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    Network Analysis Algorithm for the Solution of Discrete Time-Cost Trade-off Problem
    (Korean Society Of Civil Engineers-Ksce, 2017) Bettemir, Onder Halis; Birgonul, M. Talat
    Optimum solution of time-cost trade-off problem has significant importance since it provides the highest profit opportunity. For this reason, exact, heuristic, and meta-heuristic algorithms are adapted to obtain the optimum or near-optimum solution. However, heuristic algorithms may not always converge into the global optimum, while meta-heuristic algorithms require significant computation to converge into global optimum and exact methods are complex for construction planners to implement. Therefore, minimum cost-slope based fast converging network analysis algorithm, which provides optimum or near-optimum solutions, is proposed for discrete time-cost trade-off problem. The algorithm searches the global optimum through the feasible crashing options. Number of feasible crashing options increase tremendously in large projects. Therefore, an elimination algorithm is embedded to reduce the number of crashing options. The crashing option with the lowest unit crashing cost is executed and global optimum is searched by stepwise crashing. Tests on 18 and 63-Activity projects revealed that the network analysis algorithm converges to optimum or near-optimum solution by only one percent of the computational demand of meta-heuristic algorithms. Consequently, the proposed heuristic algorithm is a convenient optimization method for the solution of time-cost trade-off problem.
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    Solution of discrete time-cost trade-off problem with adaptive search domain
    (Emerald Group Publishing Ltd, 2023) Bettemir, Onder Halis; Birgonul, M. Talat
    PurposeExact solution of time-cost trade-off problem (TCTP) by the state-of-the-art meta-heuristic algorithms can be obtained for small- and medium-scale problems, while satisfactory results cannot be obtained for large construction projects. In this study, a hybrid heuristic meta-heuristic algorithm that adapts the search domain is developed to solve the large-scale discrete TCTP more efficiently.Design/methodology/approachMinimum cost slope-based heuristic network analysis algorithm (NAA), which eliminates the unfeasible search domain, is embedded into differential evolution meta-heuristic algorithm. Heuristic NAA narrows the search domain at the initial phase of the optimization. Moreover, activities with float durations higher than the predetermined threshold value are eliminated and then the meta-heuristic algorithm starts and searches the global optimum through the narrowed search space. However, narrowing the search space may increase the probability of obtaining a local optimum. Therefore, adaptive search domain approach is employed to make reintroduction of the eliminated activities to the design variable set possible, which reduces the possibility of converging into local minima.FindingsThe developed algorithm is compared with plain meta-heuristic algorithm with two separate analyses. In the first analysis, both algorithms have the same computational demand, and in the latter analysis, the meta-heuristic algorithm has fivefold computational demand. The tests on case study problems reveal that the developed algorithm presents lower total project costs according to the dependent t-test for paired samples with alpha = 0.0005.Research limitations/implicationsIn this study, TCTP is solved without considering quality or restrictions on the resources.Originality/valueThe proposed method enables to adapt the number of parameters, that is, the search domain and provides the opportunity of obtaining significant improvements on the meta-heuristic algorithms for other engineering optimization problems, which is the theoretical contribution of this study. The proposed approach reduces the total construction cost of the large-scale projects, which can be the practical benefit of this study.