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A two-stage-priority-rule-based algorithm for robust resource-constrained project scheduling

机译:基于两阶段优先权规则的鲁棒资源受限项目调度算法

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Traditionally, the resource-constrained project scheduling problem (RCPSP) is modeled as a static and deterministic problem and is solved with the objective of makespan minimization. However, many uncertainties, such as unpredictable increases in processing times caused by rework or supplier delays, random transportation and/or setup, may render the proposed solution obsolete. In this paper, we present a two-stage algorithm for robust resource-constrained project scheduling. The first stage of the algorithm solves the RCPSP for minimizing the makespan only using a priority-rule-based heuristic, namely an enhanced multi-pass random-biased serial schedule generation scheme. The problem is then similarly solved for maximizing the schedule robustness while considering the makespan obtained in the first stage as an acceptance threshold. Selection of the best schedule in this phase is based on one out of 12 alternative robustness predictive indicators formulated for the maximization purpose. Extensive simulation testing of the generated schedules provides strong evidence of the benefits of considering robustness of the schedules in addition to their makespans. For illustration purposes, for 10 problems from the well-known standard set J30, both robust and non-robust schedules are executed with a 10% duration increase that is applied to the same randomly picked 20% of the project activities. Over 1000 iterations per instance problem, the robust schedules display a shorter makespan in 55% of the times while the non-robust schedules are shown to be the best performing ones in only 6% of the times.
机译:传统上,将资源受限的项目计划问题(RCPSP)建模为静态的确定性问题,并以最小化制造期为目标进行解决。但是,许多不确定性,例如由于返工或供应商延迟,随机运输和/或设置而导致的处理时间的不可预测的增加,可能会使建议的解决方案过时。在本文中,我们提出了一种鲁棒的资源受限项目调度的两阶段算法。该算法的第一阶段解决了RCPSP,仅使用基于优先级规则的启发式方法(即增强型多遍随机偏置串行调度生成方案)来最小化有效期。然后类似地解决该问题以最大化调度鲁棒性,同时将在第一阶段中获得的制造期视为接受阈值。此阶段中最佳计划的选择是基于为最大化目的而制定的12种替代性稳健性预测指标中的一种。对生成的计划表进行广泛的模拟测试,提供了强有力的证据,证明了在考虑计划表的健壮性之外还考虑了计划表的鲁棒性。出于说明目的,对于来自著名标准集J30的10个问题,执行健壮和不健壮的计划时,会增加10%的持续时间,并将其应用于相同的随机抽取的20%的项目活动。每个实例问题进行了1000次以上的迭代,健壮的计划在55%的时间内显示较短的生成时间,而非健壮的计划仅在6%的时间内显示为性能最佳的计划。

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