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KAYNAK KISITLI ÇOKLU PROJE PROGRAMLAMA PROBLEMİ İÇİN TAVLAMA BENZETİMİ ALGORİTMASI

Yıl 2009, Cilt: 23 Sayı: 2, 101 - 118, 27.11.2010

Öz

Bu makalede kaynak kısıtlı çoklu proje programlama problemini
(KÇPP) çözmek için geliştirilen için bir tavlama benzetimi (TB) algoritması
sunulmaktadır. Programlamanın amacı, proje gecikmeleri ve faaliyet
beklemelerinden kaynaklanan maliyetler toplamını en küçüklemektir. Bu yönü
ile çalışma benzer çalışmalardan ayrılmaktadır. Kısıtlara uygun çözüm
gösterimi, öncelik ilişkilerine uygun bir faaliyet listesine ve kaynak tahsislerine
dayanmaktadır. TB algoritmalarında büyük çoğunlukla kullanılandan farklı
olarak bu çalışmada daha yavaş bir soğutma planı ve iki ayrı durdurma ölçütü
kullanılmaktadır. Geliştirilen algoritma rastgele oluşturulan büyük bir problem
üzerinde test edilmekte ve elde edilen sonuçlar önerilen algoritmanın etkinliğini
doğrulamaktadır.

Kaynakça

  • Aarts, E.H.L and Korst, J.H.M. (1989), Simulated Annealing and Boltzmann Machines: A stochastic Approach to Combinatorial Optimization and Neural Computing, Wiley, Chichester,.ss. 284.
  • Ash, R. (1989) “Activity Scheduling in the Dynamic, Multiple-Project setting: Choosing Heuristics Through Deterministic Simulation”, Proceedings of the 1999 Winter Simulation Conference, ss. 937–941.
  • Blazewicz, J., Lenstra, J.K, Kan, A., Rinnooy, H.G. (1983). “Scheduling Subject to Resource Constraints: Classification and Complexity “, Discrete Applied Mathematics, 5, ss. 11-24.
  • Bock, D.B. and Patterson, J.H. (1990), “A Comparison of Due Date Setting, Resource Assignment and Job Preemption Heuristics for the Multiproject Scheduling Problem”, Decision Sciences, 21, ss. 387– 402.
  • Boctor, F.F. (1990) “Some Efficent Multi-Heuristic Procedures for Resource Constrained Project Scheduling”, European Journal of Operational Research, 49, ss. 3–13.
  • Bouleimen, K. and Lecocq, H. (2003) “A New Efficent Simulated Annealing Algorithm for the Resource Constrained Project Scheduling Problem and Its Multiple Mode Version”, European Journal of Operational Research, 149, ss. 268–281.
  • Cerny, V. (1985) “Thermodynamical Approach to the Traveling Saesman Problem: An Efficent Simulation Algorithm”, Journal of Optimization Theory and Applications, 45, ss. 41–5.
  • Chiu, H.N.and Tsai, D.M. (2002) “An Efficent Search Procedure for the Resource Constrained Multi-Project Scheduling Problem with Discounted Cash Flows”, Construction Management and Economics, 20, ss. 55–66.
  • Connoly, D. (1990) “An Improved Annealing Scheme for the QAP”, European Journal of Operational Research, 46, ss. 93–100.
  • Dantzig, G.B, Wolfe, P. Decomposition principle for linear programs, Operations Research, 8, ss. 101–111.
  • Deckro, R.F., Winkofsky , E.P., Hebert , J.E., Gagnon , R. (1991) “A Decomposition Approach to Multi-project Scheduling”, European Journal of Operational Research, 51, ss. 110–118.
  • Drexl, A. (1991) “Scheduling of Project Networks by Job Assignment”, Management Science, 37(12), ss. 1590–1602.
  • Dumond, J.and Mabert, V.A. (1988) “Evaluating Project Scheduling and Due Date Assignment Procedures: An Experimental Analysis”, Management Science, 34(1), ss. 101–118.
  • Fendley, L.G. (1968) “Towards the Development of A Complete Multiproject Scheduling System”, Journal of Industrial Engineering,October, ss. 505–515.
  • Gonçalves, J.F., Mendes, J.J.M., Resende, M.G.C. (2004) “A Genetic Algorithm for the Resource Constrained Multi-Project Scheduling Problem”, AT&T Labs Technical Report, ss.1-19.
  • Homberger, J. (2007) “A Multi Agent System for the Decentralized Resource- Constrained Multi-Project Scheduling Problem”, Int. Transactions in Operational Research, 14 (6), ss. 565–589.
  • Huang, M., Romeo, F., Sangiovanni-Vincentelli, A. (1986) “An Efficent General Cooling Scedule for Simulated Annealing”, IEEE Transact. on Computer Aided Design, 5 (1), ss. 381–384.
  • Johnson, D.S,.Aragon, C.R., McGeoch, L.A, Schevon, C.(1989) “Optimization by Simulated Annealing: An Experimental Evaluation; Part 1, Graph Partitioning”, Operations Research, 37, ss 865-892.
  • Jozefowska, J., Mika, M., Rozycki, R., Waligora, G., Weglarz, J. (2001) “Simulated Annealing for Multi-Mode Resource-Constrained Project Scheduling”, Annals of Operations Research, 102,ss. 137–155.
  • Kim, K.W., Yun, Y., Yoon, J., Gen, M., Yamazaki, G. (2005) “Hybrid Genetic Algorithm with Adaptive Abilities for Resource-Consrained Multiple Project Scheduling”, Computers in Industry, 56 (2), ss. 143–160.
  • Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P. (1983) “Optimization by Simulated Annealing”, Science, 220, ss. 671–680.
  • Kolish, R.and Hartmann, S. (1998) “Heuristic Algorithms for the Resource- constrained Project Scheduling Problem: Classification and Computational analysis”, J. Weglarz, ed., Kluwer Academic, Amsterdam, ss. 147–178.
  • Kolish R. and Sprecher, A. (1996) “PSLIB-a Project Scheduling Problem Library“, European Journal of Operational Research, 96, ss. 205–216.
  • Kurtuluş, I.S. and Davis, E.W. (1982) “Multi-project Scheduling: Categorization of Heuristic Rules Performances”, Management Science, 28 (2), ss. 161–172.
  • Kurtuluş, I.S. and Narula, S.C. (1985) “Multi-project Scheduling: Analysis of Project Performance”, IIE Transactions, 17 (1), ss 58–66.
  • Lawrence, S.R.and Morton, T.E. (1993) “Resource-Constrained Multi-project Scheduling with Tardy Costs: Comparing Myopic, Bottleneck and Resource Pricing Heuristics”, European Journal of Operational Research, 64, ss. 168–187.
  • Linyi, D. and Yan, L. (2007) “A Particle Swarm Optimization or Resource- Constrained Multi-Project Scheduling Problem”, International Computational Intelligence and Security Conference, 15(9), ss. 1010– 1014.
  • Lova, A., Maroto, C., Tormos, P. (2000) “A Multicriteria Heuristic Method to Improve Resource Allocation in Multiproject Scheduling”, European Journal of Operational Research, 127, ss. 40–424.
  • Lundy, M.and Mees, A. (1986) “Convergence of Annealing Algorithm”, Mathematical Programming, 34, ss. 111–124.
  • Mendes, JJM. (2003) “Sistema de apoio â decisão para planeamento de sistemas de produção do tipo projecto” PhD thesis, Departamento de Engenharia Mecânica e Gestão Industrial, Faculdade de Engenharia da Universidade do Porto, Portugal.
  • Metropolis, N., Rosembluth, A., Rosenbluth, M., Teller, A. (1953) “Equation of State Calculationsby Fast Computing Machines”, Jornal of Chemical Physics, 21, ss. 1087–1092.
  • Mohanty, R.P.and Siddiq, M.K. (1989) “Multiple Projects Multiple Resources- Constrained Scheduling”, International Journal of Production Research, 27 (2), ss. 261–280.
  • Okada, I., Lin, L., Gen, M. (2008) “Solving ReourceConstrained Multiple Project Scheduling Problems by Random Key-Based Genetic Algorithm”, IEEJEISS, 128 (3), ss. 441–449.
  • Park, M.W., and Kim, Y.D.(1998) “A Systematic Procedure for Setting Paameters in Simulated Annealing Algorithms”, Computers and Operations Research, 3, ss. 207–217.
  • Potts, C.N.and Van Wassenhove, L.N. (1991) “Single Machine Tardiness Sequencing Heuristics”, IIE Transactions, 23, ss. 346–354.
  • Pritsker, A.B., Wattres, L.J., Wolfe, P.M. (1969) “Multi-Project Scheduling with Limited Resources: A Zero-One Programming Approach”, Management Sciene, 16 (1), ss. 93–109.
  • Shankar, V. and Nagi, R. (1996) “A Flexible Optimization Approach to Multi- Resource, Multi-Project Planning and Scheduling”, Proceedings of 5th Industrial Engineering Research Conference, Minneapolis, May, USA.
  • Tsubakitani, S. and Decro, R.F. (1990) “A Heuristic for Multi-Project Scheduling with Limited Resources in Housing Industry”, European Journal of Operational Research, 49, ss. 80–91.
  • Van Laarhoven, P.J.M.and Aarts, E.H.L. (1987) Simulated Annealing: Theory and Applications, Kluwer Academic Publisher,.ss. 204.
  • Van Laarhoven, P.J.M,. Aarts, E.H.L., Lenstra, J.K. (1992) “Job Shop Scheduling by Simulated Annealing”, Operations Research, 40, ss. 113–126.
  • Vercellis, C. (1994) “Constrained Multi-Project Planning Problems: A Lagrangean Decomposition Approach”, European Journal of Operational Research, 78, ss. 267–275.
  • Wang, T.Y.and Wu, K.B. (1999) “A Parameter Set Design Procedure for the Simulated Annealing Algorihm under the Computational Time Constrained”, Computers and Operations Research, 26, ss. 665–678.
  • Wiley, V.D., Deckro, R.F., Jackson, J.A. (1998) “Optimization Analysis for Design and Planning of Multi-Project Programs”, European Journal of Operational Research, 107, ss. 492–506.
Yıl 2009, Cilt: 23 Sayı: 2, 101 - 118, 27.11.2010

Öz

Kaynakça

  • Aarts, E.H.L and Korst, J.H.M. (1989), Simulated Annealing and Boltzmann Machines: A stochastic Approach to Combinatorial Optimization and Neural Computing, Wiley, Chichester,.ss. 284.
  • Ash, R. (1989) “Activity Scheduling in the Dynamic, Multiple-Project setting: Choosing Heuristics Through Deterministic Simulation”, Proceedings of the 1999 Winter Simulation Conference, ss. 937–941.
  • Blazewicz, J., Lenstra, J.K, Kan, A., Rinnooy, H.G. (1983). “Scheduling Subject to Resource Constraints: Classification and Complexity “, Discrete Applied Mathematics, 5, ss. 11-24.
  • Bock, D.B. and Patterson, J.H. (1990), “A Comparison of Due Date Setting, Resource Assignment and Job Preemption Heuristics for the Multiproject Scheduling Problem”, Decision Sciences, 21, ss. 387– 402.
  • Boctor, F.F. (1990) “Some Efficent Multi-Heuristic Procedures for Resource Constrained Project Scheduling”, European Journal of Operational Research, 49, ss. 3–13.
  • Bouleimen, K. and Lecocq, H. (2003) “A New Efficent Simulated Annealing Algorithm for the Resource Constrained Project Scheduling Problem and Its Multiple Mode Version”, European Journal of Operational Research, 149, ss. 268–281.
  • Cerny, V. (1985) “Thermodynamical Approach to the Traveling Saesman Problem: An Efficent Simulation Algorithm”, Journal of Optimization Theory and Applications, 45, ss. 41–5.
  • Chiu, H.N.and Tsai, D.M. (2002) “An Efficent Search Procedure for the Resource Constrained Multi-Project Scheduling Problem with Discounted Cash Flows”, Construction Management and Economics, 20, ss. 55–66.
  • Connoly, D. (1990) “An Improved Annealing Scheme for the QAP”, European Journal of Operational Research, 46, ss. 93–100.
  • Dantzig, G.B, Wolfe, P. Decomposition principle for linear programs, Operations Research, 8, ss. 101–111.
  • Deckro, R.F., Winkofsky , E.P., Hebert , J.E., Gagnon , R. (1991) “A Decomposition Approach to Multi-project Scheduling”, European Journal of Operational Research, 51, ss. 110–118.
  • Drexl, A. (1991) “Scheduling of Project Networks by Job Assignment”, Management Science, 37(12), ss. 1590–1602.
  • Dumond, J.and Mabert, V.A. (1988) “Evaluating Project Scheduling and Due Date Assignment Procedures: An Experimental Analysis”, Management Science, 34(1), ss. 101–118.
  • Fendley, L.G. (1968) “Towards the Development of A Complete Multiproject Scheduling System”, Journal of Industrial Engineering,October, ss. 505–515.
  • Gonçalves, J.F., Mendes, J.J.M., Resende, M.G.C. (2004) “A Genetic Algorithm for the Resource Constrained Multi-Project Scheduling Problem”, AT&T Labs Technical Report, ss.1-19.
  • Homberger, J. (2007) “A Multi Agent System for the Decentralized Resource- Constrained Multi-Project Scheduling Problem”, Int. Transactions in Operational Research, 14 (6), ss. 565–589.
  • Huang, M., Romeo, F., Sangiovanni-Vincentelli, A. (1986) “An Efficent General Cooling Scedule for Simulated Annealing”, IEEE Transact. on Computer Aided Design, 5 (1), ss. 381–384.
  • Johnson, D.S,.Aragon, C.R., McGeoch, L.A, Schevon, C.(1989) “Optimization by Simulated Annealing: An Experimental Evaluation; Part 1, Graph Partitioning”, Operations Research, 37, ss 865-892.
  • Jozefowska, J., Mika, M., Rozycki, R., Waligora, G., Weglarz, J. (2001) “Simulated Annealing for Multi-Mode Resource-Constrained Project Scheduling”, Annals of Operations Research, 102,ss. 137–155.
  • Kim, K.W., Yun, Y., Yoon, J., Gen, M., Yamazaki, G. (2005) “Hybrid Genetic Algorithm with Adaptive Abilities for Resource-Consrained Multiple Project Scheduling”, Computers in Industry, 56 (2), ss. 143–160.
  • Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P. (1983) “Optimization by Simulated Annealing”, Science, 220, ss. 671–680.
  • Kolish, R.and Hartmann, S. (1998) “Heuristic Algorithms for the Resource- constrained Project Scheduling Problem: Classification and Computational analysis”, J. Weglarz, ed., Kluwer Academic, Amsterdam, ss. 147–178.
  • Kolish R. and Sprecher, A. (1996) “PSLIB-a Project Scheduling Problem Library“, European Journal of Operational Research, 96, ss. 205–216.
  • Kurtuluş, I.S. and Davis, E.W. (1982) “Multi-project Scheduling: Categorization of Heuristic Rules Performances”, Management Science, 28 (2), ss. 161–172.
  • Kurtuluş, I.S. and Narula, S.C. (1985) “Multi-project Scheduling: Analysis of Project Performance”, IIE Transactions, 17 (1), ss 58–66.
  • Lawrence, S.R.and Morton, T.E. (1993) “Resource-Constrained Multi-project Scheduling with Tardy Costs: Comparing Myopic, Bottleneck and Resource Pricing Heuristics”, European Journal of Operational Research, 64, ss. 168–187.
  • Linyi, D. and Yan, L. (2007) “A Particle Swarm Optimization or Resource- Constrained Multi-Project Scheduling Problem”, International Computational Intelligence and Security Conference, 15(9), ss. 1010– 1014.
  • Lova, A., Maroto, C., Tormos, P. (2000) “A Multicriteria Heuristic Method to Improve Resource Allocation in Multiproject Scheduling”, European Journal of Operational Research, 127, ss. 40–424.
  • Lundy, M.and Mees, A. (1986) “Convergence of Annealing Algorithm”, Mathematical Programming, 34, ss. 111–124.
  • Mendes, JJM. (2003) “Sistema de apoio â decisão para planeamento de sistemas de produção do tipo projecto” PhD thesis, Departamento de Engenharia Mecânica e Gestão Industrial, Faculdade de Engenharia da Universidade do Porto, Portugal.
  • Metropolis, N., Rosembluth, A., Rosenbluth, M., Teller, A. (1953) “Equation of State Calculationsby Fast Computing Machines”, Jornal of Chemical Physics, 21, ss. 1087–1092.
  • Mohanty, R.P.and Siddiq, M.K. (1989) “Multiple Projects Multiple Resources- Constrained Scheduling”, International Journal of Production Research, 27 (2), ss. 261–280.
  • Okada, I., Lin, L., Gen, M. (2008) “Solving ReourceConstrained Multiple Project Scheduling Problems by Random Key-Based Genetic Algorithm”, IEEJEISS, 128 (3), ss. 441–449.
  • Park, M.W., and Kim, Y.D.(1998) “A Systematic Procedure for Setting Paameters in Simulated Annealing Algorithms”, Computers and Operations Research, 3, ss. 207–217.
  • Potts, C.N.and Van Wassenhove, L.N. (1991) “Single Machine Tardiness Sequencing Heuristics”, IIE Transactions, 23, ss. 346–354.
  • Pritsker, A.B., Wattres, L.J., Wolfe, P.M. (1969) “Multi-Project Scheduling with Limited Resources: A Zero-One Programming Approach”, Management Sciene, 16 (1), ss. 93–109.
  • Shankar, V. and Nagi, R. (1996) “A Flexible Optimization Approach to Multi- Resource, Multi-Project Planning and Scheduling”, Proceedings of 5th Industrial Engineering Research Conference, Minneapolis, May, USA.
  • Tsubakitani, S. and Decro, R.F. (1990) “A Heuristic for Multi-Project Scheduling with Limited Resources in Housing Industry”, European Journal of Operational Research, 49, ss. 80–91.
  • Van Laarhoven, P.J.M.and Aarts, E.H.L. (1987) Simulated Annealing: Theory and Applications, Kluwer Academic Publisher,.ss. 204.
  • Van Laarhoven, P.J.M,. Aarts, E.H.L., Lenstra, J.K. (1992) “Job Shop Scheduling by Simulated Annealing”, Operations Research, 40, ss. 113–126.
  • Vercellis, C. (1994) “Constrained Multi-Project Planning Problems: A Lagrangean Decomposition Approach”, European Journal of Operational Research, 78, ss. 267–275.
  • Wang, T.Y.and Wu, K.B. (1999) “A Parameter Set Design Procedure for the Simulated Annealing Algorihm under the Computational Time Constrained”, Computers and Operations Research, 26, ss. 665–678.
  • Wiley, V.D., Deckro, R.F., Jackson, J.A. (1998) “Optimization Analysis for Design and Planning of Multi-Project Programs”, European Journal of Operational Research, 107, ss. 492–506.
Toplam 43 adet kaynakça vardır.

Ayrıntılar

Birincil Dil tr;en
Bölüm Makaleler
Yazarlar

Tuba Yakıcı Ayan Bu kişi benim

Yayımlanma Tarihi 27 Kasım 2010
Yayımlandığı Sayı Yıl 2009 Cilt: 23 Sayı: 2

Kaynak Göster

APA Yakıcı Ayan, T. (2010). KAYNAK KISITLI ÇOKLU PROJE PROGRAMLAMA PROBLEMİ İÇİN TAVLAMA BENZETİMİ ALGORİTMASI. Atatürk Üniversitesi İktisadi Ve İdari Bilimler Dergisi, 23(2), 101-118.

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