Genetic algorithms and classifier systems: foundations and future directions
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
A Tabu Search Approach for the Resource ConstrainedProject Scheduling Problem
Journal of Heuristics
Static priority scheduling for ATM networks
RTSS '97 Proceedings of the 18th IEEE Real-Time Systems Symposium
A Dynamic Clustering Heuristic for Jobs Scheduling on Grid Computing Systems
SKG '05 Proceedings of the First International Conference on Semantics, Knowledge and Grid
An Application-Oriented On-Demand Scheduling Approach in the Computational Grid Environment
GCC '06 Proceedings of the Fifth International Conference on Grid and Cooperative Computing
Task Scheduling in Grid Based on Particle Swarm Optimization
ISPDC '06 Proceedings of the Proceedings of The Fifth International Symposium on Parallel and Distributed Computing
Expert Systems with Applications: An International Journal
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Ant colony optimization for resource-constrained project scheduling
IEEE Transactions on Evolutionary Computation
A neural-network packet switch controller: scalability, performance, and network optimization
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
A modified particle swarm optimization for aggregate production planning
Expert Systems with Applications: An International Journal
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The studied resource-constrained project scheduling problem (RCPSP) is a classical well-known problem which involves resource, precedence, and temporal constraints and has been applied to many applications. However, the RCPSP is confirmed to be an NP-hard combinatorial problem. Restated, it is hard to be solved in a reasonable time. Therefore, there are many metaheuristics-based schemes for finding near optima of RCPSP were proposed. The particle swarm optimization (PSO) is one of the metaheuristics, and has been verified being an efficient nature-inspired algorithm for many optimization problems. For enhancing the PSO efficiency in solving RCPSP, an effective scheme is suggested. The justification technique is combined with PSO as the proposed justification particle swarm optimization (JPSO), which includes other designed mechanisms. The justification technique adjusts the start time of each activity of the yielded schedule to further shorten the makespan. Moreover, schedules are generated by both forward scheduling particle swarm and backward scheduling particle swarm in this work. Additionally, a mapping scheme and a modified communication mechanism among particles with a designed gbest ratio (GR) are also proposed to further improve the efficiency of the proposed JPSO. Simulation results demonstrate that the proposed JPSO provides an effective and efficient approach for solving RCPSP.