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Logistical support scheduling under stochastic travel times given an emergency repair work schedule

机译:给定紧急维修工作时间表,根据随机旅行时间制定后勤支持计划

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Stochastic factors during the operational stage could have a significant influence on the planning results of logistical support scheduling for emergency roadway repair work. An optimal plan might therefore lose its optimality when applied in real world operations where stochastic disturbances occur. In this study we employ network flow techniques to construct a logistical support scheduling model under stochastic travel times. The concept of time inconsistency is also proposed for precisely estimating the impact of stochastic disturbances arising from variations in vehicle trip travel times during the planning stage. The objective of the model is to minimize the total operating cost with an unanticipated penalty cost for logistical support under stochastic traveling times in short term operations, based on an emergency repair work schedule, subject to related operating constraints. This model is formulated as a mixed-integer multiple-commodity network flow problem and is characterized as NP-hard. To solve the problem efficiently, a heuristic algorithm, based on problem decomposition and variable fixing techniques, is proposed. A simulation-based evaluation method is also presented to evaluate the schedules obtained using the manual method, the deterministic model and the stochastic model in the operation stage. Computational tests are performed using data from Taiwan's 1999 Chi-Chi earthquake. The preliminary test results demonstrate the potential usefulness of the proposed stochastic model and solution algorithm in actual practice.
机译:运营阶段的随机因素可能会对应急巷道维修工作的后勤保障计划的计划结果产生重大影响。因此,当在发生随机干扰的实际操作中使用最优计划时,最优计划可能会失去其最优性。在这项研究中,我们采用网络流量技术来构造随机旅行时间下的后勤支持调度模型。还提出了时间不一致的概念,以精确估计在规划阶段因车辆行程时间的变化而引起的随机干扰的影响。该模型的目的是根据紧急维修工作时间表,在相关操作约束的基础上,以短期旅行中随机旅行时间内的后勤支持的意外损失成本,将总运营成本降至最低。该模型被公式化为一个混合整数的多商品网络流问题,并且具有NP-hard的特征。为了有效地解决问题,提出了一种基于问题分解和变量修复技术的启发式算法。还提出了一种基于仿真的评估方法,以评估在操作阶段使用手动方法,确定性模型和随机模型获得的进度表。使用来自台湾1999年集集地震的数据进行计算测试。初步的测试结果证明了所提出的随机模型和求解算法在实际中的潜在实用性。

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