research-article

Bi-objective multi-mode resource-constrained multi-project scheduling using combined NSGA II and Q-learning algorithm

Authors:

Wei ZhouAuthors Info & Claims

Volume 152, Issue C

https://doi.org/10.1016/j.asoc.2023.111201

Published: 01 February 2024 Publication History

Abstract

Multi-mode resource-constrained multi-project scheduling problem (MRCMPSP) plays a pivotal role in project management, serving as a critical component in production management for Engineering-to-Order manufacturing companies to enhance productivity, reduce costs, and minimize project completion time. This paper investigates the challenging problem of a bi-objective MRCMPSP, considering resource and ﬁnish time constraints, and develops a mathematical model to reduce project cycles and achieve better resource load balancing. By leveraging on the independent nature of start time selection for each activity, which aligns with the characteristics of a Markov decision process, we propose a two-layer iterative algorithm that combines the Nondominated Sorting Genetic Algorithm II (i.e., NSGA II) and Q-learning algorithm to solve the model eﬀectively. Hence, the NSGA II algorithm generates mode combinations, while its ﬁtness function employs the Q-learning algorithm to search for optimal activity time selections within each mode. We verify the performance superiority of the proposed algorithm by conducting a comparative analysis against classical approaches, encompassing classical NSGA II, Particle Swarm Optimization, and Ant Colony Optimization algorithms. Furthermore, this study’s experimental results therefore unequivocally demonstrate the eﬀectiveness of our algorithm in achieving optimized project scheduling outcomes.

Highlights

•

A bi-objective algorithm is proposed for solving the multi-mode resource-constrained multi-project scheduling problem.

•

The novel proposed algorithm demonstrates superior performance compared to classical algorithms such as NSGA II, PSO, and ACO.

•

The method addresses the challenges of solution space and infeasible solutions, leading to improved project cycle time and resource load balance.

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Published In

cover image Applied Soft Computing

Applied Soft Computing Volume 152, Issue C

Feb 2024

1017 pages

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Elsevier B.V.

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Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 February 2024

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