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.     

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.     

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.     

Modification of Minimum Moment Approach in Resource Leveling

This paper proposes a modification to the minimum moment approach that is used for resource leveling as presented by Harris and based upon the critical path method. The proposed and the traditional methods were developed with the assumption of no activity splitting and a fixed project duration with unlimited availability of resources. The difference between these methods is in the criteria of selecting the activity that has to be shifted from its original position to a better position. This is judged by the change in the statical moment of the resource histogram before and after such movement. In the proposed method, and for the activities that lie at the same sequence step, the activity that is to be shifted first is selected based upon both the value of its free float (S) and the value of its resource rate (R). In this way, the calculation of the improvement factor is needed only to determine the extent to which an activity is to be shifted. On the other hand, using the traditional method, the activity with the maximum improvement factor found for each possible day of shifting is selected first. The process is then repeated for all remaining activities using the updated histogram resulting from the shifted activity. The proposed method significantly reduces the calculations so that the number of iterations in each sequence step is equal to the number of its noncritical activities (n) as compared to (n!) in the traditional method. In addition, the calculation process using the proposed method is easier—especially for manual computations—than the traditional one. The results were insignificantly different, and in many cases they were identical. In this paper, the traditional and the proposed methods will be presented along with an example problem that was solved using the two methods. It should be noted that neither of the two methods provides the true minimum moment.     

Curvature and Residual Stress Analysis in Rotational Leveling of Bars

Leveling process plays an important role in delivering the desired material properties and product stand-ards.An analytical method for the rotational leveling process of bars was presented.First,each cross section of the bar in the leveling area was discretized with the roller gap-curvature relations established in both planes XY and XZ. Second,a numerical procedure with two steps was developed to simulate both pressing and leveling processes.This approach can be easily implemented to produce simulation results of the curvature and traj ectory distributions during the leveling process,as well as the bending and residual stresses.It is found that curvature and traj ectory distribu-tions follow a sine-shape due to the characteristic of rotational movement,which also results in a helical pattern of residual stress after leveling.Based on the results obtained,it is also observed that the rotational movement is benefi-cial for adding the number of bending cycle.This is the reason why there are only a few pairs of rollers on the bar leveler.     

Models for Accurate Computation of Earliest and Latest Start Times and Optimal Compression in Project Networks

Operations research techniques, especially linear and integer programming, have been suggested for use in project management. Most frequently these techniques are applied to finding critical path(s) in project networks and for compressing activities for an early project completion. The simplest of these models are usually found in introductory operations research/management science textbooks and they often suffer from a common deficiency: when they employ earliest start times (ES) as variables in the model, ES of activities that are not on the critical path may be computed incorrectly. This may lead to inefficient resource reallocation and leveling. Furthermore, activities outside the critical path may be compressed unnecessarily, as will be demonstrated in this paper. We present a zero–one mixed integer programming model to correctly compute earliest and latest start times of all activities and determine the optimal compression schedule. Several extensions to the model are offered to provide different perspectives of the project and enable a project manager to answer “what if” type questions. A small example is used to demonstrate the effectiveness of the models.     

Catastrophic Transitions of Construction Contracting Behavior

The ways to manage a construction project very much depend on the attitude of the people involved. Collectively this is identified as construction contracting behavior (CCB). The CCB of the construction industry is adversarial as pinpointed in many industry-wide reviews. A more cooperative project delivery approach has therefore been advocated. In fact, drive for efficiency provides the incentive for cooperation. Nevertheless, members of a project team, in representing their respective organizations, are often in conflict. The dichotomous pair of cooperation and aggression forces therefore coexist. It is not uncommon to note that CCB turns aggressive as the construction activities of a project intensify. This change is often sudden and thus matches well with the phenomenon of hysteresis described by the catastrophe theory (CT). It is hypothesized that the dynamics of CCB can be modeled by CT. The three-variable CT models include CCB (as dependent variable), cooperation forces (as normal factor) and aggression forces (as splitting factor). With data collected from a survey fitted by the Cuspfit program, it was found that trust intensity is an effective normal factor. Contract incompleteness and competitive inertia are splitting factors that trigger aggression.     

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.     

Construction Project Scheduling with Time, Cost, and Material Restrictions Using Fuzzy Mathematical Models and Critical Path Method

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.     

Fuzzy Optimization Model for Earthwork Allocations with Imprecise Parameters

Existing linear programming (LP) models of earthwork allocations in roadway construction assume that unit cost coefficients of earthwork activities and borrow pits/disposal sites capacities are certain and deterministic numbers. However in real-world problems there are naturally some uncertainties inherited in these values, which make it difficult to represent a single value as the candidate of entire possible values. This paper presents a fuzzy linear programming (FLP) model of earthwork allocations based on the fact of assuming unit cost coefficients and borrow pits/disposal sites capacities as fuzzy numbers while minimizing total earth-moving cost as an objective function. A method based on α cuts of a fuzzy set is used to take the uncertainty in borrow pits/disposal sites capacities into account. The uncertainty in fuzzy cost coefficients of the objective function and its effects on decision variables of the earthwork allocations model are also considered using the method presented by Chanas and Kuchta in 1994. Subsequently, a more general model is suggested which considers both uncertainties in borrow pits/disposal sites capacities and cost coefficients simultaneously. It is demonstrated that the presented FLP, compared to a deterministic LP, introduces a more robust solution; as the result of giving fuzziness to the uncertain parameters. Such a solution could be beneficial in real world decision making where uncertainties on resources and activities cost exist.     

Reducing Variability to Improve Performance as a Lean Construction Principle

Variability is common on construction projects and must be managed effectively. New management thinking, like that of lean production, has suggested that better labor and cost performance can be achieved by reducing output variability. Efforts to utilize lean thinking in construction, so far, have generated limited evidence to support this claim. This paper investigates the relationship between variability and project performance to test the notion that reducing output variability will result in improved labor performance. Using productivity data from concrete formwork activities on multiple projects, various measures of output variability are tested against construction performance. It is shown that variability in output is inevitable and that there is little correlation between output variability and project performance, but that variability in labor productivity is closely correlated to project performance. It is concluded that lean improvement initiatives should be redirected to adaptable workforce management capabilities to reduce variability in labor productivity instead of output in order to improve project performance.     

Numerical Simulation of Continuous Tension Leveling Process of Thin Strip Steel and Its Application

 Cold rolled thin strip steel of high flatness quality undergoes multistage deformation during tension leveling. Thus, the parameters of set up and manipulating are more difficult. With the aid of FE code MSCMARC, the tension leveling process of thin strip steel was numerically simulated. Concentrating on the influence of the roll intermeshes in 2# anti cambering on the distribution and magnitude of residual stresses in leveled strip steel, several experiments were done with the tension leveler based on the results from the simulation. It was found from the simulation that the magnitude of longitudinal residual stresses in the cross section of the leveled strip steel regularly presents obvious interdependence with the roll intermeshes in 2# anti cambering. In addition, there is a steady zone as the longitudinal residual stresses of the surface layers in leveled strip steel vary with the roll intermeshes of 2# anti cambering, which is of importance in the manipulation of tension levelers. It was also found that the distribution of strains and stresses across the width of strip steel is uneven during leveling or after removing the tension loaded upon the strip, from which it was found that 3D simulation could not be replaced by 2D analysis because 2D analysis in this case cannot represent the physical behavior of strip steel deformation during tension leveling.     

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.     

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.     

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.     

Modelling and design of cross-over trials

There are many diseases and conditions that can be studied using a cross-over clinical trial, where the subjects receive sequences of treatments. The treatments are then compared using the repeated measurements taken 'within' subjects. The actual plan or design of the trial is usually obtained by consulting a published table of designs or by applying relatively simple rules such as using all possible permutations of the treatments. However, there is a danger is this approach because the model assumed for the data when the tables or rules were constructed may not be appropriate for the new trial being planned. Also, there may be restrictions in the new trial on the number of treatment sequences that can be used or on the number of periods of treatment particular subjects can be given. Such restrictions may mean that a published design of the ideal size cannot be found unless compromises are made. A better approach is to make the design satisfy the objectives of the trial rather than vice versa. In this paper we describe an approach to constructing such tailor-made designs which we hope will lead to ill-fitting 'off the peg' designs being a thing of the past. We use a computer algorithm to search for optimal designs and illustrate it using a number of examples. The criterion of optimality used in this paper is A-optimality but our approach is not restricted to one particular criterion. The model used in the search for the optimal design is chosen to suit the nature of the trial at hand and as an example a variety of models for three treatments are considered. We also illustrate the construction of designs for the comparison of two active treatments and a placebo where it can be assumed that the carry-over effects of the active treatments are similar. Finally, we illustrate an augmentation of a design that could arise when the objectives of a trial change.     

Identification of Effective Management Practices and Technologies for Lessons Learned Programs in the Construction Industry

Organizations in the construction industry cannot afford to make repetitive mistakes on major projects. Conversely, there are great benefits to repeating positive experiences from past projects. This need for institutional memory is amplified by the reality that in the course of normal turnover and retirement, people with years of experience leave their organizations. An effective lessons learned program is a critical element in the management of institutional knowledge; it will facilitate the continuous improvement of processes and procedures and provide a direct advantage in an even more competitive industry. This paper describes a research study that aimed to identify effective management practices and technologies for lessons learned programs in the construction industry. Data from 70 organizations that participated in this study are presented. Research found that even though more lessons learned programs are being initiated, the potential for savings and improvement has not been fully met. The study has also identified seven crucial characteristics of a successful program and verified that most organizations are stronger in some categories and weaker in others. The key to an effective program is to address all seven areas and guidance is provided for those wishing to implement lessons learned programs.     

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.     

Describing a Beta Probability Distribution Function for Construction Simulation

A typical proposed application for a construction simulation model is to assess the productivity of a future operation. This type of application inherently presents the modeler with a situation where there is only a limited amount of data available for choosing an underlying probability distribution function (PDF). This paper presents a formulation for developing a Beta PDF for use in construction simulation modeling. The hypothesis of this paper is that there is a ratio that relates the 75th percentile to the mode of the activity duration. The research demonstrates that using such ratios, along with the minimum, mode, and maximum activity durations, results in estimates of a beta PDF that accurately describes the underlying duration distribution of construction activities. In the methodology proposed here the minimum and maximum activity durations are predicted using deterministic methods based on the physical characteristics of the job and equipment employed. The beta-shaped parameters a and b are estimated using a procedure for fitting beta distributions to activity times when sample data are not available.