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Construction projects that involve repetitive operations are often referred to as repetitive construction projects. Scheduling them proves a task more demanding than in the case of projects in other industries. Typical objectives of optimization, a characteristic set of constraints, as well as the schedule’s susceptibility to the propagation of disruptions caused by materializing risks, call for specific scheduling methods. The authors review the literature to summarize the existing repetitive scheduling methods and put forward their classification to identify the method’s aspects needing refinement. This is done to point to directions of further research. The authors hope that this study will contribute to better identification of existing problems in planning repetitive construction projects and faster development of decision support systems, eagerly anticipated by the construction practitioners. Though the focus is on applications to construction projects, the repetitive scheduling methods that account for volatile operating conditions may be of interest to researchers who develop planning techniques for other industries.

Nowadays, companies employ various project management (PM) methodologies to ensure that their projects are effective and successful. It is worth knowing that differences in principles and processes of PM methodologies influence the use of different PMs in managing non-repetitive and repetitive construction projects. This paper presents the selection and application of a rational construction PM methodology to a repetitive construction project after a comparison of two PM methodologies, namely Project Management Body of Knowledge (PMBOK) and Projects IN Controlled Environments (PRINCE2). The object of this study is a repetitive anti-corrosion works project for steel structures conducted at Company X. The research was carried out in two steps. First, a quantitative survey of the respondents from companies involved in the management and execution of construction projects was conducted with the aim to identify a rational approach to construction PM. The questionnaire consisted of fourteen closed-ended questions, six of which were generic and eight were PMBOK- and PRINCE2-specific questions. Companies that took part in the quantitative study identified the PRINCE2 project management approach as the most suitable for managing a repetitive construction project. Using the PRINCE2 PM methodology, the repetitive construction project would aim to provide as much information as possible to the project participants, form a team and assign team leaders responsible for the phases, establish a financial plan, a detailed timetable for the execution of the works, a quality control plan, and a plan of responsible persons, and detail the technological sequencing of the works. Second, a quantitative study on the selection of a rational construction project management approach for a repetitive construction project was pursued, and a qualitative assessment of construction project monitoring trends and actions was conducted. The qualitative research was performed using a structured interview method and asking the representatives of different companies X, Y, and Z the same 15 questions. The results of the qualitative research showed that a successful PM depends on the size of the project team, the PM tools and methodologies used, the PM philosophy, and the frequency of monitoring and discussing the project progress.

Despite the significance of resource levelling, project managers lack various ways to smooth resource usage fluctuation of a repetitive construction project besides changing resource usage. Tolerating interruptions is an effective way to provide flexibility for a schedule but is ignored when solving resource levelling problems. Therefore, this paper investigates the impacts of interruptions on resource usage fluctuation and develops an integrated approach that simultaneously integrates two scheduling adjusting processes: changing resource usage and tolerating interruptions. In this paper, two interruption conditions are proposed to identify which activities are suitable to be interrupted for smoothing resource usage fluctuation. The traditional resource levelling model is modified to a new scheduling model by incorporating interruptions. A two-stage GA-based scheduling algorithm is developed by integrating changing resource usage and tolerating interruptions. A commonly used pipeline project is adopted to illustrate the steps of the proposed approach and demonstrate its effectiveness and superiority through comparison with previous studies. A large-scale project further verifies the usability of the proposed approach. The results confirmed the feasibility to smooth resource usage fluctuation by interruptions, and the integrated approach can achieve a more competitive resource levelling result.

The article presents the cost optimization model for multiunit construction projects. Multiunit projects constitute a special case of repetitive projects. They consist in the realization of many different, when it comes to size, types of residential, commercial, industrial buildings or engineering structures. Due to the specific character of construction works, actual schedules of such projects should not only take into account real costs of construction, but also be subject to specific restrictions, e.g. deadlines for the completion of units imposed by the investor. To solve the NP-hard problem of choosing the order of units’ construction there was metaheuristic algorithm of simulated annealing used. The objective function in the presented optimization model was the total value of the project cost determined on the basis of the mathematical programming model, taking into account direct and indirect costs, costs of missing deadlines and costs of work group discontinuities. In the article, an experimental analysis of the proposed method of solving the optimization task was carried out in a model that showed high efficiency in obtaining suboptimal solutions. In addition, the operation of the proposed model has been presented on a calculation example. The results obtained in it are fully satisfying.

PurposeThis paper presents the development of a novel model for optimizing the scheduling of crew deployments in repetitive construction projects while considering uncertainty in crew production rates.Design/methodology/approachThe model computations are performed in two modules: (1) simulation module that integrates Monte Carlo simulation and a resource-driven scheduling technique to calculate the earliest crew deployment dates for all activities that fully comply with crew work continuity while considering uncertainty; and (2) optimization module that utilizes genetic algorithms to search for and identify optimal crew deployment plans that provide optimal trade-offs between project duration and crew deployment plan cost.FindingsA real-life example of street renovation is analyzed to illustrate the use of the model and demonstrate its capabilities in optimizing the stochastic scheduling of crew deployments in repetitive construction projects.Originality/valueThe original contribution of this research is creating a novel multiobjective stochastic scheduling optimization model for both serial and nonserial repetitive construction projects that is capable of identifying an optimal crew deployment plan that simultaneously minimizes project duration and crew deployment cost.

The article presents the profit optimization model for multi-unit construction projects. Such projects constitute a special case of repetitive projects and are common in residential, commercial, and industrial construction projects. Due to the specific character of construction works, schedules of such projects should take into account many different aspects, including durations and costs of construction works, the possibility of selecting alternative execution modes, and specific restrictions (e.g., deadlines for the completion of units imposed by the investor). To solve the NP-hard problem of choosing the order of units’ construction and the best variants of works, the authors used metaheuristic algorithms (simulated annealing and genetic search). The objective function in the presented optimization model was the total profit of the contractor determined on the basis of the mathematical programming model. This model takes into account monthly cash flows subject to direct and indirect costs, penalties for missing deadlines, costs of work group discontinuities, and borrowing losses. The presented problem is very important for maintaining a good financial condition of the enterprise carrying out construction projects. In the article, an experimental analysis of the proposed method of solving the optimization task was carried out in a model that showed high efficiency in obtaining suboptimal solutions. In addition, the operation of the proposed model has been presented on a calculation example. The results obtained in it are fully satisfying.

The time-cost tradeoff problem is to find optimal combinations of construction options with the objective of minimizing project time and cost. In order to search for such a set of optimal solutions, the total time and cost of projects needs to be calculated properly. In repetitive construction projects (RCPs), due to division of work into several units and involvement of many resources for activities completion, scheduling is unique and more complicated. Scheduling method in this paper besides addressing common constraints such as precedence relationships, required lead time and lag distance between activities, enables project managers to consider all resources and their shortages in scheduling. To raise the practicality of model, in addition to direct and indirect costs, resource idle cost is considered as a cost element in estimating total cost of project. The time variant multi-objective particle swarm optimization is applied to find non-dominated solutions on the basis of minimizing time and cost of project. An application example is presented at the end to illustrate the performance of the model. This research presents a resource-constrained time-cost tradeoff model to find the optimal set of crew combinations for project activities in RCPs, considering time and cost simultaneously.

In the face of the high variability in the completion of construction projects, the following research is generated. In the literature we can find several proposals to program projects, however, the variability of the activities causes high variability in the completion date of the projects. We hope that the proposed method, by controlling the start of activities, will ensure the completion date of the projects, by fixing the start of every activity with a high level of probabilistic confidence for the planned project duration. The proposed fixed start method (FSM) was tested in two case studies by using discrete event simulation. Project completion duration results were compared with the critical path method (CPM) and the program evaluation and review technique (PERT). Project completion was evaluated in the case studies by the coefficient of variance (COV), mean, and variance. The new method decreased the scheduled duration variability while meeting project completion times.

Scheduling repetitive construction projects poses a significant challenge in optimally utilizing multiple concurrent crews and sequencing their work to minimize project duration and cost. To address this challenge, this study presents the development of a multi-objective scheduling optimization model for repetitive construction projects consisting of three modules. First, a scheduling module ensures the harmonious coordination of multiple concurrent crews for each activity while considering varying productivity rates, work continuity constraints, and project precedence relationships. Second, a cost module incorporates various contractual cost components to enable a thorough evaluation of project costs and provides flexibility to contractors encountering different contract terms. Third, an optimization module utilizes a genetic algorithm to identify optimal combinations of crews working in parallel and their optimal work sequence, to simultaneously minimize project duration and cost. An application example from the literature was analyzed to validate the model and demonstrate its superiority over previous models. The results showed significant reductions of 8% and 0.78% in project duration and overall costs, respectively, compared to previous models. Furthermore, the capabilities of the model were demonstrated through a real-life case study involving a highway development and renovation project, highlighting its practical benefits and effectiveness in a real-world construction scenario.

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.