A novel Multiple Objective Symbiotic Organisms Search (MOSOS) for time–cost–labor utilization tradeoff problem

Multiple work shifts are commonly utilized in construction projects to meet project requirements. Nevertheless, evening and night shifts raise the risk of adverse events and thus must be used to the minimum extent feasible. Tradeoff optimization among project duration (time), project cost, and the utilization of evening and night work shifts while maintaining with all job logic and resource availability constraints is necessary to enhance overall construction project success. In this study, a novel approach called “Multiple Objective Symbiotic Organisms Search” (MOSOS) to solve multiple work shifts problem is introduced. The MOSOS algorithm is new meta-heuristic based multi-objective optimization techniques inspired by the symbiotic interaction strategies that organisms use to survive in the ecosystem. A numerical case study of construction projects were studied and the performance of MOSOS is evaluated in comparison with other widely used algorithms which includes non-dominated sorting genetic algorithm II (NSGA-II), the multiple objective particle swarm optimization (MOPSO), the multiple objective differential evolution (MODE), and the multiple objective artificial bee colony (MOABC). The numerical results demonstrate MOSOS approach is a powerful search and optimization technique in finding optimization of work shift schedules that is it can assist project managers in selecting appropriate plan for project

Optimizing Multiple-Resources Leveling in Multiple Projects Using Discrete Symbiotic Organisms Search

Resource leveling is used in project scheduling to reduce fluctuation in resource usage over the period of project implementation. Fluctuating resource usage frequently creates the untenable requirement of regularly hiring and firing temporary staff to meet short-term project needs. Construction project decision makers currently rely on experience-based methods to manage fluctuations. However, these methods lack consistency and may result in unnecessary waste of resources or costly schedule overruns. This research introduces a novel discrete symbiotic organisms search for optimizing multiple resources leveling in the multiple projects scheduling problem (DSOS-MRLMP). The optimization model proposed is based on a recently developed metaheuristic algorithm called symbiotic organisms search (SOS). SOS mimics the symbiotic relationship strategies that organisms use to survive in the ecosystem. Experimental results and statistical tests indicate that the proposed model obtains optimal results more reliably and efficiently than do the other optimization algorithms considered. The proposed optimization model is a promising alternative approach to assisting project managers in handling MRLMP effectively

Infrastructure Model Development to Enhance Resilience against future changes using InfraWorks & GIS

The Smart City idea is becoming more popular because cities are getting too crowded. This study looks at how BIM and Geographical Information Systems (GIS) work together. The study focused on infrastructure and figuring out how long a building would last, as early stages of infrastructure development would help the public organization to better plan and design. The GIS mapping shows which areas are more likely to become cities or towns. The AEC industry stakeholders can benefit from mapping to achieve Sustainable Development Goals (SGDs). The AEC industry has also been constantly moving towards Building Information Modelling (BIM). In this study, the city of London was considered, and Land Cover predictions from 2000 to 2025 were made. The predicted Map of 2025 would help developers and planning authorities on decision making on housing development. Based on the current study, more research could be extended on how BIM and GIS could work together for urban development

Fuzzy clustering chaotic-based differential evolution for resource leveling in construction projects

 Project scheduling is an important part of project planning in many management companies. Resource lev­eling problem describes the process of reducing the fluctuations in resource usage over the project duration. The goal of resource leveling is to minimize the incremental demands that cause fluctuations of resources, and thus avoid unde­sirable cyclic hiring and firing during project execution. In this research, a novel optimization model, named as Fuzzy Clustering Chaotic-based Differential Evolution for solving Resource leveling (FCDE-RL), is introduced. Fuzzy Cluster­ing Chaotic-based Differential Evolution (FCDE) is developed by integrating original Differential Evolution with fuzzy c-means clustering and chaotic techniques to tackle complex optimization problems. Chaotic was exploited to prevent the optimization algorithm from premature convergence. Meanwhile, fuzzy c-means clustering acts as several multi-par­ent crossover operators to utilize the information of the population efficiently to enhance the convergence. Experimental results revealed that the new optimization model is a promising alternative to assist project managers in dealing with construction project resource leveling. First published online: 13 Jul 201

Multi-objective symbiotic organisms optimization for making time-cost tradeoffs in repetitive project scheduling problem

Time-cost problems that arise in repetitive construction projects are commonly encountered in project scheduling. Numerous time-cost trade-off approaches, such as mathematical, metaheuristic, and evolutionary methods, have been extensively studied in the construction community. Currently, the scheduling of a repetitive project is conducted using the traditional precedence diagramming method (PDM), which has two fundamental limitations: (1) progress is assumed to be linear from start to finish; and (2) activities in the schedule are connected each other only at the end points. This paper proposes a scheduling method that allows the use of continuous precedence relationships and piece-wise linear and nonlinear activity-time-production functions that are described by the use of singularity functions. This work further develops an adaptive multiple objective symbiotic organisms search (AMOSOS) algorithm that modifies benefit factors in the basic SOS to balance exploration and exploitation processes. Two case studies of its application are analyzed to validate the scheduling method, as well as to demonstrate the capabilities of AMOSOS in generating solutions that optimally trade-off minimizing project time with minimizing the cost of non-unit repetitive projects. The results thus obtained indicate that the proposed model is feasible and effective relative to the basic SOS algorithm and other state-of-the-art algorithms

Combining machine learning models via adaptive ensemble weighting for prediction of shear capacity of reinforced‑concrete deep beams

This study presents a novel artificial intelligence (AI) technique based on two support vector machine (SVM) models and symbiotic organisms search (SOS) algorithm, called “optimized support vector machines with adaptive ensemble weight- ing” (OSVM-AEW), to predict the shear capacity of reinforced-concrete (RC) deep beams. This ensemble learning-based system combines two supervised learning models—the support vector machine (SVM) and least-squares support vector machine (LS-SVM)—with the SOS optimization algorithm as the optimizer. In OSVM-AEW, SOS is integrated to simulta- neously select the optimal parameters of SVM and LS-SVM, and control the coordination process of the learning outputs. Experimental results show that OSVM-AEW achieves the greatest evaluation criteria for coefficient of correlation (0.9620), coefficient of determination (0.9254), mean absolute error (0.3854 MPa), mean absolute percentage error (7.68%), and root- mean-squared error (0.5265 MPa). This paper demonstrates the successful application of OSVM-AEW as an efficient tool for helping structural engineers in the RC deep beams design process

Chaotic initialized multiple objective differential evolution with adaptive mutation strategy (CA-MODE) for construction project time-cost-quality trade-off

Time, cost and quality are three factors playing an important role in the planning and controlling of construc­tion. Trade-off optimization among them is significant for the improvement of the overall benefits of construction pro­jects. In this paper, a novel optimization model, named as Chaotic Initialized Multiple Objective Differential Evolution with Adaptive Mutation Strategy (CA-MODE), is developed to deal with the time-cost-quality trade-off problems. The proposed algorithm utilizes the advantages of chaos sequences for generating an initial population and an external elitist archive to store non-dominated solutions found during the evolutionary process. In order to maintain the exploration and exploitation capabilities during various phases of optimization process, an adaptive mutation operation is introduced. A numerical case study of highway construction is used to illustrate the application of CA-MODE. It has been shown that non-dominated solutions generated by CA-MODE assist project managers in choosing appropriate plan which is other­wise hard and time-consuming to obtain. The comparisons with non-dominated sorting genetic algorithm (NSGA-II), multiple objective particle swarm optimization (MOPSO), multiple objective differential evolution (MODE) and previ­ous results verify the efficiency and effectiveness of the proposed algorithm. First published online: 24 Aug 201

Impact of Fintech’s Development on Bank Performance: An Empirical Study from Vietnam

In recent years, fintech has exploded in popularity and importance in the finan- cial industry. Its impacts have spread widely throughout the world, including Vietnam. This study aims to investigate the effect of fintech’s development on bank performance in Vietnam. Based on the unstructured data about fintech on the financial expert web- sites from Vietnam, the word frequency statistic technique of the text mining approach is applied for measuring fintech’s development under the support of Python-based solu- tions. The bank-level data of 15 Vietnamese banks for the period from the first quarter of 2019 to the second quarter of 2021 are collected from the quarterly financial statements in the Vietstock organization. Python programming and text mining techniques are used to compile this dataset by gathering information from popular and relevant websites. The generalized least squares method is used for estimating the panel models. The estimation result shows the significant impact of fintech’s development on bank profitability, but the net interest margin does not associate with the fintech variable. Besides, some interesting findings are revealed: The slow banking transformation to adapt to the rise of fintech and the COVID-19 pandemic increased bank profitability. Furthermore, suggestions for the banks and fintech companies are recommended, and the limitations and directions for further research are also proposed

Theoretical predictions of melting behaviors of hcp iron up to 4000 GPa

The high-pressure melting diagram of iron is a vital ingredient for the geodynamic modeling of planetary interiors. Nonetheless, available data for molten iron show an alarming discrepancy. Herein, we propose an efficient one-phase approach to capture the solid-liquid transition of iron under extreme conditions. Our basic idea is to extend the statistical moment method to determine the density of iron in the TPa region. On that basis, we adapt the work-heat equivalence principle to appropriately link equation-of-state parameters with melting properties. This strategy allows explaining cutting-edge experimental and ab initio results without massive computational workloads. Our theoretical calculations would be helpful to constrain the chemical composition, internal dynamics, and thermal evolution of the Earth and super-Earths

Optimization model for construction project resource leveling using a novel modified symbiotic organisms search

In the construction industry, determining project schedules has become one of the most critical subjects among project managers. These schedules oftentimes result in significant resource fluctuations that are costly and impractical for the construction company. Thus, construction managers are required to adjust the resource profile through a resource leveling process. In this paper, a novel optimization model is presented for resource leveling, called the “modified symbiotic organisms search” (MSOS). MSOS is developed based on the standard symbiotic organisms search, but with an improvement in the parasitism phase to better tackle complex optimization problems. A case study is employed to investigate the performance of the proposed optimization model in coping with the resource leveling problem. The experimental results show that the proposed model can find a better quality solution in comparison with existing optimization models