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A platform for research: civil engineering, architecture and urbanism
Multi-project cash flow optimization: non-inferior solution through neuro-multiobjective algorithm
Modelling of the multiproject cash flow decisions in a contracting firm facilitates optimal resource utilization, financial planning, profit forecasting and enables the inclusion of cash-flow 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 recurrent-neural-network model that integrates multi-objective 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. risk-avoiding scheme A and risk-seeking scheme B) of cash flow based on the decision maker's preference is described in this paper.
Multi-project cash flow optimization: non-inferior solution through neuro-multiobjective algorithm
Modelling of the multiproject cash flow decisions in a contracting firm facilitates optimal resource utilization, financial planning, profit forecasting and enables the inclusion of cash-flow 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 recurrent-neural-network model that integrates multi-objective 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. risk-avoiding scheme A and risk-seeking scheme B) of cash flow based on the decision maker's preference is described in this paper.
Multi-project cash flow optimization: non-inferior solution through neuro-multiobjective algorithm
LAM, K.C. (author) / HU, TIESONG (author) / CHEUNG, S.O. (author) / YUEN, R.K.K. (author) / DENG, Z.M. (author)
Engineering, Construction and Architectural Management ; 8 ; 130-144
2001-02-01
15 pages
Article (Journal)
Electronic Resource
English
Multi-project cash flow optimization: non-inferior solution through neuro-multiobjective algorithm
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