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\"A Handbook for Construction Planning & Scheduling presents the key issues of planning and programming in scheduling in a clear, concise and practical way\"-- Provided by publisher.

In recent years, automation in construction scheduling has gained popularity due to advancements in digital construction, yet it has not achieved widespread adoption. Significant challenges remain in developing adaptive schedules that effectively manage unforeseen events and construction delays. This study addresses a critical research gap by evaluating the automation levels of individual construction planning processes, an area previously underexplored. Employing a systematic literature review, this study investigates the state of the art in automated, dynamic and adaptive scheduling techniques. The review examined proposed planning procedures, assessing the extent of automation in key aspects of construction scheduling, including task sequencing, resource allocation and task duration estimation, with a focus on building information modelling (BIM) integration. The analysis reveals limited adoption of automated scheduling, BIM technologies and adaptive scheduling methods. Future research should explore advanced automation approaches, enhance BIM integration and develop adaptive scheduling solutions to improve efficiency and responsiveness in construction management.

To improve the efficiency and safety of intelligent construction scheduling, this work explores an optimization method for construction schedules based on multi-objective optimization (MOO) algorithms. This work focuses on the generation and optimization processes of scheduling plans and conducts safety assessments and resource efficiency analyses of the generated plans. The proposed optimized model is compared with classical MOO algorithms. These algorithms include Multi-Objective Evolutionary Algorithm based on Decomposition with Differential Evolution (MOEA/D-DE), Strength Pareto Evolutionary Algorithm 2 with Shift-based Density Estimation (SPEA2+SDE), and Non-dominated Sorting Genetic Algorithm III (NSGA-III). Based on the experimental results, the proposed optimized model outperforms three classic MOO algorithms across multiple key performance indicators. In terms of Hypervolume, the value achieved by the proposed model is 0.722, indicating that its solution set covers the objective space more effectively, demonstrating stronger diversity and global search capability. Furthermore, on the indicators of Generative Distance and Inverse Generative Distance, the proposed model attains lower values of 0.008 and 0.061, suggesting that the solution set is closer to the optimal front, with higher precision. In addition, the Spacing Metric value of 0.011 further shows that the solution set generated by the proposed model is more evenly distributed in the objective space. It avoids excessive clustering and enhances the uniformity and adaptability of the solutions. This uniformity is critical in practical construction scheduling optimization. This is because, under multiple conflicting objectives, a well-distributed solution set provides decision-makers with more options, enabling a better balance between safety and resource efficiency. Regarding safety assessment, Plan C has a high score of 4.63, indicating that under the optimization of the proposed model, the construction plan can achieve excellent performance in resource utilization and provide better safety guarantees. Similarly, Plan D, which demonstrates the highest resource efficiency, receives an overall score of 4.72, showcasing its outstanding advantages in resource usage and scheduling efficiency. These results validate the proposed model's applicability and flexibility under different constraints and objective functions.

Building Information Modelling (BIM) is now a globally recognised phenomenon, though its adoption remains inconsistent and variable between and within the construction sectors of different countries. BIM technology has enabled a wide range of functional applications, one of which, ‘4D BIM’, involves linking the tasks in a project’s construction schedule to its object-orientated 3D model to improve the logistical decision making and delivery of the project. Ideally, this can be automatically generated but in reality, this is not currently possible, and the process requires considerable manual effort. The level of maturity and expertise in the use of BIM amongst the project participants still varies considerably; adding further obstacles to the ability to derive full benefits from BIM. Reflecting these challenges, two case studies are presented in this paper. The first describes a predominantly manual approach that was used to ameliorate the implementation of 4D BIM on a project in Paris. In fact, there is scope for automating the process: a combination of BIM and Artificial Intelligence (AI) could exploit newly-available data that are increasingly obtainable from smart devices or IoT sensors. A prerequisite for doing so is the development of dedicated ontologies that enable the formalisation of the domain knowledge that is relevant to a particular project typology. Perhaps the most challenging example of this is the case of renovation projects. In the second case study, part of a large European research project, the authors propose such an ontology and demonstrate its application by developing a digital tool for application within the context of deep renovation projects.

Efficient planning and scheduling are critical for the success of repetitive construction projects, particularly highway infrastructure, which underpins economic growth in developing regions. Traditional scheduling methods often rely heavily on planner experience, limiting their ability to manage uncertainties and resource fluctuations in large-scale projects. This study proposes a Monte Carlo simulation-based framework to enhance planning efficiency by systematically modeling activity prioritization, resource allocation, and schedule optimization. Eighteen hypothetical project cases were analyzed under varying conditions to capture a wide range of uncertainties. The results demonstrated substantial improvements in project duration and resource utilization efficiency compared to conventional methods. Validation using three real-world highway projects in Egypt confirmed the framework’s practical applicability, achieving efficiency improvements of up to 80%. This research offers a data-driven, adaptable approach to repetitive project planning, providing planners with a robust tool to mitigate uncertainties and optimize project outcomes.

The emergence of immersive technologies, such as virtual reality (VR) headsets, has revolutionized the way we experience the physical world by creating a virtual, interactive environment. In the field of education, this technology has immense potential to provide students with a safe and controlled environment in which to experience real-world scenarios that may be otherwise unfeasible or unsafe. However, limited research exists on the effectiveness of integrating immersive technologies into technical education delivery. This research investigated the potential use of immersive virtual reality (IVR) in university-level construction management courses, with a focus on integrating IVR technology into traditional education for construction project planning and control. The experiment involved comparing the students’ learning and understanding of the subject matter using a set of two-dimensional construction drawings and a critical path method (CPM)-based construction schedule, with and without the use of an immersive environment. The findings suggested that the use of immersive technology significantly improved the students’ ability to understand technical concepts and identify any errors in the construction sequence when compared to traditional teaching methods. This paper presents the details of the experiment and a comparative analysis of both approaches in terms of students’ learning and understanding of project planning, sequencing, and scheduling.

Purpose The integration of building information modelling (BIM) and integrated project delivery (IPD) is highly recommended for better project delivery. Although there is a methodology for this integration, the BIM requires some improvements to foster the adoption of IPD. The purpose of this paper is to present an innovative way to support 4D BIM automation/optimisation within the IPD approach. Similar to structural and architectural design libraries, this research proposes a planning library to enable automating the formulation of schedule, as well as embedding the multi-objective optimisation into the 4D BIM. Design/methodology/approach The literature review was used to highlight the existing attempts to support the automation process for 4D BIM and the multi-objective schedule optimisation for construction projects. A case study was done to validate the developed framework and measure its applicability. Findings The results show that there is a cost-saving of 22.86 per cent because of using the proposed automated multi-objective optimisation. The case study shows the significance of integrating activity-based costing into 4D BIM to configure the hierarchy level of overhead activities with the IPD approach; therefore, the maximum level of contribution in managing the IPD project is 33.33 per cent by the trade package level and the minimum contribution is around 8.33 per cent by the project level. Originality/value This research presents a new philosophy to develop the 4D BIM model – planning and scheduling – a BIM library of the project activities is developed to enable the automation of the creation of the project schedule with respect to the 3D BIM design sequence. The optimisation of the project duration is considered to be automated within the creation process by using the proposed genetic algorithm model.

Generative Artificial Intelligence (AI) holds significant potential for revolutionizing the Architecture, Engineering, and Construction (AEC) industry by automating complex tasks such as construction scheduling, hazard recognition, resource leveling, information retrieval from BIM, etc. However, realizing this potential requires a strategic approach to ensure effective utilization and maximum benefit. This paper presents guidelines for prompt design and engineering to elicit desired responses from ChatGPT, a Generative AI tool, in AEC applications. Key steps include understanding user intent, leveraging model capabilities, and optimizing prompt structures. By following these guidelines, stakeholders in the AEC industry can harness the power of Generative AI to improve construction scheduling processes, increase project efficiency, and ultimately drive innovation and growth in the industry. Several illustrative examples on construction scheduling and hazard recognition are provided to demonstrate the methodology proposed in this research. It is concluded that Generative AI, when effectively utilized, significantly enhances project scheduling and hazard recognition capability in the AEC industry with minimal error.