Physics-inspired Metaheuristics for Construction Site Layout Planning Problem

In the construction industry, material handling plays an important role. Finding proper locations for construction facilities not only can affect the expenses, but also it can impact on the process of handling of construction materials. Therefore, in order to supply engineering demands and materials, the construction site layout planning problem (CSLP) within a short-distance transportation is considered as an NP-hard problem. Thus, the researchers are extensively using metaheuristics in order to solve the construction site layout planning problems. This study presents a comparative study of ten physics-inspired metaheuristics with regard to their efficacy in how they can address a real construction site layout problem. In this vein, two case studies are examined in terms of the site layout planning. Finally, the findings reveal that Gravitational Search Algorithm (GSA) and Thermal Exchange Optimization (TEO) have the ability to come up with better solutions, in comparison to other considered optimization algorithms

An Integrated Model for Multi-Mode Resource-Constrained Multi-Project Scheduling Problems Considering Supply Management with Sustainable Approach in the Construction Industry under Uncertainty Using Evidence Theory and Optimization Algorithms

In this study, the multi-mode resource-constrained multi-project scheduling problems (MMRCMPSPs) considering supply management and sustainable approach in the construction industry under uncertain conditions have been investigated using evidence theory to mathematical modeling and solving by multi-objective optimization algorithms. In this regard, a multi-objective mathematical model has been proposed, in which the first objective function aims to maximize a weighted selection of projects based on economic, environmental, technical, social, organizational, and competitive factors; the second objective function is focused on maximizing profit, and the third objective function is aimed at minimizing the risk of supply management. Moreover, various components, such as interest rates, carbon penalties, and other implementation limitations and additional constraints, have also been considered in the modeling and mathematical relationships to improve the model’s performance and make it more relevant to real-world conditions and related issues, leading to better practical applications. In the mathematical modeling adopted, the processing time of project activities has been considered uncertain, and the evidence theory has been utilized. This method can provide a flexible and rational approach based on evidence and knowledge in the face of uncertainty. In addition, to solve the proposed multi-objective mathematical model, metaheuristic optimization algorithms, such as the differential evolution (DE) algorithm based on the Pareto archive, have been used, and for evaluating the results, the non-dominated sorting genetic algorithm II (NSGA-II) has also been employed. Furthermore, the results have been compared based on multi-objective evaluation criteria, such as quality metric (QM), spacing metric (SM), and diversity metric (DM). It is worth noting that to investigate the performance and application of the proposed model, multiple evaluations have been conducted on sample problems with different dimensions, as well as a case study on residential apartment construction projects by a contracting company. In this respect, the answers obtained from solving the model using the multi-objective DE algorithm were better and superior to the NSGA-II algorithm and had a more favorable performance. Generally, the results indicate that using the integrated multi-objective mathematical model in the present research for managing and scheduling multi-mode resource-constrained multi-project problems, especially in the construction industry, can lead to an optimal state consistent with the desired objectives and can significantly improve the progress and completion of projects

Integration of resource supply management and scheduling of construction projects using multi-objective whale optimization algorithm and NSGA-II

This study explores the intricate integration and synchronization of supplier selection with the optimal scheduling of multi-mode resource-constrained projects, which is a genuine and complex challenge prevalent in the construction industry, by proposing new multi-objective mathematical modeling considering various items. Within this context, a multifaceted network of concurrent projects (multiproject) is examined with different suppliers' resources (multi-supplier) to minimize the overall projects' delay times and associated costs. The mathematical model formulation also incorporates diverse implementation modes (multi-mode) and the time value of money (TVM). In order to use and unravel the complexities of the proposed model, two distinct algorithms, including a multi-objective whale optimization algorithm (WOA) based on the Pareto archive and the well-known non-dominated sorting genetic algorithm II (NSGA-II), are employed. The algorithms were subjected to a comparative analysis of several sample problems and evaluated against multi-objective criteria, including quality metric (QM), diversity metric (DM), spacing metric (SM), number of solutions (NOS), mean Ideal distance (MID), and computational time. The evaluation reveals that the tailored multi-objective WOA outperforms NSGA-II, exhibiting greater solution precision and diversity. The WOA demonstrates an enhanced ability to efficiently explore the problem's feasible solution space, albeit at the increased computational time to pinpoint optimal solutions. Notably, the validity and practicality of the proposed model and method were field-tested within the context of construction projects in Iran, with the obtained results juxtaposed against the real-world data. The comparative analysis indicates that implementing the scheduling approach and solution methodology espoused by the multi-objective WOA led to significant improvements, with financial gains of up to 6% and time savings reaching 16%. Overall, this research substantiates the proposed model and algorithms' benefits in reducing project costs and delays, offering valuable insights for construction industry practitioners

Integration of resource supply management and scheduling of construction projects using multi-objective whale optimization algorithm and NSGA-II

This study explores the intricate integration and synchronization of supplier selection with the optimal scheduling of multi-mode resource-constrained projects, which is a genuine and complex challenge prevalent in the construction industry, by proposing new multi-objective mathematical modeling considering various items. Within this context, a multifaceted network of concurrent projects (multiproject) is examined with different suppliers' resources (multi-supplier) to minimize the overall projects' delay times and associated costs. The mathematical model formulation also incorporates diverse implementation modes (multi-mode) and the time value of money (TVM). In order to use and unravel the complexities of the proposed model, two distinct algorithms, including a multi-objective whale optimization algorithm (WOA) based on the Pareto archive and the well-known non-dominated sorting genetic algorithm II (NSGA-II), are employed. The algorithms were subjected to a comparative analysis of several sample problems and evaluated against multi-objective criteria, including quality metric (QM), diversity metric (DM), spacing metric (SM), number of solutions (NOS), mean Ideal distance (MID), and computational time. The evaluation reveals that the tailored multi-objective WOA outperforms NSGA-II, exhibiting greater solution precision and diversity. The WOA demonstrates an enhanced ability to efficiently explore the problem's feasible solution space, albeit at the increased computational time to pinpoint optimal solutions. Notably, the validity and practicality of the proposed model and method were field-tested within the context of construction projects in Iran, with the obtained results juxtaposed against the real-world data. The comparative analysis indicates that implementing the scheduling approach and solution methodology espoused by the multi-objective WOA led to significant improvements, with financial gains of up to 6% and time savings reaching 16%. Overall, this research substantiates the proposed model and algorithms' benefits in reducing project costs and delays, offering valuable insights for construction industry practitioners

Towards developing a predictive model for interpersonal communication quality in construction projects: An ensemble artificial intelligence-based approach

High-quality communication among stakeholders is salient to securing and maintaining collaboration in construction projects. Indeed, the absence of such communication among the workforce leads to inefficiency, low productivity, and substandard deliverables. Against this backdrop, the body of relevant knowledge is bereft of a study investigating the association between workers' interpersonal skills and interpersonal communication (IC) quality. Thus, this study aims to predict the quality of professionals' IC through a multi-pronged artificial intelligence-based methodological approach. In doing so, the literature is reviewed to capture noticable interpersonal skills (IPSs), followed by utilizing a fuzzy-based algorithm to prioritize them. Then, an extreme Gradient Boosting (XGBoost)-based algorithm is developed to predict the quality of workers' IC. The developed XGBoost is finally applied to three real-life construction projects to check its efficacy. Based on the application of the developed model to the selected case studies, the following conclusions are drawn: (1) the significant skills are "Leadership Style," "Listening," "Team Building," and "Clarifying Expectations"; and (2) the predictions of the developed model equal to what happens to the workers' IC quality in more than 78% of the cases. The developed algorithm can warn interpersonal conflicts before they escalate, enhance job-site productivity, team development, and human resources management, and guide construction managers in developing IPSs training