Repetitive Projects Research Articles

Minimizing durations in repetitive projects through adaptive large neighborhood search

This study proposes an integer linear programming model based on line of balance, which takes the makespan as the optimization objective and characterizes various construction scenarios that may exist in different types of repetitive projects. The model is easy to solve, convenient for on-site use, and provides a solid foundation for exploring theoretical optimal solutions. Further, a novel matheuristic algorithm that integrates adaptive large neighborhood search with exact algorithms is proposed. Based on two different types of repetitive projects, the practicality of the model and the effectiveness of the algorithm are verified in ten different construction scenarios, and managerial insights are provided. A comparison with Gurobi’s results shows that in small-scale case scenarios, the matheuristic algorithm achieves solutions of the same quality with a shorter running time. In large-scale scenarios, the matheuristic algorithm outperforms Gurobi in terms of both solution quality and computational efficiency.

Low-Carbon Multiobjective Optimization for Repetitive Projects Based on Crew-Based Scheduling Strategy

Low-Carbon Multiobjective Optimization for Repetitive Projects Based on Crew-Based Scheduling Strategy

Fuzzy repetitive scheduling for construction projects considering risk preferences and time-cost tradeoffs

PurposeThe purpose of this study is to develop a novel approach that addresses time-cost tradeoffs in repetitive construction projects while considering the uncertainty in activity durations and the risk preferences of planners.Design/methodology/approachOur study involves work in three aspects. Firstly, it employs triangular fuzzy numbers to represent activity durations in different units, which facilitates the management of scenarios characterized by limited historical data or the presence of ambiguous information. Secondly, it introduces a fuzzy chance-constrained programming model, which is aimed at minimizing the project budget while ensuring that the risks associated with cost overruns and schedule delays are confined to specified limits. Thirdly, it advances an enhanced genetic algorithm, integrating an electromagnetism-like mechanism and a scheduling repair process, to improve the efficiency and effectiveness of the optimization process.FindingsA real-life street renovation project was analyzed to demonstrate the applicability of the proposed algorithm. The analysis explored three common types of risk preferences: risk-averse, risk-neutral and risk-loving. The results indicate that the proposed algorithm surpasses existing fuzzy repetitive scheduling methods in terms of risk management. It effectively generates schedules that align with the risk preferences of planners and provides worst-case estimates of project performance.Originality/valueThis research makes a significant contribution to the field by developing a fuzzy chance-constrained programming model and an associated optimization algorithm that is specifically designed for time-cost tradeoffs in repetitive construction projects. A key distinction is that this study considers the risk preferences of planners, which sets it apart from previously developed models. As a result, it provides a practical approach for effective risk management.

Metaheuristic Multi-Objective Optimization Approach for Repetitive Construction Project Scheduling

The optimization of schedules for repetitive projects is a crucial phase in establishing an effective project timeline concerning both duration and cost. This study presents an innovative metaheuristic multi-objective optimization model tailored for repetitive projects. Distinguished by its original features, this model empowers schedulers to identify an optimal schedule that concurrently minimizes project duration, total cost, and interruption time. It encompasses various constraints and factors, including learning and forgetting effects, inter-activity soft logic, limited work interruption allowances, multiple crew formations, multiple construction methods per activity, while retaining all Critical Path Method (CPM) scheduling capabilities. The effectiveness of this new approach was validated through application to two case studies, demonstrating its capability to deliver optimal schedules for repetitive projects with heightened accuracy in minimizing project duration, cost, and interruption. Furthermore, the integration of learning effects, soft logic, and work interruption allowances within the optimization process substantially reduced project duration and cost, thereby yielding more precise and dependable optimal solutions.

Multi-Objective Resource-Constrained Scheduling in Large and Repetitive Construction Projects

Effective resource management constitutes a cornerstone of construction project success. This is a challenging combinatorial optimization problem with multiple and contradictory objectives whose complexity rises disproportionally with the project size and special characteristics (e.g., repetitive projects). While relevant work exists, there is still a need for thorough modeling of the practical implications of non-optimal decisions. This study proposes a multi-objective model, which can realistically represent the actual loss from not meeting the resource utilization priorities and constraints of a given project, including parameters that assess the cost of exceeding the daily resource availability, the cost of moving resources in and out of the worksite, and the cost of delaying the project completion. Optimization is performed using Genetic Algorithms, with problem setups organized in a spreadsheet format for enhanced readability and the solving is conducted via commercial software. A case study consisting of 16 repetitive projects, totaling 160 activities, tested under different objective and constraint scenarios is used to evaluate the algorithm effectiveness in different project management priorities. The main study conclusions emphasize the importance of conducting multiple analyses for effective decision-making, the increasing necessity for formal optimization as a project’s size and complexity increase, and the significant support that formal optimization provides in customizing resource allocation decisions in construction projects.

Modeling and optimizing linear projects using LSM and Non-dominated Sorting Genetic Algorithm (NSGA-II)

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.

Scheduling of Repetitive Projects with Multiple Objectives by Multiple Crew Model

Scheduling of Repetitive Projects with Multiple Objectives by Multiple Crew Model

Tugas Berbasis Proyek Untuk Pencapaian Keterampilan Kolaborasi Dan Komunikasi Di Tingkat Perguruan Tinggi

This research presents practical and innovative practices in the form of proposed class activities for college-level learning designs (assignments) focusing on collaboration and communication skills through behaviorist learning theory and social learning theory approaches. These two skills emphasize 21st-century competencies with the integration of Education 5.0. and Social Emotional Learning concepts. So, the main objectives of this paper are (a) how students are prepared to understand concepts and practice collaboration and communication skills and (b) form project assignments suitable for collaboration skills and active thinking from the themes understood. A qualitative method utilizes a literature review approach to investigate the process of developing project-based learning plans for classroom activities. Analysis of the discussion focuses on collaboration and communication skills through behaviorist learning theory and social learning theory approaches. The results of the learning design and class assignments show that collaboration and communication skills can be achieved through repeated continuous project assignments and feedback from colleagues in the class and lecturers. To hone collaboration and communication skills, repetitive project assignments, reinforcement, and rewards should be considered when designing special assignments for early semester students to equip them with people skills in an increasingly connected and rapidly evolving global landscape. Apart from that, this approach from the two theories in project assignments makes students accustomed to cooperation, time management, and communication. In conclusion, students need to convey ideas in oral and written form. Higher education needs to provide opportunities for students to participate in collaborative projects, group discussions, and presentations and be strengthened using digital communication technologies.

Optimization Study of Construction Repetitive Project Scheduling Based on Big Data Background

This paper proposes the use of LOB method for repetitive project scheduling solution, and the use of branch limit algorithm to optimize LOB resource allocation to provide the best resource distribution scheme for construction projects. The control process types are analyzed and the time parameters of the LOB method are calculated. Introduce the resource constraint problem of repetitive projects, establish the objective function, put forward the relevant constraints of LOB resource allocation, combine the branching strategy, limiting strategy and pruning strategy, and construct the resource allocation process of LOB scheduling based on the branching and limiting algorithm. Select the parameters number of processes ($n$), maximum employable amount of processes ( k j ), and deadline tightness ( θ ), and conduct random generation tests to compare the performance of different LOB scheduling methods. The LOB scheduling schedule problem is described in the context of a construction project example, and the LOB resource allocation problem is solved by the branch-and-bound algorithm to obtain the optimal resource allocation scheme. Calculate the duration of each process after transformation is 12, 23, 13, 17.1, 12, 12. And the end time of the last process is 44.3, which satisfies the contract duration.

Review of Research on Uncertain Repetitive Project Scheduling

With the continuous development of project management practice, the project often suffers from disturbance events in the actual execution activity, which leads to project delay and great economic losses.Uncertainty project scheduling studies have also received attention.Repetitive project is a type of project with its own characteristics in which the construction site can be divided into several independent units and all activities are repetitive on multiple units, such as high-rise buildings, highways, and pipeline works. The characteristics of repetitive project controlling paths and controlling activities have their own advantages in responding to uncertain events. This paper takes repetitive project scheduling as the object to sort out its uncertain scheduling research. Firstly, the uncertain research based on probability random theory, fuzzy set theory and robustness theory is summarized, and then the research on different scheduling strategies such as interruptions soft logic and multi-mode in repetitive project scheduling is further summarized. Finally, the current research status of uncertain reactive scheduling is summarized,and further research directions are pointed out.

Collaborative planning of subcontractors using the Last Planner System: a case study on a gas subcontractor in repetitive housing projects

The efficient management of subcontractors turns into a challenge in contracts with fixed prices and short terms. Non-conformances due to poor deliverables are common, generating delays and cost overruns for all parties involved, especially for the main contractor. In addition, in recent years some collaborative systems have appeared, such as the Last Planner System (LPS), which has proven to be very efficient in the generation of value and the optimization of resources. However, there is still little information about successful results in the management of subcontractors. The main objective of this study is to propose a method that adapts the LPS for the management of the subcontract of natural gas facilities in highly repetitive housing projects, as well as to apply it successfully. The methodology was applied to a massive multi-family low-cost housing project in the city of Lima, Peru, which was modeled by using BIM technology. The most frequent root causes of non-conformances were determined for the first buildings, and this information was used for the collaborative planning of the following buildings. This allowed to determine the optimal sectioning, Takt Time Planning, subcontractor restrictions, weekly and daily planning, and finally, the lessons learned from the implementation of the LPS for future projects, which can be adapted to other types of subcontracts

Optimization of Tunnel Construction Schedule Considering Soft Logic

Tunnel construction projects are a classic type of repetitive project, and hold a crucial position in the construction industry. The linear scheduling method (LSM) has been in the spotlight in scheduling optimization for repetitive construction projects since it was first proposed. However, the possibility of changing the construction sequence during application is very often overlooked, which is not in line with actual engineering practices. Incorporating soft logic into LSM and considering the characteristics of tunnel construction, we propose an optimization method for the tunnel construction schedule to shorten the duration by opening up an additional working surface. A mixed-integer programming (MIP) model is developed, which considers various constraints, such as construction sequence and work continuity. The exact algorithm and genetic algorithm (GA) are designed via Python, and the validity is verified through practical cases. A comparative analysis was conducted between the two algorithms and their outcomes demonstrates that the method can satisfy the restrictive constraints of tunnel construction projects. In addition, improvement strategies for tunnel construction management are recommended.

ADAPTIVE LOB SCHEDULING FOR OPTIMIZING RESOURCE LEVELING AND CONSUMPTION

Several techniques were developed to improve resource allocation in construction projects, yet few in repetitive construction projects. Accordingly, this research aims at minimizing resource consumption and fluctuations using Line-of-Balance (LOB) repetitive scheduling technique. An optimization model has been developed consisting of three modules: LOB schedule module, resource leveling module, and optimization module. The model helps determine the optimum (a) start time for each activity considering any needed delays to level resources, (b) number of crews travelling from one unit to another, (c) unit to change the number of crews, and (d) the new changed number of crews. Unlike the existing efforts, the developed model provides the capabilities of (a) allowing the change of number of crews of an activity at a certain repetitive unit to increase or decrease the progress rate; and (b) accommodates non-serial repetitive projects to enhance the model’s practicality. Using a pipeline project, the model outperformed the existing LOB-based models in minimizing resource consumption and fluctuations within the desired project duration. This study offers the project planners a useful tool to efficiently utilize their projects’ resources and avoid hidden costs due to inefficient resource utilization on-site as well as overcoming shortage in resource availability.

Optimum planning and scheduling of repetitive construction projects: multi-objective optimization using the cuckoo search algorithm

Optimum planning and scheduling of repetitive construction projects: multi-objective optimization using the cuckoo search algorithm

Resource-constrained repetitive project scheduling with soft logic

PurposeIn repetitive projects, repetition offers more possibilities for activity scheduling at the sub-activity level. However, existing resource-constrained repetitive scheduling problem (RCRSP) models assume that there is only one sequence in performing the sub-activities of each activity, resulting in an inefficient resource allocation. This paper proposes a novel repetitive scheduling model for solving RCRSP with soft logic.Design/methodology/approachIn this paper, a constraint programming model is developed to solve the RCRSP using soft logic, aiming at the possible relationship between parallel execution, orderly execution or partial parallel and partial orderly execution of different sub activities of the same activity in repetitive projects. The proposed model integrated crew assignment strategies and allowed continuous or fragmented execution.FindingsWhen solving RCRSP, it is necessary to take soft logic into account. If managers only consider the fixed logic between sub-activities, they are likely to develop a delayed schedule. The practicality and effectiveness of the model were verified by a housing project based on eight different scenarios. The results showed that the constraint programming model outperformed its equivalent mathematical model in terms of solving speed and solution quality.Originality/valueAvailable studies assume a fixed logic between sub-activities of the same activity in repetitive projects. However, there is no fixed construction sequence between sub-activities for some projects, e.g. hotel renovation projects. Therefore, this paper considers the soft logic relationship between sub-activities and investigates how to make the objective optimal without violating the resource availability constraint.

Optimization of the Cost of Repetitive Construction Projects Using Computerized Iteration Method

This study introduces an iterative scheduling method that combines two approaches for managing repetitive construction projects: the Critical Path Method (CPM) and the Repetitive Scheduling Method (RSM). The primary objective of this study is to demonstrate how optimization techniques can be applied to minimize the cost of construction projects within a defined range, spanning from the shortest to the longest possible project durations. In the shortest project duration (as determined by CPM), all activities are allocated idle times based on precedence constraints, while in the longest duration (as determined by RSM), there is no idle time allocated. To calculate the optimal schedule, a computerized iterative method specifically designed for this purpose considers all possible combinations of activities with and without idle time. The optimum schedule is the one that minimizes the total project cost. The study reveals that by using an Excel spreadsheet, it is feasible to deterministically optimize the cost of repetitive construction projects, achieving the minimum cost. This minimization process can also be implemented as a Python application. Notably, this proposed system provides multiple optimal solutions, enabling managers to select the most suitable one. This advantage distin-guishes it from conventional methods, such as genetic algorithms and other optimization techniques. However, there are some limitations when applying this application, one of which is the maximum capacity available to run the application.

The strategic use of learning in repetitive projects

The strategic use of learning in repetitive projects

Reactive Scheduling for Repetitive Projects Based on Integrated Crew Interruption and Soft Logic Strategies

Reactive Scheduling for Repetitive Projects Based on Integrated Crew Interruption and Soft Logic Strategies

Proactive-reactive repetitive project scheduling method – the concept of risk consideration at the project planning and execution stage

Proactive-reactive repetitive project scheduling method – the concept of risk consideration at the project planning and execution stage

Multimode variable resource-constrained time/cost trade-off problem in repetitive projects with adjustment strategy

Resource availability is the crucial factor of construction project scheduling decision, as it impacts the time and cost of the project. However, traditional researches assume that resource availability is fixed, which does not reflect the actual situation. This article proposes a model for addressing the multimode variable resource-constrained time/cost trade-off problem (Mvrc-DTCTP) in repetitive projects. To better capture the characteristics of repetitive projects, an adjustment strategy is designed to mitigate the impact of work continuity interruptions on idle time. Through simulation analysis, it was found that increasing resource supply can reduce project duration, but it is not directly proportional to cost. This phenomenon further increases the uncertainty in the relationship between duration and cost, inevitably affecting managers’ ability to develop effective scheduling plans. By employing the Mvrc-DTCTP model, project managers can obtain more and better solutions than traditional methods, with shorter duration, and lower total cost. The proposed adjustment strategy results in a 54% reduction in idle time, a $4000 cost reduction and more feasible solutions. Based on calculations for 30 randomly generated cases, the adjustment strategy achieves optimization rates ranging from 24.43% to 84.24%. The adjustment strategy demonstrates the best optimization effect when the resource supply level is moderate.