Line‐of‐Balance Scheduling in Pavement Construction

Linear Scheduling Methods are best suited to projects that display repetitive characteristics, but their use in the construction industry is limited. Line‐of‐Balance (LOB) is a Linear Scheduling Method that also makes use of network technology. Its benefits and shortcomings are investigated in a high‐way surface treatment project where LOB has been used experimentally. It was determined that LOB is extremely sensitive to errors in man hour, crew size, and activity duration estimates. There are also problems of a visual nature with the presentation of the diagram. On the other hand, LOB allows a better grasp of the project than any other scheduling technique because it is possible to adjust activities' rates of production. It provides a smooth and efficient flow of resources and requires less time and effort to produce than network schedules. Research to make Linear Scheduling Methods more attractive is recommended.     

Feasibility Study of 4D CAD in Commercial Construction

This paper concludes that 4D models are a useful alternative to project scheduling tools like CPM networks and bar charts. They enable more people to understand a schedule quickly and identify potential problems. By developing a 4D model for a commercial construction project, we were able to detect the incompleteness of the original schedule, find inconsistencies in the level of detail among the schedule activities, and discover an impossible schedule sequence. We were also able to anticipate potential time-space conflicts and accessibility problems. The results of the case study show that 4D models are effective in evaluating the executability of a construction schedule. The case study also highlighted the need for improvements to 4D tools. 4D tools should include bar charts, component lists, and annotation tools in their graphical user interface. Automating schedule data preparation and 4D model generation in the design stages of a project can expedite 4D model development and use. Users need to be able to generate 4D models at multiple levels of detail and generate and evaluate alternative scenarios rapidly.     

Network Creation and Development for Repetitive-Unit Projects

Network scheduling is typically performed in three phases—network creation, analysis, and development. Although the critical path method (CPM) constitutes a well-established logic in network analysis, human intuition and experience are required for the creation and development of the network. Because of this, a variety of alternative CPM networks can be created in scheduling the same project. The use of the most desirable network can lead to a considerable reduction in the duration of the projects. This can be achieved by accurately identifying activities and linking them in an appropriate manner. Many researchers insisted that network scheduling lacks efficiency in scheduling repetitive-unit projects. Because of this, many scheduling methods have been developed to model such types of projects. However, most are not network based and require a large amount of input data, although most leading scheduling software remains network based and field engineers desire networklike forms of the schedule. In an effort to overcome this limitation, this paper presents a procedure for creating and developing networks for repetitive-unit projects. This network-based model incorporates a two-dimensional arrangement of activities, resource-space coordinates, for ease in creating a network and optimizes the activity linkage, thus resulting in the most desirable results. The model is applied to a typical repetitive-unit project to illustrate the use and capabilities of the model. The model can serve as an aid for inexperienced schedulers in creating a network as well as its optimization. An experienced scheduler can also check the desirability of his or her own created network via the use of this model.     

Work Continuity Constraints in Project Scheduling

Repetitive projects involve the repetition of activities along the stages of the project. Since the resources required to perform these activities move from one stage to the other, a main objective of scheduling these projects is to maintain the continuity of work of these resources so as to minimize the idle time of resources. This requirement, often referred to as work continuity constraints, involves a tradeoff between total project duration and the resource idle time. The contribution of this paper is threefold. First, we provide an extensive literature summary of the topic under study. Although most research papers deal with the scheduling of construction projects, we show that this can be extended to many other environments. Second, we propose an exact search procedure for scheduling repetitive projects with work continuity constraints. This algorithm iteratively shifts repeating activities further in time in order to decrease the resource idle time. We have embedded this recursive search procedure in a horizon-varying algorithm in order to detect the complete tradeoff profile between resource idle time and project duration. The procedure has been coded in Visual C++ and has been validated on a randomly generated problem set. Finally, we illustrate the concepts on three examples. First, the use of our new algorithm is illustrated on a small fictive problem example from literature. In a second example, we show that work continuity constraints involve a tradeoff between total project duration and the resource idle time. A last example describes the scheduling of a well-known real-life project that aims at the construction of a tunnel at the Westerschelde in The Netherlands.     

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.     

Improvement Algorithm for Limited Space Scheduling

Selecting construction methods, scheduling activities, and planning the use of site space are key to constructing a project efficiently. Site layout and activity scheduling have been tackled as independent problems. Their interdependence is often ignored at the planning stage and may be dealt with—if at all—when construction is underway. Problems that may have had easy solutions if dealt with earlier, may then be expensive to remedy. This paper addresses the combined problem termed “space scheduling” and presents an algorithmic time-space trade-off model for adjusting activity durations and start dates to decrease the need for space over congested time periods. The model characterizes resource space requirements over time and establishes a time-space relationship for each activity in the schedule, based on alternative resource levels. An example illustrates the presented algorithm that generates a feasible space schedule.     

Precedence-Preserving GAs Operators for Scheduling Problems with Activities’ Start Times Encoding

Precedence-preserving crossover and mutation operators for scheduling problems with activities’ start times encoding are proposed and employed in this paper. The objective is to tackle the incapability of the genetic algorithms (GAs) operators to preserve the precedence relationships among activities and generate feasible solutions in scheduling problems. The proposed operators employ an embedded precedence-preserving algorithm that determines the activities’ forward free float and backward free float and utilize them in randomly selected backward and forward paths, respectively. The proposed operators were evaluated using finance-based scheduling problems for large-scale projects of 120 repetitive activities. Moreover, the proposed operators were validated by comparing the results with the optimum results of a resource-constrained scheduling problem reported in the literature. The results exhibited the robustness of the proposed operators to reduce the computational costs. In addition, the results demonstrated the high potential and effectiveness of the proposed operators to capture the optimal solutions of the problems considered.     

Probability of Project Completion Using Stochastic Project Scheduling Simulation

This paper introduces a software, Stochastic Project Scheduling Simulation (SPSS), developed to measure the probability to complete a project in a certain time specified by the user. To deliver a project by a completion date committed to in a contract, a number of activities need to be carried out. The time that an entire project takes to complete and the activities that determine total project duration are always questionable because of the randomness and stochastic nature of the activities’ durations. Predicting a project completion probability is valuable, particularly at the time of bidding. The SPSS finds the longest path in a network and runs the network a number of times specified by the user and calculates the stochastic probability to complete the project in the specified time. The SPSS can be used by a contractor: (1) to predict the probability to deliver the project in a given time frame and (2) to assess its capabilities to meet the contractual requirement before bidding. The SPSS can also be used by a construction owner to quantify and analyze the risks involved in the schedule. The benefits of the tool to researchers are: (1) to solve program evaluation and review technique problems; (2) to complement Monte Carlo simulation by applying the concept of project network modeling and scheduling with probabilistic and stochastic activities via a web based Java Simulation which is operateable over the Internet, and (3) to open a way to compare a project network having different distribution functions.     

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.     

Time-Cost Optimization of Construction Projects with Generalized Activity Constraints

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.     

Object-Oriented Model for Repetitive Construction Scheduling

This paper presents the development of an object-oriented model for scheduling of repetitive construction projects such as high-rise buildings, housing projects, highways, pipeline networks, bridges, tunnels, railways, airport runways, and water and sewer mains. The paper provides an overview of the analysis, design, and implementation stages of the developed object-oriented model. These stages are designed to provide an effective model for scheduling repetitive construction projects and to satisfy practical scheduling requirements. The model incorporates newly developed procedures for resource-driven scheduling of repetitive activities, optimization of repetitive construction scheduling, and integration of repetitive and nonrepetitive scheduling techniques. The model is named LSCHEDULER and is implemented as a windows application that supports user-friendly interface including menus, dialogue boxes, and windows. LSCHEDULER can be applied to perform regular scheduling as well as optimized scheduling. In optimized scheduling, the model can assist in identifying an optimum crew utilization option for each repetitive activity in the project that provides a minimum duration or cost for the scheduled repetitive construction project.     

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.     

Measuring and Modeling Labor Productivity Using Historical Data

Labor productivity is a fundamental piece of information for estimating and scheduling a construction project. The current practice of labor productivity estimation relies primarily on either published productivity data or an individual’s experience. There is a lack of a systematic approach to measuring and estimating labor productivity. Although historical project data hold important predictive productivity information, the lack of a consistent productivity measurement system and the low quality of historical data may prevent a meaningful analysis of labor productivity. In response to these problems, this paper presents an approach to measuring productivity, collecting historical data, and developing productivity models using historical data. This methodology is applied to model steel drafting and fabrication productivities. First, a consistent labor productivity measurement system was defined for steel drafting and shop fabrication activities. Second, a data acquisition system was developed to collect labor productivity data from past and current projects. Finally, the collected productivity data were used to develop labor productivity models using such techniques as artificial neural network and discrete-event simulation. These productivity models were developed and validated using actual data collected from a steel fabrication company.     

Probabilistic Optimal-Cost Scheduling

During the planning and execution of construction projects, it often becomes necessary to shorten the duration of the project. A widely used technique for reducing the duration of a project is commonly referred to as least-cost scheduling. This procedure is based on deterministically arriving at the shortest project duration for the minimum cost possible. There is, however, one major problem with the typical application of this technique. It does not address the variability inherent in the duration and cost of the project activities. Thus, the resulting compressed schedule value cannot be applied with any stated level of statistical confidence. This paper presents a new procedure that addresses some of the major shortcomings of least-cost scheduling. It does so by accounting for the variability inherent in the duration and cost of the scheduled activities by simultaneously applying range estimating and probabilistic scheduling to the historical data. The resulting data set is then analyzed to provide a compressed schedule duration and cost estimate that have a higher overall confidence of being achieved.     

Linear versus Network Scheduling: A Critical Path Comparison

Linear scheduling methods provide an alternative way of scheduling repetitive projects, to the commonly used network methods. Critical path identification is a major attribute for both methods; therefore, it is very important for practitioners to understand the function of the two methods in this area. The present paper compares the critical path of the recently developed Kallantzis-Lambropoulos repetitive project model against the network scheduling critical path method (CPM), aiming at delving into and pointing out the differences and similarities between them. Initially, the rules for transforming the linear project into an equivalent CPM network are proposed. Then, the rules are applied on a sample linear project. Due to the additional constraint for maintaining resource continuity that the linear method takes into account, the critical paths vary. The constraint is subsequently removed from selected activities and comparison is repeated; the critical paths then coincide. In order to validate the findings and ensure impartiality of results, a random linear project generator is developed. A group of twenty-five random linear projects and their equivalent networks is produced. Their critical paths are analyzed, compared and classified. Conclusions support that the proposed comparison could be beneficial to users of linear scheduling methods, while the random project generator can serve other related research.     

Developing a Traffic Closure Integrated Linear Schedule for Highway Rehabilitation Projects

In recent years, the state departments of transportation have implemented a number of highway rehabilitation projects across the country. These projects differ fundamentally from new highway projects in that they require an uninterrupted flow of traffic throughout both the duration and geometric length of the project. Synchronization of traffic closure with the construction activities is crucial in such projects to avoid the traffic conflicts and prevent idle time for equipment and labor. Although most highway rehabilitation projects involve predominantly linear activities, the techniques of linear scheduling are not readily applicable to highway rehabilitation projects due to the conflict between the workzone and traffic flow. This paper documents the development of a traffic closure integrated linear schedule (TCILS) that addresses both traffic closure and work progress issues. The TCILS generates a single schedule for both the construction activities and the associated traffic closures. Visual and graphical features are also applied in the system, which makes it particularly applicable for highway rehabilitation projects. An actual concrete pavement rehabilitation project using the TCILS is presented as a sample of application. The findings from the sample project, although they are limited, show that the TCILS can be applied to an actual project. With recommended future development, the system is believed to be beneficial for both construction practitioners and academics.     

Developing a Complexity Measure for Project Schedules

Several factors contribute to the complexity of project schedules, including the number of activities, the level of detail, and the shape of the project network. This paper presents a measure that assesses the complexity of project schedules in terms of the connectivity of the activities. Unlike similar complexity measures, the proposed complexity measure does not consider redundant relationships in the project’s schedule. In addition, the measure is expressed as a percentage and therefore has the advantage of being intuitively understand by project managers. The measure considers the degree of interrelationships between the activities in the project’s schedule. The measure has been implemented in a computerized tool to help managers assess the complexity of their projects. The tool is developed as an add-in to popular commercial scheduling software like MS Project.     

Productivity Scheduling Method Compared to Linear and Repetitive Project Scheduling Methods

The analysis of project schedules is among the central tasks of construction managers. Parallel to the well-known critical path method, linear scheduling techniques have been researched. The two most fully developed existing methods, the linear scheduling model and the repetitive scheduling method, are reviewed. Based on a discussion of a published example, the new mathematical analysis method for linear and repetitive schedules is introduced. The productivity scheduling method is based on singularity functions that provide a flexible and powerful mathematical model for construction activities and their buffers that are characterized by their linear or repetitive nature. The steps of formulating initial equations, stacking and consolidating them, and deriving information about their criticality are described in detail. The mathematical approach of the new method allows an integrated treatment of activities regardless of the number of changes in productivity within them and does not depend on the graphical representation of the schedule.     

Permutation-Based Particle Swarm Optimization for Resource-Constrained Project Scheduling

In the light of particle swarm optimization (PSO) which utilizes both local and global experiences during search process, a permutation-based scheme for the resource-constrained project scheduling problem (RCPSP) is presented. In order to handle the permutation-feasibility and precedence-constraint problems when updating the particle-represented sequence or solution for the RCPSP, a hybrid particle-updating mechanism incorporated with a partially mapped crossover of a genetic algorithm and a definition of an activity-move-range is developed. The particle-represented sequence should be transformed to a schedule (including start times and resource assignments for all activities) through a serial method and accordingly evaluated against the objective of minimizing project duration. Experimental analyses are presented to investigate the performances of the permutation-based PSO. The study aims at providing an alternative for solving the RCPSP in the construction field by utilizing the advantages of PSO.     

Float Consumption Impact on Cost and Schedule in the Construction Industry

Quantifying and minimizing the risks associated with delays in the construction industry are the main challenges for all parties involved. Float loss impact in noncritical activities is one of the complicated delays to assess on a project’s duration and cost. This is due to the fact that the deterministic critical path method cannot cope with such delays unless they exceed the total float values. Further, stochastic analysis, which is used in this research to assess the impact of such delays, is perceived by many planners to be complicated and time consuming. This paper presents a method to control the risks associated with float loss in construction projects. The method uses a recently developed multiple simulation analysis technique that combines the results of cost range estimates and stochastic scheduling, using Monte Carlo simulation. The proposed method quantifies the float loss impact on project duration and cost. Least-squares nonlinear regression is used to convert the stochastic results into a polynomial function that quantifies the float loss impact by relating directly the float loss value to project duration and cost at a specified confidence level.