Abstract
In recent years, the restructure of the customers’ supply chain and logistics network has redefined the way a logistics service is operated. Various kinds of logistics information systems have been well developed to store and process all sorts of data and information to support daily logistics operations. The logistics planning or decision-making of logistics activity, however, is still executed manually. In this paper, a Knowledge-based Logistics Management System (KLMS) is designed to support logistic service providers in making decisions during the stage of logistics planning and operations by extracting, sharing and storing real-time logistics knowledge. KLMS is developed by integrating Case-Based Reasoning (CBR), Radio Frequency Identification (RFID), Online Analytical Processing (OLAP) technologies and a branch-and-bound resource route optimising programming model seamlessly together, which is suitable for usage in different business processes in a warehouse operating environment. Through applying KLMS in GENCO, a US-based logistic service company, the overall logistics servicing level is enhanced through accurate decision-making and planning of warehouse operations.
| Original language | English |
|---|---|
| Pages (from-to) | 5-28 |
| Number of pages | 24 |
| Journal | International Journal of Enterprise Network Management |
| Volume | 1 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 1 Jan 2006 |
Keywords
- branch-and-bound optimisation
- Case-Based Reasoning (CBR)
- knowledge-based systems
- logistics management
- Online Analytical Processing (OLAP)
- Radio Frequency Identification (RFID)
ASJC Scopus subject areas
- Business and International Management
- Management Science and Operations Research
- Management of Technology and Innovation