Modelling industrial construction operations using a multi‐agent resource allocation framework

Abstract

Purpose

Modelling construction resources and their dynamic interactions and constraints are a challenging problem. The allocation of these resources to competing activities is usually a function required in any scheduling process. Performing such allocation under a dynamic and diverse set of constraints adds more complexity to the problem. This study seeks a structured approach for representing resources and their allocation to different activities through the use of an agent‐oriented modelling framework.

Design/methodology/approach

A model is developed for a real case of assembly operations of industrial construction modules. The model follows a multi‐agent resource allocation structure and is implemented within an agent‐based simulation environment. The model is used to evaluate the effects of different optimization algorithms and modelling parameters on the generation of a construction schedule. Different experiments run through the model and their results are analyzed and discussed.

Findings

The model showed sensitivity only under large and continuous workloads. Overall the structured approach followed in developing the model provided a flexible medium for experimenting with different elements of the resource allocation problem.

Research limitations/implications

The work is limited to the studied case and the results cannot be generalized beyond similar cases. The modelling approach used in the study provides a platform that can facilitate future research in construction resource allocation strategies.

Originality/value

The presented work demonstrates a new approach for modelling construction resource allocation problems that enables structured experimentation with alternative allocation algorithms. It also presents a novel way for modelling modular industrial construction operations.

Citation

Liu, Y. and Mohamed, Y. (2012), "Modelling industrial construction operations using a multi‐agent resource allocation framework", Engineering, Construction and Architectural Management, Vol. 19 No. 4, pp. 406-427. https://doi.org/10.1108/09699981211237111