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Subject: Pareto Front

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    Article
    Effects of Topological Structure of Project Network on Computational Cost
    (Golden Light Publ, 2024) Aminbakhsh, Saman
    Understanding how network complexity affects optimization algorithms is crucial for improving computational efficiency. This study investigates how variations in network complexity impact the performance of optimization algorithms. By examining networks with different serial/parallel indicator (I2) values, the research uncovers several key insights into how topology influences computational requirements. The experiments show that higher I2 values, which are closer to serial configurations, heighten the problem's complexity. This study reveals that networks with lower I2 values, which exhibit steeper time-cost curves with fewer solutions over their efficient frontiers, require significantly more CPU time, indicating that project complexity does not necessarily scale with the extend of the Pareto fronts. This contradicts the expectation that more Pareto front solutions would inherently demand greater computational resources. Lastly, the study highlights that while the number of time-cost realizations is often used to gauge project complexity, it may not be conclusive on its own and that one complexity measure can outperform another. Although it can be an effective indicator, it does not fully capture the computational challenges posed by different network topologies. This study further acknowledges the difficulty in establishing a clear link between project performance and complexity due to the multifaceted nature of the problem. The findings suggest that exploring similar problems in other contexts could provide valuable insights into understanding and managing computational complexity.
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    Article
    Citation - WoS: 3
    Citation - Scopus: 3
    Ε-Constraint Procedures for Pareto Front Optimization of Large Size Discrete Time/Cost Trade-Off Problem
    (Elsevier, 2025) Aminbakhsh, Saman; Sonmez, Rifat; Atan, Tankut
    The discrete time/cost trade-off problem (DTCTP) optimizes the project duration and/or cost while considering the trade-off between activity durations and their direct costs. The complete and non-dominated time-cost profile over the set of feasible project durations is achieved within the framework of Pareto front problem. Despite the importance of Pareto front optimization in project and portfolio management, exact procedures have achieved very limited success in solving the problem for large size instances. This study develops exact procedures based on combinations of mixed-integer linear programming (MILP), epsilon-constraint method, network and problem reduction techniques, and present new bounding strategies to solve the Pareto problem for large size instances. This study also provides new large size benchmark problem instances aiming to represent the size of real-life projects for the DTCTP. The new instances, therefore, are generated to include up to 990 activities and nine execution modes. Computational experiments reveal that the procedures presented herein can remarkably outperform the state-of-the-art exact methods. The new exact procedures enabled obtaining the optimal Pareto front for instances with serial networks that include more than 200 activities for the first time.