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Elazouni, A M and Metwally, F G (2007) Expanding Finance-Based Scheduling to Devise Overall-Optimized Project Schedules. Journal of Construction Engineering and Management, 133(01), 86–90.

Goldenberg, M and Shapira, A (2007) Systematic Evaluation of Construction Equipment Alternatives: Case Study. Journal of Construction Engineering and Management, 133(01), 72–85.

Hanna, A S, Chang, C, Lackney, J A and Sullivan, K T (2007) Impact of Overmanning on Mechanical and Sheet Metal Labor Productivity. Journal of Construction Engineering and Management, 133(01), 22–28.

Jung, Y and Kang, S (2007) Knowledge-Based Standard Progress Measurement for Integrated Cost and Schedule Performance Control. Journal of Construction Engineering and Management, 133(01), 10–21.

Li, Y, Nie, X and Chen, S (2007) Fuzzy Approach to Prequalifying Construction Contractors. Journal of Construction Engineering and Management, 133(01), 40–49.

Liu, L and Zhu, K (2007) Improving Cost Estimates of Construction Projects Using Phased Cost Factors. Journal of Construction Engineering and Management, 133(01), 91–95.

Lucko, G, Vorster, M C and Anderson-Cook, C M (2007) Unknown Element of Owning Costs—Impact of Residual Value. Journal of Construction Engineering and Management, 133(01), 3–9.

Moussa, M, Ruwanpura, J and Jergeas, G (2007) CTAN for Risk Assessments Using Multilevel Stochastic Networks. Journal of Construction Engineering and Management, 133(01), 96–101.

Polat, G, Arditi, D and Mungen, U (2007) Simulation-Based Decision Support System for Economical Supply Chain Management of Rebar. Journal of Construction Engineering and Management, 133(01), 29–39.

  • Type: Journal Article
  • Keywords: Decision support systems; Simulation models; Construction materials; Turkey;
  • ISBN/ISSN: 0733-9364
  • URL: https://doi.org/10.1061/(ASCE)0733-9364(2007)133:1(29)
  • Abstract:
    The economics of a materials management system is defined by the size of the shipments, the scheduling strategy that allows contractors to handle uncertainty and variability in the supply chain, and the timing of the shipments, which in turn depend on the environment in which the project is taking place. This study presents a simulation-based decision support system to assist contractors in selecting the most economical rebar management system prior to the start of construction by recommending lot sizes (large, small), a scheduling strategy (optimistic, neutral, pessimistic), and buffer sizes (large, medium, small) given the conditions of the project. This model is of benefit to contractors and researchers because it generates the probable cost of inventory of 18 alternative rebar management systems ranging from just in case (JIC) to just in time (JIT) and including different variations in between. It allows contractors to select the alternative with least cost of inventory at the planning stages of a project. The simulation model was tested by using actual data obtained from a trade center project in Istanbul, Turkey. As expected, the test indicated that JIC was the most economical rebar management system in a case study conducted in a developing country, as it generated a savings of 4.8% over JIT.

Salman, A F M, Skibniewski, M J and Basha, I (2007) BOT Viability Model for Large-Scale Infrastructure Projects. Journal of Construction Engineering and Management, 133(01), 50–63.

Subprasom, K and Chen, A (2007) Effects of Regulation on Highway Pricing and Capacity Choice of a Build-Operate-Transfer Scheme. Journal of Construction Engineering and Management, 133(01), 64–71.