Profitability Optimization of Construction Project Using Genetic Algorithm Cash Flow Model

Ismail M. Basha¹, Ahmed H. Ibrahim¹ and Ahmed N. Abd El-Azim^1,

¹Construction Engineering &Utilities Department, Zagazig University, Zagazig, Egypt

Cite this paper:
Ismail M. Basha, Ahmed H. Ibrahim and Ahmed N. Abd El-Azim. Profitability Optimization of Construction Project Using Genetic Algorithm Cash Flow Model. American Journal of Civil Engineering and Architecture. 2016; 4(1):17-27. doi: 10.12691/ajcea-4-1-3

Abstract

Cash issues are various and complicated. The contractor starts with a forecast for the flow of the cash through the lifetime of the project. Cash shortages can lead to project failure and business bankruptcy. Researchers have studied cash flow in the context of project delay, business failure, and forecasting. However, negative cash flow trends and patterns themselves are not closely examined despite the amount, duration and distribution of negative cash flow are critical factors in construction performance. This study investigates cash flow management and profit optimization by reducing the extent and amount of negative cash flow on the construction projects and completes the project as scheduled by rescheduling construction activities based on the minimum cash flow availability. The study utilizes genetic algorithm’s technique to devise finance-based schedules that minimize project negative cash flow and profit optimization by identifying the amount and timing of individual inflow or outflow at the end of each period. The study also presents a case study project to illustrate the capability of the proposed model and adopts various constraints, including project profit and due dates, for scenario analysis. The analysis result demonstrates that minimizing negative flow ensures smooth financial pressure by properly shifting activities, and assigning due dates for projects helps planners avoid project duration extension while maximizing overall project profit.

Keywords:
negative cash flow profit optimization genetic algorithm

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