Anchor-robust project scheduling with non-availability periods

IF 0.9 4区 数学 Q3 MATHEMATICS, APPLIED Discrete Optimization Pub Date : 2024-11-01 DOI:10.1016/j.disopt.2024.100864
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Abstract

In large-scale scheduling applications, it is often decisive to find reliable schedules prior to the execution of the project. Most of the time however, data is affected by various sources of uncertainty. Robust optimization is used to overcome this imperfect knowledge. Anchor robustness, as introduced in the literature for processing time uncertainty, makes it possible to guarantee job starting times for a subset of jobs. In this paper, anchor robustness is extended to the case where uncertain non-availability periods must be taken into account. Three problems are considered in the case of budgeted uncertainty: checking that a given subset of jobs is anchored in a given schedule, finding a schedule of minimal makespan in which a given subset of jobs is anchored and finding an anchored subset of maximum weight in a given schedule. Polynomial time algorithms are proposed for the first two problems while an inapproximability result is given for the third one.
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具有不可用时段的锚固型项目进度安排
在大规模日程安排应用中,在项目执行前找到可靠的日程安排往往具有决定性意义。然而,在大多数情况下,数据会受到各种不确定因素的影响。稳健优化就是用来克服这种不完善的知识。文献中针对处理时间不确定性提出的锚稳健性,可以保证工作子集的工作开始时间。本文将锚稳健性扩展到必须考虑不确定不可用时段的情况。在预算不确定的情况下,本文考虑了三个问题:检查给定计划中是否锚定了给定的工作子集;找到一个最小有效期的计划,其中锚定了给定的工作子集;找到给定计划中权重最大的锚定子集。针对前两个问题提出了多项式时间算法,针对第三个问题给出了不可逼近性结果。
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来源期刊
Discrete Optimization
Discrete Optimization 管理科学-应用数学
CiteScore
2.10
自引率
9.10%
发文量
30
审稿时长
>12 weeks
期刊介绍: Discrete Optimization publishes research papers on the mathematical, computational and applied aspects of all areas of integer programming and combinatorial optimization. In addition to reports on mathematical results pertinent to discrete optimization, the journal welcomes submissions on algorithmic developments, computational experiments, and novel applications (in particular, large-scale and real-time applications). The journal also publishes clearly labelled surveys, reviews, short notes, and open problems. Manuscripts submitted for possible publication to Discrete Optimization should report on original research, should not have been previously published, and should not be under consideration for publication by any other journal.
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