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Çok Modlu Kaynak Kısıtlı Proje Çizelgeleme Problemlerinin Belirsizlik Ortamında Modellenmesi

Year 2021, Volume: 21 Issue: 3, 586 - 605, 30.06.2021

Ömer Atlı Serhat Aydın

https://doi.org/10.35414/akufemubid.864896

Abstract

Bu çalışmada, belirsizlik ortamında proje süreçlerinin çizelgelenmesine olanak tanıyan bulanık etkinlik sürelerinden oluşan çok modlu, kaynak kısıtlı proje çizelgeleme problemleri incelenmiştir. Proje çizelgeleme problemlerinin çözümü için Microsoft C# programlama dili kullanılarak “Proje Çizelgeleme Programı” olarak isimlendirilen bir paket program geliştirilmiş, literatürde Proje Çizelgeleme Problemleri Kütüphanesi (PSPLib) olarak bilinen örnek problem setleri üzerinde test edilerek çıktı sonuçları kıyaslanmıştır. Kaynak kısıtlı bulanık çok modlu proje çizelgeleme problemleri, geliştirilen program ile çözülerek toplam proje süreleri ve toplam çizelgeleme maliyetleri en küçüklenmektedir.

Keywords

proje çizelgeleme Bulanık çok modlu kaynak kısıtlı proje çizelgeleme Tabu arama algoritması Bulanık mantık.

References

  • Atlı, Ö. and Kahraman, C. 2013. Fuzzy critical path analysis, Sigma Journal of Engineering and Natural Science, 31, 128-140.
  • Atli, Ö. 2011. Tabu search and an exact algorithm for the solutions of resource-constrained project scheduling problems.International Journal of Computational Intelligence Systems, 2, 255-267. https://doi.org/10.1080/18756891.2011.9727781
  • Arauj, J.S.S., Santos, H.G., Gendron, B., Dominik J.S., Brito, S.S. and Souza, D.S., 2020. Strong bounds for resource constrained project scheduling: Preprocessing and cutting planes. Computers and Operations Research, 113, 104782. https://doi.org/10.1016/j.cor.2019.104782
  • Birjandi A. and Mousavi S.M., 2019. Fuzzy resource-constrained project scheduling with multiple routes: A heuristic solution. Automation in Construction, 100, 84–102. https://doi.org/10.1016/j.autcon.2018.11.029
  • Bouleimen, K. and Lecocq, H. A new efficient simulated annealing algorithm for the resource constrained project scheduling problem. Tech. rep., Service de Robotique et Automatisation, Universite de Liege, 1998.
  • Bouleimen, K. and Lecocq, H., 2003. A new efficient simulated annealing algorithm for the resource-constrained project scheduling problem and its multiple mode version. In European Journal of Operational Research, 49(2), 268-281. https://doi.org/10.1016/S0377-2217(02)00761-0
  • Bofill, M., Coll,J., Suy, J. and Villaret, M. 2020. SMT encodings for Resource-Constrained Project Scheduling Problems. Computers & Industrial Engineering, 149, 106777. https://doi.org/10.1016/j.cie.2020.106777
  • Buckley, J. J., 1985. Fuzzy Hierarchical Analysis. Fuzzy Sets and Systems, 17(3), 233-247.
  • Brucker,P., Drexl,A., Möhrıng, R. Neumann,K. and Pesch.E., 1999. Resources-Constrained Project Scheduling: Notation, Classification, Model and Methods. European Journal of Operational Research, 112,. 3-41. https://doi.org/10.1016/S0377-2217(98)00204-5
  • Chanas, S. and Kamburowski, J., 1981. The use of fuzzy variables in pert. Fuzzy Sets and Systems, 5(1), 11-19. https://doi.org/10.1016/0165-0114(81)90030-0
  • Chen, S. P. and Hsueh, Y. J., 2008. A simple approach to fuzzy critical path analysis in project networks. Applied Mathematical Modelling, 32(7), 1289-1297. https://doi.org/10.1016/j.apm.2007.04.009
  • Drexl, A. and Gruenewald, J. 1993. Nonpreemptive multi-mode resource-constrained project scheduling. IIE transactions, 25(5), 74-81. https://doi.org/10.1080/07408179308964317
  • Drexl,A., Nıssen R., Patterson J. and Salewskı F. 2000. PROGEN/ πX – An ınstance generator for resources-constrained project scheduling problems with partially renewable resources and further extensions. European Journal of Operational Research, 125, 59-72. https://doi.org/10.1016/S0377-2217(99)00205-2
  • Dubois, D. and Prade, H., 1988. Possibility theory: An Approach to Computerized Processing of Uncertainty. Computational Complexity. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-1800-9_139
  • Dubois, D. and Prade, H. 1983. Ranking fuzzy numbers in the setting of possibility theory. Information Sciences, 30(3), 183-224. https://doi.org/10.1016/0020-0255(83)90025-7
  • Elsayed, E. A., 1982. Algorithms for project scheduling with resource constraints. International Journal of Production Research, 20(1), 95–103. https://doi.org/10.1080/00207548208947751
  • Glover, F. 1990a. Tabu Search—Part II. ORSA Journal on Computing, 2(1), 4-32. https://doi.org/10.1287/ijoc.2.1.4
  • Glover, F. 1990b. Tabu Search: A Tutorial. Interfaces, 20(4), 74-94. https://doi.org/10.1287/inte.20.4.74
  • Hapke, M., Jaszkiewicz, A., and Słowiński, R. 2000. Pareto simulated annealing for fuzzy multi-objective combinatorial optimization. Journal of Heuristics, 6, 29–345. https://doi.org/10.1023/A:1009678314795
  • Hartmann, S. and Drexl, A. 1998. Project scheduling with multiple modes: A comparison of exact algorithms. Networks: An International Journal, 32(4), 283-297. https://doi.org/10.1002/(SICI)10970037(199812)32:4<283::AID-NET5>3.0.CO;2-I
  • Jarboui, B., Damak, N., Siarry, P. and Rebai, A., 2008. A combinatorial particle swarm optimization for solving multi-mode resource-constrained project scheduling problems. Applied Mathematics and Computation, 195(1), 299-308. https://doi.org/10.1016/j.amc.2007.04.096
  • Kassandraa, T.R. and Suhartonob, D., 2018. Resource-Constrained Project Scheduling Problem using Firefly Algorithm. Procedia Computer Science, 135, 534–543. https://doi.org/10.1016/j.procs.2018.08.206
  • Knyazevaa, M., Bozhenyuka, A. and Rozenbergb, I. 2015. Resource-constrained project scheduling approach under fuzzy Conditions. Procedia Computer Science 77, 56 – 64. https://doi.org/10.1016/j.procs.2015.12.359
  • Kulejewski, J., Ibadov, N. and Krzemiński, M. 2018. Scheduling Construction Projects Under Fuzzy Modelling of Resource Constraints. MATEC Web of Conferences, 196, 04045. https://doi.org/10.1051/matecconf/201819604045
  • Kurt, P.A., 2018. Çok projeli kaynak kısıtlı proje çizelgeleme problemi: bir yazılım firmasında uygulama çalışması (Yüksek lisans), Başkent Üniversitesi Fen Bilimleri Enstitüsü,104.
  • Mori, M. and Tseng, C.C., 1997. A genetic algorithm for multi mode resource constrained project scheduling problem. European Journal of operational Research. 100, 134-141. https://doi.org/10.1016/S0377-2217(96)00180-4
  • Nasution, S. H., 1994. Fuzzy Critical Path Method. IEEE Transactions on Systems, Man and Cybernetics, 24(1), 48-57. https://doi.org/10.1109/21.259685
  • Özdamar, L. and Ulusoy, G. 1995. A survey on the resource-constrained project scheduling problem. IIE Transactions, 27: 574-586. https://doi.org/10.1080/07408179508936773
  • Patterson, J. H., 1984. A Comparison of Exact Approaches for Solving the Multiple Constrained Resource, Project Scheduling Problem. Management Science, 30(7), 854-867. https://doi.org/10.1287/mnsc.30.7.854
  • Pellerin, R., Perrier, N. and Berthaut, F., 2020. A survey of hybrid metaheuristics for the resource-constrained project scheduling problem. European Journal of Operational Research, 280, 395–416. https://doi.org/10.1016/j.ejor.2019.01.063
  • Rahman, H.F., Chakrabortty, R.K. and Ryan, M.J., 2020 Memetic algorithm for solving resource constrained project scheduling Problems. Automation in Construction, 111, 103052. https://doi.org/10.1016/j.autcon.2019.103052
  • Sharma, K. and Trivedi, M. K., 2020 Latin hypercube sampling-based NSGA-III optimization model for multimode resource constrained time–cost–quality–safety trade-off in construction projects International Journal of Construction Management (online). https://doi.org/10.1080/15623599.2020.1843769
  • Talbot, F. B., 1982. Resource-Constrained Project Scheduling with Time-Resource Tradeoffs: The Nonpreemptive Case. Management Science, 28(10), 1197-1210. https://doi.org/10.1287/mnsc.28.10.1197
  • Wang, X., and Huang, W., 2010. Fuzzy resource-constrained project scheduling problem for software development. Wuhan University Journal of Natural Sciences, 15(1), 25-30. https://doi.org/10.1007/s11859-010-0106-z
  • Weglarz,J, 1980. On certain models of resource allocation, problems, Kybernetes 9, 61-66.
  • Yager, R. R., 1981. A procedure for ordering fuzzy subsets of the unit interval. Information Sciences, 24(2), 143-161. https://doi.org/10.1016/0020-0255(81)90017-7
  • Zadeh, L. A., 1965. Fuzzy Sets. Information and Control, 8(3), 338-353. https://doi.org/10.1109/2.53
  • Zaman, F., Elsayed, F., Sarker, R., Essam, D. 2020. Hybrid evolutionary algorithm for large-scale project scheduling problems. Computers & Industrial Engineering, 146, 106567. https://doi.org/10.1016/j.cie.2020.106567
  • Zaman,F., Elsayed,S., Sarker, R., Essam, D., Carlos A. Coello C. 2021. An evolutionary approach for resource constrained project scheduling with uncertain changes. Computers and Operations Research, 125, 105104. https://doi.org/10.1016/j.cor.2020.105104
  • Zimmermann, H.-J., 2001. Fuzzy Relations and Fuzzy Graphs. In Fuzzy Set Theory—and Its Applications, 321-367. https://doi.org/10.1007/978-94-010-0646-0_6

Multi-Mode Resource Constrained Project Scheduling Under Fuzzy Enviroment

Year 2021, Volume: 21 Issue: 3, 586 - 605, 30.06.2021

Ömer Atlı Serhat Aydın

https://doi.org/10.35414/akufemubid.864896

Abstract

In this paper, we consider multi-mode resource-constrained project scheduling problems with multiple execution modes for each activity under uncertainty conditions. A software package named as “Project Scheduling Programming” was developed by using Microsoft C# and its performance was tested on some sample projects and PSPLib data sets. Project scheduling problems defined with constrained resources and uncertainty issues can be solved by by Project Scheduling Software in order to minimize total project makespan and scheduling cost.

Keywords

Project scheduling Multi-Mode resource-constrained project scheduling Tabu Search Algorithm Fuzzy Logic

References

  • Atlı, Ö. and Kahraman, C. 2013. Fuzzy critical path analysis, Sigma Journal of Engineering and Natural Science, 31, 128-140.
  • Atli, Ö. 2011. Tabu search and an exact algorithm for the solutions of resource-constrained project scheduling problems.International Journal of Computational Intelligence Systems, 2, 255-267. https://doi.org/10.1080/18756891.2011.9727781
  • Arauj, J.S.S., Santos, H.G., Gendron, B., Dominik J.S., Brito, S.S. and Souza, D.S., 2020. Strong bounds for resource constrained project scheduling: Preprocessing and cutting planes. Computers and Operations Research, 113, 104782. https://doi.org/10.1016/j.cor.2019.104782
  • Birjandi A. and Mousavi S.M., 2019. Fuzzy resource-constrained project scheduling with multiple routes: A heuristic solution. Automation in Construction, 100, 84–102. https://doi.org/10.1016/j.autcon.2018.11.029
  • Bouleimen, K. and Lecocq, H. A new efficient simulated annealing algorithm for the resource constrained project scheduling problem. Tech. rep., Service de Robotique et Automatisation, Universite de Liege, 1998.
  • Bouleimen, K. and Lecocq, H., 2003. A new efficient simulated annealing algorithm for the resource-constrained project scheduling problem and its multiple mode version. In European Journal of Operational Research, 49(2), 268-281. https://doi.org/10.1016/S0377-2217(02)00761-0
  • Bofill, M., Coll,J., Suy, J. and Villaret, M. 2020. SMT encodings for Resource-Constrained Project Scheduling Problems. Computers & Industrial Engineering, 149, 106777. https://doi.org/10.1016/j.cie.2020.106777
  • Buckley, J. J., 1985. Fuzzy Hierarchical Analysis. Fuzzy Sets and Systems, 17(3), 233-247.
  • Brucker,P., Drexl,A., Möhrıng, R. Neumann,K. and Pesch.E., 1999. Resources-Constrained Project Scheduling: Notation, Classification, Model and Methods. European Journal of Operational Research, 112,. 3-41. https://doi.org/10.1016/S0377-2217(98)00204-5
  • Chanas, S. and Kamburowski, J., 1981. The use of fuzzy variables in pert. Fuzzy Sets and Systems, 5(1), 11-19. https://doi.org/10.1016/0165-0114(81)90030-0
  • Chen, S. P. and Hsueh, Y. J., 2008. A simple approach to fuzzy critical path analysis in project networks. Applied Mathematical Modelling, 32(7), 1289-1297. https://doi.org/10.1016/j.apm.2007.04.009
  • Drexl, A. and Gruenewald, J. 1993. Nonpreemptive multi-mode resource-constrained project scheduling. IIE transactions, 25(5), 74-81. https://doi.org/10.1080/07408179308964317
  • Drexl,A., Nıssen R., Patterson J. and Salewskı F. 2000. PROGEN/ πX – An ınstance generator for resources-constrained project scheduling problems with partially renewable resources and further extensions. European Journal of Operational Research, 125, 59-72. https://doi.org/10.1016/S0377-2217(99)00205-2
  • Dubois, D. and Prade, H., 1988. Possibility theory: An Approach to Computerized Processing of Uncertainty. Computational Complexity. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-1800-9_139
  • Dubois, D. and Prade, H. 1983. Ranking fuzzy numbers in the setting of possibility theory. Information Sciences, 30(3), 183-224. https://doi.org/10.1016/0020-0255(83)90025-7
  • Elsayed, E. A., 1982. Algorithms for project scheduling with resource constraints. International Journal of Production Research, 20(1), 95–103. https://doi.org/10.1080/00207548208947751
  • Glover, F. 1990a. Tabu Search—Part II. ORSA Journal on Computing, 2(1), 4-32. https://doi.org/10.1287/ijoc.2.1.4
  • Glover, F. 1990b. Tabu Search: A Tutorial. Interfaces, 20(4), 74-94. https://doi.org/10.1287/inte.20.4.74
  • Hapke, M., Jaszkiewicz, A., and Słowiński, R. 2000. Pareto simulated annealing for fuzzy multi-objective combinatorial optimization. Journal of Heuristics, 6, 29–345. https://doi.org/10.1023/A:1009678314795
  • Hartmann, S. and Drexl, A. 1998. Project scheduling with multiple modes: A comparison of exact algorithms. Networks: An International Journal, 32(4), 283-297. https://doi.org/10.1002/(SICI)10970037(199812)32:4<283::AID-NET5>3.0.CO;2-I
  • Jarboui, B., Damak, N., Siarry, P. and Rebai, A., 2008. A combinatorial particle swarm optimization for solving multi-mode resource-constrained project scheduling problems. Applied Mathematics and Computation, 195(1), 299-308. https://doi.org/10.1016/j.amc.2007.04.096
  • Kassandraa, T.R. and Suhartonob, D., 2018. Resource-Constrained Project Scheduling Problem using Firefly Algorithm. Procedia Computer Science, 135, 534–543. https://doi.org/10.1016/j.procs.2018.08.206
  • Knyazevaa, M., Bozhenyuka, A. and Rozenbergb, I. 2015. Resource-constrained project scheduling approach under fuzzy Conditions. Procedia Computer Science 77, 56 – 64. https://doi.org/10.1016/j.procs.2015.12.359
  • Kulejewski, J., Ibadov, N. and Krzemiński, M. 2018. Scheduling Construction Projects Under Fuzzy Modelling of Resource Constraints. MATEC Web of Conferences, 196, 04045. https://doi.org/10.1051/matecconf/201819604045
  • Kurt, P.A., 2018. Çok projeli kaynak kısıtlı proje çizelgeleme problemi: bir yazılım firmasında uygulama çalışması (Yüksek lisans), Başkent Üniversitesi Fen Bilimleri Enstitüsü,104.
  • Mori, M. and Tseng, C.C., 1997. A genetic algorithm for multi mode resource constrained project scheduling problem. European Journal of operational Research. 100, 134-141. https://doi.org/10.1016/S0377-2217(96)00180-4
  • Nasution, S. H., 1994. Fuzzy Critical Path Method. IEEE Transactions on Systems, Man and Cybernetics, 24(1), 48-57. https://doi.org/10.1109/21.259685
  • Özdamar, L. and Ulusoy, G. 1995. A survey on the resource-constrained project scheduling problem. IIE Transactions, 27: 574-586. https://doi.org/10.1080/07408179508936773
  • Patterson, J. H., 1984. A Comparison of Exact Approaches for Solving the Multiple Constrained Resource, Project Scheduling Problem. Management Science, 30(7), 854-867. https://doi.org/10.1287/mnsc.30.7.854
  • Pellerin, R., Perrier, N. and Berthaut, F., 2020. A survey of hybrid metaheuristics for the resource-constrained project scheduling problem. European Journal of Operational Research, 280, 395–416. https://doi.org/10.1016/j.ejor.2019.01.063
  • Rahman, H.F., Chakrabortty, R.K. and Ryan, M.J., 2020 Memetic algorithm for solving resource constrained project scheduling Problems. Automation in Construction, 111, 103052. https://doi.org/10.1016/j.autcon.2019.103052
  • Sharma, K. and Trivedi, M. K., 2020 Latin hypercube sampling-based NSGA-III optimization model for multimode resource constrained time–cost–quality–safety trade-off in construction projects International Journal of Construction Management (online). https://doi.org/10.1080/15623599.2020.1843769
  • Talbot, F. B., 1982. Resource-Constrained Project Scheduling with Time-Resource Tradeoffs: The Nonpreemptive Case. Management Science, 28(10), 1197-1210. https://doi.org/10.1287/mnsc.28.10.1197
  • Wang, X., and Huang, W., 2010. Fuzzy resource-constrained project scheduling problem for software development. Wuhan University Journal of Natural Sciences, 15(1), 25-30. https://doi.org/10.1007/s11859-010-0106-z
  • Weglarz,J, 1980. On certain models of resource allocation, problems, Kybernetes 9, 61-66.
  • Yager, R. R., 1981. A procedure for ordering fuzzy subsets of the unit interval. Information Sciences, 24(2), 143-161. https://doi.org/10.1016/0020-0255(81)90017-7
  • Zadeh, L. A., 1965. Fuzzy Sets. Information and Control, 8(3), 338-353. https://doi.org/10.1109/2.53
  • Zaman, F., Elsayed, F., Sarker, R., Essam, D. 2020. Hybrid evolutionary algorithm for large-scale project scheduling problems. Computers & Industrial Engineering, 146, 106567. https://doi.org/10.1016/j.cie.2020.106567
  • Zaman,F., Elsayed,S., Sarker, R., Essam, D., Carlos A. Coello C. 2021. An evolutionary approach for resource constrained project scheduling with uncertain changes. Computers and Operations Research, 125, 105104. https://doi.org/10.1016/j.cor.2020.105104
  • Zimmermann, H.-J., 2001. Fuzzy Relations and Fuzzy Graphs. In Fuzzy Set Theory—and Its Applications, 321-367. https://doi.org/10.1007/978-94-010-0646-0_6
There are 40 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Articles
Authors

Ömer Atlı This is me 0000-0002-4861-5587

Serhat Aydın 0000-0003-0861-8297

Publication Date June 30, 2021
Submission Date January 19, 2021
Published in Issue Year 2021 Volume: 21 Issue: 3

Cite

APA Atlı, Ö., & Aydın, S. (2021). Çok Modlu Kaynak Kısıtlı Proje Çizelgeleme Problemlerinin Belirsizlik Ortamında Modellenmesi. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, 21(3), 586-605. https://doi.org/10.35414/akufemubid.864896
AMA Atlı Ö, Aydın S. Çok Modlu Kaynak Kısıtlı Proje Çizelgeleme Problemlerinin Belirsizlik Ortamında Modellenmesi. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi. June 2021;21(3):586-605. doi:10.35414/akufemubid.864896
Chicago Atlı, Ömer, and Serhat Aydın. “Çok Modlu Kaynak Kısıtlı Proje Çizelgeleme Problemlerinin Belirsizlik Ortamında Modellenmesi”. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi 21, no. 3 (June 2021): 586-605. https://doi.org/10.35414/akufemubid.864896.
EndNote Atlı Ö, Aydın S (June 1, 2021) Çok Modlu Kaynak Kısıtlı Proje Çizelgeleme Problemlerinin Belirsizlik Ortamında Modellenmesi. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi 21 3 586–605.
IEEE Ö. Atlı and S. Aydın, “Çok Modlu Kaynak Kısıtlı Proje Çizelgeleme Problemlerinin Belirsizlik Ortamında Modellenmesi”, Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, vol. 21, no. 3, pp. 586–605, 2021, doi: 10.35414/akufemubid.864896.
ISNAD Atlı, Ömer - Aydın, Serhat. “Çok Modlu Kaynak Kısıtlı Proje Çizelgeleme Problemlerinin Belirsizlik Ortamında Modellenmesi”. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi 21/3 (June 2021), 586-605. https://doi.org/10.35414/akufemubid.864896.
JAMA Atlı Ö, Aydın S. Çok Modlu Kaynak Kısıtlı Proje Çizelgeleme Problemlerinin Belirsizlik Ortamında Modellenmesi. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi. 2021;21:586–605.
MLA Atlı, Ömer and Serhat Aydın. “Çok Modlu Kaynak Kısıtlı Proje Çizelgeleme Problemlerinin Belirsizlik Ortamında Modellenmesi”. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, vol. 21, no. 3, 2021, pp. 586-05, doi:10.35414/akufemubid.864896.
Vancouver Atlı Ö, Aydın S. Çok Modlu Kaynak Kısıtlı Proje Çizelgeleme Problemlerinin Belirsizlik Ortamında Modellenmesi. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi. 2021;21(3):586-605.
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