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- ArticleYalnız künye2026A novel multi-objective optimization model for the time–cost–environmental impact trade-off in construction projects using a strength Pareto-based whale optimization algorithm
Yılmaz, Mehmet; Dede, Tayfun
The construction industry, a key contributor to global economic development, significantly impacts environmental pollution through greenhouse gas emissions. While conventional construction project management prioritizes cost and time reduction, environmental considerations are increasingly critical. This study addresses the time-cost-environmental impact trade-off problem by introducing a strength Pareto-based whale optimization algorithm (SP2WOA). The algorithm enhances solution diversity and achieves well-distributed Pareto fronts by integrating strength Pareto operators with the whale optimization algorithm. Its effectiveness is demonstrated through three diverse construction projects. Compared to previous algorithms (non-dominated sorting genetic algorithm-II, opposition-based multi-objective differential evolution, golden ratio-based opposite teaching-learning-based optimization and enhanced multi-objective grasshopper optimization algorithm), SP2WOA achieves superior balances between time, cost and environmental impact, using 33-60% fewer function evaluations. Statistical analysis using Wilcoxon rank tests confirms SP2WOA's superiority in both hypervolume and spread metrics. The results demonstrate that SP2WOA offers a valuable tool for sustainable construction project management.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2026Multi-objective time-cost-safety risk trade-off optimization for the construction scheduling problem
Yılmaz, Mehmet; Dede, Tayfun
Purpose – The purpose of this study is to enable the planning of construction projects with simultaneous consideration of time, cost and safety risks. It also aims to improve the decision-making process by evaluating the effectiveness of the Rao-2 algorithm in solving multi-objective time-cost-safety risk problems. In the end, this model is designed to support project managers in enhancing management approaches by addressing project challenges and constraints more efficiently. Design/methodology/approach – In this study, the Rao-2 algorithm, along with Grey Wolf Optimization (GWO) and Whale Optimization algorithm (WOA), were improved using the crowding distance-based non-dominated sorting method. Rao-2 was first compared to GWO and WOA. Subsequently, it was compared with well-established algorithms in the literature, including genetic algorithm (GA), particle swarm optimization (PSO) and differential evolution (DE). The C-metric, hypervolume and spread metrics were employed for performance measurement. The performance of the algorithms was evaluated on four case studies consisting of 11, 13, 18 and 25 activities. Findings – The results revealed that Rao-2 performs better than other algorithms as the number of activities increases, when compared using the Hypervolume, Spread and C-metric measures. In terms of performance measures, the GWO algorithm outperformed Rao-2 in some evaluation metrics for the instance involving 11 activities. However, as the number of activities grew, the Rao-2 method consistently generated higher-quality Pareto fronts and outperformed GWO and WOA in all evaluation metrics. The solutions generated by Rao-2 were also superior to those obtained from GA, PSO and DE in all case studies, further demonstrating the capability of our framework to produce a wide range of optimal solutions with high diversity across different case studies. Originality/value – This research demonstrates that Rao-2 not only improves solution quality when generating Pareto fronts but also achieves better results with fewer function evaluations compared to GA, PSO and DE. The algorithm's efficiency makes it particularly well-suited for optimizing time, cost and safety risks in large-scale construction projects, which in turn positions Rao-2 as a better choice for such projects by producing superior results compared to other algorithms. By providing high-quality solutions with reduced computational demands, Rao-2 offers a faster and more resource-efficient tool for decision-making, contributing to advancements in both the theory and practice of construction project management.
RDL Kalıcı Kaydıİncele - Conference ObjectYalnız künye2026Application of Arithmetic Optimization Algorithm (AOA) for Time–Cost–Quality–Environmental Impact Optimization in Construction Projects
Yılmaz, Mehmet; Dede, Tayfun; Rao, Ravipudi Venkata; Rao, R.V.; Taler, J.; Taler, D.
This study aims to investigate the relationship between time, cost, quality, and environmental impact in construction projects using arithmetic optimization algorithm. Project managers have prioritized time and cost concepts in traditional project planning. It is seen that other factors affecting the success of the project are ignored. However, with the impact of technological and sociocultural changes, awareness of sustainability and environmental factors has increased. For this reason, there are studies showing that environmental impact is taken into account at least as much as time, cost, and quality in project management. Therefore, this study evaluates the performance of the arithmetic optimization algorithm (AOA) on the many-objective time–cost–quality–environmental impact problem. The performance of the AOA was compared with the adaptive selection slime mold algorithm (ASSMA), which is known to give good results in the literature. The results show that the AOA outperforms the ASSMA in terms of cost, environmental impact, and quality with 25% less function evaluation.
RDL Kalıcı Kaydıİncele - Book ChapterYalnız künye2026Optimization of Time–Cost-Environmental Impact Problems in Construction Projects with Grey Wolf Algorithm
Dede, Tayfun; Yılmaz, Mehmet
In the conventional construction project management, the main focus is generally on the completion time and budget of the project. One of the main objectives of many decision-makers is to complete the project quickly and stay within the specified cost. However, the concepts of sustainability and environmental impact have recently gained prominence on a global scale, which has necessitated greater consideration of the environmental impacts of construction projects. Various scenarios and alternatives are generated during the project planning phase and each of these alternatives has different impacts on the total duration, cost and environmental impact of the project. Therefore, it has become critical for decision makers to identify the most appropriate option. The options selected for the activities can directly affect the overall success, efficiency and sustainability of the project. In the literature, many optimization algorithms have been proposed to solve such multi-objective problems. In this study, the effect of the grey wolf optimization algorithm, inspired by the hunting behavior of grey wolves in nature, on multi-objective time–cost-environmental impact problems encountered in the planning phase of construction projects is investigated. The performance and efficiency of the algorithm are compared with the algorithms in the literature.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2025Integrated risk management in construction project: utilizing NDSII-BMR and NDSII-BWR for time-cost-safety trade-offs
Dede, Tayfun; Sin, Theint Shwe; Yılmaz, Mehmet; Uçan, Hacı Abdullah; Rao, Ravipudi Venkata
Purpose – This research introduces novel algorithms (NDS-II-BMR and NDS-II-BWR) that combine the simplicity of best-mean-random (BMR) and best-worst-random (BWR) approaches with the opting mechanisms of NDS-II. Design/methodology/approach – This study expands on earlier comparison assessments by adding a broader range of algorithms, including MOPSO, NSGAII, PESA2 and WOA, to provide a more comprehensive evaluation approach for the proposed NDSII-BMR and NDSII-BWR algorithms. In addition to typical performance measurements, this study utilizes sophisticated statistical validation through the Wilcoxon signed-rank test to determine the statistical significance of performance differences across this varied range of multi-objective optimization approaches. Findings – The results demonstrate the effectiveness of NDSII-BMR and NDSII-BWR in optimizing costs and decreasing safety risks while minimizing time efficiency. These outcomes underscore the applicability of these models for managing complex construction projects in real-world scenarios. Originality/value – This study employs multi-objective functions to minimize the total project time and cost while reducing overall safety risks. The proposed model integrates the NDS-II framework with innovative strategies, best-mean-random (BMR) and best-worst-random (BWR).
RDL Kalıcı Kaydıİncele - Conference ObjectYalnız künye2024Application of Grey Wolf Algorithm with Archive and Grid Mechanisms for Solving Construction Time–Cost–Quality Problems
Yılmaz, Mehmet; Dede, Tayfun; Venkata Rao, R.; Taler, J.
Multi-objective optimization problems are an important research area widely used to solve complex and competitive problems in various industries. In the construction sector, time, cost, and quality factors hold critical importance for the success of projects. Minimizing time, managing costs, and enhancing quality are essential elements for achieving success in many application areas. grey wolf optimization (GWO) is a natural search algorithm that has been effectively utilized in optimization problems in recent years. GWO offers an optimization approach that mimics the interaction between leader wolves, sub-leader wolves, and hunting wolves. The aim of this study is to enhance the performance of GWO algorithm in solving multi-objective construction time–cost–quality problems by integrating archive and grid mechanisms. The archive serves as a data structure to preserve diversity within the solution population and store better solutions. On the other hand, the grid mechanism aims to improve the search process by dividing the solution space. This study evaluates the performance of the GWO algorithm by addressing multi-objective construction time–cost–quality problems with 7 and 13 activities. The results obtained demonstrate that GWO algorithm outperforms genetic algorithm (NSGA-II). These findings highlight the impact and advantages of the GWO algorithm in solving multi-objective construction time–cost–quality optimization problems.
RDL Kalıcı Kaydıİncele - Conference ObjectYalnız künye2023Optimization of Construction Scheduling with Rao Algorithm
Yılmaz, Mehmet; Dede, Tayfun; Venkata Rao, Ravipudi; Venkata Rao, R.; Taler, J.
The two most important factors to consider in construction scheduling are cost and time. To maximize profits in today’s competitive environment, construction stakeholders will seek to minimize project cost and duration at the same time. Construction projects consist of direct and indirect costs. While resources allocated to activities to reduce project duration can increase direct costs, they can also decrease indirect costs. Time–cost trade-off problems involve more than one alternative in each activity. In these problems, as the number of activities and alternatives increases, time required to solve the problem becomes longer. Many evolutionary and metaheuristic algorithms have been used in solving such problems in recent years. With the help of these algorithms, the solution times are reduced, and more than one solution proposal is presented to the decision-makers. In this study, the Rao algorithm is used to solve the multi-objective cost-time trade-off problem.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2023Multi-objective time–cost trade-off optimization for the construction scheduling with Rao algorithms
Yılmaz, Mehmet; Dede, Tayfun
Determining the time and cost of activities is one of the most important stages of construction project planning. Measures taken to shorten the time, such as the need for more or more qualified employees, equipment and machinery, increase project direct cost. The process of deciding which option to use in activities with more than one time–cost option has revealed the time–cost trade-off problem which is discrete optimization. Within the scope of this study, the non-dominant sorting (NDS) method is integrated into the Rao-1 and Rao-2 algorithms and applied to small and medium-scale problems. The numerical results show that the NDS-Rao-2 algorithm has found a better result with a significant number of function evaluations compared to algorithms such as PSO, ACO, GA and TLBO used in previous studies. In addition, the NDS-based Rao-2 algorithm not only find optimal /near results, but also suggest more than one pareto result to the decision maker for time–cost trade-off problem. For this reason, the developed multi-objective Rao-2 algorithm can be considered as a very good alternative for time–cost trade-off problems which are combinatorial problems.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2022Experimental and numerical investigations on reinforcement arrangements in RC deep beams
Husem, Metin; Yılmaz, Mehmet; Cosgun, Suleyman I.
Reinforced concrete (RC) deep beams are critical structural elements used in offshore pile caps, rectangular crosssection water tanks, silo structures, transfer beams in high-rise buildings, and bent caps. As a result of the low shear span ratio to effective depth (a/d) in deep beams, arch action occurs, which leads to shear failure. Several studies have been carried out to improve the shear resistance of RC deep beams and avoid brittle fracture behavior in recent years. This study was performed to investigate the behavior of RC deep beams numerically and experimentally with different reinforcement arrangements. Deep beams with four different reinforcement arrangements were produced and tested under monotonic static loading in the study’s scope. The horizontal and vertical shear reinforcement members were changed in the test specimens to obtain the effects of different reinforcement arrangements. However, the rebars used for tension and the vertical shear reinforcement ratio were constant. In addition, the behavior of each deep beam was obtained numerically with commercial finite element analysis (FEA) software ABAQUS, and the findings were compared with the experimental results. The results showed that the reinforcements placed diagonally significantly increased the load-carrying and energy absorption capacities of RC deep beams. Moreover, an apparent plastic plateau was seen in the load-displacement curves of these test specimens in question (DE-2 and DE-3). This finding also indicated that diagonally located reinforcements improve displacement ductility. Also, the numerical results showed that the FEM method could be used to accurately predict RC deep beams’behavior with different reinforcement arrangements.
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