Finance-based Scheduling for Cash-flow Management of Maintenance Portfolios: Multi-objective Optimization Approach
DOI:
https://doi.org/10.31181/dmame7220241136Keywords:
Finance-based Scheduling, Multi-objective optimization, Cash-flow management, Budget allocation, PortfoliosAbstract
Bridge agencies are often challenged to develop maintenance programs under given budgets. Numerous studies developed budget-allocation models for maintenance programs during defined planning horizons of multiple fiscal years while totally ignoring the crucial aspect of cash flow. The payment schedules (both timing and amount) for contractors might indicate agencies’ cash needs that exceed the available budgets during certain fiscal years, which create cash flow problems. While numerous finance-based scheduling (FBS) models were developed to manage cash flow for contractors, this function was totally overlooked for portfolio owners. Thus, this research reintroduces the FBS from the perspective of portfolio owners. A FBS model is developed to schedule the activities of the portfolio projects, utilize the schedules to define the payment schedules of projects’ contractors, calculate the agencies’ cash needs during the fiscal years, and utilize the multi-objective optimization algorithms of NSGA-II, SPEA-II, and MOPSO to optimize the projects’ schedules to achieve a balance between the cash needs during the fiscal years and the available budgets. The anticipated extensions in projects’ completion represent the conflicting objectives. Finally, the optimized schedules make the contractors’ payment schedules affordable by the agencies’ budgets, which help to complete projects successfully and achieve the programs’ strategic goals.
Downloads
References
OECD. Road transport research. Paris: Bridge Management; 1992.
Shim, H., Lee, S., & Kang, B. (2017). Pareto front generation for bridge deck management system using bi-objective optimization. KSCE Journal of Civil Engineering, 21, 1563-1572. https://doi.org/10.1007/s12205-016-2569-8
Hegazy, T., Elbeltagi, E., & El-Behairy, H. (2004). Bridge deck management system with integrated life-cycle cost optimization. Transportation Research Record: Journal of the Transportation Research Board, 1866, 44-50. https://doi.org/10.3141/1866-06
Gao, L., Xie, C., Zhang, Z., & Waller, S. T. (2012). Network‐level road pavement maintenance and rehabilitation scheduling for optimal performance improvement and budget utilization. Computer‐Aided Civil and Infrastructure Engineering, 27(4), 278-287. https://doi.org/10.1111/j.1467-8667.2011.00733
Mohammadi, A., Igwe, C., Amador-Jimenez, L., & Nasiri, F. (2022). Applying lean construction principles in road maintenance planning and scheduling. International journal of construction management, 22(12), 2364-2374. https://doi.org/10.1080/15623599.2020.1788758
Kordestani Ghalenoeei, N., Saghatforoush, E., Athari Nikooravan, H., & Preece, C. (2021). Evaluating solutions to facilitate the presence of operation and maintenance contractors in the pre-occupancy phases: a case study of road infrastructure projects. International Journal of Construction Management, 21(2), 140-152. https://doi.org/10.1080/15623599.2018.1512027
Gharaibeh, N. G., Chiu, Y. C., & Gurian, P. L. (2006). Decision methodology for allocating funds across transportation infrastructure assets. Journal of infrastructure systems, 12(1), 1-9. https://doi.org/10.1061/(ASCE)1076-0342(2006)12:1(1)
Mrawira, D., & Amador, L. (2009). Cross-assets trade-off analysis: Why are we still talking about it. Proceedings, 88th TRB Annual Meeting Compendium of Papers, Transportation Research Board, Washington, DC.
Kuhn, K. D. (2010). Network-level infrastructure management using approximate dynamic programming. Journal of Infrastructure Systems, 16(2), 103-111. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000019
National Cooperative Highway Research Program (2009). An asset-management framework for the interstate highway system. National Cooperative Highway Research Program Synthesis Rep. No. 632, Transportation Research Board. Washington, DC.
Fwa, T., & Farhan, J. (2012). Optimal Multiasset Maintenance Budget Allocation in Highway Asset Management. Journal of Transportation Engineering, 138, 1179-1187. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000414
Yeo, H., Yoon, Y., & Madanat, S. (2013). Algorithms for bottom-up maintenance optimisation for heterogeneous infrastructure systems. Structure and Infrastructure Engineering, 9(4), 317-328. https://doi.org/10.1080/15732479.2012.657649
Dehghani, M. S., Giustozzi, F., Flintsch, G. W., & Crispino, M. (2013). Cross-asset resource allocation framework for achieving performance sustainability. Transportation research record, 2361(1), 16-24. https://doi.org/10.3141/2361-03
Porras-Alvarado, J., Han, Z., & Zhanmin, Z. (2015). A fair division approach to performance-based cross-asset resource allocation. In 9th International Conference on Managing Pavement Assets.
Shi, Y., Xiang, Y., Xiao, H., & Xing, L. (2021). Joint optimization of budget allocation and maintenance planning of multi-facility transportation infrastructure systems. European Journal of Operational Research, 288(2), 382-393. https://doi.org/10.1016/j.ejor.2020.05.050
Xu, G., & Guo, F. (2022). Sustainability-oriented maintenance management of highway bridge networks based on Q-learning. Sustainable Cities and Society, 81, 103855. https://doi.org/10.1016/j.scs.2022.103855
Ghafoori, M., Abdallah, M., & Ozbek, M. E. (2024). Machine Learning–Based Bridge Maintenance Optimization Model for Maximizing Performance within Available Annual Budgets. Journal of Bridge Engineering, 29(4), 04024011. https://doi.org/10.1061/JBENF2.BEENG-6436
Mohammadi, M., Rashidi, M., Yu, Y., & Samali, B. (2023). Integration of TLS-derived Bridge Information Modeling (BrIM) with a Decision Support System (DSS) for digital twinning and asset management of bridge infrastructures. Computers in Industry, 147, 103881. https://doi.org/10.1016/j.compind.2023.103881
Cao, Q., Zou, X., & Zhang, L. (2022). Multiobjective robust optimization model for generating stable and makespan-protective repetitive schedules. Journal of construction Engineering and Management, 148(9), 04022099. https://doi.org/10.1061/(ASCE)CO.1943-7862.0002348
Tavakolan, M., & Nikoukar, S. (2022). Developing an optimization financing cost-scheduling trade-off model in construction project. International Journal of Construction Management, 22(2), 262-277. https://doi.org/10.1080/15623599.2019.1619439
Dabirian, S., Ahmadi, M., & Abbaspour, S. (2021). Analyzing the impact of financial policies on construction projects performance using system dynamics. Engineering, Construction and Architectural Management, 30(3), 1201-1221. https://doi.org/10.1108/ECAM-05-2021-0431
Andalib, R., Hoseini, A., & Gatmiri, B. (2018). A stochastic model of cash flow forecasting considering delays in owners' payments. Construction Management and Economics, 36(10), 545-564. https://doi.org/10.1080/01446193.2018.1433309
Alavipour, S., & Arditi, D. (2018). Optimizing Financing Cost in Construction Projects with Fixed Project Duration. Journal of Construction Engineering and Management, 144(4), 04018012. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001451
Su, Y., & Lucko, G. (2015). Synthetic cash flow model with singularity functions. I: Theory for periodic phenomena and time value of money. Journal of construction engineering and management, 141(3), 04014078. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000938
Lee, D. E., Lim, T. K., & Arditi, D. (2012). Stochastic project financing analysis system for construction. Journal of Construction Engineering and Management, 138(3), 376-389. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000432
Motawa, I., & Kaka, A. (2009). Modelling payment mechanisms for supply chain in construction. Engineering, Construction and Architectural Management, 16(4), 325-336. https://doi.org/10.1108/09699980910970824
Tang, C. M., Leung, A. Y., & Lam, K. C. (2006). Entropy application to improve construction finance decisions. Journal of construction engineering and management, 132(10), 1099-1113. https://doi.org/10.1061/(ASCE)0733-9364(2006)132:10(1099)
Lee, C. K., Bujna, M., Abd Jamil, A. H., & Ee, P. T. (2023). A cause and effect of a nonpayment model based on the DEMATEL algorithm. Journal of Legal Affairs and Dispute Resolution in Engineering and Construction, 15(1), 04522050. https://doi.org/10.1061/(ASCE)LA.1943-4170.0000592
Dorrah, D. H., & McCabe, B. (2023). Integrated Agent-Based Simulation and Game Theory Decision Support Framework for Cash Flow and Payment Management in Construction Projects. Sustainability, 16(1), 244. https://doi.org/10.3390/su16010244
Shalaby, A., & Ezeldin, A. S. (2022). A model for work packages optimization in results-based-finance projects. Engineering, Construction and Architectural Management, 29(7), 2810-2835. https://doi.org/10.1108/ECAM-10-2019-0556
Liang, Y., Ashuri, B., & Li, M. (2021). Forecasting the construction expenditure cash flow for transportation design-build projects with a case-based reasoning model. Journal of Construction Engineering and Management, 147(6), 04021043. https://doi.org/10.1061/(ASCE)CO.1943-7862.0002054
Farshchian, M. M., Heravi, G., & AbouRizk, S. (2017). Optimizing the owner’s scenarios for budget allocation in a portfolio of projects using agent-based simulation. Journal of Construction Engineering and Management, 143(7), 04017022. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001315
Huang, W. H., Tserng, H. P., Jaselskis, E. J., & Lee, S. Y. (2014). Dynamic threshold cash flow–based structural model for contractor financial prequalification. Journal of construction Engineering and management, 140(10), 04014047. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000902
Jarrah, R. E., Kulkarni, D., & O’Connor, J. T. (2007). Cash flow projections for selected TxDoT highway projects. Journal of construction engineering and management, 133(3), 235-241. https://doi.org/10.1061/(ASCE)0733-9364(2007)133:3(235)
Elazouni, A. M., & Gab-Allah, A. A. (2004). Finance-based scheduling of construction projects using integer programming. Journal of construction engineering and management, 130(1), 15-24. https://doi.org/10.1061/(ASCE)0733-9364(2004)130:1(15)
Liu, S. S., & Wang, C. J. (2010). Profit optimization for multiproject scheduling problems considering cash flow. Journal of Construction Engineering and Management, 136(12), 1268-1278.https://doi.org/10.1061/(ASCE)CO.1943-7862.0000235
Alghazi, A., Elazouni, A., & Selim, S. (2013). Improved genetic algorithm for finance-based scheduling. Journal of Computing in Civil Engineering, 27(4), 379-394. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000227
Gajpal, Y., & Elazouni, A. (2015). Enhanced heuristic for finance-based scheduling of construction projects. Construction Management and Economics, 33(7), 531-553. https://doi.org/10.1080/01446193.2015.1063676
Al‐Shihabi, S., & AlDurgam, M. M. (2020). The contractor time–cost–credit trade‐off problem: integer programming model, heuristic solution, and business insights. International Transactions in Operational Research, 27(6), 2841-2877. https://doi.org/10.1111/itor.12764
Liu, W., Zhang, J., & Liu, W. (2021). Heuristic methods for finance-based and resource-constrained project scheduling problem. Journal of Construction Engineering and Management, 147(11), 04021141. https://doi.org/10.1061/(ASCE)CO.1943-7862.0002174
Liu, W., Zhang, J., Liu, C., & Qu, C. (2023). A bi-objective optimization for finance-based and resource-constrained robust project scheduling. Expert Systems with Applications, 231, 120623. https://doi.org/10.1016/j.eswa.2023.120623
Zitzler, E., Laumanns, M., & Thiele, L. (2001). SPEA2: Improving the strength Pareto evolutionary algorithm. TIK report, 103, 1-18. https://doi.org/10.3929/ethz-a-004284029
Zitzler, E., & Thiele, L. (1999). Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE transactions on Evolutionary Computation, 3(4), 257-271. https://doi.org/10.1109/4235.797969
Gharari, R., Poursalehi, N., Abbasi, M., & Aghaie, M. (2016). Implementation of strength pareto evolutionary algorithm ii in the multiobjective burnable poison placement optimization of kwu pressurized water reactor. Nuclear Engineering and Technology, 48(5), 1126-1139. https://doi.org/10.1016/j.net.2016.04.004
Kaucic, M., Moradi, M., & Mirzazadeh, M. (2019). Portfolio optimization by improved NSGA-II and SPEA 2 based on different risk measures. Financial Innovation, 5, 26. https://doi.org/10.1186/s40854-019-0140-6
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. A. M. T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation, 6(2), 182-197. https://doi.org/10.1109/4235.996017
Alothaimeen, I., & Arditi, D. (2020). Overview of Multi-Objective Optimization Approaches in Construction Project Management. Multicriteria Optimization-Pareto-Optimality and Threshold-Optimality. https://doi.org/10.5772/intechopen.88185
Wang, Y., Ma, J., & Zhang, Y. (2024). Application of Improved Frog Leaping Algorithm in Multi objective Optimization of Engineering Project Management. Decision Making: Applications in Management and Engineering, 7(1), 364-379. https://doi.org/10.31181/dmame712024896
Zhang, F. (2024). Constructing a Multi-Objective Optimization Model for Engineering Projects Based on NSGA-II Algorithm under the Background of Green Construction. Decision Making: Applications in Management and Engineering, 7(1), 37-53. https://doi.org/10.31181/dmame712024895
Ünal, A. N., & Kayakutlu, G. (2020). Multi-objective particle swarm optimization with random immigrants. Complex & Intelligent Systems, 6(3), 635-650. https://doi.org/10.1007/s40747-020-00159-y
Abido, M. A. (2010). Multiobjective particle swarm optimization with nondominated local and global sets. Natural Computing, 9, 747-766. https://doi.org/10.1007/s11047-009-9171-7
Bean, J. C. (1994). Genetic algorithms and random keys for sequencing and optimization. ORSA journal on computing, 6(2), 154-160. https://doi.org/10.1287/ijoc.6.2.154
Dhillon, J., Parti, S. C., & Kothari, D. P. (1993). Stochastic economic emission load dispatch. Electric Power Systems Research, 26(3), 179-186. https://doi.org/10.1016/0378-7796(93)90011-3
Schott, J. R. (1995). Fault tolerant design using single and multicriteria genetic algorithm optimization (Doctoral dissertation, Massachusetts Institute of Technology).
El-Abbasy, M. S., Elazouni, A., & Zayed, T. (2020). Finance-based scheduling multi-objective optimization: Benchmarking of evolutionary algorithms. Automation in Construction, 120, 103392. https://doi.org/10.1016/j.autcon.2020.103392
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Decision Making: Applications in Management and Engineering
This work is licensed under a Creative Commons Attribution 4.0 International License.
