Concreting operations – the relationship between unit costs and unit emissions

References

• Abolhasani S, Frey HC, Kim K, Rasdorf W, Lewis P, Pang S-H. 2008. Real-world in-use activity, fuel use, and emissions for nonroad construction vehicles: a case study for excavators. J Air Waste Manage Assoc. 58:1033–1046. https://doi.org/10.3155/1047-3289.58.8.1033
   Web of Science ®Google Scholar
• Ahn CR, Lee S-H, Peña-Mora F. 2013. Application of low-cost accelerometers for measuring the operational efficiency of a construction equipment fleet. J Comput Civil Eng. 29(2):1–11. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000337.
   Web of Science ®Google Scholar
• Ahn C, Rekapalli PV, Martinez JC, Pena-Mora FA. 2009. Sustainability analysis of earthmoving operations. Proceedings of the 2009 Winter Simulation Conference; Dec 13–16; Austin (TX). Piscataway (NJ): IEEE (Institute of Electrical and Electronics Engineers). p. 2605–2611.
   Google Scholar
• Alkass S, Arouian A, Moselhi O. 1993. Computer-aided equipment selection for transporting and placing concrete. J Constr Eng Manage. 119(3):445–465.
   Web of Science ®Google Scholar
• Alkoc E, Erbatur F. 1998. Simulation in concreting operations: a comparison of models and resource combinations. Eng Constr Archit Manage. 5(2):159–173.
   Google Scholar
• Anson M, Shou-qing W. 1994. Hong Kong performance yardsticks for concrete placing during building construction. HKIE Trans. 1(1):1–18. https://doi.org/10.1080/1023697X.1994.10667660.
   Google Scholar
• Anson M, Tang SL, Ying KC. 2002. Measurement of the performance of ready mixed concreting resources as data for system simulation. Constr Manage Econ. 20(3):237–250. https://doi.org/10.1080/01446190210121297.
   Google Scholar
• Baxendale T. 1984. Construction resource models by Monte Carlo simulation. Constr Manage Econ. 2(3):201–217. https://doi.org/10.1080/01446198400000019.
   Google Scholar
• Cao M, Lu M, Zhang JP. 2004. Concrete plant operations optimization using combined simulation and genetic algorithm. Proceedings of the 3rd International Conference on Machine Learning and Cybernetics; Aug 26–29; Shanghai. Piscataway (NJ): IEEE (Institute of Electrical and Electronics Engineers). p. 4204–4209.
   Google Scholar
• [CARB] California Air Resources Board. 2009. Off-road emissions inventory program, version 2009. Sacramento: California Air Resources Board. [ accessed 2014 April 28] http://www.arb.ca.gov/msei/offroad.htm
   Google Scholar
• Carmichael DG. 1985. Concrete handling queues. Proceedings of the Japan-Thai Civil Engineering Conference, Recent Advances in Structural Engineering; Mar 14–15; Bangkok: Asian Institute of Technology. p. 657–669.
   Google Scholar
• Carmichael DG. 1987. Engineering queues in construction and mining. Chichester: Ellis Horwood Ltd.
   Google Scholar
• Carmichael DG. 1989. Production tables for earthmoving, quarrying and open cut mining operations. In: Carmichael DG, editor. Applied construction management. Sydney: Unisearch Ltd Publishers. p. 275–284. ISBN 0 909796 19 X.
   Google Scholar
• Carmichael DG, Bartlett BJ, Kaboli AS. 2014a. Surface mining operations: coincident unit cost and emissions. Int J Min Reclam Environ. 28(1):47–65. https://doi.org/10.1080/17480930.2013.772699
   Web of Science ®Google Scholar
• Carmichael DG, Lea LO, Balatbat MCA. 2014b. Emissions management of urban earthmoving fleets. In: Tamosaitiene J, Panuwatwanich K, Mishima N, Ko C-H, editors. Proceedings of the 5th International Conference on Engineering, Project, and Production Management; Nov 26–28; Port Elizabeth, South Africa: Nelson Mandela Metropolitan University. p. 262–271. ISBN 978-1-920508-31-9.
   Google Scholar
• Carmichael DG, Williams EH, Kaboli AS. 2012. Minimum operational emissions in earthmoving. Proceedings of the ASCE Construction Research Congress; May 21–23; West Lafayette (IN): Purdue University. p. 1869–1878. ISBN 9780784412329.
   Google Scholar
• Chou JS, Ongkowijoyo CS. 2015. Reliability-based decision making for selection of ready-mix concrete supply using stochastic superiority and inferiority ranking method. Reliab Eng Syst Safe. 137:29–39.
   Web of Science ®Google Scholar
• Chua DKH, Li GM. 2002. RISim: resource-interacted simulation modeling in construction. J Constr Eng Manage. 128(3):195–202.
   Web of Science ®Google Scholar
• [DCCEE] Department of Climate Change and Energy Efficiency. 2017. National greenhouse account factors, Australian national greenhouse accounts. Canberra: Department of Climate Change and Energy Efficiency. [ accessed 2017 July 11]. http://www.environment.gov.au/system/files/resources/5a169bfb-f417-4b00-9b70-6ba328ea8671/files/national-greenhouse-accounts-factors-july-2017.pdf.
   Google Scholar
• Dunlop PG, Smith SD. 2002. Simulation analysis of the UK concrete delivery and placement process–a tool for planners. Proceedings of the 18th Annual ARCOM Conference; Sep 2–4; Northumbria: ARCOM (Association of Researchers in Construction Management). p. 781–790.
   Google Scholar
• Dunlop PG, Smith SD. 2003. Estimating key characteristics of the concrete delivery and placement process using linear regression analysis. Civil Eng Environ Syst. 20(4):273–290. https://doi.org/10.1080/1028660031000091599.
   Web of Science ®Google Scholar
• [EPA] Environmental Protection Agency. 2008. NONROAD model 2008. Ann Arbor (MI): US Environmental Protection Agency, Office of Transportation and Air Quality. [ accessed 2015 September 22]. https://www3.epa.gov/otaq/nonrdmdl.htm.
   Google Scholar
• Feng C-W, Cheng T-M, Wu H-T. 2004. Optimizing the schedule of dispatching RMC trucks through genetic algorithms. Autom Constr. 13:327–340.
   Web of Science ®Google Scholar
• Feng C-W, Wu H-T. 2006. Integrating fmGA and CYCLONE to optimize the schedule of dispatching RMC trucks. Autom Constr. 15:186–199.
   Web of Science ®Google Scholar
• Frey HC, Kim K. 2009. In-use measurement of the activity, fuel use, and emissions of eight cement mixer trucks operated on each of petroleum diesel and soy-based B20 biodiesel. Transp Res Part D. 14:585–592.
   Google Scholar
• Frey HC, Rasdorf W, Kim K, Pang S-H, Lewis P. 2008. Comparison of real world emissions of B20 biodiesel versus petroleum diesel for selected nonroad vehicles and engine tiers. Transp Res Record J Transp Res Board. 2058:33–42.
   Web of Science ®Google Scholar
• Galić M, Kraus I. 2016. Simulation model for scenario optimization of the ready-mix concrete delivery problem. Sel Sci Pap-J Civ Eng. 11(2):7–18.
   Google Scholar
• Graham LD, Forbes DR, Smith SD. 2006. Modeling the ready mixed concrete delivery system with neural networks. Autom Constr. 15:656–663.
   Web of Science ®Google Scholar
• Hasan MS. 2013. Decision support system for crane selection and location optimization on construction sites [PhD thesis]. Alberta: University of Alberta. [ accessed 2016 October 20]. https://era.library.ualberta.ca/files/4b29b596v/Hasan_Md%20Shafiul_Fall%202013.pdf.
   Google Scholar
• Hashemi MH, Yuksel O. 2014. Minimizing the distribution of ready-mixed concrete with linear programming. Int J Appl Eng Res. 9(20):7835–7846.
   Google Scholar
• Hegazy T, Kassab M. 2003. Resource optimization using combined simulation and genetic algorithms. J Constr Eng Manage. 129(6):698–705.
   Web of Science ®Google Scholar
• Heydarian A, Memarzadeh M, Golparvar-Fard M. 2012. Automated benchmarking and monitoring of an earthmoving operation's carbon footprint using video cameras and a greenhouse gas estimation model. Comput Civ Eng. 26:509–516. https://doi.org/10.1061/9780784412343.0064.
   Web of Science ®Google Scholar
• Jukic D, Carmichael DG. 2016. Emission and cost effects of training for earthmoving equipment operators – a field study. Smart Sustain Built Environ. 5(2):96–110. https://doi.org/10.1108/SASBE-11-2015-0040.
   Web of Science ®Google Scholar
• Kaboli AS, Carmichael DG. 2014. Optimum scraper load time and fleet size for minimum emissions. Int J Constr Manag. 14(4):209–226. https://doi.org/10.1080/15623599.2014.967924.
   Google Scholar
• Kaboli AS, Carmichael DG. 2016. An examination of the DRET model and the influence of payload, haul grade and truck type on earthmoving emissions. Int J Constr Manage. 16(2):95–108. https://doi.org/10.1080/15623599.2015.1130600.
   Google Scholar
• Kinable J, Wauters T, Berghe GV. 2014. The concrete delivery problem. Comput Oper Res. 48:53–68.
   Web of Science ®Google Scholar
• Lewis MP. 2009. Estimating fuel use and emission rates of nonroad diesel construction equipment performing representative duty cycles [PhD thesis]. Raleigh (NC): North Carolina State University. [ accessed 2015 April 15]. http://gradworks.umi.com/33/57/3357743.html.
   Google Scholar
• Liu Z, Zhang Y, Li M. 2014. Integrated scheduling of ready-mixed concrete production and delivery. Autom Constr. 48:31–43. https://doi.org/10.1016/j.autcon.2014.08.004.
   Web of Science ®Google Scholar
• Liu Z, Zhang Y, Yu M, Zhou X. 2017. Heuristic algorithm for ready-mixed concrete plant scheduling with multiple mixers. Autom Constr. 84:1–13.
   Web of Science ®Google Scholar
• Lu M, Anson M, Tang SL, Ying YC. 2003. HKCONSIM: a practical simulation solution to planning concrete plant operations in Hong Kong. J Constr Eng Manage. 129(5):547–554.
   Web of Science ®Google Scholar
• Lu M, Lam H-C. 2009. Simulation-optimization integrated approach to planning ready mixed concrete production and delivery: validation and applications. Proceedings of the IEEE 2009 Winter Simulation Conference; Dec 13–16; Austin (TX). Piscataway (NJ): IEEE (Institute of Electrical and Electronics Engineers). p. 2593–2604.
   Google Scholar
• Maghrebi M, Sammut C, Waller TS. 2014a. Predicting the duration of concrete operations via artificial neural network and by focusing on supply chain parameters. Build Res J. 61(1):1–14.
   Google Scholar
• Maghrebi M, Waller ST, Sammut C. 2014b. Assessing the accuracy of expert-based decisions in dispatching ready mixed concrete. J Constr Eng Manage. 140(6):06014004.
   Web of Science ®Google Scholar
• Maghrebi M, Waller ST, Sammut C. 2015. Optimality gap of experts’ decisions in concrete delivery dispatching. J Build Eng. 2:17–23.
   Web of Science ®Google Scholar
• Marklund J, Berling P. 2016. Green inventory management, in sustainable supply Chains. Berlin: Springer. p. 189–218.
   Google Scholar
• Mayteekrieangkrai N, Wongthatsanekorn W. 2015. Optimized ready mixed concrete truck scheduling for uncertain factors using bee algorithm. Songklanakarin J Sci Technol. 37(2):221–230.
   Google Scholar
• Naso D, Surico M, Turchiano B, Kaymak U. 2007. Genetic algorithms for supply-chain scheduling: a case study in the distribution of ready-mixed concrete. Eur J Oper Res. 177(3):2069–2099.
   Web of Science ®Google Scholar
• Nellickal AG, Rajendra AV, Palaniappan S. 2015. A simulation-based model for evaluating the performance of ready-mixed concrete (RMC) production processes. In: Crawford RH, Stephan A, editors. Living and learning: research for a better built environment. Proceedings of the 49th International Conference of the Architectural Science Association; Dec 2–4; Melbourne: ASA (Architectural Science Association). p. 658–667.
   Google Scholar
• Sawhney A, Abudayyeh O, Chaitavatputtiporn T. 1999. Modeling and analysis of concrete production plant using Petri Nets. J Comput Civil Eng. 13(3):178–186.
   Web of Science ®Google Scholar
• Smith SD. 1998. Concrete placing analysis using discrete-event simulation. PI Civ Eng Struct Build. 128:351–358. ISSN 0965-0911.
   Web of Science ®Google Scholar
• Smith SD. 1999. Modelling and experimentation of the concrete supply and delivery process. Civ Eng Environ Syst. 16(2):93–114. https://doi.org/10.1080/02630259908970255.
   Web of Science ®Google Scholar
• Srichandum S, Rujirayanyong T. 2010. Production scheduling for dispatching ready mixed concrete trucks using bee colony optimization. Am J Eng Appl Sci. 3(1):7–14.
   Google Scholar
• Tang SL, Ying KC, Anson M, Lu M. 2005. RMCSIM: a simulation model of a ready‐mixed concrete plant serving multiple sites using multiple truckmixers. Constr Manage Econ. 23(1):15–31. https://doi.org/10.1080/0144619032000124661.
   Google Scholar
• Wongthatsanekorn W, Matheekrieangkrai N. 2014. A case study of bee algorithm for ready mixed concrete problem. WASET IJMAIMME. 8(7):1253–1258.
   Google Scholar
• Yan S, Lin HC, Jiang XY. 2012. A planning model with a solution algorithm for ready mixed concrete production and truck dispatching under stochastic travel times. Eng Optim. 44(4):427–447. https://doi.org/10.1080/0305215X.2011.580741.
   Web of Science ®Google Scholar
• Yan S, Wang WC, Chang GW, Lin HC. 2016. Effective ready mixed concrete supply adjustments with inoperative mixers under stochastic travel times. Transp Lett. 8(5):286–300.
   Web of Science ®Google Scholar
• Ying KC, Tang SL, Anson M, Lu M. 2005. An experiment to explore the potential of simulation for improving ready mixed concrete delivery to construction sites. HKIE Trans. 12(3):6–13. https://doi.org/10.1080/1023697X.2005.10668005.
   Google Scholar
• Zayed TM, Halpin D. 2001. Simulation of concrete batch plant production. J Constr Eng Manage. 127(2):132–141.
   Web of Science ®Google Scholar
• Zayed TM, Halpin DW, Basha IM. 2005. Productivity and delays assessment for concrete batch plant‐truck mixer operations. Constr Manage Econ. 23(8):839–850. https://doi.org/10.1080/01446190500184451.
   Google Scholar
• Zayed TM, Minkarah I. 2004. Resource allocation for concrete batch plant operation: case study. J Constr Eng Manage. 130(4):560–569.
   Web of Science ®Google Scholar
• Zhang G-C, Zeng J-C. 2013. Modelling and solving for ready-mixed concrete scheduling problems with time dependence. Int J Comput Sci Math. 4(2):163–175.
   Google Scholar
• Zhang G-C, Zeng J-C, Zhang J-H. 2016. Modelling and optimising of ready-mixed concrete vehicle scheduling problem with stochastic transportation time. Int J Wirel Mobile Comput. 10(2):104–111.
   Google Scholar