Using novel particle swarm optimization scheme to solve resource-constrained scheduling problem in PSPLIB

Authors:
Ruey-Maw Chen;Chung-Lun Wu;Chuin-Mu Wang;Shih-Tang Lo
Affiliations:
Department of Computer Science and Information Engineering, National Chin-yi University of Technology, Taichung 411, Taiwan, ROC;Department of Electrics Engineering, National Chin-yi University of Technology, Taichung 411, Taiwan, ROC;Department of Computer Science and Information Engineering, National Chin-yi University of Technology, Taichung 411, Taiwan, ROC;Department of Information Management, Kun Shan University, Tainan 710, Taiwan, ROC
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

Citing 12
Cited 4

Genetic algorithms and classifier systems: foundations and future directions

Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Course and classroom scheduling: an interactive computer graphics approach

Journal of Systems and Software
Noise strategies for improving local search

AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
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
Data popularity and shortest-job-first scheduling of network transfers

ICDT '06 Proceedings of the international conference on Digital Telecommunications
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
A simulation-GA based model for production planning in precast plant

Proceedings of the 38th conference on Winter simulation
Combining competitive scheme with slack neurons to solve real-time job scheduling problem

Expert Systems with Applications: An International Journal
GA-Based resource-constrained project scheduling with the objective of minimizing activities' cost

ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
Ant colony system: a cooperative learning approach to the traveling salesman problem

IEEE Transactions on Evolutionary Computation
Scheduling multiprocessor job with resource and timing constraintsusing neural networks

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Solving project scheduling problems with resource constraints via an event list-based evolutionary algorithm

Expert Systems with Applications: An International Journal
Task allocation in distributed computing systems using adaptive particle swarm optimisation

International Journal of Computer Applications in Technology
Solving resource-constrained project scheduling problems: Conceptual validation of FLP formulation and efficient permutation-based ABC computation

Computers and Operations Research
A new multi-objective multi-mode model for solving preemptive time-cost-quality trade-off project scheduling problems

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	12.05

Visualization

Abstract

This investigation proposes an improved particle swam optimization (PSO) approach to solve the resource-constrained scheduling problem. Two proposed rules named delay local search rule and bidirectional scheduling rule for PSO to solve scheduling problem are proposed and evaluated. These two suggested rules applied in proposed PSO facilitate finding global minimum (minimum makespan). The delay local search enables some activities delayed and altering the decided start processing time, and being capable of escaping from local minimum. The bidirectional scheduling rule which combines forward and backward scheduling to expand the searching area in the solution space for obtaining potential optimal solution. Moreover, to speed up the production of feasible solution, a critical path is adopted in this study. The critical path method is used to generate heuristic value in scheduling process. The simulation results reveal that the proposed approach in this investigation is novel and efficient for resource-constrained class scheduling problem.