نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مدیریت، واحد دهاقان، دانشگاه آزاد اسلامی، دهاقان، ایران.

2 گروه مدیریت، دانشکده مهندسی صنایع و مدیریت، دانشگاه صنعتی شاهرود، شاهرود، ایران.

چکیده

هدف: هدف تحقیق حاضر ارایه یک مدل ریاضی چند‌هدفه با رویکرد یکپارچه زمان‌بندی و جریان مالی در پروژه‌های تولیدی با استفاده از الگوریتم ژنتیک مرتب‌سازی غیر مسلط II (NSGA-II) است.
روش‌شناسی پژوهش: این تحقیق یک مدل ریاضی چندهدفه را ارایه می‌کند که زمان‌بندی و بهینه‌سازی جریان مالی را در پروژه‌های عمرانی ادغام می‌کند. این تحقیق به چالش‌های زمان‌بندی و جریان مالی در پروژه‌های تولیدی در شرکت‌های ساختمانی می‌پردازد. هدف توسعه یک مدل ریاضی چندهدفه است که ملاحظات زمان‌بندی و مالی را با هدف بهینه‌سازی تخصیص منابع و به حداقل رساندن هزینه‌ها ادغام می‌کند. جامعه آماری به‌صورت مطالعه موردی است و اطلاعات و داده‌های موردنیاز از طریق مصاحبه با مدیران شرکت عمرانی کیسون جمع‌آوری شد.
یافته‌ها: الگوریتم ژنتیک مرتب‌سازی غیر مسلط II به‌عنوان الگوریتم بهینه‌سازی برای یافتن راه‌حل‌های کارآمد در زمینه چند‌هدفه استفاده و نتایج بهینه جهت انتخاب پروژه‌های عمرانی و ساختمانی ارایه شد.
اصالت/ارزش‌افزوده علمی: این تحقیق با پیشنهاد یک مدل ریاضی چندهدفه جدید که ملاحظات زمان‌بندی و جریان مالی را در پروژه‌های تولیدی یکپارچه می‌کند، به این حوزه کمک می‌نماید. استفاده از الگوریتم NSGA-II، کارایی یافتن راه‌حل‌های بهینه را افزایش می‌دهد. یافته‌ها می‌تواند برای تصمیم‌گیری در انتخاب پروژه‌های ساخت‌وساز و تولید ارزشمند باشد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Presenting a multi-objective mathematical model with an integrated approach to scheduling and financial flow in production projects using NSGA-II

نویسندگان [English]

  • Sajad Janbaz 1
  • Seyed Mohammadreza Davoodi 1
  • Abdolmajid Abdolbaghi Ataabadi 2

1 Department of Management, Dehaghan Branch, Islamic Azad University, Dehaghan, Iran.

2 Department of Management, School of Industrial Engineering and Management, Shahrood, Iran.

چکیده [English]

Purpose: The current research aims to present a multi-objective mathematical model with an integrated approach to scheduling and financial flow in production projects using Non-dominated Sorting Genetic Algorithm II (NSGA-II).
Methodology: This research presents a multi-objective mathematical model integrating scheduling and financial flow optimization in civil engineering projects. This research addresses the scheduling and financial flow challenges in construction companies' production projects. The objective is to develop a multi-objective mathematical model that integrates scheduling and financial considerations to optimize resource allocation and minimize costs. The statistical population is in the form of a case study, and the required information and data were collected through interviews with managers of Kisson Construction Company.
Findings: NSGA-II was used as an optimization algorithm to find efficient multi-objective solutions, and optimal results were presented to select civil and construction projects.
Originality/Value: This research contributes to the field by proposing a novel multi-objective mathematical model that integrates scheduling and financial flow considerations in production projects. The use of the NSGA-II algorithm enhances the efficiency of finding optimal solutions. The findings can be valuable for decision-making when selecting construction and production projects.

کلیدواژه‌ها [English]

  • Scheduling of production projects
  • Non-linear mixed integer multi-objective mathematical programming model
  • NSGA-II
[1]     Hartmann, S., & Briskorn, D. (2022). An updated survey of variants and extensions of the resource-constrained project scheduling problem. European journal of operational research, 297(1), 1–14.
[2]     Pellerin, R., Perrier, N., & Berthaut, F. (2020). A survey of hybrid metaheuristics for the resource-constrained project scheduling problem. European journal of operational research, 280(2), 395–416. DOI:10.1016/j.ejor.2019.01.063
[3]     Deng, W., Zhang, X., Zhou, Y., Liu, Y., Zhou, X., Chen, H., & Zhao, H. (2022). An enhanced fast non-dominated solution sorting genetic algorithm for multi-objective problems. Information sciences, 585, 441–453. DOI:10.1016/j.ins.2021.11.052
[4]     Herroelen, W. S., Van Dommelen, P., & Demeulemeester, E. L. (1997). Project network models with discounted cash flows a guided tour through recent developments. European journal of operational research, 100(1), 97–121. DOI:10.1016/S0377-2217(96)00112-9
[5]     Kolisch, R., & Hartmann, S. (2006). Experimental investigation of heuristics for resource-constrained project scheduling: An update. European journal of operational research, 174(1), 23–37. DOI:10.1016/j.ejor.2005.01.065
[6]     Hartmann, S., & Briskorn, D. (2010). A survey of variants and extensions of the resource-constrained project scheduling problem. European journal of operational research, 207(1), 1–14. DOI:10.1016/j.ejor.2009.11.005
[7]     He, Z., Wang, N., Jia, T., & Xu, Y. (2009). Simulated annealing and tabu search for multi-mode project payment scheduling. European journal of operational research, 198(3), 688–696. DOI:10.1016/j.ejor.2008.10.005
[8]     Russell, A. H. (1970). Cash flows in networks. Management science, 16(5), 357–373. DOI:10.1287/mnsc.16.5.357
[9]     Özdamar, L., Ulusoy, G., & Bayyigit, M. (1998). A heuristic treatment of tardiness and net present value criteria in resource constrained project scheduling. International journal of physical distribution & logistics management, 28(9/10), 805–824. DOI:10.1108/09600039810248181
[10]   Dayan, N., & Padman, R. (1997). On modelling payments in projects. Journal of the operational research society, 48(9), 906–918. DOI:10.1057/palgrave.jors.2600440
[11]   Ulusoy, G., & Cebelli, S. (2000). Equitable approach to the payment scheduling problem in project management. European journal of operational research, 127(2), 262–278. DOI:10.1016/S0377-2217(99)00499-3
[12]   He, Z., & Xu, Y. (2008). Multi-mode project payment scheduling problems with bonus-penalty structure. European journal of operational research, 189(3), 1191–1207. DOI:10.1016/j.ejor.2006.07.053
[13]   Özdamar, L., & Dündar, H. (1997). A flexible heuristic for a multi-mode capital constrained project scheduling problem with probabilistic cash inflows. Computers and operations research, 24(12), 1187–1200. DOI:10.1016/S0305-0548(96)00058-5
[14]   Mika, M., Waligóra, G., & Wkeglarz, J. (2005). Simulated annealing and tabu search for multi-mode resource-constrained project scheduling with positive discounted cash flows and different payment models. European journal of operational research, 164(3), 639–668.
[15]   Chen, W. N., & Zhang, J. (2012). Scheduling multi-mode projects under uncertainty to optimize cash flows: A Monte Carlo ant colony system approach. Journal of computer science and technology, 27(5), 950–965. DOI:10.1007/s11390-012-1276-2
[16]   Aboutalebi, R. S., Najafi, A. A., & Ghorashi, B. (2012). Solving multi-mode resource-constrained project scheduling problem using two multi objective evolutionary algorithms. African journal of business management, 6(11), 4057–4065.
[17]   Hosseini, Z. S., Hassan Pour, J., & Roghanian, E. (2014). A bi-objective pre-emption multi-mode resource constrained project scheduling problem with due dates in the activities. Journal of optimization in industrial engineering, 7(15), 15–25.
[18]   Leyman, P., & Vanhoucke, M. (2016). Payment models and net present value optimization for resource-constrained project scheduling. Computers and industrial engineering, 91, 139–153. DOI:10.1016/j.cie.2015.11.008
[19]   Sebt, M. H., Afshar, M. R., & Alipouri, Y. (2017). Hybridization of genetic algorithm and fully informed particle swarm for solving the multi-mode resource-constrained project scheduling problem. Engineering optimization, 49(3), 513–530. DOI:10.1080/0305215X.2016.1197610
[20]   Geiger, M. J. (2017). A multi-threaded local search algorithm and computer implementation for the multi-mode, resource-constrained multi-project scheduling problem. European journal of operational research, 256(3), 729–741. DOI:10.1016/j.ejor.2016.07.024
[21]   Oztemel, E., & Selam, A. A. (2017). Bees algorithm for multi-mode, resource-constrained project scheduling in molding industry. Computers and industrial engineering, 112, 187–196. DOI:10.1016/j.cie.2017.08.012
[22]   Ghafoori, S., & Taghizadeh Yazdi, M. (2016). Proposing a multi-objective mathematical model for RCPSP and solving It with firefly and simulated annealing algorithms. Modern researches in decision making, 1(4), 117–142.
[23]   Kazemi, A., Sarrafha, K., & Alinezhad, A. (2018). Presenting a bi-objective integrated production – distribution planning problem model in a multi echelon supply chain with considering service level. Production and operations management, 8(2), 115–134.
[24]   Baradaran, V., & Hosseinian, A. H. (2021). A multi-objective mathematical formulation for the airline crew scheduling problem: MODE and NSGA-II solution approaches. Journal of industrial management perspective, 11(1), 247–269.
[25]   Rezaie Moghadam, S., & Doosti, A. (2022). Designing a multi _objective mathematical model for integrated production planning in a reversible supply chain with the uncertainty approach and using the NSGA-II meta_industry. Journal of decisions and operations research, 6(Spec. Issue), 1–24.
[26]   Janbaz, S., Davoodi, S. M. R., & Abdolbaghi Ataabadi, A. (2023). An integrated multi-objective model of scheduling and financial flow of production projects and useing of MOSA and MOKA meta-heuristic algorithms. Journal of industrial engineering research in production systems, 10(20), 17–31. DOI:magiran.com/p2572744
[27]   Torabi Yeganeh, F., & Zegordi, S. H. (2020). A multi-objective optimization approach to project scheduling with resiliency criteria under uncertain activity duration. Annals of operations research, 285(1–2), 161–196. DOI:10.1007/s10479-019-03375-z
[28]   Peng, W., Mu, J., Chen, L., & Lin, J. (2021). A novel non-dominated sorting genetic algorithm for solving the triple objective project scheduling problem. Memetic computing, 13(2), 271–284. DOI:10.1007/s12293-021-00332-x
[29]   Liu, Y., You, K., Jiang, Y., Wu, Z., Liu, Z., Peng, G., & Zhou, C. (2022). Multi-objective optimal scheduling of automated construction equipment using non-dominated sorting genetic algorithm (NSGA-III). Automation in construction, 143, 104587. DOI:10.1016/j.autcon.2022.104587
[30]   Babor, M., Pedersen, L., Kidmose, U., Paquet-Durand, O., & Hitzmann, B. (2022). Application of non-dominated sorting genetic algorithm (NSGA-II) to increase the efficiency of bakery production: a case study. Processes, 10(8), 1623. DOI:10.3390/pr10081623
[31]   Hou, J., Du, J., & Chen, Z. (2023). Time-optimal trajectory planning for the manipulator based on improved non-dominated sorting genetic algorithm II. Applied sciences (switzerland), 13(11), 6757. DOI:10.3390/app13116757
[32]   Mircioiu, C., Voicu, V., Anuta, V., Tudose, A., Celia, C., Paolino, D., … & Mircioiu, I. (2019). Mathematical modeling of release kinetics from supramolecular drug delivery systems. Pharmaceutics, 11(3), 1–140. DOI:10.3390/pharmaceutics11030140
[33]   Mainardi, F. (2022). Fractional calculus and waves in linear viscoelasticity: an introduction to mathematical models. World Scientific.
[34]   Kurihara, K., Maruyama, H., & Masuda, K. (2010). Hierarchical planning method for product supply based on multi objective genetic algorithm. Proceedings of the 15th IEEE international conference on emerging technologies and factory automation, ETFA 2010 (pp. 1–8). IEEE. DOI: 10.1109/ETFA.2010.5641164
[35]   Gerstel, O., Filsfils, C., Telkamp, T., Gunkel, M., Horneffer, M., Lopez, V., & Mayoral, A. (2014). Multi-layer capacity planning for IP-optical networks. IEEE communications magazine, 52(1), 44–51.
[36]   Chen, H., Deng, T., Du, T., Chen, B., Skibniewski, M. J., & Zhang, L. (2022). An RF and LSSVM–NSGA-II method for the multi-objective optimization of high-performance concrete durability. Cement and concrete composites, 129, 104446. DOI:10.1016/j.cemconcomp.2022.104446
[37]   Zhang, P., Qian, Y., & Qian, Q. (2021). Multi-objective optimization for materials design with improved NSGA-II. Materials today communications, 28, 102709. DOI:10.1016/j.mtcomm.2021.102709
[38]   Rahimi, I., Gandomi, A. H., Deb, K., Chen, F., & Nikoo, M. R. (2022). Scheduling by NSGA-II: review and bibliometric analysis. Processes, 10(1), 98. DOI:10.3390/pr10010098
[39]   Kumar, A., & Kumar, T. V. V. (2022). Multi-objective big data view materialization using NSGA-III. International journal of decision support system technology, 14(1), 1–28. DOI:10.4018/IJDSST.311066
[40]   Hojjati, A., Monadi, M., Faridhosseini, A., & Mohammadi, M. (2018). Application and comparison of NSGA-II and MOPSO in multi-objective optimization of water resources systems. Journal of hydrology and hydromechanics, 66(3), 323–329. DOI:10.2478/johh-2018-0006
[41]   Nebro, A. J., Barba-González, C., López-Ibáñez, M., & García-Nieto, J. (2019). Automatic configuration of nsga-ii with jmetal and irace. GECCO 2019 companion - proceedings of the 2019 genetic and evolutionary computation conference companion (pp. 1374–1381). Association for computing machinery. DOI: 10.1145/3319619.3326832
[42]   Altuwaim, A., & El-Rayes, K. (2018). Minimizing duration and crew work interruptions of repetitive construction projects. Automation in construction, 88, 59–72. DOI:10.1016/j.autcon.2017.12.024
[43]   García-Nieves, J. D., Ponz-Tienda, J. L., Ospina-Alvarado, A., & Bonilla-Palacios, M. (2019). Multipurpose linear programming optimization model for repetitive activities scheduling in construction projects. Automation in construction, 105, 102799. DOI:10.1016/j.autcon.2019.03.020