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.     

Minimum Moment Method for Resource Leveling Using Entropy Maximization

The minimum moment method for resource leveling is revisited and restated as an entropy-maximization problem. The minimum moment method assumes that the moment of the daily resource demands about the horizontal axis of a project’s resource histogram is a good measure of the resource utilization and that the optimal resource allocation exists when the total moment is at a minimum, thus when the resource histogram is of rectangular shape. The entropy-maximization method proposed in this paper makes use of the general theory of entropy and two of its principal properties (subadditivity and maximality) to revisit the minimum moment method for resource leveling. The entropy-maximization method presented allows for activity stretching and provides resource allocation solutions that show improvement over previous approaches. A case study is also presented that validates the results.     

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.     

Project Planning for Construction under Uncertainty with Limited Resources

Much of the project scheduling literature treats task durations as deterministic. In reality, however, task durations are subject to considerable uncertainty, and that uncertainty can be influenced by the resources assigned. The purpose of this paper is to provide the means for contractors to optimally allocate their skilled workers among individual tasks for a single project. Instead of the traditional use of schedules, we develop control policies in the form of planned resource allocation to tasks that capture the uncertainty associated with task durations and the impact of resource allocation on those durations. We develop a solution procedure for the model and illustrate the ideas in an example. The data for the example is collected from a real project.     

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.     

Cost Contingency Management

In the practice of project management, cost contingencies have the objective of covering probable cost increases (risks) above target estimates. Due to the variable performance nature for a wide range of activities, contingencies not only should be properly calculated but also assigned in the budget estimation process and wisely controlled during project execution. In this article, the Monte Carlo simulation approach is recommended as part of a proposed methodology for cost contingency management, which also includes a heuristics for contingency assignment (allocation) among project activities, as long as the activities have some degree of uncertainty regarding their future costs.     

Resource Management of Bridge Deck Rehabilitation: Jacques Cartier Bridge Case Study

The condition and performance of bridges vary widely across North America. The large amount of expenditures on bridges needs significant efforts to optimize budget and resource allocation and to select the best rehabilitation or replacement method, which reduces project cost and duration. Simulation has been widely used in the construction area to optimize productivity and resource allocation. Current research optimizes resource combination for bridge deck rehabilitation projects using discrete event simulation. The Jacques Cartier Bridge redecking project is selected as a case study. Data related to productivity and duration of different activities were collected from the project. Probability distributions are fitted, which show the robustness of normal distribution to fit most variables. A simulation model is developed for this project in order to experiment with and perform sensitivity analysis. Based on the simulation results, an optimum resource combination of deck rehabilitation is obtained, which is [five teams, two saws, three old section trucks, and five new panel trucks] TSON 5235 with the unit (panel) cost of $747/h (direct cost only). The model developed is tested against real productivity where it shows reasonable results. The present research is relevant to both researchers and practitioners. It provides bridge redecking researchers with a real case study, a simulation model, and an approach to analyze projects. It also provides practitioners with an approach to optimize the usage of their resources considering direct project cost.     

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.     

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.     

Applying Modified Minimum Moment Method to Multiple Resource Leveling

The purpose of this paper is to extend the modification of the minimum moment approach applied to networks with single resource leveling to networks with multiple resources. Specifically, the contribution of this paper is to outline a procedure to level multiple resources using the modified minimum moment approach. The heuristic procedure is applied to both methods of leveling multiple resources: leveling multiple resources in series and combined resource leveling. For comparison purposes, the proposed procedure is demonstrated through the same examples that were used to illustrate the use of the traditional minimum moment to both methods of multiple resource leveling. The application of the modified minimum moment approach showed the same results of the traditional minimum moment method. In some cases, the obtained results were even better than those of the traditional method.     

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.     

Resource Scheduling Using Neural Dynamics Model of Adeli and Park

This paper presents a mathematical model for resource scheduling considering project scheduling characteristics generally ignored in prior research, including precedence relationships, multiple crew-strategies, and time cost trade-off. Previous resource scheduling formulations have traditionally focused on project duration minimization. The proposed model considers the total project cost minimization. Furthermore, resource leveling and resource-constrained scheduling have traditionally been solved independently. In the new formulation, resource leveling and resource-constrained scheduling are performed simultaneously. The proposed model is solved using the patented neural dynamics model of Adeli and Park. A case study is presented to demonstrate the performance of the method under various resource availability profiles.     

Project Management in Construction: Software Use and Research Directions

This paper focuses on future research and the use of project management software in the construction industry. Data are drawn from an empirical study of project management professionals that yielded 240 replies (35% response rate), 42 of which were from the construction industry. Data were collected on: demographics and work environment, project management software usage patterns, analytical technique usage, data management, and suggestions for future research. The results indicate that construction professionals have different characteristics, needs and preferences, as compared to the overall sample. The study shows that construction professionals are more experienced and educated than the respondents in the overall study, they tend to work on fewer projects with larger numbers of activities, and they are more likely to use Primavera (Primavera, Inc., Bala Cynwyd, Pa.) than Microsoft Project (Microsoft Corp., Redmond, Wash.). Construction respondents are heavy users of critical path analysis for planning and control, resource scheduling for planning, and earned value analysis for control. The number of activities in a typical project and the use of software for all active projects were the key determinants of the usage of specific analytical techniques. These factors are also significant determinants of the types of information entered and updated, although the effect is weaker. Although construction professionals are generally satisfied with the quality of schedules produced by the software, they still expressed a clear interest in future research on resource scheduling∕leveling in general and a net present value option in particular. To maximize the impact on practice, development of new planning and control methods should include their integration into project management software.     

Competency-Based Model for Predicting Construction Project Managers’ Performance

Using behavioral competencies to influence human resource management decisions is gaining popularity in business organizations. This study identifies the core competencies associated with the construction management role and, further, develops a predictive model to inform human resource selection and development decisions within large construction organizations. A range of construction managers took part in behavioral event interviews where staff were asked to recount critical management incidents, decisions, and actions from which their key competencies could be identified. By delineating the sample according to their levels of performance measured against a range of role-specific performance criteria, the competencies defining superior management performance could be determined. These were then used to construct a logistic regression model from which a project manager’s performance can be predicated. The validated results reveal that “self-control” and “team leadership” are the most predictive behaviors of effective project management performance within the framework of the model. The paper explores the potential role and application of the framework to underpin human resource management decision making with regards to recruitment, performance management, succession planning, and resource allocation.     

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.     

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.     

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.     

Optimal Allocation of Construction Planning Resources

Efficient allocation of resources for construction planning activities requires construction planning resource requirements to be determined on a cost-effective and value-adding basis. However, although some research studies have indicated that increasing resource allocations to construction planning activities will lead to improved project performance, other research studies have indicated that investing in construction planning beyond an optimum point will lead to a deterioration in project performance. This study explored the concept of optimal planning of construction projects by examining 52 building projects undertaken in Australia. The relationships between planning input (ratio of planning costs to total project costs) and the probabilities of achieving poor performance and good performance were modeled using logistic, linear, and curvilinear regression analyses. A probable optimum planning input based on the sample studied was derived. It is suggested that any additional planning efforts beyond this optimum point would be essentially wasted because the additional planning costs would not achieve any savings in project cost but merely add to the overhead costs and therefore increase the overall project cost. A model for optimal planning is presented and discussed.     

Business Strategy and Capital Allocation Optimization Model for Practitioners

Construction engineering companies usually provide a variety of services. To be competitive, companies have to organize their operations strategically based on market demands within the limitations of their own resources. Optimization of these resources is of vital importance for these companies. Historically, decisions on resource allocations to various construction market segments were made exclusively based on intuitive judgment. In previous literature, the proposed models on capital allocation place emphasis on formulating cash-flow forecasting and planning strategy on project level. However, existing technologies and established mathematical methods provide a sound base for quantitative analysis on company-level business strategy and capital allocation. This note proposes a linear programming model that can be conveniently applied by construction practitioners. The model incorporates the project cost structure and considers the business constraints such as bonding capacity and borrowing capital capacity. Its objective is to achieve maximum profit through improving project management efficiency and setting appropriate sales goals in various market segments based on market demands. It is a decision-making tool that provides “what-if” analysis. The solutions and alternatives of the model give the decision makers an excellent insight for making the best choice.     

Conjoining MMAS to GA to Solve Construction Site Layout Planning Problem

An optimal construction site layout planning (CSLP) is vital for project management. It can reduce the transportation flows and thus the costs of a project. Genetic algorithm (GA) is the most used algorithm to solve site layout problems, but randomly generated initial population in GA will decrease solution quality. Max-min ant system (MMAS) can offer a better initial population than the randomly generated initial population at the beginning of GA. In this study, a modified GA (MMAS-GA) formed by conjoining MMAS to the step of initialization of GA is proposed to solve CSLP problems. In order to reveal the computational capability of MMAS-GA to solve CSLP problems, the results of MMAS-GA and traditional GA are compared by solving an equal-area CSLP problem. The results showed that the proposed MMAS-GA algorithm provided a better optimal solution under the objective function of minimizing the transportation flows between the site facilities. The proposed MMAS-GA algorithm could assist project managers and planners to design optimal construction site layout, and thus to reduce construction costs.