Use of Genetic Algorithms in Resource Scheduling of Construction Projects

This paper presents an augmented Lagrangian genetic algorithm model for resource scheduling. The algorithm considers scheduling characteristics that were ignored in prior research. Previous resource scheduling formulations have primarily focused on project duration minimization. Furthermore, resource leveling and resource-constrained scheduling have traditionally been solved independently. The model presented here considers all precedence relationships, multiple crew strategies, total project cost minimization, and time-cost trade-off. In the new formulation, resource leveling and resource-constrained scheduling are performed simultaneously. The model presented uses the quadratic penalty function to transform the resource-scheduling problem to an unconstrained one. The algorithm is general and can be applied to a broad class of optimization problems. An illustrative example is presented to demonstrate the performance of the proposed method.     

Cost Optimization Model for the Multiresource Leveling Problem with Allowed Activity Splitting

Resource leveling aims at minimizing the resource usage fluctuations, which is accomplished by moving noncritical activities within their float. The project duration is fixed and is not affected by the leveling. Most of resource leveling techniques assumes that activities cannot be split. Although this assumption is valid for most construction activities, there are several activities that can be split to achieve better resource leveling. However, there is an added cost associated with splitting such as startup and restarting costs. This paper presents an optimization model for resource leveling that allows activity splitting and minimizes its associated costs. The objective is to level resources in a way that provides a tradeoff between the extra cost of acquiring and releasing resources versus the extra cost of activity splitting. The model can be used to determine at what values of the splitting cost, the preemption of an activity is recommended. One example problem is solved at the end of the paper in order to illustrate the proposed model.     

GA-Based Multicriteria Optimal Model for Construction Scheduling

Resources for construction activities are limited in the real construction world. To avoid the waste and shortage of resources on a construction jobsite, scheduling must include resource allocation. A multicriteria computational optimal scheduling model, which integrates the time∕cost trade-off model, resource-limited model, and resource leveling model, is proposed. A searching technique using genetic algorithms (GAs) is adopted in the model. Furthermore, the nondominated solutions are found by the multiple attribute decision-making method, technique for order preference by similarity to ideal solution. The model can effectively provide the optimal combination of construction durations, resource amounts, minimum direct project costs, and minimum project duration under the constraint of limited resources.     

Efficient Hybrid Genetic Algorithm for Resource Leveling via Activity Splitting

Resource leveling problem is an attractive field of research in project management. Traditionally, a basic assumption of this problem is that network activities could not be split. However, in real-world projects, some activities can be interrupted and resumed in different time intervals but activity splitting involves some cost. The main contribution of this paper lies in developing a practical algorithm for resource leveling in large-scale projects. A novel hybrid genetic algorithm is proposed to tackle multiple resource-leveling problems allowing activity splitting. The proposed genetic algorithm is equipped with a novel local search heuristic and a repair mechanism. To evaluate the performance of the algorithm, we have generated and solved a new set of network instances containing up to 5,000 activities with multiple resources. For small instances, we have extended and solved an existing mixed integer programming model to provide a basis for comparison. Computational results demonstrate that, for large networks, the proposed algorithm improves the leveling criterion at least by 76% over the early schedule solutions. A case study on a tunnel construction project has also been examined.     

Fuzzy Optimal Model for Resource-Constrained Construction Scheduling

Activity duration is uncertain due to the variation in the outside environment, such as weather, site congestion, productivity level, etc. Furthermore, resources for construction activities are limited in the real construction world so that scheduling must include resource allocation. A new optimal resource-constrained construction scheduling model is proposed in this paper, in which the effects of both uncertain activity duration and resource constraints are taken into account. Fuzzy set theory is used to model the uncertainties of activity duration. A genetic algorithm-based searching technique is adopted to search for the fuzzy optimal project duration under resource constraints. The model can effectively provide the optimal fuzzy profiles of project duration and resource amounts under the constraint of limited resources.     

Critical Path Scheduling under Resource Calendar Constraints

Resource calendars specify nonworking days of driving resources involved in construction projects. As part of the resource availability constraints in critical path method (CPM) scheduling, resource calendars may postpone activity start time, extend activity duration, and hence prolong the total project duration. Ultimately, resource calendars bring about changes to the critical path identification. Research has yet to address how to incorporate the effects of multiple resource calendars on the total float determination. In this research, the popular P3 software is used as a tool for investigating the current practice of CPM scheduling under resource limit and calendar constraints. We assess P3’s advanced resource scheduling functions (including resource leveling and resource calendars) and identify P3’s potential errors in total float determination. Further, we propose a new method based on the forward pass analysis alone for accurately evaluating activity total float subject to resource calendar constraints. The application of the new method is illustrated with an activity-on-node case and a precedence-diagram-method case, with the results compared against those produced from P3. Our research has elucidated on some critical issues of resource-constrained scheduling in the application domain of construction project management. The findings will provide useful input for the vendors and users of the CPM software—which is not limited to P3—to improve the scheduling methodology as well as the accuracy of the resulting project schedules.     

Evaluation of the Resource-Constrained Critical Path Method Algorithms

This study evaluates the resource-constrained critical path method (RCPM), which the writers have recently proposed. RCPM establishes a critical path method (CPM)-like, resource-constrained schedule by resource-dependent activity relationships (or resource links) that the five-step RCPM technique identifies. With its CPM-like feature, RCPM provides the critical path and float data that are not available in traditional resource-constrained scheduling techniques. In addition, RCPM provides more flexibility to the schedule through identified alternative schedules, which allow certain activities to be executed beyond their late finish times without delaying the project completion. This paper evaluates the RCPM’s performance by comparing it with five related previous studies. A brief review of each study is also included in this paper. This comparison shows that RCPM performs well in identifying resource links and alternative schedules, compared to other methods. This study is of interest to academics because it highlights the advantages and disadvantages of different algorithms that have attempted to overcome present problems in traditional resource-constrained scheduling techniques.     

Resource-Activity Critical-Path Method for Construction Planning

In this paper, a practical method is developed in an attempt to address the fundamental matters and limitations of existing methods for critical-path method (CPM) based resource scheduling, which are identified by reviewing the prior research in resource-constrained CPM scheduling and repetitive scheduling. The proposed method is called the resource-activity critical-path method (RACPM), in which (1) the dimension of resource in addition to activity and time is highlighted in project scheduling to seamlessly synchronize activity planning and resource planning; (2) the start/finish times and the floats are defined as resource-activity attributes based on the resource-technology combined precedence relationships; and (3) the “resource critical” issue that has long baffled the construction industry is clarified. The RACPM is applied to an example problem taken from the literature for illustrating the algorithm and comparing it with the existing method. A sample application of the proposed RACPM for planning a footbridge construction project is also given to demonstrate that practitioners can readily interpret and utilize a RACPM schedule by relating the RACPM to the classic CPM. The RACPM provides schedulers with a convenient vehicle for seamlessly integrating the technology/process perspective with the resource use perspective in construction planning. The effect on the project duration and activity floats of varied resource availability can be studied through running RACPM on different scenarios of resources. This potentially leads to an integrated scheduling and cost estimating process that will produce realistic schedules, estimates, and control budgets for construction.     

Simplified Spreadsheet Solutions.?II: Overall Schedule Optimization

Overall schedule optimization, considering time, cost, and resource constraints is a difficult task due to the inherent complexity of projects, the difficulties associated with modeling all aspects combined, and the inability of traditional optimization tools to solve this large-size problem. In this paper, a practical approach is presented for the modeling and optimization of overall construction schedules. To simplify modeling, a spreadsheet-based model is developed to be easily usable by practitioners. The spreadsheet model integrates critical-path network scheduling with time-cost trade-off analysis, resource allocation, resource leveling, and cash flow management. The model uses the total project cost as the objective function to be minimized. To facilitate this large-size optimization, a nontraditional optimization technique, genetic algorithms, is used to locate the globally optimal solution, considering all aspects simultaneously. Details of the proposed model are described, and a hypothetical case study was used to experiment with it. Integration of the model with a simple information system is described to automate the development of optimal construction schedules.     

Stochastic Time-Cost-Resource Utilization Optimization Using Nondominated Sorting Genetic Algorithm and Discrete Fuzzy Sets

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.     

Optimization of Resource Allocation and Leveling Using Genetic Algorithms

Resource allocation and leveling are among the top challenges in project management. Due to the complexity of projects, resource allocation and leveling have been dealt with as two distinct subproblems solved mainly using heuristic procedures that cannot guarantee optimum solutions. In this paper, improvements are proposed to resource allocation and leveling heuristics, and the Genetic Algorithms (GAs) technique is used to search for near-optimum solution, considering both aspects simultaneously. In the improved heuristics, random priorities are introduced into selected tasks and their impact on the schedule is monitored. The GA procedure then searches for an optimum set of tasks' priorities that produces shorter project duration and better-leveled resource profiles. One major advantage of the procedure is its simple applicability within commercial project management software systems to improve their performance. With a widely used system as an example, a macro program is written to automate the GA procedure. A case study is presented and several experiments conducted to demonstrate the multiobjective benefit of the procedure and outline future extensions.     

Investigating the Effectiveness of Certain Priority Rules on Resource Scheduling of Housing Estate Projects

The heuristic method is one of the methods used for the scheduling of resource-constrained projects. This method is commonly used in programming the projects with high number of activities and resources such as construction investments. This paper investigates the effectiveness of three heuristic method priority rules applied in the resource scheduling of ten Turkish housing estate projects which were scheduled according to three preselected priority rules [maximum remaining path length (MRPL), latest finish time (LFT), and minimum slack time (MNSLCK)] in resource-constrained conditions. The performance of each priority rule was evaluated in relation to the duration of the project. The results revealed that MRPL priority reduced the project duration to minimum in six projects, whereas LFT priority yielded the best duration results in three projects and MNSLCK priority in only one project.     

Forecasting Construction Staffing for Transportation Agencies

When the volume of construction projects let to contract increases significantly, state departments of transportation must critically examine internal construction project management staffing capabilities and accurately forecast the manpower required to execute future projects. This paper describes the development of a model or process to forecast manpower requirements as a function of project type and cost for selected employee classifications. Using data from 130 recently completed highway construction projects and over 11,000 employee payroll entries, regression analysis plots were generated to predict overall manpower requirements for projects of a given type and cost. These overall requirements were then adjusted to predict manpower requirements for individual employee classifications using typical task allocation percentages obtained from questionnaire data. The output from the model serves as input into commercially available critical path method scheduling software to facilitate manpower planning and resource leveling.     

Finance-Based Scheduling of Construction Projects Using Integer Programming

Construction scheduling is the process of devising schemes for sequencing activities. A realistic schedule fulfills the real concerns of users, thus minimizing the chances of schedule failure. The minimization of total project duration has been the concept underlying critical-path method/program evaluation and review technique (CPM/PERT) schedules. Subsequently, techniques including resource management and time-cost trade-off analysis were developed to customize CPM/PERT schedules in order to fulfill users’ concerns regarding project resources, cost, and time. However, financing construction activities throughout the course of the project is another crucial concern that must be properly treated otherwise, nonrealistic schedules are to be anticipated. Unless contractors manage to procure adequate cash to keep construction work running according to schedule, the pace of work will definitely be relaxed. Therefore, always keeping scheduled activities in balance with available cash is a potential contribution to producing realistic schedules. This paper introduces an integer-programming finance-based scheduling method to produce financially feasible schedules that balance the financing requirements of activities at any period with the cash available during that same period. The proposed method offers twofold benefits of minimizing total project duration and fulfilling finance availability constraints.     

Multiobjective Linear Programming Model for Scheduling Linear Repetitive Projects

Linear repetitive construction projects require large amounts of resources which are used in a sequential manner and therefore effective resource management is very important both in terms of project cost and duration. Existing methodologies such as the critical path method and the repetitive scheduling method optimize the schedule with respect to a single factor, to achieve minimum duration or minimize resource work breaks, respectively. However real life scheduling decisions are more complicated and project managers must make decisions that address the various cost elements in a holistic way. To respond to this need, new methodologies that can be applied through the use of decision support systems should be developed. This paper introduces a multiobjective linear programming model for scheduling linear repetitive projects, which takes into consideration cost elements regarding the project’s duration, the idle time of resources, and the delivery time of the project’s units. The proposed model can be used to generate alternative schedules based on the relative magnitude and importance of the different cost elements. In this sense, it provides managers with the capability to consider alternative schedules besides those defined by minimum duration or maximizing work continuity of resources. The application of the model to a well known example in the literature demonstrates its use in providing explicatory analysis of the results.     

Optimal Repetitive Scheduling Model with Shareable Resource Constraint

An optimal repetitive scheduling model for precast production with the consideration of resource constraints and resource sharing is proposed in this paper. A repetitive schedule, like precast production, needs to take into consideration the impact of limited resources, including equipment (such as cranes, forms, and steaming curing facilities) and skilled labor, on production makespan. Moreover, to economically and effectively utilize these valuable resources (such as cranes and work crews), it is worth taking resource sharing into consideration in precast production. A genetic algorithm-based searching technique is adopted to establish an optimal resource-constrained repetitive precast production scheduling system with the consideration of resource sharing. The system can effectively provide the optimal or near-optimal combination of production durations, resource amounts, and minimum makespan under the constraint of limited resources and with the consideration of resource sharing.     

Permutation-Based Elitist Genetic Algorithm for Optimization of Large-Sized Resource-Constrained Project Scheduling

The resource-constrained project scheduling problem (RCPSP) has received the attention of many researchers because its general model can be used in a wide variety of construction planning and scheduling applications. The exact procedures and priority-rule-based heuristics fail to search for the optimum solution to the RCPSP of large-sized project networks in a reasonable amount of time for successful application in practice. This paper presents a permutation-based elitist genetic algorithm for solving the problem in order to fulfill the lack of an efficient optimal solution algorithm for project networks with 60 activities or more as well as to overcome the drawback of the exact solution approaches for large-sized project networks. The proposed algorithm employs the elitist strategy to preserve the best individual solution for the next generation so the improved solution can be obtained. A random number generator that provides and examines precedence feasible individuals is developed. A serial schedule generation scheme for the permutation-based decoding is applied to generate a feasible solution to the problem. Computational experiments using a set of standard test problems are presented to demonstrate the performance and accuracy of the proposed algorithm.     

Optimizing Resource Leveling in Construction Projects

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.     

Non-Unit-Based Planning and Scheduling of Repetitive Construction Projects

Repetitive scheduling methods are more effective than traditional critical path methods in the planning and scheduling of repetitive construction projects. Nevertheless, almost all the repetitive scheduling methods developed so far have been based on the premise that a repetitive project is comprised of many identical production units. In this research a non-unit-based algorithm for the planning and scheduling of repetitive projects is developed. Instead of repetitive production units, repetitive or similar activity groups are identified and employed for scheduling. The algorithm takes into consideration: (1) the logical relationship of activity groups in a repetitive project; (2) the usage of various resource crews in an activity group; (3) the maintaining of resource continuity; and (4) the time and cost for the routing of resource crews. A sample case study and a case study of a sewer system project are conducted to validate the algorithm, as well as to demonstrate its application. Results and findings are reported.     

Binary Resource Leveling Model: Activity Splitting Allowed

The resource leveling problem is common and has been studied numerous times. In these studies and the resulting solutions, there exists a common element, which is once an activity is started, it cannot be stopped and restarted again. That is, it cannot be split. In many instances in actual construction, there exist activities that can be stopped and restarted. However, not all activities have this characteristic. This paper presents a linear program binary variable model to level resources that permits selected activities to stop and restart. This splitting of activities results in improvement to the leveling solution that is traditionally achieved when splitting is not permitted. Examples are presented that illustrate the improvement in the solution obtained from the proposed model compared to models that do not allow splitting and compares the result to that obtained using commercially available software. The results are beneficial to construction professionals who may be unaware of the impacts of using activity splitting.