Multiple Modular Building Construction Project Scheduling Using Genetic Algorithms

This paper examines the time comparing efficiency between the two approaches namely the modular building system and the common ways of building a construction and cost efficiency with an emphasis on whether Primavera P6 is used as a means of project scheduling and project optimization. Using modules, prefabricated off site and assembled on site, modular construction does provide the opportunity to shorten project schedules through simultaneous work on activities, reduced occurrence of weather delays, and enhanced quality management. Nevertheless, adoption by industry is a problem related to difficulty in initial manufacturing costs, involved logistics of production, specialized labour, and also having an equal scarcity in domestic supply chains. Case study method was used which is comparative, focusing on two same-type multi-story buildings constructions, with the same type of products, but one is built using standard methods and the other with modular construction. Primavera P6 was used to model both scenarios so that they could be handled with the same delivery timing, career and cost control. Also, 25 professionals in the industry have been surveyed and the relative importance index (RII) was applied to their responses to measure benefits and barriers impression. The findings indicated that the modular approach to construction saved 65 days (12.7 percent) on the project and this was largely due to concurrent fabrication of the modules off-site and on-site site preparations. The total costs however, rose up by about 8.1 percent with the catalyst being fabrication, transport, crane procedures and special assembly needs. RII analysis proved that the most appreciated benefits were time savings and improved profitability and the most hindering barriers were high initial costs and constraints regarding logistics. This conclusion implies that modular construction can be vitally fast in delivering the projects when coupled with an advanced project management tool such as Primavera P6. Greater investments in strategic supply chain management development, training of the workforce and regulatory backing of the modular practices should be encouraged to render the modular practices cost-competitive and more popular. Keywords- Building performance, Cost optimization, Modular construction, Prefabricated systems, Primavera P6 scheduling, Time efficiency

This study explores the enhancement of Supply Chain Management (SCM) efficiency in the Philippines' modular construction sector through Integrated Project Delivery (IPD). It highlights the critical alignment of SCM success factors with IPD principles, emphasizing the importance of collaboration in modular construction projects. Quantitative analysis via surveys among modular construction professionals assesses IPD's impact on SCM efficiency, uncovering a positive correlation between IPD adoption and compliance with SCM success factors, particularly in larger organizations. This reveals the benefits of early collaboration and shared management practices. The research proposes a comprehensive framework tailored to the Philippine construction industry to optimize SCM efficiency by integrating modular construction with IPD principles. This strategy aims to tackle unique organizational and individual challenges, offering insights for improving SCM practices through IPD. The study paves the way for superior project delivery in developing economies and calls for future research into IPD and SCM risks, the influence of organizational structures, and the role of emerging technologies and cultural factors in modular construction.

Modular construction (MC) is increasingly recognised as a viable solution to the growing need for affordable, sustainable social housing, especially in rapidly urbanising countries like Vietnam. Despite its potential benefits in terms of construction speed, waste reduction, and quality control, the uptake of MC in Vietnam remains limited. This study identifies and evaluates 22 barriers and 12 drivers of MC adoption in the Vietnamese social housing sector, drawing on survey data from 256 construction professionals representing five key stakeholder groups: clients, contractors, consultants, policymakers, and academics. Statistical analyses, including reliability testing, mean score ranking, and ANOVA, reveal significant divergence in stakeholder perceptions. The results indicate that the most critical barriers, including the lack of experience among suppliers and designers, inflexible design processes, and high upfront investment costs. In addition, key drivers identified, including early design finalisation, dimensional standardisation, pilot projects, and international knowledge transfer. Based on the findings, the study proposes tailored policy implications for key stakeholders. Policymakers should mandate BIM for MC projects, offer financial incentives, and establish R&D centres to foster innovation. Contractors and consultants are encouraged to adopt BIM comprehensively and engage in pilot MC projects to strengthen technical capacity. Clients and developers would benefit from access to benchmarking data and international partnerships to reduce perceived risks. Educational and research institutions should introduce targeted training and innovation hubs to bridge skill gaps. These recommendations offer a practical roadmap for advancing MC in sustainable social housing development in Vietnam and similar contexts.

Recent advances in manufacturing methods, construction equipment, information technology tools, and project delivery systems have resulted in increased use of prefabrication and modularization. This research study identifies critical factors and constraints that can help designers, engineers, and constructors in selecting modular construction over the conventional “stick-built” technique for commercial building projects. The decision-making process of selecting one construction method over another is complex and based on a number of factors, some of which are site conditions, skilled labor availability, transportation conditions, organizational readiness, local codes, project schedule and budget, sustainability requirements, and design complexity. The aim of this study is to identify the most critical factors and constraints that must be considered by the decision-makers for selecting modular or stick-built construction. The study employed a mixed methods research design and was divided into three phases. In the first phase, through an in-depth literature review and interviews with six industry professionals, ninety seven decision-making factors and forty three constraints were identified. In the following phase, a questionnaire survey was conducted to determine their quantitative ratings and rakings. In the final phase, with the help of a focus group of five modular construction experts, twelve critical decision-making factors and six major constraints were shortlisted and validated. It is hoped that the findings will help the Architecture, Engineering and Construction (AEC) industry in making informed decisions about the appropriate construction method selection.

In software development projects, it is desired to complete multiple projects at minimum cost and time while ensuring that the completion date is the same for all projects to meet certain operational and strategic objectives. Also, full-time employees assigned to projects should be reallocated smoothly to other tasks without any idle time during project execution to minimize costs even further. This study describes a model that enables the use of efficient continuous variable nonlinear solvers for finding the optimal schedule for possibly a large number of multiple software projects that make use of shared resources. The study validates the proposed solution using a random generator of multiple software project instances while interfacing to online optimization solvers to find a solution. Our continuous variable model was solved in the cloud for optimality for large instances of upto 40 different software projects and 100 employees in less than 21 min using nonlinear programming algorithms.•A continuous variable nonlinear model is developed to efficiently schedule large-scale software projects.•The model enables scheduling for multiple projects with identical completion times while ensuring work continuity.•A cloud-based program architecture is designed to facilitate the testing of multiple solvers online.

This paper presents a multiobjective optimization model for the planning and scheduling of repetitive construction projects. The model enables construction planners to generate and evaluate optimal construction plans that minimize project duration and maximize crew work continuity, simultaneously. The computations in the present model are organized in three major modules: scheduling, optimization, and ranking modules. First, the scheduling module uses a resource-driven scheduling algorithm to develop practical schedules for repetitive construction projects. Second, the optimization module utilizes multiobjective genetic algorithms to search for and identify feasible construction plans that establish optimal tradeoffs between project duration and crew work continuity. Third, the ranking module uses multiattribute utility theory to rank the generated plans in order to facilitate the selection and execution of the best overall plan for the project being considered. An application example is analyzed to illustrate the use of the model demonstrate its new capabilities in optimizing the planning and scheduling of repetitive construction projects.

In modular construction—a type of industrialized construction—production planning is very important, as it is closely related to the project’s duration, quality, and sustainability. The constraints (production area, delivery due date) often differ for each project, yet production planning in modular construction has failed to change with the project characteristics. As a result, bottlenecks and construction delays are common problems seen in modular construction, which, in turn, decreases the production ratio, causing the production to be inefficient. To this end, this paper applied a prefabricated component in the modular production process. The paper developed a process analysis model considering constraint factors (production period, production area) to derive the optimal configuration of the prefabricated components in various alternatives. The developed analysis model was then applied to a virtual case to analyze the productivity improvement and select the optimal process. The optimal production process was derived by simulating the possible production planning within a limited production area and production timeline. The result of a simulation indicates that the production period has been halved by optimizing the process. Furthermore, by applying prefabricated components, the production efficiency was further increased because the existing linear production process’s bottleneck disappeared. The model is deemed to have the potential to optimize various production methods across production facilities or modular factories that simultaneously perform multiple projects.

With the rapid development of projects, firms are facing challenges in planning and controlling complex multiple construction projects. This research, therefore, aims at developing blockchain of optimal scheduling and sequencing of multiple construction projects under probabilistic arrival and stochastic durations. Each project task was considered as a block. Then, a framework for electronic project recording (EPR) system was developed. The EPRs are records for project tasks that make information available directly and securely to authorized users. In this framework, two optimization models were developed for scheduling and sequencing project blocks. The scheduling model aims to assign project tasks to available resources at minimal total cost and maximal the number of assigned project tasks. On the other hand, the sequencing model seeks to determine the start time of block execution while minimizing delay costs and minimizing the sum of task’s start times. The project arrival date and the task’s execution duration were assumed probabilistic and stochastic (normally distributed), respectively. The developed EPR system was implemented on a real case study of five projects with total of 121 tasks. Further, the system was developed when the task’s execution duration follows the Program Evaluation and Review Technique (PERT) model with four replications. The project costs (idle time and overtime costs) at optimal plan were then compared between the task’s execution duration normally distributed and PERT modelled. The results revealed negligible differences between project costs and slight changes in the sequence of project activities. Consequently, both distributions can be used interchangeably to model the task’s execution duration. Furthermore, the project costs were also compared between four solution replications and were found very close, which indicates the robustness of model solutions to random generation of task’s execution duration at both models. In conclusion, the developed EPR framework including the optimization models provided an effective planning and monitoring of construction projects that can be used to make decisions through project progress and efficient sharing of project resources at minimal idle and overtime costs. Future research considers developing a Blockchain of optimal maintenance planning.

Modular construction has been reported to lead to a number of advantages when contrasted with conventional construction approaches, including time and cost savings, along with reduced environmental impacts. Given that the operations involved in modular construction take place within a controlled environment, an integral factor that impacts the productivity of works involved is the timely scheduling of the associated activities. Unlike conventional on-site construction methods, operations in modular construction take place in a sequential and linear manner, with large dependence on adequate resource allocation to specific workstations. In a bid to address the lack of mathematical optimisation methods that target the scheduling of operations in a modular factory setting, this paper proposes a novel mixed integer non-linear programming model for optimising the scheduling of modular construction activities on the shop floor of modular factories. The impact of scheduling of resources, such as overhead cranes, and the distance between stations to which activities are scheduled are factored in the proposed method. The solution approach presented for solving the scheduling problem involves the linearisation of the model to enable the use of efficient off-the-shelf solvers. A realistic case study is implemented, and an extensive computational experiment is also conducted to test the robustness and tractability of the proposed method. The performance of the model is also contrasted with a commonly adopted heuristic, with the model producing project durations reductions that are 43% less than that of the heuristic.

Successful implementation of Building Information Model (BIM) -based modular construction projects is not always guaranteed in different regions and their associated contexts, because success depends heavily on combinations of multiple conditions, including technological, political, social and cultural, and economic ones. Such difference in conditions often hinders a successful modular construction company in a region from continuing its success in other regions; however, understanding the complex causality between the conditions and the success in implementation from previous BIM-based modular construction cases is very challenging because (1) each case omits some conditions and focuses too much on others, which makes the comparison difficult, and (2) cases are insufficient in number for dealing with various conditions, i.e., a small-N or intermediate-N situation. To address this problem, based on the review of previous case studies and modular construction theories, this paper classifies and defines nine condition variables that can be utilized in developing and analyzing BIM-based modular construction cases more comprehensively and systematically. This paper then discusses how the qualitative comparative analysis (QCA) approach can be used to find sufficient and necessary combinations of conditions for successful BIM-based modular construction projects. Upon successful completion, the QCA approach will contribute more structured and generalized explanations of success and failure in BIM-based modular construction to the industrialized construction theory.

The industrialization of the construction industry, driven by 3D Concrete Printing (3DCP), is revolutionizing the sector by enabling the creation of complex structural elements without the need for formwork, resulting in reduced material usage and increased precision. Despite challenges such as the mechanical strength of materials and the need for adequate reinforcement, the ongoing search for design solutions is overcoming these barriers. The combination of 3DCP with Building Information Modeling (BIM) promises to reduce costs and waste, optimizing processes from the design phase to execution. Modular construction standardizes components, allowing for greater quality control and agility in production, making it a viable solution for emergency housing and accommodation for vulnerable populations. The path to the effective, productive, and sustainable implementation of off-site concrete 3D printing systems is not yet fully defined. This article aims to present what has been done with modular construction applied to 3D printing. For this purpose, a systematic literature review was conducted to identify the state of the art of Off-site Construction applied to 3D Concrete Printing (3DCP). Nine articles were selected based on selection criteria, which provide important guidelines and insights for the creation of a modular construction project for 3D in the country. The results also highlighted the need for in-depth studies on the subject and the customization of modules for each application, as well as the need to understand the connections between these modules. This article establishes a theoretical foundation that guides future research for the design and implementation of these systems in local construction industries.

Purpose – The purpose of this paper is to characterize the relationships between air pollutant emissions from heavy duty diesel equipment and highway construction project scope, schedule, and budget. Objectives included estimating total project emissions; developing a daily emissions profile; and developing new emissions estimating metrics based on project scope, schedule, and budget. Design/methodology/approach – The research approach involved collecting real-world data related to project scope, schedule, and budget from two highway case study projects. The data were used to establish an emissions inventory estimating methodology to calculate total emissions for each case study. The total emissions were normalized based on project size, duration, and cost in order to develop new emissions estimating metrics. Findings – The results proved that it is possible to characterize total equipment emissions based on project size, duration, and cost. The new emissions estimating metrics were quantitatively similar for both case studies. Research limitations/implications – The results were based on two case study projects. Additional data from more projects is needed to provide more highly refined numerical results. Practical implications – This approach enables project planners and managers to assess the environmental impacts of highway projects along with the financial and time impacts. Social implications – Construction equipment is a major contributor to the nation’s air pollution problem. Before pollutant emissions can be managed they must first be measured. Originality/value – The new emissions estimating metrics are a novel approach to comparing environmental impacts of two or more projects, as well as estimating total emissions for future highway construction projects.

Hybrid Algorithm for Multi-Contractor, Multi-Resource Project Scheduling in the Industrial Field

Scheduling plays a fundamental role in construction projects’ success and thus has drawn attention from both academic researchers and industry practitioners. A large number of research articles tend to solve emerging challenges in construction project schedule (CPS). Therefore, there is a strong need of systematic review on existing studies. In this study, a total of 332 articles were retrieved from Scopus database using title, abstract and keywords with respect to CPS and filtered by document type, language type and abstract content. In particular, science mapping approach was adopted to analyse selected journal articles. These articles were examined using three sequential processes, including bibliometric search, scientometric analysis, and in-depth qualitative discussion. It could demonstrate the most influential journals, researchers, published articles, and active countries/regions in this area. In addition, major CPS knowledge areas were identified and summarized as CPS constructability, applications of variety of CPS methods, CPS optimization models and algorithms, identification and quantification of schedule risks and uncertainties, CPS performance management, and adopting new emerging CPS technologies and methods. Furthermore, knowledge gaps and future potential research directions were also discussed in detail. Finally, a comprehensive CPS framework was proposed as a sound reference in future research.

A GA optimization model for workgroup-based repetitive scheduling (WoRSM)