An Improved Multi-Objective Particle Swarm Optimization Algorithm for Solving Multi-mode Resource-constrained Multi-project Scheduling Problem

To address the challenging Multi-Mode Resource-Constrained Multi-Project Scheduling Problem (MRCMPSP) that involves scheduling multiple instances of projects with various execution modes, subject to resource constraints and precedence relations, this paper proposes the Delay Parallel Decoupled Sched...

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Vydáno v:	2023 4th International Conference on Information Science, Parallel and Distributed Systems (ISPDS) s. 437 - 441
Hlavní autoři:	Chen, Dejun, Wang, Ben
Médium:	Konferenční příspěvek
Jazyk:	angličtina
Vydáno:	IEEE 14.07.2023
Témata:	Clustering algorithms Delays Dynamic scheduling Heuristic algorithms Multi-mode Resource-constrained Multi-project Scheduling Problem Multi-objective optimization problem Particle Swarm Optimization Algorithm Schedules Scheduling algorithms Topology
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Popis
Shrnutí:	To address the challenging Multi-Mode Resource-Constrained Multi-Project Scheduling Problem (MRCMPSP) that involves scheduling multiple instances of projects with various execution modes, subject to resource constraints and precedence relations, this paper proposes the Delay Parallel Decoupled Schedule Generation Scheme (DPDSGS) and the Dynamic-Cluster Dynamic-Weight Multi-Objective Particle Swarm Optimization (DCDW-MO-PSO) based on a dynamic topology structure. These methods can generate multi-project baseline schedules that meet practical requirements and optimize three objectives: total makespan (TMP), total project delay (TPD), and max project delay (MPD). Moreover, the superior optimization performance of DCDW-MO-PSO is demonstrated by comparing it with other multi-objective optimization algorithms using example problems.
DOI:	10.1109/ISPDS58840.2023.10235484