Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.dss.2012.12.014 | Decision Support Systems |
Keywords | Field | DocType |
experienced schedulers,pam modeling approach,different disruption,knowledge-based modeling approach,large-scale problem,pam approach,real-time vehicle,urban distribution process,specific distribution state,current distribution plan,real time,modeling approach,knowledge representation,local search algorithm | Object-oriented modeling,Knowledge representation and reasoning,Heuristic,Vehicle routing problem,Mathematical optimization,Current distribution,Scheduling (computing),Computer science,Local search (optimization),Unexpected events | Journal |
Volume | Issue | ISSN |
54 | 3 | 0167-9236 |
Citations | PageRank | References |
7 | 0.42 | 21 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiangpei Hu | 1 | 93 | 17.52 |
| Lijun Sun | 2 | 82 | 17.07 |
| Linlin Liu | 3 | 7 | 0.42 |