2017, 2(1): 39-58. doi: 10.3934/bdia.2017007

A moving block sequence-based evolutionary algorithm for resource investment project scheduling problems

Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Xi'an 710071, China

* Corresponding author: Jing Liu

Published  September 2017

Inspired by the representation designed for floorplanning problems, in this paper, we proposed a new representation, namely the moving block sequence (MBS), for resource investment project scheduling problems (RIPSPs). Since each activity of a project in RIPSPs has fixed duration and resource demand, we consider an activity as a rectangle block whose width is equal to the duration of the activity and height the resource needed by the activity. Four move modes are designed for activities, by using which the activity can move to the appropriate position. Therefore, the new representation of the project of RIPSPs consists of two parts: an activity list and a move mode list. By initializing the move modes randomly for each activity and moving it appropriately, the activity list can be decoded into valid solutions of RIPSPs. Since the decoding method of MBS guarantees that after moved, each activity is scheduled in the left-most and bottom-most position within a coordinate, which means that each activity in the corresponding project is arranged as early as possible when the precedence constraints and resource demands are satisfied. In addition, the multiagent evolutionary algorithm (MAEA) is employed to incorporate with the newly designed MBS representation in solving RIPSPs. With the intrinsic properties of MBS in mind, four behaviors, namely the crossover, mutation, competition, and self-learning operators are designed for agents in MAEA. To test the performance of our algorithm, 450 problem instances are used and the experimental results demonstrate the good performance of the proposed representation.

Citation: Xiaoxiao Yuan, Jing Liu, Xingxing Hao. A moving block sequence-based evolutionary algorithm for resource investment project scheduling problems. Big Data & Information Analytics, 2017, 2 (1) : 39-58. doi: 10.3934/bdia.2017007
References:
[1]

H. A. Abbass, A. Bender, H. Dam, S. Baker, J. M. Whitacre, R. A. Sarker, Computational scenario-based capability planning, in Genetic and Evolutionary Computation Conference (GECCO), ACM, Atlanta, Georgia, (2008), 1437-1444. doi: 10.1145/1389095.1389378.

[2]

P. Brucker, A. Drexl, R. Möhring, K. Neumann, E. Pesch, Resource-constrained project scheduling: Notation, classification, models, and methods, European Journal of Operational Research, 112 (1999), 3-41. doi: 10.1016/S0377-2217(98)00204-5.

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L. T. Bui, M. Barlow, H. A. Abbass, A multi-objective risk-based framework for mission capability planning, New Mathematics and Natural Computation, 5 (2009), 459-485. doi: 10.1142/S1793005709001428.

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F. Chicano, F. Luna, A. J. Nebro, 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. doi: 10.1145/2001576.2001833.

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S.-H. Cho, S. D. Eppinger, A simulation-based process model for managing complex design projects, IEEE Trans. Engineering Management, 52 (2005), 316-328. doi: 10.1109/TEM.2005.850722.

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D. Debels, B. D. Reyck, R. Leus, 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. doi: 10.1016/j.ejor.2004.08.020.

[7]

E. Demeulemeester, Minimizing resource availability costs in time-limited project networks, Management Science, 41 (1995), 1590-1598. doi: 10.1287/mnsc.41.10.1590.

[8]

B. Depenbrock, T. Balint, J. Sheehy, Leveraging design principles to optimize technology portfolio prioritization, in 2015 IEEE Aerospace Conference, (2015), 1-10. doi: 10.1109/AERO.2015.7119203.

[9]

A. Drexl, A. Kimms, Optimization guided lower and upper bounds for the resource investment problem, The Journal of the Operational Research Society, 52 (2001), 340-351. doi: 10.1057/palgrave.jors.2601099.

[10]

K. S. Hindi, H. Yang, K. Fleszar, An evolutionary algorithm for resource-constrained project scheduling, IEEE Transactions on Evolutionary Computation, 6 (2002), 512-518. doi: 10.1109/TEVC.2002.804914.

[11]

R. Kolisch, Serial and parallel resource-constrained project scheduling methods revisited: Theory and computation, European Journal of Operational Research, 90 (1996), 320-333. doi: 10.1016/0377-2217(95)00357-6.

[12]

R. Kolisch, S. Hartmann, Heuristic algorithms for the resource-constrained project scheduling problem: Classification and computational analysis, Project Scheduling, (1999), 147-178. doi: 10.1007/978-1-4615-5533-9_7.

[13]

R. Kolisch, S. Hartmann, Experimental investigation of heuristics for resource-constrained project scheduling: An update, European Journal of Operational Research, 174 (2006), 23-37. doi: 10.1016/j.ejor.2005.01.065.

[14]

R. Kolisch, A. Sprecher, A. Drexl, Characterization and generation of a general class of resource-constrained project scheduling problems, Management Science, 41 (1995), 1693-1703. doi: 10.1287/mnsc.41.10.1693.

[15]

J. Liu, W. Zhong, 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, 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, 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, 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. doi: 10.1145/2330163.2330332.

[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. doi: 10.1007/PL00013357.

[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, R. Leus, R&d project scheduling when activities may fail, IIE Transactions, 40 (2008), 367-384. doi: 10.1080/07408170701413944.

[23]

S. R. Schultz, J. Atzmon, A simulation based heuristic approach to a resource investment problem (rip), in Proceedings of the Winter Simulation Conference, (2014), 3411-3422. doi: 10.1109/WSC.2014.7020174.

[24]

S. Shadrokh, F. Kianfar, A genetic algorithm for resource investment project scheduling problem, tardiness permitted with penalty, European Journal of Operational Research, 181 (2007), 86-101. doi: 10.1016/j.ejor.2006.03.056.

[25]

J. Xiong, J. Liu, Y. Chen, 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, Y.wu. Chen, A two-stage preference-based evolutionary multi-objective approach for capability planning problems, Knowledge-Based Systems, 31 (2012), 128-139. doi: 10.1016/j.knosys.2012.02.003.

[27]

W. Zhong, J. Liu, M. Xue, L. Jiao, A multiagent genetic algorithm for global numerical optimization, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 34 (2004), 1128-1141.

show all references

References:
[1]

H. A. Abbass, A. Bender, H. Dam, S. Baker, J. M. Whitacre, R. A. Sarker, Computational scenario-based capability planning, in Genetic and Evolutionary Computation Conference (GECCO), ACM, Atlanta, Georgia, (2008), 1437-1444. doi: 10.1145/1389095.1389378.

[2]

P. Brucker, A. Drexl, R. Möhring, K. Neumann, E. Pesch, Resource-constrained project scheduling: Notation, classification, models, and methods, European Journal of Operational Research, 112 (1999), 3-41. doi: 10.1016/S0377-2217(98)00204-5.

[3]

L. T. Bui, M. Barlow, H. A. Abbass, A multi-objective risk-based framework for mission capability planning, New Mathematics and Natural Computation, 5 (2009), 459-485. doi: 10.1142/S1793005709001428.

[4]

F. Chicano, F. Luna, A. J. Nebro, 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. doi: 10.1145/2001576.2001833.

[5]

S.-H. Cho, S. D. Eppinger, A simulation-based process model for managing complex design projects, IEEE Trans. Engineering Management, 52 (2005), 316-328. doi: 10.1109/TEM.2005.850722.

[6]

D. Debels, B. D. Reyck, R. Leus, 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. doi: 10.1016/j.ejor.2004.08.020.

[7]

E. Demeulemeester, Minimizing resource availability costs in time-limited project networks, Management Science, 41 (1995), 1590-1598. doi: 10.1287/mnsc.41.10.1590.

[8]

B. Depenbrock, T. Balint, J. Sheehy, Leveraging design principles to optimize technology portfolio prioritization, in 2015 IEEE Aerospace Conference, (2015), 1-10. doi: 10.1109/AERO.2015.7119203.

[9]

A. Drexl, A. Kimms, Optimization guided lower and upper bounds for the resource investment problem, The Journal of the Operational Research Society, 52 (2001), 340-351. doi: 10.1057/palgrave.jors.2601099.

[10]

K. S. Hindi, H. Yang, K. Fleszar, An evolutionary algorithm for resource-constrained project scheduling, IEEE Transactions on Evolutionary Computation, 6 (2002), 512-518. doi: 10.1109/TEVC.2002.804914.

[11]

R. Kolisch, Serial and parallel resource-constrained project scheduling methods revisited: Theory and computation, European Journal of Operational Research, 90 (1996), 320-333. doi: 10.1016/0377-2217(95)00357-6.

[12]

R. Kolisch, S. Hartmann, Heuristic algorithms for the resource-constrained project scheduling problem: Classification and computational analysis, Project Scheduling, (1999), 147-178. doi: 10.1007/978-1-4615-5533-9_7.

[13]

R. Kolisch, S. Hartmann, Experimental investigation of heuristics for resource-constrained project scheduling: An update, European Journal of Operational Research, 174 (2006), 23-37. doi: 10.1016/j.ejor.2005.01.065.

[14]

R. Kolisch, A. Sprecher, A. Drexl, Characterization and generation of a general class of resource-constrained project scheduling problems, Management Science, 41 (1995), 1693-1703. doi: 10.1287/mnsc.41.10.1693.

[15]

J. Liu, W. Zhong, 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, 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, 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, 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. doi: 10.1145/2330163.2330332.

[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. doi: 10.1007/PL00013357.

[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, R. Leus, R&d project scheduling when activities may fail, IIE Transactions, 40 (2008), 367-384. doi: 10.1080/07408170701413944.

[23]

S. R. Schultz, J. Atzmon, A simulation based heuristic approach to a resource investment problem (rip), in Proceedings of the Winter Simulation Conference, (2014), 3411-3422. doi: 10.1109/WSC.2014.7020174.

[24]

S. Shadrokh, F. Kianfar, A genetic algorithm for resource investment project scheduling problem, tardiness permitted with penalty, European Journal of Operational Research, 181 (2007), 86-101. doi: 10.1016/j.ejor.2006.03.056.

[25]

J. Xiong, J. Liu, Y. Chen, 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, Y.wu. Chen, A two-stage preference-based evolutionary multi-objective approach for capability planning problems, Knowledge-Based Systems, 31 (2012), 128-139. doi: 10.1016/j.knosys.2012.02.003.

[27]

W. Zhong, J. Liu, M. Xue, L. Jiao, A multiagent genetic algorithm for global numerical optimization, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 34 (2004), 1128-1141.

Figure 1.  An example of precedence graph
Figure 2.  An example of precedence graph
Figure 3.  The types of overlaps. (a) All kinds of top-overlaps, (b) all kinds of right-overlaps
Figure 4.  Relative positions of $CoverLeftX$ and $CoverRightX$. (a) and (b) are the cases without violating precedence constraints. (c) and (d) are the cases violating precedence constraints
Figure 5.  The decoding process
Figure 6.  The agent lattice of MBS$_{\rm {MAEA}}$-RIPSP
Table 1.  The Percentages of Finding Optimal Solutions for MBS$_{\rm MAEA}$-RIPSP on Möhring Instances with 1000 Evaluations
$C_1/C_2/C_3/C_4$$\theta = 1.0$$\theta = 1.1$$\theta = 1.2$$\theta = 1.3$$\theta = 1.4$$\theta = 1.5$
1/1/1/11.001.001.001.000.0670.033
3/1/1/11.000.900.900.800.1330.067
1/3/1/11.001.001.000.800.7000.333
1/1/3/11.001.001.001.000.9500.067
1/1/1/31.001.001.000.601.000.067
3/3/1/11.000.500.800.800.000.333
3/1/3/11.001.000.900.850.600.333
3/1/1/31.000.750.950.750.5330.067
1/3/3/11.001.001.001.000.700.033
1/3/1/31.001.001.001.000.800.00
1/1/3/31.001.001.001.000.600.00
3/3/3/11.001.000.901.000.3330.20
3/3/1/31.000.451.000.750.1330.033
3/1/3/31.001.000.950.850.1670.067
1/3/3/31.001.001.001.000.700.067
$C_1/C_2/C_3/C_4$$\theta = 1.0$$\theta = 1.1$$\theta = 1.2$$\theta = 1.3$$\theta = 1.4$$\theta = 1.5$
1/1/1/11.001.001.001.000.0670.033
3/1/1/11.000.900.900.800.1330.067
1/3/1/11.001.001.000.800.7000.333
1/1/3/11.001.001.001.000.9500.067
1/1/1/31.001.001.000.601.000.067
3/3/1/11.000.500.800.800.000.333
3/1/3/11.001.000.900.850.600.333
3/1/1/31.000.750.950.750.5330.067
1/3/3/11.001.001.001.000.700.033
1/3/1/31.001.001.001.000.800.00
1/1/3/31.001.001.001.000.600.00
3/3/3/11.001.000.901.000.3330.20
3/3/1/31.000.451.000.750.1330.033
3/1/3/31.001.000.950.850.1670.067
1/3/3/31.001.001.001.000.700.067
Table 2.  The Comparison of Numbers of Generation to Reach to the Optimal Solutions between MBS$_{\rm MAEA}$-RIPSP and GA for Möhring Test Sets
$C_1/C_2/C_3/C_4$$\theta = 1.0$$\theta = 1.1$$\theta = 1.2$$\theta = 1.3$$\theta = 1.4$$\theta = 1.5$
MBSGAMBSGAMBSGAMBSGAMBSGAMBSGA
1/1/1/111121116621141
3/1/1/111611212720271
1/3/1/1121331411243131
1/1/3/11111111112343
1/1/1/31111132112145
3/3/1/11112812125171
3/1/3/11111121181189
3/1/1/31212162198102
1/3/3/11111182111523
1/3/1/31112111186201
1/1/3/313131212537121
3/3/3/111121212914131
3/3/1/31141113288221
3/1/3/3121211121311845
1/3/3/3121213311934923
$C_1/C_2/C_3/C_4$$\theta = 1.0$$\theta = 1.1$$\theta = 1.2$$\theta = 1.3$$\theta = 1.4$$\theta = 1.5$
MBSGAMBSGAMBSGAMBSGAMBSGAMBSGA
1/1/1/111121116621141
3/1/1/111611212720271
1/3/1/1121331411243131
1/1/3/11111111112343
1/1/1/31111132112145
3/3/1/11112812125171
3/1/3/11111121181189
3/1/1/31212162198102
1/3/3/11111182111523
1/3/1/31112111186201
1/1/3/313131212537121
3/3/3/111121212914131
3/3/1/31141113288221
3/1/3/3121211121311845
1/3/3/3121213311934923
Table 3.  Parameter Settings for J10, J14 and J20 Test Sets
Test Set#AgentMaxGenExcuteNum$SelfLTime$$P_{cro}$$P_{mut}$$P_{com}$
J10$20\times 20$1010120.950.850.9
J14$20\times 20$108120.950.851.0
J20$20\times 19$108120.950.851.0
Test Set#AgentMaxGenExcuteNum$SelfLTime$$P_{cro}$$P_{mut}$$P_{com}$
J10$20\times 20$1010120.950.850.9
J14$20\times 20$108120.950.851.0
J20$20\times 19$108120.950.851.0
Table 4.  Experimental Results of MBS$_{\rm MAEA}$-RIPSP for J10, J14 and J20 Test Sets
$\theta$J10J14J20
Opt.%Dev.%Eva.Opt.%Dev.%Eva.Opt.%Dev.%Eva.
1.040.06.8896698876.50.7970371932.54.91267135
1.141.03.4006722563.01.0457498135.05.43357327
1.255.02.5922582143.1252.5851768333.04.92587273
1.351.52.4822617049.3752.2975736943.02.61737188
1.456.02.8036606547.51.9321677143.02.00797279
1.568.51.8743449655.01.8272746543.01.97287154
$\theta$J10J14J20
Opt.%Dev.%Eva.Opt.%Dev.%Eva.Opt.%Dev.%Eva.
1.040.06.8896698876.50.7970371932.54.91267135
1.141.03.4006722563.01.0457498135.05.43357327
1.255.02.5922582143.1252.5851768333.04.92587273
1.351.52.4822617049.3752.2975736943.02.61737188
1.456.02.8036606547.51.9321677143.02.00797279
1.568.51.8743449655.01.8272746543.01.97287154
Table 5.  Comparisons between MBS$_{\rm MAEA}$-RIPSP and GA for J10, J14 and J20 Test Sets
Test SetOpt.%Dev.%
MBSGAMBSGA
J1052.0048.203.34040.23
J1455.7540.001.74740.32
J2038.2533.333.64454.68
Test SetOpt.%Dev.%
MBSGAMBSGA
J1052.0048.203.34040.23
J1455.7540.001.74740.32
J2038.2533.333.64454.68
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