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论文类型：期刊论文

发表时间：2013-02-01

发表刊物：DECISION SUPPORT SYSTEMS

收录刊物：SCIE、EI、Scopus

卷号：54

期号：3

页面范围：1380-1393

ISSN号：0167-9236

关键字：Knowledge representation; Object-oriented modeling; Local search algorithm; Disruption; Real-time Vehicle Routing Problem (RVRP)

摘要：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. (C) 2012 Elsevier B.V. All rights reserved.