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Linear projects are a special type of repetitive projects. Major disadvantages have been shown when using the critical path method (CPM) for linear project problems. So, to overcome these shortcomings linear scheduling method (LSM) was used to analyze linear projects. There are different types of activities in linear projects. However, existing mathematical models for LSM cannot represent all types of linear project activities. Therefore, this paper presents a mathematical model that can represent all types of activities using matrices. Moreover, the mathematical model is then used to create an optimization model that minimizes the project duration and cost using non-dominated sorting genetic algorithm II (NSGA-II). The two models can help project managers in scheduling real-life linear projects and find optimum linear project schedules. This paper introduces an optimization model for linear projects that utilizes matrices, offering a different approach in the field of linear project optimization. • Different types of activities in linear schedule method (LSM) are identified • New mathematical formation for (LSM) is developed using matrices • Mathematical formation helps in representing all types activities • Mathematical formation was used to build an optimization model • Optimization model solves time cost tradeoff problem for linear project [ABSTRACT FROM AUTHOR]

This research presents a transformative framework to tackle challenges within the construction industry, where efficiency is hindered by limited digitalization and disjointed project phases. The framework seamlessly integrates state-of-the-art technologies, such as Building Information Modeling (BIM), Genetic Algorithms (GAs) for schedule optimization, 5D simulation, and a Business Intelligence (BI) Dashboard. By doing so, it effectively enhances productivity while bridging the gap between the realms of management and engineering. A comprehensive literature review underpins the framework's design, focusing mainly on BIM-based schedule generation and optimization. The approach's validity is demonstrated through a compelling case study, showcasing automated BIM model creation, BIM-Based schedule generation limited to structural elements of bottom-up Cast-in-situ construction method, GAs-driven schedule optimization, 5D project simulation, and the integration of the BI dashboard. This research propels operational efficiency and promotes harmonious integration with pre-existing software systems, thus facilitating smoother adoption and adaptation. [Display omitted] • Innovative framework integrates BIM, genetic scheduling, 5D simulation & BI Dashboard. • Addresses global construction challenges: low productivity, high costs, and schedule overruns. • Comprehensive literature review informs BIM-based schedule optimization framework. • Innovates-centred data for cohesive engineering-management workflow. [ABSTRACT FROM AUTHOR]

Optimal production scheduling in off-site construction is hindered by the lack of comprehensive methods that simultaneously addresses workforce allocation, realistic labor productivity, workstation idle time (WIT), work-in-progress (WIP), and project completion time (PCT). To overcome these limitations, this paper introduces a hybrid planning and scheduling method that integrates the linear scheduling method reflecting workforce allocation, Monte Carlo simulation for estimating realistic labor productivity, and a multi-objective optimization model to minimize WIT, WIP, and PCT concurrently. The proposed method is validated using the Taillard instance benchmark dataset, and a panelized production line located in Edmonton, Canada. As a result, the proposed method is effective in generating realistic and optimal production schedules, achieving reductions of 5.2 % in PCT, 41.45 % in WIT, and 56.42 % in WIP. These findings are crucial for industry professionals aiming to improve efficiency in the production line and provide a foundation for further research in off-site construction scheduling. • A hybrid scheduling method is proposed for off-site construction (OSC) manufacturing. • Advanced linear scheduling method is proposed to create production schedule in OSC. • A multi-objective optimization model is proposed using NSGA-II. • Probabilistic process time is estimated using Monte Carlo simulation. • Proposed method can be used to generate an effective and optimum production schedule in OSC. [ABSTRACT FROM AUTHOR]

Decision-making in construction planning and scheduling is complex because of budget and resource constraints, uncertainty, and the dynamic nature of construction environments. A knowledge gap in the construction literature exists regarding decision-making frameworks with the ability to learn and propose an optimal set of solutions for construction scheduling problems, such as activity sequencing and work breakdown structure formulations under uncertainty. The objective of this paper is to propose a hybrid reinforcement learning–graph embedding network model that 1) simulates complex construction planning environments using agent-based modeling and 2) minimizes computational burdens in establishing activity sequences and work breakdown formations. Three case studies with practical construction scheduling problems were used to demonstrate applicability of the developed model. This paper contributes to the body of knowledge by proposing the hybridization of reinforcement learning and simulation approaches to optimize project durations with resource constraints and support construction practitioners in making project planning decision-making. • Implementation of agent-based simulation in construction processes • Optimization of construction project scheduling • Hybridization between reinforcement learning, agent-based simulation modeling and graph embedding methods • Practical solutions to aid decision-making processes in construction project activity sequencing and work-breakdown formation [ABSTRACT FROM AUTHOR]

Prefabrication or off-site construction is a growing trend contributing to productivity improvements. It motivates specialty contractors and suppliers to operate multiple fabrication shops close to market regions, where a shop can produce and delivery prefabricated components in a timely fashion and at a minimal cost. Few quantitative models are available to assist companies with scheduling and allocation questions. This research utilizes optimization to answer these questions supporting the production planning in prefabrication supply chains. The paper presents an optimization model that seeks minimal cost while considering job demands and shop capacities. Computational results suggest that the model generates a lower-cost production schedule than the early due date (EDD) method. It also indicates that varying due dates cause changes in total cost. Moreover, this research supports decision-makers by analyzing the impacts of changing shop capacities regarding available machines. It provides further insight into construction supply chain management with multiple shops. • The paper presents an optimization model that seeks minimal cost while considering job demands and shop capacities. • Computational results indicate that the model can generate a more cost-effective schedule than the early due date method. • This research supports decision-makers by analyzing the impacts of changing machine availability on shop performance. • The research provides insight into planning of construction prefabrication supply chain with multiple fabrication shops. [ABSTRACT FROM AUTHOR]

This paper addresses a challenging category of repetitive projects that involves scattered sites, such as rehabilitation programs for multiple bridges, multiple schools, etc. The paper proposes a schedule optimization framework with four novel components: (1) scheduling algorithm that efficiently schedules the crews among scattered sites, considering the activities independent site sequences and the time and cost of moving among sites; (2) cost optimization that considers deadlines, crew limits, and optional construction methods to determine the activities' optimum crews, methods, and site sequences; (3) interactive documentation of all progress events to support re-scheduling during construction; and (4) legible schedule representation of scattered projects. A computer prototype was developed and validated on a real-life case study and literature examples. Activity-specific site sequences proved uniquely flexible to optimize the scheduling of scattered projects without extra cost. The proposed framework enables efficient delivery planning of large infrastructure rehabilitation program within the execution constraints. • This paper identifies the unique scheduling challenges of scattered repetitive projects (SRPs). • Based on a field study, SRPs require activity-independent site sequences and productivity factors for operational facilities. • A cost optimization framework was developed to determine the optimum crews, methods, and activity sequences that minimize cost. • The framework has functions to record progress events and develop optimum corrective-action plans. • A real case study proved the powerful optimization of the developed framework. [ABSTRACT FROM AUTHOR]

Abstract: To date, few construction methods or models in the literature have discussed about helping the project managers decide the near-optimum distributions of manpower, material, equipment and space according to their project objectives and project constraints. Thus, the traditional scheduling methods or models often result in a “seat-of-the-pants” style of management, rather than decision making based on an analysis of real data. This paper presents an intelligent scheduling system (ISS) that can help the project managers to find the near-optimum schedule plan according to their project objectives and project constraints. ISS uses simulation techniques to distribute resources and assign different levels of priorities to different activities in each simulation cycle to find the near-optimal solution. ISS considers and integrates most of the important construction factors (schedule, cost, space, manpower, equipment and material) simultaneously in a unified environment, which makes the resulting schedule that will be closer to optimal. Furthermore, ISS allows for what-if analyses of possible scenarios, and schedule adjustments based on unforeseen conditions (change orders, late material delivery, etc.). Finally, two sample applications and one real-world construction project are utilized to illustrate and compare the effectiveness of ISS with two widely used software packages, Primavera Project Planner and Microsoft Project. [Copyright &y& Elsevier]

Abstract: Rework has been a primary cause of cost and schedule overruns in large construction projects. While several research efforts have analyzed the causes and effects of rework and provided guidelines to reduce rework, almost no research exists to analyze the impact of rework timing and quantity on schedule delays and to support decisions on cost effective recovery. This research presents a quantitative mechanism for schedule analysis considering rework. The mechanism has three aspects: (1) a new schedule representation of rework magnitude as negative percentage complete for affected activities, documented on the specific date on which the rework is detected; (2) a modified daily-windows delay analysis to apportion project delays among the responsible parties; and (3) an optimization technique for determining the least costly corrective action strategy that recovers project delays. The proposed approach is applied to a case study to demonstrate its ability to consider rework impact, in combination with other progress events by other project parties. This research offers an innovative quantitative approach to consider rework timing and amount in delay analysis and corrective action optimization. [Copyright &y& Elsevier]

Abstract: Under cash-constrained conditions, the fulfillment of cash demands of the ongoing projects within a contractor''s portfolio constitutes a set of conflicting objectives. As the profit values of the individual projects are maximized should their cash demands be fulfilled, the profit values of the individual projects constitute a set of multiple conflicting objectives. A Strength Pareto Evolutionary Algorithm (SPEA) employing a logic-preserving crossover and mutation operators is developed to devise Pareto-optimal finance-based schedules of multiple projects. The Pareto-optimal solutions allow the decision makers select the best solution based on their own preference. The developed SPEA reproduced the same results of an existing GAs-based multi objective technique in the literature. The proposed approach has been developed and implemented on multiple projects of different sizes. The results proved the effectiveness of the SPEA to solve finance-based scheduling problems of multiple projects considering the conflict in their profit realization. [Copyright &y& Elsevier]

The time-space conflicts may have severe negative impacts on the productivity and safety level in a construction site. However, little attention is paid to propose an automated, integrated system for managing potential time-space conflicts, considering the site space system's dynamic nature. This study contributes to the body of knowledge by proposing a novel integrated approach to manage time-space conflicts automatically. The proposed hybrid approach is based on Discrete Event Simulation (DES), Building Information Modeling (BIM), and Rapidly-exploring Random Tree (RRT) path planning. All types of time-space conflicts during the entire project timeline are automatically detected, evaluated, and resolved based on predefined if-then rules. The proposed approach is applied successfully in a real case study to estimate the project duration considering site space limitations. The results indicate that the proposed system can considerably prevent the negative impacts of time-space conflicts, particularly in congested construction sites. • Contribution to the body of knowledge is by proposing an integrated system that automatically detects, evaluates, and resolves time-space conflicts and modifies the project's schedule. • The dynamic changes in the space requirements of the process-level activities are considered. • The on-site motions of the mobile machinery are simulated. • An integrated framework based on 4D BIM, DES, and RRT path planning algorithm is provided. • The system is applied successfully in a real case study. [ABSTRACT FROM AUTHOR]

Scheduling heavy industrial projects is challenging due to their size and complexity. Three-dimensional space and its discipline-specific tasks are an important asset in congested industrial sites, yet are not easily scheduled early in planning when detailed information is lacking. Although recent approaches have focused on enhancing space and crew management, effective scheduling of large-scale industrial projects with several interdependent constraints remains difficult. A comprehensive, hybrid planning framework capable of developing an efficient, high-level schedule accounting for uncertainties in bidding and execution is proposed. The framework employs simulation and optimization tools to determine best durations/modes in multi-mode activity networks and uses dynamic data adoption to continuously plan during project progression. The proposed framework uses historical information and expert opinion to automatically create a near-optimized schedule while considering various uncertainties. The proposed approach is tested and demonstrated with an illustrative example and an actual case study of industrial construction in Alberta, Canada. • This research proposes a high-level scheduling framework for industrial projects • Subarea-planning serves as an extension to workface planning • The proposed framework uses hybrid simulation and optimization engines • It employs historical data and makes an optimized, automated high-level schedule • It considers uncertainties, e.g. alternative durations along with space management [ABSTRACT FROM AUTHOR]

Hybrid concrete (HC) structures are being promoted in China, as well as in other countries. The construction process of HC structures differs from that of traditional cast-in-situ (CiS) structures in two aspects: 1) the construction unit for HC structures is no longer a floor or a construction zone, but a single component, 2) both PC components and CiS components exist in HC structures, and are usually being constructed in parallel. Thus, the optimization of component-level construction schedule considering the construction sequence of different PC and CiS components is necessary for HC structures. However, existing studies about construction sequence optimization or schedule optimization have not taken such aspects into account. To fill the research gaps, this study proposes an approach to optimize the component-level construction schedule for HC structures. An optimization model for the component-level resource-constrained project scheduling problem for HC structures (C-RCPSP-HC) is formulated, involving the precedence relationships and resource constraints, with the objectives of the minimization of the construction makespan and the construction cost. To solve the C-RCPSP-HC model, a multi-objective discrete symbiotic organisms search (MODSOS) is developed to obtain the non-dominated solutions. A case study originated from a real project is conducted to verify the proposed approach. The result shows: 1) the component-level construction schedule optimized by the proposed approach is superior to the traditional construction zone-level construction schedule in eliminating construction conflicts, and contributes to shortening the makespan, decreasing the cost, and enhancing the resource utilization efficiency, 2) the proposed MODSOS has better comprehensive performance than other competitive multi-objective optimization algorithms in solving the C-RCPSP-HC model, as far as convergence, spread, and spacing are concerned. For future improvement, the estimation of the construction durations of activities from historical data will be done by using artificial intelligence methods. Besides, Building Information Modeling will be integrated to automate the data extraction. • A model for component-level resource-constrained project scheduling problem for hybrid concrete structures is proposed. • A multi-objective discrete symbiotic organisms search is developed to solve the proposed model. • A case study is carried out on the construction of a hybrid concrete shearing wall structure. • The component-level schedule contributes to improving the makespan, cost, and resource utilization. [ABSTRACT FROM AUTHOR]

Current planning tools provide insufficient support for the scheduling and control of Mechanical and Electrical works in construction projects. Consequently, installation activities often deviate from project plans. However, BIM data on the spatial layout of Mechanical and Electrical systems can be used for more detailed and accurate planning of their installation, and to identify points where work progress should be tracked. This paper presents a novel method for the automated scheduling and control of Mechanical and Electrical Works. A topological analysis of the precise position of components is used to define relationships between the installation tasks, and to identify the optimal schedule. The definition of specific control points supports effective progress tracking to ensure that tasks adhere in practice to this schedule. • A novel method is presented for automated scheduling and control of M&E Works. • BIM is used for a topological analysis of the components' precise positions. • Relationships between tasks are then defined based on the topological analysis. • The optimal schedule is identified taking into account constraints such as resources. • Control points in the schedule support effective progress tracking. [ABSTRACT FROM AUTHOR]