TY - CHAP
T1 - Smart Strategies for Project Scheduling
T2 - An Adaptive BR-DEH Approach
AU - Martin, Xabier A.
AU - Panadero, Javier
AU - Juan, Angel A.
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025/1/1
Y1 - 2025/1/1
N2 - In recent years, management of multiple complex projects has become increasingly common among large-scale companies, posing significant challenges for company managers. These projects are often identified by numerous tasks that have precedence relations and share a set of limited resources. The main goal of project scheduling is to minimize the total time required to complete these projects. However, this requires careful consideration of resource allocation, careful scheduling choices, and exact sequencing to maximize efficiency while managing the complex interactions between task dependencies. A novel discrete-event heuristic is presented to solve this project scheduling problem, which is later extended into a probabilistic algorithm using biased-randomization techniques. In addition, an adaptive mechanism was introduced to tune the parameters of the algorithm for optimal scheduling in different problem instances. Computational results demonstrate the effectiveness of our approach, finding high-quality solutions for difficult problem sets in short computational times. This intuitive and efficient approach can empower company managers to efficiently improve operational efficiency, reduce project costs, and ultimately increase their business’s success.
AB - In recent years, management of multiple complex projects has become increasingly common among large-scale companies, posing significant challenges for company managers. These projects are often identified by numerous tasks that have precedence relations and share a set of limited resources. The main goal of project scheduling is to minimize the total time required to complete these projects. However, this requires careful consideration of resource allocation, careful scheduling choices, and exact sequencing to maximize efficiency while managing the complex interactions between task dependencies. A novel discrete-event heuristic is presented to solve this project scheduling problem, which is later extended into a probabilistic algorithm using biased-randomization techniques. In addition, an adaptive mechanism was introduced to tune the parameters of the algorithm for optimal scheduling in different problem instances. Computational results demonstrate the effectiveness of our approach, finding high-quality solutions for difficult problem sets in short computational times. This intuitive and efficient approach can empower company managers to efficiently improve operational efficiency, reduce project costs, and ultimately increase their business’s success.
KW - Adaptive Algorithms
KW - Discrete-Event Heuristic
KW - Project Scheduling
UR - https://www.scopus.com/pages/publications/85219198946
UR - https://www.mendeley.com/catalogue/d2390527-8c0d-3b18-abf0-c65a88b1e910/
UR - https://portalrecerca.uab.cat/en/publications/b50472b0-afac-4149-922f-d5af06b68c00
U2 - 10.1007/978-3-031-78241-1_35
DO - 10.1007/978-3-031-78241-1_35
M3 - Chapter
AN - SCOPUS:85219198946
SN - 9783031782404
VL - 14779
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 383
EP - 393
BT - Decision Sciences - 2nd Decision Science Alliance International Summer Conference, DSA ISC 2024, Proceedings
A2 - Juan, Angel A.
A2 - Faulin, Javier
A2 - Lopez-Lopez, David
PB - Springer Science and Business Media Deutschland GmbH
ER -