Optimum finance-based scheduling

Abstract

Financial issues are among the most common causes of business failure in the construction industry. Hence, construction contractors must acknowledge the financial parameters in the project's planning phase. However, the schedulers mostly neglected integrating work scheduling and finance management until finance-based scheduling methods were introduced. Although several researchers developed models to provide solutions for finance-based scheduling problems, they neglected essential issues that must be visited. Some of these researchers used the activities' total floats to shift the activities' start times to minimize the contractor's financing cost. However, they never considered that there could be more than one work schedule to provide the minimum financing cost. Also, except for a few of them, they never indicated that using the total floats of the activities may cause more critical work schedules. Moreover, the studies in finance-based scheduling literature involved parameters and used techniques for resource-leveling. Still, none considered the fact that there are nine different resource-leveling objective functions widely used to obtain smoother resource histograms. Lastly, most of the researchers in the finance-based scheduling literature use only the line of credit as the sole financing alternative. A few researchers considered other financing alternatives, such as short-term loans and long-term loans, but they neglected the use of more financing alternatives to minimize the financing cost of the contractor. In this study, three different models are proposed to fill the gaps mentioned above. Using the first model ensures the selection of one schedule among multiple schedules with the minimum financing cost by calculating the float consumption cost and selecting the schedule with the minimum float consumption cost. In the first proposed model, a line of credit and short-term and long-term loans are used as the financing alternatives. Line of credit, short-term loans, and long-term loans are the financing alternatives to minimize the contractor's financing cost in the second proposed model of this study, which is similar to the first model. Besides, using the second model enables contractors to select the optimal work schedule by considering their priorities. If the contractor gives more importance to minimizing the financing cost of the project than the minimization of resource fluctuations, the contractor can obtain the work schedule with the minimum resource fluctuations among the schedules with the minimum financing cost by using the second proposed model of this study. On the contrary, if the contractor prioritizes the minimization of the resource fluctuations rather than the minimization of the financing cost, then the reverse of the first process becomes available to the contractor. Nine different resource-leveling objective functions are considered in the scope of the second proposed model to ensure that the selected work schedule provides the smoothest resource histogram possible. In the last proposed model of this study, balloon loans are considered in addition to other financing alternatives used in the first and second proposed models. Also, the contractors can frontload their offers along with the usage of the different financing alternatives, as mentioned earlier, to minimize the financing cost further. The users of all three models can obtain financing schedules that involve the borrowing and repaying time of the funds and the repaying time of the interest payments. The amounts of these financial parameters can also be determined as the outputs of the three proposed models in this study. However, the proposed models in this study have several limitations that can be addressed in future studies. First, none of the three models considered the likelihood of the contractor having many projects in their portfolio. Contractors in the construction sector typically manage multiple projects in their portfolios simultaneously, so improvements to the proposed models are needed to accommodate this aspect. Second, none of the models provided in this study use a stochastic method to forecast the uncertain environment of the construction industry. There is a need for additional research to close this gap. As a third limitation, the presented models do not incorporate any time-cost tradeoff analysis, which may assist contractors in reducing financing costs even more. Future research must take these assessments into account in order to increase the proposed models' performance. Fourth, all three models can incorporate a broader range of financing alternatives employed by contractors in the construction industry that have never been addressed in the present finance-based scheduling literature.