A bi-objective robust resource allocation model for the RCPSP considering resource transfer costs
A bi-objective robust resource allocation model for the RCPSP considering resource transfer costs
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DOI：
10.1080/00207543.2019.1695168
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发表时间：
2019-11
影响因子：
9.2
通讯作者：
Jianjiang Wang;Xuejun Hu;E. Demeulemeester;Yan Zhao
中科院分区：
文献类型：
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作者：
Jianjiang Wang;Xuejun Hu;E. Demeulemeester;Yan Zhao
资源分配是项目调度中确保稀缺再生资源有效利用的核心问题之一，在制造业和服务业的生产系统中经常遇到。可再生资源在活动之间的转移通常会产生一定的调度成本，并影响特定调度在不确定环境下的鲁棒性。为了解决这个问题，提出了一种双目标优化模型来做出资源转移决策，其目的是在存在活动持续时间变化的情况下最小化转移成本并最大化解决方案的鲁棒性。所提出的模型采用了一种新颖的面向资源的流公式，与之前的文献不同。 NSGA-II 和 Pareto 模拟退火 (PSA) 算法已被用作求解方法。此外，还与约束方法进行比较来评估元启发法的有效性。具体来说，算法在一组基准上执行，并根据四个性能指标进行比较以测试其效率：非支配解的数量、一般距离、超体积和间距。最后，通过实际项目的案例研究进一步表明所提出的模型和算法对于实际问题是适用且有益的。
Resource allocation is one of the core issues in project scheduling to ensure the effective use of scare renewable resources, and has been regularly encountered in production systems in the manufacturing and service industries. The transfers of renewable resources between activities generally incur certain scheduling costs and affect the robustness of a certain schedule in an uncertain environment. To address this issue, a bi-objective optimisation model is proposed to make the resource transfer decisions, which aims to minimise the transfer cost and maximise solution robustness in the presence of activity duration variability. The proposed model employs a novel resource-oriented flow formulation that is different from those of the previous literature. A NSGA-II and a Pareto simulated annealing (PSA) algorithm have been applied as the solution methodologies. Besides, the effectiveness of the metaheuristics are evaluated in comparison with a -constraint method. In detail, the algorithms are carried out on a set of benchmarks and are compared to test their efficiencies based on four performance metrics: number of non-dominated solutions, general distance, hypervolume and spacing. Finally, a case study of a real project further indicates that the suggested model and algorithms are applicable and beneficial to the problem in practice.