International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 07 | Jul 2023
p-ISSN: 2395-0072
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Understanding the effect of Financing Variability Using ChanceConstrained Approach in Time-Cost Tradeoff Shantanu Tiwari1, Manoj Kumar Trivedi2 1M.E. student, civil engineering department, MITS Gwalior
2Head of Civil Engineering department, Madhav Institute of Technology and Science, Gwalior
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Abstract - The issue of balancing trade-offs between
funding becomes imperative for effectively handling unanticipated expenses.
project duration and associated costs can be effectively addressed through the implementation of the linear optimization methodology. It’s important to consider that this particular approach solely focuses on the monetary aspects. During our study, we decided to analyze the influence of financing variability within the scheduling process. To achieve this, we used chance-constrained programming (CCP) allowing us to estimate the coefficient of variation for financing feasibility at a desired confidence level. Evaluating uncertainty involves calculating CV within a specified confidence level. In order to understand its effect, an objective function along with a set of constraints is used. An effective way to find direct costs. The utilization of Excel Solver helps in successfully completing the study. To better comprehend how financing variability impacts our study, two scenarios were taken during our analysis.
Chance-constrained programming (CCP) is an optimization technique that can provide valuable insights into how to handle the variability of financing. By incorporating the probability of events into the optimization equation CCP enables a more precise estimation of potential outcomes. This programming approach serves as a powerful tool for evaluating the risks based on the desired confidence level. [1] According to our model. The optimal solution is determined to have a risk level of 15%. Based on statistics there is an 85% probability of fulfilling the constraints and only a 15% chance of breaching them. As a result. It can be assumed that the solutions presented in this study have a commendable success rate of complying with constraints approximately 85% of the time.
Key Words: Financing feasibility, Linear optimization, Chance constrained programming (CCP), Coefficients of financing variability (CVAF), Time-Cost Tradeoff (TCT)
This study aims to develop a comprehensive mathematical model that considers various network Precedence relationships as well as the financing variability within a project.
1. INTRODUCTION
2. LITERATURE REVIEW
In the early stages of construction projects, it is important to formulate a plan and estimate the time required for completion as well as the projected expenses. This process, commonly referred to as "time and cost estimation of the project," is crucial in order to achieve efficiency from beginning to end. By accurately estimating these factors, it becomes possible to implement effective planning strategies and maintain concise control over costs. [1]
In the realm of construction project management striking an effective equilibrium between time and cost holds significant importance. To tackle conventional time cost problems associated with these projects. Various mathematical models exist alongside diverse approaches like heuristics and metaheuristics. Modern advancements in machine learning and artificial intelligence have also opened up avenues for addressing these challenges effectively.
Accomplishing a construction project successfully is truly a challenging endeavor. As it requires careful planning and precise execution. Regrettably, unforeseen circumstances frequently lead to modifications in time and cost estimates even when plans are carefully formulated.
Two broad groups of mathematical programming models are used to address the time-cost tradeoff problem in various ways. One group utilizes linear programming (LP) models, as seen in studies conducted by researchers such as Meyer and Shaffer [2] . Another approach involves integer programming (IP), demonstrated by Liuet al.'s combined LP/IP hybrid method that establishes both lower bounds and exact solutions for project time-cost relationships through LP and IP techniques respectively [3]. Butcher's dynamic programming-based approach is
As deviations emerge in the project, obtaining adequate financing presents a critical task, resulting in the suspension of operations until securing necessary fundsthus causing delays and hindering momentum. As such, it often proves impractical to limit potential divergences entirely; instead, ensuring ready access to supplementary
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