This paper investigates the projectscheduling problem to minimize the maximum cash flow gap of contractors under random activity duration. A comprehensive optimization model with time buffers added to the baseline sc...
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This paper investigates the projectscheduling problem to minimize the maximum cash flow gap of contractors under random activity duration. A comprehensive optimization model with time buffers added to the baseline schedule by projectproactivescheduling and the schedule adjustment cost determined by projectreactivescheduling is constructed to manage disruptions caused by the randomness of activity durations. Due to the problem's intractability, two hybrid metaheuristic algorithms, namely, tabu simulated annealing (tabu-SA) and variable neighborhood tabu search (VNTS), are developed, and several improvement measures are proposed to enhance the algorithms' performance. Based on a randomly generated data set, a computational experiment is performed to evaluate the algorithms, with the effects on results of certain key parameters also analyzed. The conclusions are as follows: tabu-SA outperforms VNTS for large-scale problems, however, the reverse holds for small-scale problems. The key parameters, including the project deadline, the cost per time buffer, the activity instability weight, the activity duration variability, the number of payments, and the payment proportion can exert important effects on the contractor's maximum cash flow gap.
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