مروری بر الگوریتم‌های حل مسئله زمان‌بندی پروژه با محدودیت منابع با در نظر گرفتن مسائل کشاورزی

نویسندگان

1 گروه مهندسی کامپیوتر - دانشکده مهندسی برق و کامپیوتر - دانشگاه تبریز - تبریز- ایران

2 گروه مهندسی بیوسیستم - دانشکده کشاورزی - دانشگاه تبریز - تبریز- ایران

3 گروه مهندسی برق - دانشکده مهندسی برق و کامپیوتر - دانشگاه تبریز - تبریز- ایران

چکیده

پروژه‌های زمان­بندی در کشاورزی شامل عملیات و فعالیت هایی است که با ترتیب معین و در یک بازه زمانی مشخص انجام میگیرد. چنانچه این عملیات و فعالیت ها به موقع انجام نشوند، به دلیل افت کمی و کیفی محصول، کاهش سنگینی در درآمد کشاورز یا واحد کشاورزی ایجاد می‌شود که این هزینه‌ها نامشهود است. از طرفی دیگر عملیات کشاورزی برای اجرا نیازمند به کارگیری منابع هستند که معمولاً محدود بوده و در صورت عدم تخصیص بهینه به فعالیتها، احتمال به موقع نبودن فعالیت ها افزایش می‌یابد. به منظور کاستن از هزینه‌های به موقع نبودن، این پروژه‌ها نیازمند برنامه‌ریزی، زمان‌بندی و مدیریت علمی و منطقی زمان و منابع هستند. این به یک مسئله استاندارد در زمینه زمان‌بندی پروژه تبدیل شده که محققان زیادی را به خود علاقه‌مند کرده است و آن‌ها از روش‌های زمان‌بندی مختلف از جمله روش­های دقیق و روش­های اکتشافی و فراکتشافی استفاده کرده­اند. در نتیجه، روش­های مختلفی از مسئله زمان‌بندی پروژه با محدودیت منابع اولیه توسعه یافته‌اند. این مقاله یک مرور کلی بر روش­ها و تحقیقاتی و تحلیلی بر روش­های موجود می­باشد که تاکنون منتشر شده است. در این مقاله به بررسی اهداف و رویکردهای حل مسئله زمان­بندی پروژه  با در نظر گرفتن برخی کارهای موجود در حیطه کشاورزی و همچنین به داده­های مربوط پرداخته شده است. نتایج نشان میدهد پژوهش­های زیادی در راستای زمان‌بندی پروژه با منابع محدود انجام شده است. اما کارهایی که برای حل مسئله زمان‌بندی پروژه در حوزه کشاورزی باشد بسیار کم و انگشت شمار بوده است. بنابراین برای کارهای آینده و تحقیقات آتی می‌توان از روش‌های ارائه شده در سایر حوزه­ها، برای حل مسئله زمان­بندی پروژه با منابع محدود در حوزه کشاورزی استفاده بنماییم.

کلیدواژه‌ها


عنوان مقاله [English]

A review of algorithms for solving the project scheduling problem with resource-constrained considering agricultural problems

نویسندگان [English]

  • kimia shirini 1
  • Adel Taherihajivand 2
  • sina samadi Gharehveran 3
1 Department of Computer Engineering - Faculty of Electrical and Computer Engineering - Tabriz University - Tabriz - Iran
2 Department of Biosystem Engineering - Faculty of Agriculture - Tabriz University - Tabriz - Iran
3 Department of Electrical Engineering - Faculty of Electrical and Computer Engineering - Tabriz University - Tabriz – Iran
چکیده [English]

Scheduling projects in agriculture include operations and activities that are carried out in a certain order and within a certain period of time. If these operations and activities are not performed on time, due to the quantitative and qualitative decline of the product, a heavy decrease in the income of the farmer or agricultural unit will occur, and these costs are invisible. On the other hand, agricultural operations require the use of resources for implementation, which are usually limited, and if the activities are not optimally allocated, the possibility of the activities not being on time increases. In order to reduce the costs of not being on time, these projects require planning, scheduling and scientific and logical management of time and resources. This has become a standard problem in the field of project scheduling, which has attracted many researchers and they have used different scheduling methods, including exact methods and exploratory and meta-exploratory methods. As a result, various methods of project scheduling problem with limited primary resources have been developed. This article is an overview of research methods and analysis of existing methods that have been published so far. In this article, the goals and approaches to solving the project scheduling problem have been investigated, taking into account some existing works in the field of agriculture, as well as related data.

کلیدواژه‌ها [English]

  • scheduling
  • agriculture
  • algorithm
  • resource constraints
  • meta-huristic
  1. Hartmann, S., A competitive genetic algorithm for resource‐constrained project scheduling. Naval Research Logistics (NRL), 1998. 45(7): p. 733-750.
  2. Hartmann, S., A self‐adapting genetic algorithm for project scheduling under resource constraints. Naval Research Logistics (NRL), 2002. 49(5): p. 433-448.
  3. Leon, V.J. and R. Balakrishnan, Strength and adaptability of problem-space based neighborhoods for resource-constrained scheduling. Operations-Research-Spektrum, 1995. 17(2): p. 173-182.
  4. Lee, J.-K. and Y.-D. Kim, Search heuristics for resource constrained project scheduling. Journal of the Operational Research Society, 1996. 47(5): p. 678-689.
  5. Kohlmorgen, U., H. Schmeck, and K. Haase, Experiences with fine‐grainedparallel genetic algorithms. Annals of Operations Research, 1999. 90: p. 203-219.
  6. Valls, V., F. Ballestin, and S. Quintanilla, A hybrid genetic algorithm for the resource-constrained project scheduling problem. European journal of operational research, 2008. 185(2): p. 495-508.
  7. Mendes, J.J., J.F. Gonçalves, and M.G. Resende, A random key based genetic algorithm for the resource constrained project scheduling problem. Computers & operations research, 2009. 36(1): p. 92-109.
  8. 8. Dadres et al., planning agricultural mechanization projects with scattered networks. 1390 Tabriz University.
  9. Zapata, J.C., B.M. Hodge, and G.V. Reklaitis, The multimode resource constrained multiproject scheduling problem: Alternative formulations. AIChE Journal, 2008. 54(8): p. 2101-2119.
  10. Chen, V.Y., A 0–1 goal programming model for scheduling multiple maintenance projects at a copper mine. European Journal of Operational Research, 1994. 76(1): p. 176-191.
  11. Kolisch, R. and C. Heimerl, An efficient metaheuristic for integrated scheduling and staffing IT projects based on a generalized minimum cost flow network. Naval Research Logistics (NRL), 2012. 59(2): p. 111-127.
  12. Rostami, M. and M. Bagherpour, A lagrangian relaxation algorithm for facility location of resource-constrained decentralized multi-project scheduling problems. Operational Research, 2020. 20(2): p. 857-897.
  13. Lova, A., C. Maroto, and P. Tormos, A multicriteria heuristic method to improve resource allocation in multiproject scheduling. European journal of operational research, 2000. 127(2): p. 408-424.
  14. Kim, S.O. and M.J. Schniederjans, Heuristic framework for the resource constrained multi-project scheduling problem. Computers & operations research, 1989. 16(6): p. 541-556.
  15. Dumond, J., In a multi-resource environment, how much is enough? THE INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 1992. 30(2): p. 395-410.
  16. Dumond, J. and V.A. Mabert, Evaluating project scheduling and due date assignment procedures: an experimental analysis. Management Science, 1988. 34(1): p. 101-118.
  17. Zhu, H., et al., Modeling and algorithm for resource-constrained multi-project scheduling problem based on detection and rework. Assembly Automation, 2021.
  18. Chen, H., et al., Research on priority rules for the stochastic resource constrained multi-project scheduling problem with new project arrival. Computers & Industrial Engineering, 2019. 137: p. 106060.
  19. Wang, Y., et al., On the performance of priority rules for the stochastic resource constrained multi-project scheduling problem. Computers & industrial engineering, 2017. 114: p. 223-234.
  20. Gonçalves, J.F., J.J.d. Magalhães Mendes, and M.G. Resende, The basic multi-project scheduling problem, in Handbook on Project Management and Scheduling Vol. 2. 2015, Springer. p. 667-683.
  21. Krüger, D. and A. Scholl, Managing and modelling general resource transfers in (multi-) project scheduling. OR spectrum, 2010. 32(2): p. 369-394.
  22. Adhau, S., M. Mittal, and A. Mittal, A multi-agent system for decentralized multi-project scheduling with resource transfers. International journal of production economics, 2013. 146(2): p. 646-661.
  23. Chakrabortty, R.K., R.A. Sarker, and D.L. Essam, Resource constrained multi-project scheduling: a priority rule based evolutionary local search approach, in Intelligent and evolutionary systems. 2017, Springer. p. 75-86.
  24. Pérez, E., M. Posada, and A. Lorenzana, Taking advantage of solving the resource constrained multi-project scheduling problems using multi-modal genetic algorithms. Soft Computing, 2016. 20(5): p. 1879-1896.
  25. Van Eynde, R. and M. Vanhoucke, Resource-constrained multi-project scheduling: benchmark datasets and decoupled scheduling. Journal of Scheduling, 2020. 23(3): p. 301-325.
  26. Vázquez, E.P., M.P. Calvo, and P.M. Ordóñez, Learning process on priority rules to solve the RCMPSP. Journal of Intelligent Manufacturing, 2015. 26(1): p. 123-138.
  27. Confessore, G., S. Giordani, and S. Rismondo, A market-based multi-agent system model for decentralized multi-project scheduling. Annals of Operations Research, 2007. 150(1): p. 115-135.
  28. Mao, X., N. Roos, and A. Salden. Stable multi-project scheduling of airport ground handling services by heterogeneous agents. in Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems-Volume 1. 2009.
  29. Jędrzejowicz, P. and E. Ratajczak-Ropel, A-team solving distributed resource-constrained multi-project scheduling problem. Vietnam Journal of Computer Science, 2019. 6(04): p. 423-437.
  30. Li, F. and Z. Xu, A multi-agent system for distributed multi-project scheduling with two-stage decomposition. PloS one, 2018. 13(10): p. e0205445.
  31. Sánchez, M.G., A.F. Gil, and C. Castro. Integrating a SMT solver based local search in ant colony optimization for solving RCMPSP. in 2019 IEEE Latin American Conference on Computational Intelligence (LA-CCI). 2019. IEEE.
  32. Shi, Y., Z. Du, and J. Li. Hierarchy Model for Distributed Resource Constrained Multi-project Scheduling Problem. in Proceedings of the 2019 5th International Conference on Industrial and Business Engineering. 2019.
  33. Tian, M., R.J. Liu, and G.J. Zhang, Solving the resource-constrained multi-project scheduling problem with an improved critical chain method. Journal of the Operational Research Society, 2020. 71(8): p. 1243-1258.
  34. Villafáñez, F., et al., A generic heuristic for multi-project scheduling problems with global and local resource constraints (RCMPSP). Soft Computing, 2019. 23(10): p. 3465-3479.
  35. Zhang, Z. and M. Chen, A bi-level multi-agent system model for decentralized multi-project scheduling of wind power plants. Journal of Renewable and Sustainable Energy, 2018. 10(3): p. 035502.
  36. Adhau, S.R. and M.L. Mittal, A multi-agent based approach for dynamic multi-project scheduling. International Journal of Advanced Operations Management ICOREM, 2011. 3(3-4): p. 230-238.
  37. Ahmeti, A. and N. Musliu. Hybridizing constraint programming and meta-heuristics for multi-mode resource-constrained multiple projects scheduling problem. in Proceedings of the 13th International Conference on the Practice and Theory of Automated Timetabling-PATAT. 2021.
  38. Fu, F. and H. Zhou, A combined multi-agent system for distributed multi-project scheduling problems. Applied Soft Computing, 2021. 107: p. 107402.
  39. Gonçalves, J.F., J.J. Mendes, and M.G. Resende, A genetic algorithm for the resource constrained multi-project scheduling problem. European journal of operational research, 2008. 189(3): p. 1171-1190.
  40. He, Y., Z. He, and N. Wang, Tabu search and simulated annealing for resource-constrained multi-project scheduling to minimize maximal cash flow gap. Journal of Industrial & Management Optimization, 2021. 17(5): p. 2451.
  41. Beşikci, U., Ü. Bilge, and G. Ulusoy, Multi-mode resource constrained multi-project scheduling and resource portfolio problem. European Journal of Operational Research, 2015. 240(1): p. 22-31.
  42. Kolisch, R., Integrated scheduling, assembly area-and part-assignment for large-scale, make-to-order assemblies. International Journal of Production Economics, 2000. 64(1-3): p. 127-141.
  43. Lawrence, S.R. and T.E. Morton, Resource-constrained multi-project scheduling with tardy costs: Comparing myopic, bottleneck, and resource pricing heuristics. European Journal of Operational Research, 1993. 64(2): p. 168-187.
  44. Tasan, S.O. and M. Gen, An integrated selection and scheduling for disjunctive network problems. Computers & Industrial Engineering, 2013. 65(1): p. 65-76.
  45. Wang, X., et al., Proactive approach for stochastic RCMPSP based on multi-priority rule combinations. International Journal of Production Research, 2015. 53(4): p. 1098-1110.
  46. Xu, J. and Z. Zhang, A fuzzy random resource-constrained scheduling model with multiple projects and its application to a working procedure in a large-scale water conservancy and hydropower construction project. Journal of Scheduling, 2012. 15(2): p. 253-272.
  47. Zhu, H., Z. Lu, and X. Hu. A modified heuristic algorithm for resource constrained multi-project scheduling problem based on inspection and rework. in 2018 IEEE 14th International Conference on Automation Science and Engineering (CASE). 2018. IEEE.
  48. Li, F., Z. Xu, and H. Li, A multi-agent based cooperative approach to decentralized multi-project scheduling and resource allocation. Computers & Industrial Engineering, 2021. 151: p. 106961.
  49. Nabipoor Afruzi, E., A. Aghaie, and A.A. Najafi, Robust optimization for the resource-constrained multi-project scheduling problem with uncertain activity durations. Scientia Iranica, 2020. 27(1): p. 361-376.
  50. Pritsker, A.A.B., L.J. Waiters, and P.M. Wolfe, Multiproject scheduling with limited resources: A zero-one programming approach. Management science, 1969. 16(1): p. 93-108.
  51. Bock, D.B. and J.H. Patterson, A comparison of due date setting, resource assignment, and job preemption heuristics for the multiproject scheduling problem. Decision Sciences, 1990. 21(2): p. 387-402.
  52. Chen, J.-j., J.-l. Zhu, and D.-n. Zhang. Multi-project scheduling problem with human resources based on dynamic programming and staff time coefficient. in 2014 International Conference on Management Science & Engineering 21th Annual Conference Proceedings. 2014. IEEE.
  53. Deckro, R.F., et al., A decomposition approach to multi-project scheduling. European Journal of Operational Research, 1991. 51(1): p. 110-118.
  54. Cui, L., et al., A variable neighborhood search approach for the resource-constrained multi-project collaborative scheduling problem. Applied Soft Computing, 2021. 107: p. 107480.
  55. Hauder, V.A., et al., Resource-constrained multi-project scheduling with activity and time flexibility. Computers & Industrial Engineering, 2020. 150: p. 106857.
  56. Issa, S.B., R.A. Patterson, and Y. Tu, Solving resource-constrained multi-project environment under different activity assumptions. International Journal of Production Economics, 2021. 232: p. 107936.
  57. Kannimuthu, M., et al., Comparing optimization modeling approaches for the multi-mode resource-constrained multi-project scheduling problem. Engineering, Construction and Architectural Management, 2019.
  58. Hu, W., et al. Scheduling and Optimization of Multi-Project Resources Based on Cellular Automata. in 2010 International Conference on Logistics Engineering and Intelligent Transportation Systems. 2010. IEEE.
  59. Man, Z., et al. Research on multi-project scheduling problem based on hybrid genetic algorithm. in 2008 International Conference on computer science and software engineering. 2008. IEEE.
  60. Xin, S., et al. Optimization of resource-constrained multi-project scheduling problem based on the genetic algorithm. in 2018 15th International Conference on Service Systems and Service Management (ICSSSM). 2018. IEEE.
  61. Xu, J. and C. Feng, Multimode resource-constrained multiple project scheduling problem under fuzzy random environment and its application to a large scale hydropower construction project. The Scientific World Journal, 2014. 2014.
  62. Li, J. and W. Liu. A hybrid genetic algorithm for the resource constrained multi-project scheduling problem. in 2005 IEEE Conference on Emerging Technologies and Factory Automation. 2005. IEEE.
  63. Cheng, C.-B., C.-Y. Lo, and C.-P. Chu, Solving Multi-Mode Resource-Constrained Multi-Project Scheduling Problem with Combinatorial Auction Mechanisms. International Journal of Information and Management Sciences, 2019. 30(2): p. 143-167.
  64. Amirian, H. and R. Sahraeian, Solving a grey project selection scheduling using a simulated shuffled frog leaping algorithm. Computers & Industrial Engineering, 2017. 107: p. 141-149.
  65. Asta, S., et al., Combining Monte-Carlo and hyper-heuristic methods for the multi-mode resource-constrained multi-project scheduling problem. Information Sciences, 2016. 373: p. 476-498.
  66. Davari Ardakani, H. and A. Dehghani, Multi-objective Optimization of Multi-mode Resource-constrained Project Selection and Scheduling Problem Considering Resource Leveling and Time-varying Resource Usage. International Journal of Supply and Operations Management, 2022. 9(1): p. 34-55.
  67. Namazian, A., S. Haji Yakhchali, and M. Rabbani, Integrated bi-objective project selection and scheduling using Bayesian networks: A risk-based approach. Scientia Iranica, 2019. 26(6): p. 3695-3711.
  68. Vartouni, A.M. and L.M. Khanli, A hybrid genetic algorithm and fuzzy set applied to multi-mode resource-constrained project scheduling problem. Journal of Intelligent & Fuzzy Systems, 2014. 26(3): p. 1103-1112.
  69. Küçüksayacıgil, F., Use of genetic algorithms in multi-objective multi-project resource constrained project scheduling. 2014.
  70. Wang, H., D. Lin, and M.-Q. Li. A competitive genetic algorithm for resource-constrained project scheduling problem. in 2005 International Conference on Machine Learning and Cybernetics. 2005. IEEE.
  71. Satic, U., P. Jacko, and C. Kirkbride, Performance evaluation of scheduling policies for the dynamic and stochastic resource-constrained multi-project scheduling problem. International Journal of Production Research, 2022. 60(4): p. 1411-1423.
  72. Tian, J., X. Dong, and S. Han. Optimizing for a resource-constrained multi-project scheduling problem with planned resource unavailability. in 2018 3rd International Conference on Modelling, Simulation and Applied Mathematics (MSAM 2018). 2018. Atlantis Press.
  73. Liu, D. and Z. Xu, A Multi-PR Heuristic for Distributed Multi-Project Scheduling With Uncertain Duration. IEEE Access, 2020. 8: p. 227780-227792.
  74. Liu, J. and M. Lu, Robust dual-level optimization framework for resource-constrained multiproject scheduling for a prefabrication facility in construction. Journal of Computing in Civil Engineering, 2019. 33(2): p. 04018067.
  75. Van Den Eeckhout, M., M. Vanhoucke, and B. Maenhout, A column generation-based diving heuristic to solve the multi-project personnel staffing problem with calendar constraints and resource sharing. Computers & Operations Research, 2021. 128: p. 105163.
  76. Chen, P.-H. and S.M. Shahandashti, Hybrid of genetic algorithm and simulated annealing for multiple project scheduling with multiple resource constraints. Automation in Construction, 2009. 18(4): p. 434-443.
  77. Joo, B. and P. Chua. Multimode resource-constrained multi-project scheduling with ad hoc activity splitting. in 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). 2017. IEEE.
  78. Sonmez, R. and F. Uysal, Backward-forward hybrid genetic algorithm for resource-constrained multiproject scheduling problem. Journal of Computing in Civil Engineering, 2015. 29(5): p. 04014072.
  79. Chen, T. and C. Ju, Comparative analysis of swarm intelligence and heuristic priority rules for solving multi-project scheduling problem. International journal of computing science and mathematics, 2015. 6(6): p. 581-599.
  80. Gutjahr, W.J., et al., Competence-driven project portfolio selection, scheduling and staff assignment. Central European Journal of Operations Research, 2008. 16(3): p. 281-306.
  81. Linyi, D. and L. Yan. A particle swarm optimization for resource-constrained multi-project scheduling problem. in 2007 International Conference on Computational Intelligence and Security (CIS 2007). 2007. IEEE.
  82. Liu, M., M. Shan, and J. Wu, Multiple R&D projects scheduling optimization with improved particle swarm algorithm. The Scientific World Journal, 2014. 2014.
  83. Rokou, E., M. Dermitzakis, and K. Kirytopoulos. Multi-project flexible resource profiles project scheduling with Ant Colony Optimization. in 2014 IEEE International Conference on Industrial Engineering and Engineering Management. 2014. IEEE.
  84. Araujo, J.A., et al., Strong bounds for resource constrained project scheduling: Preprocessing and cutting planes. Computers & Operations Research, 2020. 113: p. 104782.
  85. Kim, K.W., M. Gen, and G. Yamazaki, Hybrid genetic algorithm with fuzzy logic for resource-constrained project scheduling. Applied soft computing, 2003. 2(3): p. 174-188.
  86. Boctor, F.F., An adaptation of the simulated annealing algorithm for solving resource-constrained project scheduling problems. 1994.
  87. Glover, F., Tabu search—part I. ORSA Journal on computing, 1989. 1(3): p. 190-206.
  88. Thomas, P.R. and S. Salhi, A tabu search approach for the resource constrained project scheduling problem. Journal of heuristics, 1998. 4(2): p. 123-139.
  89. Lo, S.-T., et al., Multiprocessor system scheduling with precedence and resource constraints using an enhanced ant colony system. Expert Systems with Applications, 2008. 34(3): p. 2071-2081.
  90. Merkle, D., M. Middendorf, and H. Schmeck, Ant colony optimization for resource-constrained project scheduling. IEEE transactions on evolutionary computation, 2002. 6(4): p. 333-346.
  91. Zhang, H., H. Li, and C. Tam, Particle swarm optimization for resource-constrained project scheduling. International journal of project management, 2006. 24(1): p. 83-92.
  92. Karaboga, D., An idea based on honey bee swarm for numerical optimization. 2005, Technical report-tr06, Erciyes university, engineering faculty, computer ….
  93. 93. Fahimi Fard, Kikha and Salarpour, Forecasting the price of selected agricultural products in Iran with the combined method of autoregressive neural network with Bruna inputs, 2018.
  94. Mehdi, Kh., et al., Compilation of planting operation scheduling model based on optimizing the cost of not performing the operation on time. Agricultural Mechanization Journal, 2013. Pages 61-70. 95. Gholamreza, R., b. Houshang, and D. Mohammad Javad Sheikh, the study of the cost of delay in carrying out the initial tillage operations of water wheat in Fars province using the system dynamics method. Agricultural machines, 1392. Pages 163-172.
  95. Maryam Seyedhamid et al. effective network methods in exploration project management, in Iran Mining Engineering Conference. 1383.
  96. Fadzipour Sain, Memar Yani and Hosseinzadeh Lotfi, Determining the relative efficiency of decision-making units to a certain extent inconsistent with DEA. Management future research, 2002. 14, number 2, pages 54-55

 

  1. Kolisch, R. and A. Sprecher, PSPLIB-a project scheduling problem library: OR software-ORSEP operations research software exchange program. European journal of operational research, 1997. 96(1): p. 205-216.
  2. Homberger, J., A multi‐agent system for the decentralized resource‐constrained multi‐project scheduling problem. International Transactions in Operational Research, 2007. 14(6): p. 565-589.
  3. Gholizadeh-Tayyar, S., et al., Modeling a generalized resource constrained multi project scheduling problem integrated with a forward-backward supply chain planning. IFAC-PapersOnLine, 2016. 49(12): p. 1283-1288.
  4. Song, W., et al., An agent-based simulation system for multi-project scheduling under uncertainty. Simulation Modelling Practice and Theory, 2018. 86: p. 187-203.
  5. Tayyar, S.G., J. Lamothe, and L. Dupont. Genetic algorithm for Generalized Resource Constrained Multi Project Scheduling Problem integrated with closed loop supply chain planning. in 2016 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). 2016. IEEE.
  6. Adhau, S., M.L. Mittal, and A. Mittal, A multi-agent system for distributed multi-project scheduling: An auction-based negotiation approach. Engineering Applications of Artificial Intelligence, 2012. 25(8): p. 1738-1751.
  7. Wauters, T., et al., A learning-based optimization approach to multi-project scheduling. Journal of Scheduling, 2015. 18(1): p. 61-74.
  8. Zheng, Z., et al., A critical chains based distributed multi-project scheduling approach. Neurocomputing, 2014. 143: p. 282-293.
  9. Geiger, M.J., A multi-threaded local search algorithm and computer implementation for the multi-mode, resource-constrained multi-project scheduling problem. European Journal of Operational Research, 2017. 256(3): p. 729-741.
  10. Toffolo, T.A., et al., An integer programming approach to the multimode resource-constrained multiproject scheduling problem. Journal of Scheduling, 2016. 19(3): p. 295-307.
  11. Wauters, T., et al., The multi-mode resource-constrained multi-project scheduling problem. Journal of Scheduling p. 271-283.