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

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 frequen...

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Bibliographic Details
Published in:Mathematics (Basel) Vol. 12; no. 12; p. 1873
Main Authors: Martin, Xabier A., Herrero, Rosa, Juan, Angel A., Panadero, Javier
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.06.2024
Subjects:
adaptive algorithms
agile optimization
Aircraft industry
Algorithms
biased-randomization
Constraints
discrete-event heuristics
Graph representations
Heuristic
Manufacturing
Manufacturing industry
Mathematical optimization
Project management
Resource scheduling
resource-constrained project scheduling
Scheduling
Task complexity
Task scheduling
ISSN:2227-7390, 2227-7390
Online Access:Get full text
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Summary: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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ISSN:2227-7390
2227-7390
DOI:10.3390/math12121873