Optimizations in project scheduling: A state-of-art survey

Intelligent Systems, Control and Automation: Science and Engineering

Volumn 72, Issue , 2014, Pages 161-177

  Wu, Changzhi ^a   Wang, Xiangyu ^a   Jiang, Lin ^b  

^a CURTIN UNIVERSITY   (Australia)

^b ANHUI NORMAL UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84896505809     PISSN: 22138986     EISSN: 22138994     Source Type: Book Series    
DOI: 10.1007/978-94-017-8044-5_10     Document Type: Article

References (69)

1. Egan J (1998) Rethinking construction, the report of the construction task force to the Deputy Prime Minister John Prescott, scope for improving the quality and efficiency of UK construction. HMSO, London
2. Love PED, Simpson I, Hill A, Standing C (2013) From justification to evaluation: building information modeling for asset owners, Automation in Construction (in press)
3. Queensland Department of Main Roads (2005) Roads implementation program 2004-2005 to 2008-2009. www.mainroads.qld.gov.au. Accessed 28 June 2011
4. Waldron B (2011) Scope for Improvement 2011-Project Risk Getting the Right Balance and Outcomes, 27th July 2011. (Available at: http://careers.blakedawson.com, access 15th November 2011)
5. Assaf S, Al-Hejji S (2006) Causes of delay in large construction projects. Int J Proj Manag 24:349-357
6. Galloway PD, Fasce PE (2006) Survey of the construction industry relative to the use of CPM scheduling for construction projects. J Constr Eng Manage 132:697-711
7. HorbachA(2010)Aboolean satisfiability approach to the resource-constrained project scheduling problem. Ann Oper Res 181:89-107
8. Liao TW, Egbelu PJ, Sarker BR, Leu SS (2011) Metaheuristics for project and construction management-a state-of-the-art review. Autom Constr 20:491-505
9. Kolisch R, Padman R (2001) An integrated survey of deterministic project scheduling. Omega 29:249-272
10. Harmann S, Briskorn D (2010) A survey of variants and extensions of the resource-constrained project scheduling problem. Eur J Oper Res 207:1-14
11. Hartmann S (2001) Project scheduling with multiple modes: a genetic algorithm. Ann Oper Res 102:111-135
12. Wang L, Fang C (2011) An effective shuffled frog-leaping algorithm for multi-mode resourceconstrained project scheduling problem. Inf Sci 181:4804-4822
13. Bianco L, CaramiaM(2012) An exact algorithm to minimize the makespan in project scheduling with scarce resources and generalized precedence relations. Eur J Oper Res 219:73-85
14. Jia Q, Seo Y (2013) Solving resource-constrained project scheduling problems: conceptual validation of FLP formulation and efficient permutation-based ABC computation. Comput Oper Res 40:2037-2050
15. Son J, Hong T, Lee S (2013) A mixed (continuous + discrete) time-cost trade-off model considering four different relationships with lag time. KSCE J Civil Eng 17:281-291
16. HazirO, Haouari M, ErelE(2010) Discrete time/cost trade-off problem: a decomposition-based solution algorithm for the budget version. Comput Oper Res 37:649-655
17. Klansek U, PsunderM(2012) MINLP optimization model for the nonlinear discrete time-cost trade-off problem. Adv Eng Softw 48:6-16
18. Ghoddousi P, Eshtehardian E, Jooybanpuur S, Javanmardi A (2013) Multi-mode resourceconstrained discrete time-cost-resource optimization in project scheduling using nondominated sorting genetic algorithm. Autom Constr 30:216-227
19. Koulinas GK, Anagnostopoulos KP (2013) A new tabu search-based hyper-heuristic algorithm for solving construction leveling problmes with limited resource availablitis. Autom Constr 31:169-175
20. Rieck J, Zimmermann J, Gather T (2012) Mixed-integer linear programming for resource leveling problem. Eur J Oper Res 221:27-37
21. Lin C, GenM(2007) Multiobjective resource allocation problem by multistage decision-based hybrid genetic algorithm. Appl Math Comput 187:574-583
22. Fan K, You W, Li YY (2013) An effective modified binary particle swarm optimization (mBPSO) algorithm for multi-objective resource allocation problem (MORAP). Appl Math Comput 221:257-267
23. Baradaran S, Ghomi SMTF, RanjbarM, Hashemin SS (2012) Multi-mode renewable resourceconstrained allocation in PERT networks. Appl Soft Comput 12:82-90
24. Yin PY, Wang JY (2006) Ant colony optimization for the nonlinear resource allocation problem. Appl Math Comput 174:1438-1453
25. Patriksson M (2008) A survey on the continuous nonlinear resource allocation problem. Eur J Oper Res 185:1-46
26. PengW, WangC(2009)Amulti-mode resource constrained discrete time-cost tradeoff problem and its genetic algorithm based solution. Int J Proj Manage 27:600-609
27. RanjbarM, Khalilzadeh M, Kianfar F, EtminaniK(2012) An optimal procedure for minimizing total weighted resource tardiness penalty costs in the resource-constrained project scheduling problem. Comput Ind Eng 62:264-270
28. Ranjbar M, Hosseinabadi S, Abasian F (2013) Minimizing total weighted late work in the resource-constrained project scheduling problem. Appl Math Model (in press)
29. Ranjbar M, Davari M (2013) An exact method for scheduling of the alternative technologies in R&D projects. Comput Oper Res 40:395-405
30. Klein R, Scholl A (1999) Computign lower bounds by destructive improvement: an application to resource-constrained project scheduling. Eur J Oper Res 112:322-346
31. Stinson JP, Davis EW, Khumawala BM(1978) Multiple resource-constrained scheduling using branch and bound. AIIE Trans 10:23-40
32. Drotos M, Kis T (2011) Resource leveling in a machine environment. Eur J Oper Res 212:12-21
33. Kone O, Artigues C, Lopez P, Mongeau M (2011) Event-based MILP models for resourceconstrained project scheduling problems. Comput Oper Res 38:3-13
34. Ziarati K, Akbaria R, Zeighamib V (2011) On the performance of bee algorithms for resourceconstrained project scheduling problem. Appl Soft Comput 11:3720-3733
35. BouleimenK, LecocqH(2003)Anewefficient simulated annealing algorithm for the resourceconstrained project scheduling problem and its multiple mode version. Eur J Oper Res 149:268-281
36. Mika M, Waligora G, Weglarz J (2005) Simulated annealing and tabu search for multi-mode resource-constrained project scheduling with positive discounted cash flows and different payment models. Eur J Oper Res 164:639-668
37. Mika M, Waligora G, Weglarz J (2008) Tabu search for multi-mode resource-constrained project scheduling with schedule-dependent setup times. Eur J Oper Res 187:1238-1250
38. KhoshjahanY, Najafi AA, Nadjafi BA(2013) Resource constrained project scheduling problem with discounted earliness tardiness penalties: mathematical modeling and solving procedure. Comput Ind Eng (in press)
39. He Z, Wang N, Jia T, Xu Y (2009) Simulated annealing and tabu search for multi-mode project payment scheduling. Eur J Oper Res 198:688-696
40. He Z, Liu R, Jia T (2012) Metaheuristic for multi-mode capital-constrained project payment scheduling. Eur J Oper Res 223:605-613
41. Anagnostopoulos KP, Kotsikas L (2010) Experimental evaluation of simulated annealing algorithms for the time-cost trade-off problem. Appl Math Comput 217:260-270
42. Montoya-Torres JR, Utierrez-Franco E, Pirachican-Mayorga C (2010) Project scheduling with limited resources using a genetic algorithm. Int J Project Manage 28:619-628
43. Zamani R (2013) A competitive magnet-based genetic algorithm for solving the resourceconstrained project scheduling problem. Eur J Oper Res 229:552-559
44. Peteghem V, VanhouckeM(2010) A genetic algorithm for the preemptive and non-preemptive multi-mode resource-constrained project scheduling problem. Eur J Oper Res 201:409-441
45. Buddhakulsomsiri J, Kim D (2006) Properties of multi-mode resource-constrained project scheduling problems with resource vacations and activity splitting. Eur J Oper Res 175:279-295
46. Long LD, Ohsato A (2009) A genetic algorithm-based method for scheduling repetitive construction projects. Autom Constr 18:499-511
47. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6:182-197
48. Lorenzoni LL, Ahonen H, Alvarenga AG (2006) A multi-mode resource-constrained scheduling problem in the context of port operations. Comput Ind Eng 50:55-65
49. Damaka N, Jarbouia B, Siarryb P, Loukila T (2009) Differential evolution for solving multimode resource-constrained project scheduling problems. Comput Oper Res 36:2653-2659
50. Zamani R (2013) An evolutionary search procedure for optimizing timeCcost performance of projects under multiple renewable resource constraints. Comput Ind Eng (in press)
51. Wang L, Fang C (2012) An effective estimation of distribution algorithm for the multi-mode resource-constrained project scheduling problem. Comput Oper Res 39:449-460
52. Chen SH, ChenMC (2013) Addressing the advantages of using ensemble probabilistic models in estimation of distribution algorithms for scheduling problems. Int J Prod Econ 141:24-33
53. Wang L, Wang S, Liu M (2013) A Pareto-based estimation of distribution algorithm for the multi-objective flexible job-shop scheduling problem. Int J Prod Res 51:3574-3592
54. Mahdi Mobini MD, Rabban M, Amalnik MS, Razmi J, Rahimi-Vahed AR (2009) Using an enhanced scatter search algorithm for a resource-constrained project scheduling problem. Soft Comput 13:597-610
55. Mario V (2010) A scatter search heuristic for maximizing the net present value of a resourceconstrained project with fixed activity cash flows. Int J Prod Res 48:1983-2001
56. Merkle D, MiddendorfM, SchmeckH(2002) Ant colony optimization for resource-constrained project scheduling. IEEE Trans Evol Comput 6:333-346
57. Li H, Zhang H (2013) Ant colony optimization-based multi-mode scheduling under renewable and nonrenewable resource constraints. Autom Constr 35:431-438(online publishing)
58. Chaharsooghi SK, Kermani AHM (2008) An effective ant colony optimization algorithm (ACO) for multi-objective resource allocation problem (MORAP). Appl Math Comput 200:167-177
59. Xiong Y, Kuang Y (2008) Applying an ant colony optimization algorithm-basedmultiobjective approach for time-cost trade-off. J Constr Eng Manage 134:153-156
60. Zheng DXM, Ng ST, Kumaraswamy MM (2004) Applying a genetic algorithm-based multiobjective approach for time-cost optimization. J Constr Eng Manage 130:168-176
61. Mokhtari H, Baradaran Kazemzadeh R, Salmasnia A (2011) Time-cost tradeoff analysis in project management: an ant system approach. IEEE Trans Eng Manage 58:36-43
62. Chen RM, Wu CL, Wang CM, Lo ST (2010) Using novel particle swarm optimization scheme to solve resource-constrained scheduling problem in PSPLIB. Expert Sys Appl 37:1899-1910
63. Valls V, Ballest F, Quintanilla S (2005) Justification and RCPSP: a technique that pays. Eur J Oper Res 165:375-386
64. Chen RM (2011) Particle swarm optimization with justification and designed mechanisms for resource-constrained project scheduling problem. Expert Sys Appl 38:7102-7111
65. Jia Q, Seo Y (2013) An improved particle swarm optimization for the resource-constrained project scheduling problem. Int J Adv Manuf Technol 67:2627-2638
66. Zhang H, Li H (2010) Multi-objective particle swarm optimization for construction time-cost trade off problems. Constr Manage Econ 28:75-88
67. Fang C, Wang L (2012) An effective shuffled frog-leaping algorithm for resource-constrained project scheduling problem. Comput Oper Res 39:890-901
68. Ashuri B, Tavakolan M (2013) A shuffled frog-leaping model for solving time-cost-resource optimization (TCRO) problems in construction project planning. J Comput Civil Eng (in press)
69. Huang YM, Lin JC (2011) A new bee colony optimization algorithm with idle-time-based filtering scheme for open shop-scheduling problems. Expert Sys Appl 38:5438-5447