A late-mover genetic algorithm for resource-constrained project-scheduling problems

Liu, Yongping; Huang, Linyan; Liu, Xiufeng; Ji, Guomin; Cheng, Xu; Onstein, Erling

doi:10.1016/j.ins.2023.119164

Cited by 7 publications

(1 citation statement)

References 47 publications

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“…Liu et al [32] presented a straightforward algorithm combining the "1 + 1" evolution strategy with a genetic algorithm framework, which innovatively eliminates parameter tuning and employs real-valued numbers and path representation. This method proved competitive against benchmark algorithms, though further research is needed to enhance its exploration capabilities.…”

Section: Related Workmentioning

confidence: 99%

An Agile Adaptive Biased-Randomized Discrete-Event Heuristic for the Resource-Constrained Project Scheduling Problem

Martin,

Herrero,

Juan

et al. 2024

Mathematics

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In industries such as aircraft or train manufacturing, large-scale manufacturing companies often manage several complex projects. Each of these projects includes multiple tasks that share a set of limited resources. Typically, these tasks are also subject to time dependencies among them. One frequent goal in these scenarios is to minimize the makespan, or total time required to complete all the tasks within the entire project. Decisions revolve around scheduling these tasks, determining the sequence in which they are processed, and allocating shared resources to optimize efficiency while respecting the time dependencies among tasks. This problem is known in the scientific literature as the Resource-Constrained Project Scheduling Problem (RCPSP). Being an NP-hard problem with time dependencies and resource constraints, several optimization algorithms have already been proposed to tackle the RCPSP. In this paper, a novel discrete-event heuristic is introduced and later extended into an agile biased-randomized algorithm complemented with an adaptive capability to tune the parameters of the algorithm. The results underscore the effectiveness of the algorithm in finding competitive solutions for this problem within short computing times.

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Section: Related Workmentioning

confidence: 99%