An object-oriented framework for model management and DSS development
Decision Support Systems
An object-oriented framework for model management
Decision Support Systems
Object-oriented model construction in production scheduling decisions
Decision Support Systems - Special double issue: unified programming
Towards a model and algorithm management system for vehicle routing and scheduling problems
Decision Support Systems
The vehicle routing problem
Knowledge-Based Approaches for Scheduling Problems: A Survey
IEEE Transactions on Knowledge and Data Engineering
Diversion Issues in Real-Time Vehicle Dispatching
Transportation Science
An overview of Knowledge Representation
Proceedings of the 1980 workshop on Data abstraction, databases and conceptual modeling
Real-Time Multivehicle Truckload Pickup and Delivery Problems
Transportation Science
AIMMS - Optimization Modeling
Reference metadata extraction using a hierarchical knowledge representation framework
Decision Support Systems
Model-driven decision support systems: Concepts and research directions
Decision Support Systems
Dynamic Vehicle Routing Based on Online Traffic Information
Transportation Science
Vehicle routing and scheduling with dynamic travel times
Computers and Operations Research
Knowledge-based modeling for disruption management in urban distribution
Expert Systems with Applications: An International Journal
| Hi-index | 0.00 |
During the urban distribution process, unexpected events may frequently result in disruptions to the current distribution plan, which need to be handled in real-time vehicle routing. In this paper, a knowledge-based modeling approach, PAM (disruption-handling Policies, local search Algorithms and object-oriented Modeling), is developed, which combines the scheduling knowledge of experienced schedulers with the optimization knowledge concerning models and algorithms in the field of Operations Research to obtain an effective solution in real time. Experienced schedulers can respond to different disruptions promptly with heuristic adjustment based on their experience, but their solutions may be inaccurate, inconsistent, or even infeasible. This method is limited when the problem becomes large-scale. The model-algorithm method can handle large-scale problems, but it has to predefine a specific disruption and a specific distribution state for constructing a model and algorithm, which is inflexible, time-consuming and consequently unable to promptly obtain solutions for responding to different disruptions in real time. PAM modeling approach combines the advantages and eliminates the disadvantages of the two methods aforementioned. Computational experiments show that solutions achieved by this modeling approach are practical and the speed of achieving the solutions is fast enough for responding to disruptions in real time.