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The production process of precast components is labor-intensive, involving various manual tasks. The physically demanding tasks usually result in fatigue and overexertion of workers, leading to increased occupational health risks and reduced productivity. An appropriate work–rest strategy is recognized to effectively promote both workers' health and productivity, while it has rarely been studied in the field of the construction industry. To narrow this gap, this study developed a mixed-integer linear programming approach to optimize the work–rest schedule by integrating workers' overexertion. The objective is to maximize the productive time affected by the workers' accumulative fatigue and recovery. Also, the optimized work–rest strategy can be highly customized by considering personalized factors and task characteristics. Experimenting with a case study compared the default rest schedule provided by the superintendent onsite with the optimal solution solved from the developed model. Results suggested that up to 20% improvement in productive time can be achieved, especially for the task with a relatively higher workload. Computational experiments were conducted to evaluate the sensitivity of total productive time to various personalized and task-specific factors. The proposed method provides superintendents with an applicable strategy to improve workers' productivity and reduce their occupational risks resulting from overexertion. This study can promote the implementation of personalized occupational health management and support the improvement of regulations on the required rest with quantified evidence, thereby contributing to more reliable scheduling and sustainable workforce development for the construction industry. The research scope was limited to the precast production process, and further investigation on broader applications will be conducted. [ABSTRACT FROM AUTHOR]

Thousands of aging education buildings in the US are in urgent need of maintenance to ensure their operational performance. The planning and scheduling of these maintenance activities need to be optimized to minimize their total maintenance costs and reduce the challenging deferred maintenance problems confronting education institutions in the US. This paper presents the development of a novel model for optimizing the planning of maintenance activities in education buildings. The computations of the developed model are implemented utilizing an optimization module that identifies an optimal maintenance schedule for minimizing total maintenance costs; a scheduling module that calculates the start day, start hour, finish day, and finish hour of all maintenance activities; and a cost estimating module that estimates the total maintenance cost for each generated schedule. An application example of three education buildings that include plumbing, HVAC, and electrical maintenance activities was analyzed to illustrate the use of the model and evaluate its performance by comparing its results to those generated by widely used existing methods for planning maintenance activities of education buildings. The outcome of this analysis confirms the model was capable of outperforming existing methods in reducing the total maintenance cost by more than 12%. The main contributions of the developed novel model to the body of knowledge include its innovative methodology for identifying optimal ranking of maintenance activities, selecting optimal overtime use and crew size for all maintenance activities, generating an optimal maintenance schedule that complies with all practical constraints such as the availability of classrooms during their nonoperational hours, and minimizing total maintenance cost of all scheduled activities. [ABSTRACT FROM AUTHOR]

As construction projects become more complex and challenging to execute, project managers need to develop work schedules that consider as many objectives as are critical to project success. A work schedule designed to optimize all these objectives is likely to enhance project performance. However, achieving a balance among these objectives is difficult because they frequently conflict with each other. Many optimization models have been developed over the years that can factor in different objectives. Although previous optimization studies have contributed to the body of knowledge by individually adding one or two objectives to the classical time–cost trade-off (TCT) problem, none of these studies simultaneously optimized more than three or four objectives at a time. Clearly, there is a need for a multiobjective optimization model to generate work schedules that increase the likelihood of achieving as many project objectives as necessary. This study was motivated by the need to fill this research gap. The objective of this study was to develop a mathematical multiobjective optimization model for the time–cost–quality–schedule flexibility-resource fluctuation trade-off problem in small-scale construction projects. The proposed model is novel because it (1) considers five important objectives of construction project scheduling at the same time, (2) quantifies all objectives to provide an accurate reflection of construction site conditions, and (3) objectively determines the optimal compromise solution using a distance-based method rather than subjectively from a set of Pareto-optimal solutions. The applicability of the proposed model is demonstrated with an illustrative example. The key results of this study are as follows: (1) the optimal solution obtained depends on the project manager's priorities relative to the objectives; and (2) in optimization problems with more than two objectives, considering the objectives one at a time may result in misleading solutions, because the effects of the other objectives are ignored in this optimization. Consequently, multiple objectives must be considered simultaneously rather than one at a time. The proposed model is expected to provide project managers with a work schedule that balances five or more objectives, increasing the chances of successful project performance. [ABSTRACT FROM AUTHOR]

This paper presents the development of a novel multiobjective model for optimizing the planning of airport airside expansion projects that provides the capability of minimizing both airport operations disruption cost and total construction cost. The model computations are performed in four main modules: (1) a simulation module that calculates and quantifies the impact of airport construction activities on airport operations, (2) a multiobjective genetic algorithms optimization module that identifies an optimal schedule for airport airside expansion projects, (3) a scheduling module that calculates the start and finish times of each construction activity, and (4) a cost module that computes the total cost of construction-related disruptions in airport operations and the total construction cost. A real-life case study of an airport airside expansion project is analyzed to illustrate the use of the model and highlight its original contributions to the body of knowledge including its novel methodologies for (1) quantifying and minimizing the impact of construction activities on airport operations, (2) analyzing and minimizing the impact of air traffic data and airport operations on total construction cost, and (3) generating a set of optimal construction plans including optimal project start time, activities start time, and weekly and daily work plans for all activities. The results of the case study clearly illustrate the original contributions of the developed model and its novel capabilities that are expected to support airport and construction planners in their efforts to improve the cost-effectiveness and functional performance of airports during airside expansion projects. [ABSTRACT FROM AUTHOR]

Effective handling of project risks is an important part of project risk management. Various methods have been proposed to determine the optimal set of risk-response actions (RRAs) to mitigate the project risks. However, few existing methods incorporate resource planning into their selected RRAs. This paper integrates the risk-response problem with the resource-constrained project scheduling problem (RCPSP) and presents a method for allocating and scheduling the limited risk-related (RR) resources to provide project managers with an effective and feasible project risk-response strategy. Based on the elaboration of the relationships between the risk, the RRA, and the RR resource, a mixed-integer optimization model is developed to obtain the optimal risk-response strategy and the consequent project scheduling. A full-factorial numerical experiment is conducted to validate the optimization model and investigate the effects of risk-response efficiency and risk severity on project make-span and total cost. In addition, a model comparison analysis is presented to highlight the necessity of incorporating resource planning. The computational results show that, as expected, resource utilization can be improved by incorporating allocation and scheduling in the risk-response procedure. Also, the study suggests that project managers should avoid RR resource consumption and cost increases caused by excessive risk response in specific circumstances. [ABSTRACT FROM AUTHOR]

Procuring the correct materials at the right time and for the lowest cost is essential in construction. This fact underlines the importance of an optimized material procurement schedule integrated with the construction schedule/program. However, few studies examine the development of a material procurement schedule by integrating the construction schedule and optimizing material costs as well as any material shortage impact. In addition, budget constraints and maximum storage capacity are rarely captured in existing models for material procurement optimization. To address these shortcomings, an optimization model is developed using the nondominated sorting genetic algorithm II (NSGA-II) and executed in MATLAB R2017a. This model was implemented in a construction project that developed a material delivery schedule based on the budget constraints and maximum storage capacity for five majorly used building materials, resulting in a costs saving of 31.17% and a reduction in shortage impact of 83.47% compared with the actual delivery schedule. The developed model also incorporates the minimum order quantity and standard shipping size as well as avoiding surplus materials, leading to sustainable procurement of materials. Construction practitioners can use this model for procuring materials when facing budget constraints in a construction project with the lowest cost and least shortage and without excess or insufficient purchasing. The model will aid in completing the project within the stipulated time and budgeted cost. [ABSTRACT FROM AUTHOR]

Increased emphasis is currently placed on precast production scheduling due to the upward-trending applications of precast technology. However, existing studies primarily focused on static scheduling, and few of them addressed dynamic rescheduling problems subject to disruptions from individual products without considering constraints in the practical environment. In this paper, a hybrid rescheduling optimization model for precast production is proposed to minimize rescheduling costs and ensure on-time delivery under disruptions of machine breakdown. The rescheduling was first optimized based on the genetic algorithm considering the idle time of multiple production lines, time intervals, and operation characteristics in precast production. Subsequently, the feasibility of potential reschedules was further verified via simulating production uncertainties in a real-world environment to achieve a trade-off between a high service level and profit maximization. Finally, a case study with different realistic scenarios was conducted to prove the superiority of the model in comparison with other methods. This methodology will increase the applicability of rescheduling methods in practice and reduce production costs of components in precast construction. [ABSTRACT FROM AUTHOR]

Current precast production scheduling methodologies often have limited applicability in practice largely due to the limited consideration of human factors and interactions between the scheduling and workforce development. This study systematically integrates human factors in the form of worker competence into production scheduling. More specifically, the factors of worker learning rate, professional skills, experience in each type of operation, and operation specifications are all formulated to determine the duration of each process in precast production. A two-hierarchy scheduling optimization model (WCSO_PP) considering worker competence is proposed to synchronize the production scheduling with workforce development and to optimize the scheduling from both the short-term (time-oriented) and long-term (competence-oriented) views. Finally, the performance of the proposed model is validated using a real case study. The WCSO_PP model can achieve 74.09% and 73.01% cost savings in short-term and long-term scenarios, respectively. The developed model addresses the research gap in modeling the impact of human factors on the production, contributing to more reliable scheduling and enhancing the long-term human resource development. [ABSTRACT FROM AUTHOR]

Abstract: Schedules and physical workspaces are two key elements of linear construction projects that are extremely interdependent. Any negligence in incorporating spatial and temporal constraints in developing and improving schedules of linear projects results in inevitable delays and workspace congestions and can substantially hinder the performance of the activity resources. This study augments the current linear scheduling methods by presenting an uncertainty-aware optimization framework to optimize the duration of linear projects while minimizing their potential congestions. The methodology is built upon the new concept of space-time float for explicit consideration of spatio-temporal constraints of activities and their inherent uncertainty. A constraint satisfaction approach was used for the two-tier optimization of duration and congestion. A fuzzy inference system was also incorporated to assess the inherent uncertainty in the schedule. Two case examples from literature are analyzed. The results demonstrate the effectiveness of the proposed method in planning and control of the unforeseen variations from planned schedules of linear projects. [ABSTRACT FROM AUTHOR]

A construction project can be accelerated by overlapping critical activities. This paper presents a computational method called exact time--cost tradeoff (ETCT) in concurrency-based scheduling that identifies exact global overlaps between critical activities. It calculates the rework cost slope and amount by integrating the activities' concurrency attributes, identifies the exact global solution, and computes the probability to complete a project within the project completion time (PCT) and/or the project completion cost (PCC). The study is of value to project schedulers because ETCT identifies the exact global solution (i.e., the set of activities to be overlapped and the exact overlap rates between these activities) expeditiously, and establishes a compressed schedule that satisfies a project's budget and duration constraints. This is also relevant to researchers in that it clearly defines the computational formula to compute the exact solution in detail and facilitates a comparison of the outcome of a schedule under different project objectives, such as minimizing project completion duration and/or project completion cost. The usability and validity of the computational methods was confirmed by performing two test cases. [ABSTRACT FROM AUTHOR]

Real-life construction projects are large in size and are challenged by many constraints, including strict deadlines and resource limits. In this paper, constraint programming (CP) is used as an advanced mathematical technique that suits schedule optimization problems. A practical CP optimization model has been developed to resolve both deadline and resource constraints simultaneously in large-scale projects. The proposed CP model is much faster than metaheuristic techniques and provides a set of feasible project durations that do not violate resource limits. The paper compares the CP results with several case studies from the literature to prove the practicality and usefulness of the CP approach to both researchers and practitioners. The CP model of this paper could provide solutions within 6.5% deviation from optimum schedules for a large project of 2,000 activities within minutes of processing time. This paper thus contributes to introducing a superior optimization model that is suitable for large-size projects and helps to render schedule optimization a mainstream cost-saving function within commercial scheduling systems. [ABSTRACT FROM AUTHOR]

The fast-track method is one of the most recognized methodologies for reducing construction project schedules. However, due to the lack of definitive research to date pertaining to the effects of fast-track application in terms of time and cost, it has been difficult for project owners to determine its correct application. This paper presents the time and cost optimized decision support (TACTICS) model, and it was developed based on the fast-track methodology and genetic algorithms (GAs). TACTICS was applied to two case studies, and the results indicated that the fast-track method could be expected to deliver more efficient projects compared with using the traditional method. In particular, (1) the average reduction in project duration by applying the fast-track method was 40.48% (Case I) and 18.59% (Case II) compared with using the traditional method, and (2) the average project costs were reduced by as much as 0.39% (Case I) and 4.48% (Case II). Consequently, TACTICS could be expected to help in making a decision regarding the fast-track application and further contribute to the project scheduling expertise in the construction engineering and management body of knowledge. [ABSTRACT FROM AUTHOR]

One of the most challenging tasks of a construction project planner is to simultaneously minimize the total project cost and total project duration while considering issues related to optimal resource allocation and resource leveling. Therefore, project planners face complicated multivariate, time-cost-resource optimization (TCRO) problems that require time-cost-resource tradeoff analysis. The hybrid GA-PSO approach is presented to solve complex, TCRO problems in construction project planning. The proposed approach uses the fuzzy set theory to characterize uncertainty about the input data (i.e., time, cost, and resources required to perform an activity) in this hybrid approach. The proposed fuzzy-enabled hybrid GA-PSO approach is applied to solve two optimization problems that are found in the construction project planning literature. It is shown that the proposed fuzzy enabled hybrid GA-PSO approach is superior to existing optimization algorithms at finding better project schedule solutions with less total project cost, less total project duration, and less total variation in resource allocation. The results also show that the proposed approach is faster than existing methods in processing time for solving complex TCRO problems in construction project planning. [ABSTRACT FROM AUTHOR]

In this study we propose a threshold accepting based hyperheuristic for solving in a single run both the resource-constrained project scheduling problem or resource allocation, and the resource leveling problem. Having their roots in the field of artificial intelligence, hyperheuristics operate in the “low-level” heuristics domain rather than in the solutions domain. The hyperheuristic has been implemented within a commercial project management software package. Low-level heuristics operate on the solution domain defined by the priority values that the software uses for resource allocation. A case example from the literature and computational experiments on randomly generated projects demonstrate that the hyperheuristic achieves good performance in a timely manner, improving the results provided by the software. [ABSTRACT FROM AUTHOR]

Completing a project with minimal time as well as minimal cost is a critical factor for scheduling a project. However, because completion speed tends to be correlated with cost (e.g., usually, time can be saved if more workers are hired), the relationship between time and cost is a trade-off. This study employed a phenomenon-mimicking algorithm, harmony search to perform this biobjective trade-off. The harmony search algorithm was applied to two test examples, and good Pareto solutions were obtained when compared with other algorithms, such as the genetic algorithm and the ant colony optimization algorithm. The harmony search algorithm explored only a small amount of total solution space in order to solve this combinatorial optimization problem. The model in this study has obtained good results with a network of up to 18 nodes, however it is anticipated that the model could be useful to solve bigger networks in practice without any difficult problem. [ABSTRACT FROM AUTHOR]

An issue has arisen with regard to which of the schedule generation schemes will perform better for an arbitrary instance of the resource-constrained project scheduling problem (RCPSP), which is one of the most challenging areas in construction engineering and management. No general answer has been given to this issue due to the different mechanisms between the serial scheme and the parallel scheme. In an effort to address this issue, this paper compares the two schemes using a permutation-based Elitist genetic algorithm for the RCPSP. Computational experiments are presented with multiple standard problems. From the results of a paired difference experiment, the algorithm using the serial scheme provides better solutions than the one using the parallel scheme. The results also show that the algorithm with the parallel scheme takes longer to solve each problem than the one using the serial scheme. [ABSTRACT FROM AUTHOR]

In a construction project, the cost and duration of activities could change due to different uncertain variables such as weather, resource availability, etc. Resource leveling and allocation strategies also influence total time and costs of projects. In this paper, two concepts of time-cost trade-off and resource leveling and allocation have been embedded in a stochastic multiobjective optimization model which minimizes the total project time, cost, and resource moments. In the proposed time-cost-resource utilization optimization (TCRO) model, time and cost variables are considered to be fuzzy, to increase the flexibility for decision makers when using the model outputs. Application of fuzzy set theory in this study helps managers/planners to take these uncertainties into account and provide an optimal balance of time, cost, and resource utilization during the project execution. The fuzzy variables are discretized to represent different options for each activity. Nondominated sorting genetic algorithm (NSGA-II) has been used to solve the optimization problem. Results of the TCRO model for two different case studies of construction projects are presented in the paper. Total time and costs of the two case studies in the Pareto front solutions of the TCRO model cover more than 85% of the ranges of total time and costs of solutions of the biobjective time-cost optimization (TCO) model. The results show that adding the resource leveling capability to the previously developed TCO models provides more practical solutions in terms of resource allocation and utilization, which makes this research relevant to both industry practitioners and researchers. [ABSTRACT FROM AUTHOR]

Construction schedules, generated by network scheduling techniques, often cause undesirable resource fluctuations that are impractical, inefficient, and costly to implement on construction sites. This paper presents the development of two innovative resource leveling metrics to directly measure and minimize the negative impact of resource fluctuations on construction productivity and cost. The first metric quantifies the total amount of resources that need to be temporarily released during low demand periods and rehired at a later stage during high demand periods. The second measures the total number of idle and nonproductive resource days that are caused by undesirable resource fluctuations. The two new metrics are incorporated in a robust and practical optimization model that is capable of generating optimal and practical schedules that maximize the efficiency of resource utilization. An application example is analyzed to illustrate the use of the model and demonstrate its capabilities. The results of this analysis show that the present model and metrics are capable of outperforming existing metrics and eliminating undesirable resource fluctuations and resource idle time. [ABSTRACT FROM AUTHOR]

This article evaluates the viability of using fuzzy mathematical models for determining construction schedules and for evaluating the contingencies created by schedule compression and delays due to unforeseen material shortages. Networks were analyzed using three methods: manual critical path method scheduling calculations, Primavera Project Management software (P5), and mathematical models using the Optimization Programming Language software. Fuzzy mathematical models that allow the multiobjective optimization of project schedules considering constraints such as time, cost, and unexpected materials shortages were used to verify commonly used methodologies for finding the minimum completion time for projects. The research also used a heuristic procedure for material allocation and sensitivity analysis to test five cases of material shortage, which increase the cost of construction and delay the completion time of projects. From the results obtained during the research investigation, it was determined that it is not just whether there is a shortage of a material but rather the way materials are allocated to different activities that affect project durations. It is important to give higher priority to activities that have minimum float values, instead of merely allocating materials to activities that are immediately ready to start. [ABSTRACT FROM AUTHOR]

This paper presents a multiobjective optimization model that provides new and unique capabilities including generating and evaluating optimal/near-optimal construction resource utilization and scheduling plans that simultaneously minimize the time and maximize the profit of construction projects. The computations in the present model are organized in three major modules: (1) a scheduling module that develops practical schedules for construction projects; (2) a profit module that computes the project profit; and (3) a multiobjective module that searches for and identifies optimal/near optimal trade-offs between project time and profit. A large-scale construction project is analyzed to illustrate the use of the model and to demonstrate its capabilities in generating and visualizing optimal trade-offs between construction time and profit. [ABSTRACT FROM AUTHOR]

Construction contractors often finance projects using bank credit lines that allow contractors to withdraw money up to certain credit limits. Finance-based scheduling provides schedules that ensure that the contractor’s indebtedness at any time during the construction stage does not exceed the credit limit. Generally, constricted credit limits tend to yield prolonged schedules. Provided that credit limits can be adequately relaxed, compressed schedules of compressed-duration activities can be attained. Devising a compressed schedule calls for the incorporation of time-cost trade-off (TCT) analysis to strike a balance between the decreased overhead costs and the increased direct costs of the activities. Since employing TCT analysis usually causes great fluctuations in the daily resource requirements by mixing compressed-duration activities of high resource demand with others of low resource demand, therefore, the need for resource management techniques becomes inevitable to ensure efficient utilization of resources. This note used genetic algorithms to expand finance-based scheduling to devise schedules for relaxed credit limits. A prototype system was developed and coded using VISUAL BASIC, then demonstrated using a five-activity example project. The prototype was validated by comparing the results with those obtained by using the integer programming. Expanding finance-based scheduling to handle the whole spectrum of credit limits helps devise overall-optimized schedules that consider cash, time, cost, and resources. [ABSTRACT FROM AUTHOR]

This paper introduces a particle swarm optimization (PSO)-based methodology to implement preemptive scheduling under break and resource-constraints (PSBRC) for construction projects. The PSBRC under study allows the preemptive activities to be interrupted in off-working time and not to resume immediately in the next working period because all the limited resources are to be reallocated during a break. The potential solution to the PSBRC, i.e., a set of priorities deciding the order to start the activities or restart the interrupted activities, is represented by the multidimensional particle position. Hence PSO is applied to search for the optimal schedule for the PSBRC, in which a parallel scheme is adopted to transform the particle-represented priorities to a schedule. Computational analyses are presented to verify the effectiveness of the proposed methodology. This paper provides an attempt to make use of preemption and break for the resource-constrained construction project with the objective of minimizing project duration. [ABSTRACT FROM AUTHOR]

Time-cost analysis is an important element of project scheduling, especially for lengthy and costly construction projects, as it evaluates alternative schedules and establishes an optimum one considering any project completion deadline. Existing methods for time-cost analysis have not adequately considered typical activity and project characteristics, such as generalized precedence relationships between activities, external time constraints, activity planning constraints, and bonuses/penalties for early/delayed project completion that would provide a more realistic representation of actual construction projects. The present work aims to incorporate such characteristics in the analysis and has developed two solution methods, an exact and an approximate one. The exact method utilizes a linear/integer programming model to provide the optimal project time-cost curve and the minimum cost schedule considering all activity time-cost alternatives together. The approximate method performs a progressive project length reduction providing a near-optimal project time-cost curve but it is faster than the exact method as it examines only certain activities at each stage. In addition, it can be easily incorporated in project scheduling software. Evaluation results indicate that both methods can effectively simulate the structure of construction projects, and their application is expected to provide time and cost savings. [ABSTRACT FROM AUTHOR]

Time–cost optimization (TCO) is one of the greatest challenges in construction project planning and control, since the optimization of either time or cost, would usually be at the expense of the other. Although the TCO problem has been extensively examined, many research studies only focused on minimizing the total cost for an early completion. This does not necessarily convey any reward to the contractor. However, with the increasing popularity of alternative project delivery systems, clients and contractors are more concerned about the combined benefits and opportunities of early completion as well as cost savings. In this paper, a genetic algorithms (GAs)-driven multiobjective model for TCO is proposed. The model integrates the adaptive weight to balance the priority of each objective according to the performance of the previous “generation.” In addition, the model incorporates Pareto ranking as a selection criterion and the niche formation techniques to improve popularity diversity. Based on the proposed framework, a prototype system has been developed in Microsoft Project for testing with a medium-sized project. The results indicate that greater robustness can be attained by the introduction of adaptive weight approach, Pareto ranking, and niche formation to the GA-based multiobjective TCO model. [ABSTRACT FROM AUTHOR]

This paper presents an innovative model for scheduling, resource planning, and cost optimization of large construction and/or maintenance programs that involve multiple distributed sites. The proposed distributed scheduling model (DSM) employs an information system to store data related to various work sites, activities’ optional construction methods, and available resources. The DSM also incorporates a scheduling algorithm that is resource focused, and maintains crew work continuity under any sequence of distributed sites. To minimize cost and meet project time and resource constraints, the DSM uses genetic algorithms to determine the optimum set of construction methods and the optimum routing order among sites. Details of the DSM formulation are presented in this paper and the benefits to municipalities and contractors are outlined. [ABSTRACT FROM AUTHOR]