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Günhan, S and Arditi, D (2007) Budgeting Owner’s Construction Contingency. Journal of Construction Engineering and Management, 133(07), 492–7.

Kallantzis, A, Soldatos, J and Lambropoulos, S (2007) Linear versus Network Scheduling: A Critical Path Comparison. Journal of Construction Engineering and Management, 133(07), 483–91.

Mahalingam, A and Levitt, R E (2007) Institutional Theory as a Framework for Analyzing Conflicts on Global Projects. Journal of Construction Engineering and Management, 133(07), 517–28.

Mahalingam, A and Levitt, R E (2007) Safety Issues on Global Projects. Journal of Construction Engineering and Management, 133(07), 506–16.

Sacks, R, Esquenazi, A and Goldin, M (2007) LEAPCON: Simulation of lean construction of high-rise apartment buildings. Journal of Construction Engineering and Management, 133(07), 529–39.

Yang, I (2007) Using Elitist Particle Swarm Optimization to Facilitate Bicriterion Time-Cost Trade-Off Analysis. Journal of Construction Engineering and Management, 133(07), 498–505.

  • Type: Journal Article
  • Keywords: Optimization; Algorithms; Computer aided scheduling; Multiple objective analysis; Construction industry;
  • ISBN/ISSN: 0733-9364
  • URL: https://doi.org/10.1061/(ASCE)0733-9364(2007)133:7(498)
  • Abstract:
    The present study develops a new optimization algorithm to find the complete time-cost profile (Pareto front) over a set of feasible project durations, i.e., it solves the time-cost trade-off problem. To improve existing methods, the proposed algorithm aims to achieve three goals: (1) to obtain the entire Pareto front in a single run; (2) to be insensitive to the scales of time and cost; and (3) to treat all existing types of activity time-cost functions, such as linear, nonlinear, discrete, discontinuous, and a hybrid of the above. The proposed algorithm modifies a population-based search procedure, particle swarm optimization, by adopting an elite archiving scheme to store nondominated solutions and by aptly using members of the archive to direct further search. Through a fast food outlet example, the proposed algorithm is shown effective and efficient in conducting advanced bicriterion time-cost analysis. Future applications of the proposed algorithm are suggested in the conclusion.