Constraint Programming Approach to Precast Production Scheduling

Current scheduling practices in precast plants are fairly basic and depend greatly on experience. This may lead to inefficient resource utilization, over-inventory, and/or missing delivery dates. Computer assisted scheduling may therefore be useful in producing better production schedules. This paper shows how constraint programming (CP) can be applied in production scheduling for precast plants. The paper describes a constrained precast scheduling model that incorporates the key constraints and objectives considered by production schedulers. A capacity-based backward-scheduling earliest due date rule and a CP approach are developed to solve the model. The CP approach is computationally efficient, even though it incorporates many problem-derived constraints. The efficiency of the CP approach lies in the fact that the representation (model) is separated from the algorithm (solver). Strategies to improve the performance of the CP approach are identified, and the CP approach is compared against commonly used heuristic rules on an example problem.     

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.     

Dynamic Programming Approach to Optimization of Site Remediation

Environmental restoration is a matter of national concern. Decades of abuse by industry, agriculture, and the military have caused devastating contamination of the earth, air, and water. The Department of Energy alone will spend hundreds of billions of dollars on containment and restoration. It is imperative that restoration costs are minimized. Every dollar spent on restoration is a dollar that will not go toward research, a dollar that will not go to upgrade our nation’s infrastructure. The work presented here uses cost as a decision variable in restoration projects. Contaminated sites frequently vary from one point to another in type and level of contamination. In addition, a single piece of property may contain several distinct contaminated areas, each of which has characteristics unlike any of the other areas. Thus one should look at optimizing the selection of remediation technologies to address the variation. A methodology has been developed that will optimize the selection of remediation technologies based on cost. This methodology uses geostatistics and dynamic programming to break a site into discrete cells and then select the optimal sequence of remediation technologies.     

Dynamic Site Layout Planning Using Approximate Dynamic Programming

Dynamic site layout planning requires identifying and updating the positions of all temporary construction facilities such as offices, storage areas, and workshops over the entire project duration. Existing models do not guarantee global optimal solutions because they focus on optimizing the planning and layout of successive construction stages in a chronological order, without considering the future implications of layout decisions made in early stages. This paper presents the development of an approximate dynamic programming model that is capable of searching for and identifying global optimal dynamic site layout plans. The model applies the concepts of approximate dynamic programming to estimate the future effects of layout decisions in early stages on future decisions in later stages. The model is developed in three main phases: (1) formulating the decision variables, geometric constraints, and objective function of the dynamic site layout planning problem; (2) modeling the problem using approximate dynamic programming; and (3) implementing and evaluating the performance of the model. An evaluation example is analyzed to illustrate the use of the model and demonstrate its capabilities in generating global optimal solution for dynamic site layout planning of construction projects.     

Resource Sharing in Linear Construction

In the method developed, the conventional method of allocating resources are not used. Instead, resource‐hour units are employed. This enables to allocate resources precisely and the analysis becomes realistic unlike any other method. The method uses linear programming to determine the maximum rate of construction and the resource requirements in various activities. Sharing of resources which are available in limited quantities and balanced trades are all catered for in this development. The economics of using additional resources, working overtime and the optimal usage of all resources are the more important aspects of this paper. The step by step process of introducing additional resources and overtime ease the decision making process considerably. The method also helps to determine whether it is more economical to invest in additional resources or introduce work on overtime. If there is no proper method to compute resource requirements, a considerable waste in resource utilization would result. If resources are under utilized, even by a very small quantity, a considerably large delay of time would incur.     

What Does the Construction Industry Value in Its Workers?

This paper briefly characterizes today’s United States (U.S.) construction workforce, and attempts to provide evidence for what the construction industry most values in its workers. It presents the social and demographic characteristics of a sample of 862 construction workers, from 19 project sites that were interviewed in 2002, as part of a research effort at the University of Texas at Austin, and compares them with broader-based Bureau of Labor Statistics data to establish the degree to which they represent the U.S. construction workforce. Via statistical analysis, the paper explores the relationships between workers’ attributes and how the industry compensates them as reflected in both hourly wages and average annual incomes. The statistical results reinforce what is known about the importance of years of experience; however, it also provides evidence of the importance of number of crafts each worker possessed, and computer knowledge. Less, but significant, evidence was obtained for the importance of the number of years spent with his/her current firm, craft training hours, age, or self-assessed performance.     

Improved SWOT Approach for Conducting Strategic Planning in the Construction Industry

Strength, weakness, opportunity, and threat (SWOT) analysis has been in use since the 1960s as a tool to assist strategic planning in various types of enterprises including those in the construction industry. While still widely used, the approach has called for improvements to make it more helpful in strategic management. The project described in this paper aimed to study whether the process to convert a SWOT analysis into a strategic plan could be assisted with some simple rationally quantitative model, as an augmented SWOT analysis. By using the mathematical approaches including the quantifying techniques, the “maximum subarray” method, and fuzzy mathematics, one or more heuristic rules (HRs) are derived from a SWOT analysis. These HRs bring into focus the most influential factors concerning a strategic planning situation, and thus inform strategic analysts where particular consideration should be given. A case study conducted in collaboration with a Chinese international construction company showed that the new SWOT approach is more helpful to strategic planners. The paper provides an augmented SWOT analysis approach for strategists to conduct strategic planning in the construction industry. It also contributes fresh insights into strategic planning by introducing rationally analytic processes to improve the SWOT analysis.     

Optimization of Water Distribution Networks Using Integer Linear Programming

In this study optimum design of municipal water distribution networks for a single loading condition is determined by the branch and bound integer linear programming technique. The hydraulic and optimization analyses are linked through an iterative procedure. This procedure enables us to design a water distribution system that satisfies all required constraints with a minimum total cost. The constraints include pipe sizes, which are limited to the commercially available sizes, reservoir levels, pipe flow velocities, and nodal pressures. Accuracy of the developed model has been assessed using a network with limited solution alternatives, the optimal solution of which can be determined without employing optimization techniques. The proposed model has also been applied to a network solved by others. Comparison of the results indicates that the accuracy and convergence of the proposed method is quite satisfactory.     

Optimized Scheduling of Linear Projects

This paper presents a model, designed to optimize scheduling of linear projects. The model employs a two-state-variable, N-stage, dynamic programming formulation, coupled with a set of heuristic rules. The model is resource-driven, and incorporates both repetitive and nonrepetitive activities in the optimization process to generate practical and near-optimal schedules. The model optimizes either project construction duration, total cost, or their combined impact for what is known as cost-plus-time bidding, also referred to as A+B bidding. The model has a number of interesting and practical features. It supports multiple crews to work simultaneously on any activity, while accounting for: (1) multiple successors and predecessors with specified lead and lag times; (2) the impact of transverse obstructions, such as rivers and creeks, on crew assignments and associated time and cost; (3) the effect of inclement weather and learning curve on crew productivity; and (4) variations in quantities of work in repetitive activities from one unit to another. The model is implemented in a prototype software that operates in Windows? environment. It is developed utilizing object-oriented programming, and provides for automated data entry. Several graphical and tabular output reports can be generated. An example project, drawn from the literature, is analyzed to demonstrate the features of the developed model.     

Assignment and Allocation Optimization of Partially Multiskilled Workforce

Multiskilling is a workforce strategy that has been shown to reduce indirect labor costs, improve productivity, and reduce turnover. A multiskilled workforce is one in which the workers possess a range of skills that allow them to participate in more than one work process. In practice, they may work across craft boundaries. The success of multiskilling greatly relies on the foreman’s ability to assign workers to appropriate tasks and to compose crews effectively. The foreman assigns tasks to workers according to their knowledge, capabilities, and experience on former projects. This research investigated the mechanics of allocating a multiskilled workforce and developed a linear programming model to help optimize the multiskilled workforce assignment and allocation process in a construction project, or between the projects of one company. It is concluded that the model will be most useful in conditions where full employment does not exist; however, it is also useful for short term allocation decisions. By running the model for various simulated scenarios, additional observations were made. For example, it is concluded that, for a capital project, the benefits of multiskilling are marginal beyond approximately a 20% concentration of multiskilled workers in a project workforce. Benefits to workers themselves become marginal after acquiring competency in two or three crafts. These observations have been confirmed by field experience. Extension of this model to allocation of multifunctional resources, such as construction equipment, should also be possible.     

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.     

Comparison of Using Mixed-Integer Programming and Genetic Algorithms for Construction Site Facility Layout Planning

The use of modular construction has gained wide acceptance in the industry. For a specific construction facility layout problem such as site precast standardized modular units, it requires the establishment of an on-site precast yard. Arranging the precast facilities within a construction site presents real challenge to site management. This complex task is further augmented with the involvement of several resources and different transport costs. A genetic algorithm (GA) model was developed for the search of a near-optimal layout solution. Another approach using mixed-integer programming (MIP) has been developed to generate optimal facility layout. These two approaches are applied to solve with an example in this paper to demonstrate that the solution quality of MIP outperforms that of GA. Further, another scenario with additional location constraints can also be solved readily by MIP, which, however, if modeled by GA, the solution process would be complicated. The study has highlighted that MIP can perform better than GA in site facility layout problems in which the site facilities and locations can be represented by a set of integer variables.     

Evapotranspiration Modeling Using Linear Genetic Programming Technique

The study investigates the accuracy of linear genetic programming (LGP), which is an extension to genetic programming (GP) technique, in daily reference evapotranspiration (ET0) modeling. The daily climatic data, solar radiation, air temperature, relative humidity, and wind speed from three stations, Windsor, Oakville, and Santa Rosa, in central California, are used as inputs to the LGP to estimate ET0 obtained using the FAO-56 Penman-Monteith equation. The accuracy of the LGP is compared with those of the support vector regression (SVR), artificial neural network (ANN), and those of the following empirical models: the California irrigation management system Penman, Hargreaves, Ritchie, and Turc methods. The root-mean-square errors, mean-absolute errors, and determination coefficient (R2) statistics are used for evaluating the accuracy of the models. Based on the comparison results, the LGP is found to be superior alternative to the SVR and ANN techniques.     

Strategic Perspective of Turkish Construction Companies

Whether extensively planned or not, all companies have a strategic perspective which is analogous to the personality of an individual. However, a clear strategic perspective is a must to achieve strategic integration that further leads to competitive advantage. In order to define a strategic perspective, four determinants should be put together: objectives, strategies; competencies; and competitive rules. There are two way linkages among objectives, strategies, and competencies that are affected from competitive forces acting on the market and these interrelations change with respect to company characteristics like size, age, major client, etc. The objective of this paper is to propose a conceptual framework for the analysis of a strategic perspective and present results of a questionnaire carried out to explore the strategic perspectives of Turkish contractors. Objectives, strategies, and competencies of Turkish contractors are analyzed together with competitive rules prevailing in the Turkish construction industry. The determinants of strategic perspective can further be used as a guide for setting a strategic agenda for construction companies with different characteristics and operating in different submarkets.     

Strategic Group Analysis in the Construction Industry

The aim of strategic group analysis is to determine whether clusters of firms that have a similar strategic position exist within an industry or not. Findings of strategic group analysis may further be used to investigate the performance implications of strategic group membership. The objective of this paper is to identify the possible strategic groups that could exist within the Turkish construction industry by using a theoretical framework applicable for the construction industry and alternative statistical cluster analysis techniques. The achieved results pinpoint the existence of three clusters and significant differences between the performances of firms in each cluster. All of the firms in the strategic group that have the highest average performance utilize a quality differentiation strategy and have the necessary resources and capabilities that give them the opportunity to differentiate their services from others. Also, they use a systematic approach and have a collaborative environment for strategic decision making. Findings of strategic group analysis can be used by professionals to understand the current strategic position of a firm within the competitive environment and formulate strategies to shift to a better performing cluster.     

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.     

Effect of Foremen on Construction Apprentice

Construction apprentices in a Midwestern city were surveyed to find out their perceptions on their foremen. Based on the analysis of our data, seven foreman factor scales were identified. These scales are: Performance Improvement, Work Facilitation, Achievement Orientation, Support, Work Participation, Bias, and Recognition. These dimensions have varying relationships with motivation and performance of the construction apprentice. These dimensions provide important issues that need to be addressed in developing foremen training programs and in particular how they affect apprentice. Further research may encompass various segments of the construction industry to analyze motivation of craft workers.     

Framework for Building Intelligent Simulation Models of Construction Operations

Modeling and analyzing construction operations using simulation techniques allows researchers to capture the uncertainty and randomness usually associated with these operations and can thus be an effective tool for analysis and improvement. However, the effort and knowledge required to build simulation models and experiment with them tend to limit the use of simulation in construction. A common recommendation for removing this obstacle found in the literature leans towards developing simulation tools that reduce model development and experimentation time on the construction engineer’s side by packaging most of the knowledge required into the tool itself. Such “intelligent” simulation modeling tools may significantly impact the way construction engineers use simulation techniques in day-to-day decision?making. This paper presents a framework that extends and formalizes this recommendation by providing the foundation for building intelligence into simulation objects. The proposed framework provides the structure necessary for building intelligence and autonomy into simulation objects and permits a further reduction in the knowledge required to experiment with simulation models. This approach also automates model modification, not only through changes in numeric parameters, but through topological model changes as well, which may assist the model user in making many decisions throughout the different phases of simulation experimentation.     

Localizing and Designing Computer-Based Safety Training Solutions for Hispanic Construction Workers

Despite the construction industry’s generally positive reaction to the use of information and communication technologies (ICTs) in many of its functions, some of the profession’s key players reside in a digital divide and do not benefit from advances in technology. Hispanic construction workers, an at-risk population with high rates of workplace accidents, are affected by that divide because they rarely take advantage of available ICTs at work. One application of ICTs that can help Hispanic/Latino workers is computer-based training (CBT) for occupational safety. However, the design of CBT materials for Spanish-speaking workers needs to go beyond basic localization of existing products in English. A radical localization approach that uses participatory design sessions with construction workers and their supervisors is proposed in this paper. This case study reports that Latino workers reacted positively and retained knowledge from CBT materials, including videos with elements of humor and without graphic representations of accidents, modeled after the genre of a television situation comedy.