The interaction of lean and building information modeling in construction

Lean construction and Building Information Modeling are quite different initiatives, but both are having profound impacts on the construction industry. A rigorous analysis of the myriad specific interactions between them indicates that a synergy exists which, if properly understood in theoretical terms, can be exploited to improve construction processes beyond the degree to which it might be improved by application of either of these paradigms independently. Using a matrix that juxtaposes BIM functionalities with prescriptive lean construction principles, fifty-six interactions have been identified, all but four of which represent constructive interaction. Although evidence for the majority of these has been found, the matrix is not considered complete, but rather a framework for research to explore the degree of validity of the interactions. Construction executives, managers, designers and developers of IT systems for construction can also benefit from the framework as an aid to recognizing the potential synergies when planning their lean and BIM adoption strategies

Construction scheduling using multi-constraint and genetic algorithms approach

Reliable construction schedules are important for effective co-ordination across the supply chain and various trades at the construction work face. Reliability of construction schedules can be enhanced and improved through satisfying all potential constraints prior to execution on site. Availability of resources, execution space, execution logic, physical dependency of construction products, client instructions and others can be regarded as potential constraints. Current scheduling tools and techniques are fragmented and designed to deal with a limited set of construction constraints. In this context, a methodology termed 'multi-constraint scheduling' is introduced in which four major groups of construction constraints including physical, contract, resource and information constraints are considered to demonstrate the approach. A genetic algorithm (GA) has been developed and used for a multi-constraint optimization problem. Given multiple constraints such as activity dependency, limited working area, and resource and information readiness, the GA alters tasks' priorities and construction methods so as to arrive at an optimum or near optimum set of project duration, cost, and smooth resource profiles. The multi-constraints approach has been practically developed as an embedded macro in MS Project. Several experiments were conducted using a simple project and it was concluded that GA can provide near optimum and constraint-free schedules within an acceptable searching time. This will be vital to improve the productivity and predictability of construction sites