at the Savannah River Site
检索历史
应用推荐
