Optimal Work Pattern for Construction Workers in Hot Weather: A Case Study in Hong Kong

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24 Citations (Scopus)

Abstract

Having established a Monte Carlo simulation-based algorithm to optimize work-rest schedule in a hot and humid environment, this paper attempts to develop the algorithm and identify an optimal work pattern, which may maximize the direct-work rates and minimize the health hazard due to heat stress to the workers concerned. Traditionally, construction workers in Hong Kong start work at 8:00 a.m. and finish work at 6:00 p.m., having one hour lunch break between 12:00 p.m. and 1:00 p.m., and an additional break of 30 min at 3:15 p.m. Construction workers can beat the heat by starting earlier to avoid some extreme conditions, which may occur at certain times of a day. By maintaining the current practice of 9-h working duration for a day, 21 additional work patterns with different start and finish times were proposed and evaluated by the developed optimization algorithm. An optimized schedule (direct-work rate of 87.8%) of working from 7:30 a.m. to 12:00 p.m. with a 20 min break at 9:40 a.m., having lunch break between 12:00 p.m. and 1:00 p.m., and working from 1:00 p.m. to 5:30 p.m. with a 30 min break at 3:00 p.m. is proposed. The proposed work pattern not only maximizes direct-work rates but also minimizes the occurrence of heat stress on construction site. This will enable policy makers to derive solid guidelines for working in hot weather. Because the proposed work pattern is developed specifically for the construction industry, more work is needed to further investigate other industries and other climates to provide a holistic view in the future.
Original languageEnglish
Article number5014009
JournalJournal of Computing in Civil Engineering
Volume29
Issue number5
DOIs
Publication statusPublished - 1 Jan 2015

Keywords

  • Construction industry
  • Direct-work rates
  • Hot and humid climate
  • Monte Carlo simulation
  • Work patterns
  • Work-rest schedule

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Computer Science Applications

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