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Collaborative material ordering in decentralized multi-project scheduling: a Stackelberg game perspective
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DOI：10.1080/17509653.2026.2667265.png)
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Abstract
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In a multi-project environment, collaboration in material provisioning while maintaining project managers’ autonomy in decision-making regarding scheduling and resource management leads to cost reduction and enhances project performance. This paper studies the resource investment and materials ordering problem for multi-projects with the possibility of collaboration in shared materials ordering in a decentralized approach. Due to the interactions between the project managers, a Stackelberg game is utilized for problem formulation, and a bi-level mixed integer programming model is proposed. To solve this bi-level model, two nested bi-level memetic metaheuristic algorithms, namely a nested bi-level memetic genetic algorithm (MGA) and a nested bi-level memetic particle swarm optimization (MPSO) algorithm have been developed. To evaluate the performance of the proposed solution methods, a set of sample problems was solved. The numerical results indicate that collaborative material procurement yields substantial cost savings, with reductions of up to 16% for the leader and 15% for the follower when using the MGA algorithm, and approximately 12% for both parties when using the MPSO algorithm. These findings confirm the effectiveness of the proposed methods in achieving cost-efficient project planning.
Keywords:
Project scheduling
materials ordering
Stackelberg game
bi-level programming
nested bi-level metaheuristic algorithms
C61
C72
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2.6
Papers:
237
Citations:
739
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