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Fast GA-based project scheduling for computing resources allocation in a cloud manufacturing system

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Abstract

Cloud manufacturing is becoming an increasingly popular enterprise model in which computing resources are made available on-demand to the user as needed. Cloud manufacturing aims at providing low-cost, resource-sharing and effective coordination. In this study, we present a genetic algorithm (GA) based resource constraint project scheduling, incorporating a number of new ideas (enhancements and local search) for solving computing resources allocation problems in a cloud manufacturing system. A newly generated offspring may not be feasible due to task precedence and resource availability constraints. Conflict resolutions and enhancements are performed on newly generated offsprings after crossover or mutation. The local search can exploit the neighborhood of solutions to find better schedules. Due to its complex characteristics, computing resources allocation in a cloud manufacturing system is NP-hard. Computational results show that the proposed GA can rapidly provide a good quality schedule that can optimally allocate computing resources and satisfy users’ demands.

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References

  • Abrishami, S., & Naghibzadeh, M. (2012). Deadline-constrained workflow scheduling in software as a service cloud. Scientia Iranica, 19(3), 680–689.

    Article  Google Scholar 

  • Agarwal, A., Colak, S., & Erenguc, S. (2011). A neurogenetic approach for the resource-constrained project scheduling problem. Computer and Operations Research, 38(1), 44–50.

    Article  Google Scholar 

  • Alcaraz, A., & Maroto, C. (2001). A robust genetic algorithm for resource allocation in project scheduling. Annals of Operations Research, 102, 83–109.

    Article  Google Scholar 

  • Alcaraz, J., Maroto, C., & Ruiz. R. (2004). Improving the performance of genetic algorithms for the RCPS problem. In Proceedings of the 9th International Workshop on Project Management and Scheduling (pp. 40–43).

  • Baar, T., Brucker, P., & Knust, S. (1998). Tabu search algorithms and lower bounds for the resource-constrained project scheduling problem (pp. 1–18). US: Springer.

    Google Scholar 

  • Balas, E., & Vazacopoulos, A. (1998). Guided local search with shifting bottleneck for job shop scheduling. Management Science, 44, 262–275.

    Article  Google Scholar 

  • Bardsiri, A. K., & Hashemi, S. M. (2012). A review of workflow scheduling in cloud computing environment. International Journal of Computer Science and Management Research, 1(3), 348–351.

    Google Scholar 

  • Bouleimen, K., & Lecocq, H. (2003). A new efficient simulated annealing algorithm for the resource-constrained project scheduling problem and its multiple mode version. European Journal of Operational Research, 149(2), 268–281.

    Article  Google Scholar 

  • Brucker, P., Drexl, A., Möhring, R., Neumann, K., & Pesch, E. (1999). Resource-constrained project scheduling: Notation, classification, models, and methods. European Journal of Operational Research, 112(1), 3–41.

    Article  Google Scholar 

  • Chen, R. M., Wu, C. L., Wang, C. M., & Lo, S. T. (2010). Using novel particle swarm optimization scheme to solve resource-constrained scheduling problem in PSPLIB. Expert Systems with Applications, 37(3), 1899–1910.

    Article  Google Scholar 

  • Chen, W., Shi, Y. J., Teng, H. F., Lan, X. P., & Hu, L. C. (2010). An efficient hybrid algorithm for resource-constrained project scheduling. Information Sciences, 180(5), 1031–1039.

    Article  Google Scholar 

  • Cheng, Y., Tao, F., Liu, Y., Zhao, D., Zhang, L., & Xu, L. (2013). Energy-aware resource service scheduling based on utility evaluation in cloud manufacturing system. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 227(12), 1901–1915.

    Article  Google Scholar 

  • Coelho, J., & Tavares, L. (2003). Comparative analysis of meta-heuristics for the resource constrained project scheduling problem. Technical Report, Department of Civil Engineering, Instituto Superior Tecnico.

  • Debels, D., & Vanhoucke, M. (2007). A decomposition-based genetic algorithm for the resource-constrained project-scheduling problem. Operations Research, 55(3), 457–469.

    Article  Google Scholar 

  • Eshelman, L. J., & Schaffer, J. D. (1992). Real-coded genetic algorithms and interval schemata. In L. Darrel Whitley (Ed.), Foundations of Genetic Algorithms 2. San Mateo, CA: Morgan Kaufmann Publishers.

    Google Scholar 

  • Fang, C., & Wang, L. (2012). An effective shuffled frog-leaping algorithm for resource-constrained project scheduling problem. Computers and Operations Research, 39, 890–901.

    Article  Google Scholar 

  • Goldberg, D. (1989). Genetic Algorithms in Search, Optimization and Machine Learning. Reading: Addison Wesley.

  • Gonçalves, J. F., Resende, M. G., & Mendes, J. J. (2011). A biased random-key genetic algorithm with forward-backward improvement for the resource constrained project scheduling problem. Journal of Heuristics, 17(5), 467–486.

    Article  Google Scholar 

  • Grèze, L., Pellerin, R., Leclaire, P., & Perrier, N. (2014). CIGI2011: A heuristic method for resource-constrained project scheduling with activity overlapping. Journal of Intelligent Manufacturing, 25(4), 797–811.

    Article  Google Scholar 

  • Hartmann, S. (1998). A competitive genetic algorithm for resource-constrained project scheduling. Naval Research Logistics, 45(6), 733–750.

    Article  Google Scholar 

  • Hartmann, S. (2002). A self-adapting genetic algorithm for project scheduling under resource constraints. Naval Research Logistics, 49(5), 433–448.

    Article  Google Scholar 

  • Hartmann, S., & Briskorn, D. (2010). A survey of variants and extensions of the resource-constrained project scheduling problem. European Journal of Operational Research, 207(1), 1–14.

    Article  Google Scholar 

  • Holland, J. H. (1975). Adaptation in Natural and Artificial Systems. Ann Arbor, MA: University of Michigan.

    Google Scholar 

  • Jia, Q., & Seo, Y. (2013a). An improved particle swarm optimization for the resource-constrained project scheduling problem. The International Journal of Advanced Manufacturing Technology, 67(9–12), 2627–2638.

    Article  Google Scholar 

  • Jia, Q., & Seo, Y. (2013b). Solving resource-constrained project scheduling problems: conceptual validation of FLP formulation and efficient permutation-based ABC computation. Computers and Operations Research, 40(8), 2037–2050.

    Article  Google Scholar 

  • Ji, X., & Yao, K. (2014). Uncertain project scheduling problem with resource constraints. Journal of Intelligent Manufacturing. doi:10.1007/s10845-014-0980-x.

  • Kaur, N., Aulakh, T. S., & Cheema, R. S. (2011). Comparison of workflow scheduling algorithms in cloud computing. International Journal of Advanced Computer Science and Applications, 2(10), 81–86.

  • Khanzadi, M., Soufipour, R., & Rostami, M. (2011). A new improved genetic algorithm approach and a competitive heuristic method for large-scale multiple resource-constrained project-scheduling problems. International Journal of Industrial Engineering Computations, 2(4), 737–748.

    Article  Google Scholar 

  • Kim, B., Youn, C. H., Park, Y. S., Lee, Y., & Choi, W. (2012). An adaptive workflow scheduling scheme based on an estimated data processing rate for next generation sequencing in cloud computing. JIPS, 8(4), 555–566.

    Google Scholar 

  • Kochetov, Y., & Stolyar, A. (2003). Evolutionary local search with variable neighborhood for the resource constrained project scheduling problem. In Proceedings of the 3rd International Workshop of Computer Science and Information Technologies, Russia.

  • Kolisch, R., & Drexl, A. (1996). Adaptive search for solving hard project scheduling problems. Naval Research Logistics, 43(1), 23–40.

    Article  Google Scholar 

  • Kolisch, R., & Hartmann, S. (2006). Experimental investigation of heuristics for resource-constrained project scheduling: An update. European Journal of Operational Research, 174, 23–37.

    Article  Google Scholar 

  • Kolisch, R. (1995). Project scheduling under resource constraints: Efficient heuristics for several problem classes. Heidelberg: Springer Press.

    Book  Google Scholar 

  • Kolisch, R. (1996a). Efficient priority rules for the resource-constrained project scheduling problem. Journal of Operations Management, 14(3), 179–192.

    Article  Google Scholar 

  • Kolisch, R. (1996b). Serial and parallel resource-constrained project scheduling methods revisited: Theory and computation. European Journal of Operational Research, 90(2), 320–333.

    Article  Google Scholar 

  • Kolisch, R., & Sprecher, A. (1997). PSPLIB—A project scheduling problem library: OR Software—ORSEP operations research software exchange program. European Journal of Operational Research, 96(1), 205–216.

    Article  Google Scholar 

  • Lee, G., Chun, B. G., & Katz, R. H. (2011). Heterogeneity-aware resource allocation and scheduling in the cloud. In Proceedings of HotCloud (pp. 1–5).

  • Laili, Y., Tao, F., Zhang, L., Cheng, Y., Luo, Y. L., & Sarker, B. R. (2013). A ranking chaos algorithm for dual scheduling of cloud service and computing resource in private cloud. Computers in Industry, 64, 448–463.

    Article  Google Scholar 

  • Laili, Y., Tao, F., Zhang, L. & Ren, L. (2011), The optimal allocation model of computing resources in cloud manufacturing system. In IEEE Seventh International Conference on Natural Computation.

  • Laili, Y. J., Tao, F., Zhang, L., & Sarker, B. R. (2012). A study of optimal allocation of computing resources in cloud manufacturing systems. International Journal of Advanced Manufacturing Technology, 63, 671–690.

    Article  Google Scholar 

  • Leon, V. J., & Ramamoorthy, B. (1995). Strength and adaptability of problem space based neighborhoods for resource-constrained scheduling. OR Spectrum, 17(2–3), 173–182.

    Article  Google Scholar 

  • Lodha, P. R., & Wadhe, M. A. P. (2013). Study of different types of workflow scheduling algorithm in cloud computing. International Journal of Advanced Research in Computer Science and Electronics Engineering (IJARCSEE), 2(4), 421.

    Google Scholar 

  • Mehra, M., Jayalal, M. L., Arul, A. J., Rajeswari, S., Kuriakose, K., & Satya Murty, S. A. V. (2014). Study on different crossover mechanisms of genetic algorithm for test interval optimization for nuclear power plants. International Journal of Intelligent Systems and Applications, 6(1), 20–28.

    Article  Google Scholar 

  • Mendes, J. J., Goncalves, J. F., & Resende, M. G. C. (2009). A random key based genetic algorithm for the resource constrained project scheduling problem. Computers and Operations Research, 36(1), 92–109.

  • Merkle, D., Middendorf, M., & Schmeck, H. (2002). Ant colony optimization for resource-constrained project scheduling. IEEE Transactions on Evolutionary Computation, 6(4), 333–346.

    Article  Google Scholar 

  • Nowicki, E., & Smutnicki, C. (1996). A fast taboo search algorithm for the job shop problem. Management Science, 42(6), 797–813.

    Article  Google Scholar 

  • Rahman, M., Hassan, R., Ranjan, R., & Buyya, R. (2013). Adaptive workflow scheduling for dynamic grid and cloud computing environment. Concurrency and Computation: Practice and Experience, 25(13), 1816–1842.

    Article  Google Scholar 

  • Ranjbar, M. (2008). Solving the resource constrained project scheduling problem using filter-and-fan approach. Applied Mathematics and Computation, 201, 313–318.

    Article  Google Scholar 

  • Shi, Y. J., Qu, F. Z., Chen, W., & Li, B. (2010). An artificial bee colony with random key for resource-constrained project scheduling. In Life System Modeling and Intelligent Computing (pp. 148–157). Springer, Berlin Heidelberg.

  • Shue, L. Y., & Zamani, R. (1999). An intelligent search method for project scheduling problems. Journal of Intelligent Manufacturing, 10(3–4), 279–288.

    Article  Google Scholar 

  • Tao, F., Laili, Y., Xu, L., & Zhang, L. (2012). FC-PACO-RM: A parallel method for service composition optimal-selection in cloud manufacturing system. IEEE Transactions on Industrial Informatics, 9(4), 2023–2033.

    Article  Google Scholar 

  • Valls, V., Ballestin, F., & Quintanilla, S. (2008). A hybrid genetic algorithm for the resource-constrained project scheduling problem. European Journal of Operational Research, 185(2), 495–508.

    Article  Google Scholar 

  • Varalakshmi, P., Ramaswamy, A., Balasubramanian, A., & Vijaykumar, P. (2011). An optimal workflow based scheduling and resource allocation in cloud. In Advances in Computing and Communications (pp. 411–420). Springer, Berlin Heidelberg.

  • Wall B. W. (1996). A Genetic Algorithm for Resource Constrained Scheduling, PhD Thesis, Department of Mechanical Engineering, Massachusetts Institute of Technology, USA.

  • Wu, D., Greer, M. J., Rosen, D. W., & Schaefer, D. (2013). Cloud manufacturing: Strategic vision and state-of-the-art. Journal of Manufacturing System, 32(4), 564–579.

    Article  Google Scholar 

  • Wu, L., & Yang, C. (2010). A solution of manufacturing resources sharing in cloud computing environment. Cooperative Design, Visualization and Engineering (pp. 247–252). Berlin Heidelberg: Springer.

    Chapter  Google Scholar 

  • Yassa, S., Chelouah, R., Kadima, H., & Granado, B. (2013). Multi-objective approach for energy-aware workflow scheduling in cloud computing environments. The Scientific World Journal.

  • Zhang, Q., Zhu, Q., & Boutaba, R. (2011). Dynamic resource allocation for spot markets in cloud computing environments. In Utility and Cloud Computing (UCC), 2011 Fourth IEEE International Conference on IEEE (pp. 178–185).

  • Zamani, R. (2013). A competitive magnet-based genetic algorithm for solving the resource-constrained project scheduling problem. European Journal of Operational Research, 229(2), 552–559.

    Article  Google Scholar 

  • Ziarati, K., Akbari, R., & Zeighami, V. (2011). On the performance of bee algorithms for resource-constrained project scheduling problem. Applied Soft Computing, 11(4), 3720–3733.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Industrial Engineering and Systems Management, Feng Chia University, P.O. Box 25-097, Taichung, 40724, Taiwan, ROC

    Yang-Kuei Lin

  2. Planning and Operations Management Group, Singapore Institute of Manufacturing Technology, A-star, 7 Nanyang Avenue, Singapore, 638075, Singapore

    Chin Soon Chong

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  1. Yang-Kuei Lin
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Lin, YK., Chong, C.S. Fast GA-based project scheduling for computing resources allocation in a cloud manufacturing system. J Intell Manuf 28, 1189–1201 (2017). https://doi.org/10.1007/s10845-015-1074-0

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