Publications / 2016 Proceedings of the 33rd ISARC, Auburn, USA

A Multi-Objective Mixed Integer Programming Model for Minimising Obtrusive Effects and Installation Costs of Night-time Lighting on Construction Sites

Ahmed Hammad, Ali Akbarnezhad and David Rey
Pages 505-514 (2016 Proceedings of the 33rd ISARC, Auburn, USA, ISBN 978-1-5108-2992-3, ISSN 2413-5844)
Abstract:

The increase in the rate of urbanisation worldwide has led to a boom in the construction industry sector in most major cities. To cope with the associated rising demand for further services and facilities, contractors find themselves frequently obliged to extend working hours on construction sites. Construction of major infrastructure projects, such as road works, is conducted at times of the day when less disruption is likely to result to the affected population. As a result, night work is now a common sighting on many construction sites. To allow for better vision for construction personnel on sites, floodlights are deployed during night work to light up work zones. There is an expense however to the adoption of construction lighting, particularly when considering the social and environmental impacts arising due to light pollution. In a residential district, and especially when infrastructure projects are taking place, the lighting system utilised on a construction site can be a cause of major disruption to the people and wildlife in the vicinity of the site. This paper attempts to model the problem of construction lighting through an optimisation model that minimises both construction light set-up costs and the maximum light pollution perceived at light-sensitive receivers. At the same time, the model ensures an appropriate coverage level to avoid impairing workers? vision on site. The developed formulations take the format of a Mixed Integer Programming model. An illustrative case study is applied to a construction project to demonstrate the applicability of the model.

Keywords: Construction Lighting, Light Pollution, Multi-Objective Optimisation, Mixed Integer Programming, - constraint Method, Sustainable Construction Operations.