`a`
Big Data and Information Analytics (BDIA)
 

A moving block sequence-based evolutionary algorithm for resource investment project scheduling problems
Pages: 39 - 58, Issue 1, January 2017

doi:10.3934/bdia.2017007      Abstract        References        Full text (570.6K)           Related Articles

Xiaoxiao Yuan - Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi'an 710071, China (email)
Jing Liu - Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi'an 710071, China (email)
Xingxing Hao - Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi'an 710071, China (email)

1 H. A. Abbass, A. Bender, H. Dam, S. Baker, J. M. Whitacre and R. A. Sarker, Computational scenario-based capability planning, in Genetic and Evolutionary Computation Conference (GECCO), ACM, Atlanta, Georgia, 2008, 1437-1444.
2 P. Brucker, A. Drexl, R. Möhring, K. Neumann and E. Pesch, Resource-constrained project scheduling: Notation, classification, models, and methods, European Journal of Operational Research, 112 (1999), 3-41.
3 L. T. Bui, M. Barlow and H. A. Abbass, A multi-objective risk-based framework for mission capability planning, New Mathematics and Natural Computation, 5 (2009), 459-485.
4 F. Chicano, F. Luna, A. J. Nebro and E. Alba, Using multi-objective metaheuristics to solve the software project scheduling problem, in GECCO '11 Proceedings of the 13th annual conference on Genetic and evolutionary computation, ACM, Dublin, Ireland, 2011, 1915-1922.
5 S.-H. Cho and S. D. Eppinger, A simulation-based process model for managing complex design projects, IEEE Trans. Engineering Management, 52 (2005), 316-328.
6 D. Debels, B. D. Reyck, R. Leus and M. Vanhoucke, A hybrid scatter search/electromagnetism meta-heuristic for project scheduling, European Journal of Operational Research, 169 (2006), 638-653, Feature Cluster on Scatter Search Methods for Optimization.       
7 E. Demeulemeester, Minimizing resource availability costs in time-limited project networks, Management Science, 41 (1995), 1590-1598.
8 B. Depenbrock, T. Balint and J. Sheehy, Leveraging design principles to optimize technology portfolio prioritization, in 2015 IEEE Aerospace Conference, 2015, 1-10.
9 A. Drexl and A. Kimms, Optimization guided lower and upper bounds for the resource investment problem, The Journal of the Operational Research Society, 52 (2001), 340-351.
10 K. S. Hindi, H. Yang and K. Fleszar, An evolutionary algorithm for resource-constrained project scheduling, IEEE Transactions on Evolutionary Computation, 6 (2002), 512-518.
11 R. Kolisch, Serial and parallel resource-constrained project scheduling methods revisited: Theory and computation, European Journal of Operational Research, 90 (1996), 320-333.
12 R. Kolisch and S. Hartmann, Heuristic algorithms for the resource-constrained project scheduling problem: Classification and computational analysis, Project Scheduling, (1999), 147-178.
13 R. Kolisch and S. Hartmann, Experimental investigation of heuristics for resource-constrained project scheduling: An update, European Journal of Operational Research, 174 (2006), 23-37.
14 R. Kolisch, A. Sprecher and A. Drexl, Characterization and generation of a general class of resource-constrained project scheduling problems, Management Science, 41 (1995), 1693-1703.
15 J. Liu, W. Zhong and L. Jiao, A multiagent evolutionary algorithm for combinatorial optimization problems, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 40 (2010), 229-240.
16 J. Liu, W. Zhong, L. Jiao and X. Li, Moving block sequence and organizational evolutionary algorithm for general floorplanning with arbitrarily shaped rectilinear blocks, IEEE Transactions on Evolutionary Computation, 12 (2008), 630-646.
17 J. Liu, W. Zhong and L. Jiao, A multiagent evolutionary algorithm for constraint satisfaction problems, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 36 (2006), 54-73.
18 L. L. Minku, D. Sudholt and X. Yao, Evolutionary algorithms for the project scheduling problem: runtime analysis and improved design, in GECCO '12 Proceedings of the 14th annual conference on Genetic and evolutionary computation, ACM, Philadelphia, Pennsylvania, USA, 2012, 1221-1228.
19 R. H. Möhring, Minimizing costs of resource requirements in project networks subject to a fixed completion time, Operational Research, 32 (1984), 89-120.
20 H. Nübel, The resource renting problem subject to temporal constraints, OR-Spektrum, 23 (2001), 359-381.       
21 C. Qian, Y. Yu and Z.-H. Zhou, Variable solution structure can be helpful in evolutionary optimization, Science China Information Sciences, 58 (2015), 112105, 17 pp.       
22 B. D. Reyck and R. Leus, R&d project scheduling when activities may fail, IIE Transactions, 40 (2008), 367-384.
23 S. R. Schultz and J. Atzmon, A simulation based heuristic approach to a resource investment problem (rip), in Proceedings of the Winter Simulation Conference, 2014, 3411-3422.
24 S. Shadrokh and F. Kianfar, A genetic algorithm for resource investment project scheduling problem, tardiness permitted with penalty, European Journal of Operational Research, 181 (2007), 86-101.       
25 J. Xiong, J. Liu, Y. Chen and H. A. Abbass, A knowledge-based evolutionary multiobjective approach for stochastic extended resource investment project scheduling problems, IEEE Transactions on Evolutionary Computation, 18 (2014), 742-763.
26 J. Xiong, K. wei Yang, J. Liu, Q. song Zhao and Y. wu Chen, A two-stage preference-based evolutionary multi-objective approach for capability planning problems, Knowledge-Based Systems, 31 (2012), 128-139.
27 W. Zhong, J. Liu, M. Xue and L. Jiao, A multiagent genetic algorithm for global numerical optimization, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 34 (2004), 1128-1141.

Go to top