PRODUCTIVITY IMPROVEMENT IN OPERATING AUTONOMOUS PLANTS SUBJECT TO RANDOM BREAKDOWNS IN CONSTRUCTION

Realizing continuous operations of autonomous plants subject to finite specialist crew resources for maintenance and repair is vital to achieving productivity and cost-effectiveness in construction operations. This paper presents a practical Monte Carlo simulation-based method to develop autonomous plants operations and maintenance programs. To balance the cost of plant production loss against the cost of hiring maintenance crews, we define a cost function which factors in production output value, resource utilization efficiency and direct cost in connection with both autonomous plants and maintenance crews. An illustration case of planning maintenance crew resources in operating autonomous crushing plants at a quarry site is used to shed light on required input data, simulation processing, and output analysis. The case also has increasing relevance to the construction industry in the near future in terms of planning the operation of a fleet of autonomous equipment in site operations.

[1]  Ming Lu,et al.  Error Quantification and Visualization in Using Sensors to Position Backhoe Excavator , 2017 .

[2]  Phillip S. Dunston,et al.  Methodology for Real-Time Monitoring of Construction Operations Using Finite State Machines and Discrete-Event Operation Models , 2017 .

[3]  Simaan M. AbouRizk,et al.  Zero-One Programming Approach to Determine Optimum Resource Supply under Time-Dependent Resource Constraints , 2016, J. Comput. Civ. Eng..

[4]  Jungho Yoon,et al.  Spatial factors affecting the loading efficiency of excavators , 2014 .

[5]  Remon F. Aziz,et al.  Applying lean thinking in construction and performance improvement , 2013 .

[6]  Wu Chen,et al.  Tunnel-Boring Machine Positioning during Microtunneling Operations through Integrating Automated Data Collection with Real-Time Computing , 2011 .

[7]  S. P. Dozzi,et al.  Productivity in Construction , 2011 .

[8]  A. Hiwatashi.  How to Use This Book , 2009 .

[9]  Peter Muchiri,et al.  Performance measurement using overall equipment effectiveness (OEE): literature review and practical application discussion , 2008 .

[10]  J. F. Woodward,et al.  Quantitative techniques for decision making in construction, S.L. Tang, Irtishad U. Ahmad, Syed M. Ahmad, Ming Lu. Hong Kong University Press (2004), Price US$ 24.95 (Soft-back), p. 226, ISBN: 9622097057 , 2006 .

[11]  S. Chopra,et al.  Managing Risk To Avoid Supply-Chain Breakdown , 2004 .

[12]  Boo Teng. Chong.  Project management for trenchless sewer construction. , 1998 .

[13]  W. J. Selen,et al.  A production model and maintenance planning model for the process industry , 1996 .

[14]  J. Ashayeri,et al.  A PRODUCTION AND MAINTENANCE PLANNING MODEL FOR THE PROCESS INDUSTRY , 1995 .

[15]  Byron J. Finch,et al.  Developing maintenance craft labor efficiency through an integrated planning and control system: A prescriptive model , 1986 .

[16]  John Bessant,et al.  FAILURE, UNCERTAINTY AND CONTROL: THE ROLE OF OPERATORS IN A COMPUTER INTEGRATED PRODUCTION SYSTEM , 1985 .

[17]  William R. Gibbs,et al.  Productivity in Construction , 1976 .