Multiproject cash flow optimization noninferior solution through neuromultiobjective algorithm

• Published in: Engineering, construction, and architectural management construction, and architectural management, 2001-02, Vol.8 (2), p.130-144
• Main Authors: LAM, K.C., HU, TIESONG, CHEUNG, S.O., YUEN, R.K.K., DENG, Z.M.
• Format: Article
• Language: English
• Subjects: Cashflow; Construction; Neuromultiobjective; Optimization; Riskseeking
• ISSN: 0969-9988
• DOI: 10.1108/eb021176

Modelling of the multiproject cash flow decisions in a contracting firm facilitates optimal resource utilization, financial planning, profit forecasting and enables the inclusion of cashflow liquidity in forecasting. However, a great challenge for contracting firm to manage his multiproject cash flow when large and multiple construction projects are involved manipulate large amount of resources, e.g. labour, plant, material, cost, etc.. In such cases, the complexity of the problem, hence the constraints involved, renders most existing regular optimization techniques computationally intractable within reasonable time frames. This limit inhibits the ability of contracting firms to complete construction projects at maximum efficiency through efficient utilization of resources among projects. Recently, artificial neural networks have demonstrated its strength in solving many optimization problems efficiently. In this regard a novel recurrentneuralnetwork model that integrates multiobjective linear programming and neural network MOLPNN techniques has been developed. The model was applied to a relatively large contracting company running 10 projects concurrently in Hong Kong. The case study verified the feasibility and applicability of the MOLPNN to the defined problem. A comparison undertaken of two optimal schedules i.e. riskavoiding scheme A and riskseeking scheme B of cash flow based on the decision maker's preference is described in this paper.