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Reoptimization Approaches for the Vehicle-Routing Problem with Stochastic Demands

机译：具有随机需求的车辆路径问题的重新优化方法

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摘要

We consider the vehicle-routing problem with stochastic demands (VRPSD) under reoptimization. We develop and analyze a finite-horizon Markov decision process (MDP) formulation for the single-vehicle case and establish a partial characterization of the optimal policy. We also propose a heuristic solution methodology for our MDP, named partial reoptimization, based on the idea of restricting attention to a subset of all the possible states and computing an optimal policy on this restricted set of states. We discuss two families of computationally efficient partial reoptimization heuristics and illustrate their performance on a set of instances with up to and including 100 customers. Comparisons with an existing heuristic from the literature and a lower bound computed with complete knowledge of customer demands show that our best partial reoptimization heuristics outperform this heuristic and are on average no more than 10%-13% away from this lower bound, depending on the type of instances. [PUBLICATION ABSTRACT]
机译：我们考虑了在重新优化下具有随机需求（VRPSD）的车辆路线问题。我们针对单车案件开发并分析了有限水平的马尔可夫决策过程（MDP）公式，并建立了最优策略的部分特征。我们还基于将注意力集中在所有可能状态的子集上并针对此受限状态集计算最佳策略的思想，为我们的MDP提出了一种启发式解决方案方法，称为部分重新优化。我们讨论了两个计算有效的部分重新优化启发式方法家族，并举例说明了它们在多达100个客户（包括100个客户）的一组实例上的性能。与现有文献中的启发式方法和对客户需求有完整了解的下限进行比较后，我们发现，最佳的部分重新优化启发式方法的性能优于该启发式方法，并且与该下限的平均距离不超过10％-13％，具体取决于实例类型。 [出版物摘要]

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  • 来源
    《Operations Research》 |2009年第1期|p.214-232|共19页
  • 作者

    Nicola Secomandi François Margot;

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    Nicola Secomandi, François MargotTepper School of Business, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213{ns7@andrew.cmu.edu, fmargot@andrew.cmu.edu}François Margot ("Reoptimization Approaches for the Vehicle-Routing Problem with Stochastic Demands") is an associate professor of operations research at the Tepper School of Business of Carnegie Mellon University. Professor Margot's main area of research is polyhedral combinatorics with a marked interest in branch-and-cut algorithms.Nicola Secomandi ("Reoptimization Approaches for the Vehicle-Routing Problem with Stochastic Demands") is an assistant professor of operations management and manufacturing at the Tepper School of Business at Carnegie Mellon University. His research interests include the interface between operations and finance, revenue and supply chain management, logistics under uncertainty, and applications in the energy and commodity industries.;

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