Journal article
A knowledge-based approach for facilities location planning
Expert systems with applications, v 5(1), pp 131-139
1992
Abstract
The location of facilities is important for both service and manufacturing firms seeking to start up new businesses or to expand or modify the scope of their current operations. Numerous models have been developed to assist in the location decision, both for specific types of facilities and businesses, as well as for the general case. Most available models, however, are unable to cope with symbolic, uncertain, or incomplete information. In addition, they must deal with combinatorially huge solution spaces of feasible locations, requiring excessive computation. Even recourse to many heuristic methods suffers from nonoptimal solutions and corresponding accuracy concerns. This paper describes a knowledge-based system (KBS) for the location problem that attempts to remedy the above problems. It provides a means for capturing symbolic concepts and handling qualitative aspects of the location decision. The KBS achieves this by employing user-defined heuristics and inferencing strategies to drastically reduce the solution space and thereby, the computational requirements for procedural models. Furthermore, it applies expert heuristics to identify an appropriate model for a particular facilities location problem. The structure of the KBS program is described, along with an illustration of its use.
Metrics
Details
- Title
- A knowledge-based approach for facilities location planning
- Creators
- Bay Arinze - Drexel UniversityAvijit Banerjee - Drexel University
- Publication Details
- Expert systems with applications, v 5(1), pp 131-139
- Publisher
- Elsevier
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Decision Sciences (and Management Information Systems)
- Web of Science ID
- WOS:A1992JA73300014
- Scopus ID
- 2-s2.0-22144457052
- Other Identifier
- 991019173749604721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Engineering, Electrical & Electronic
- Operations Research & Management Science