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Improving project performance has always been emphasized in construction projects. When project objectives contradict, the project manager must apply mitigating or conflict resolution strategies to remove or minimize the level of contradiction in order to enhance project performance. A typical example is conflict between cost and schedule, where cost tends to increase when the schedule is crashed (sped up). Although some of the current project control systems attempt to diagnose the root cause of performance deviations and suggest corrective action, none is designed to resolve conflicts in a systematic and innovative manner to improve the project performance. The purpose of this paper is to present a systematic and structured approach to guide project managers to propose corrective action plans that lead to minimal conflict and ultimately to better project performance. Resolving contradictions can generate effective corrective action plans that would enhance project performance. Conflicts among competing project performance indices such as cost, schedule, safety, and quality are obvious in any project. This innovative approach uses the theory of inventive problem solving (TRIZ) to develop a framework for a conflict resolution strategy called techniques for resolving project performance contradictions (TRAC), or the techniques for resolving performance contradictions. TRAC provides an algorithmic approach for solving performance contradictions in order to optimize the overall project performance. TRAC is a user-oriented methodology and presents a new model for performance problem solving to assist project managers in their corrective action exercise. TRAC methodology is demonstrated in two case studies that indicate the workability of this model and its usefulness in resolving performance contradictions in contraction projects. [ABSTRACT FROM AUTHOR]

Resource scheduling, or the allocation of resources over time, is a challenging problem in large-scale or multiple-project environments. Traditional network scheduling techniques are ineffective in modeling the dynamic nature and resource interactions of large or multiunit projects. This paper presents a simulation-based auction protocol (SBAP) to solve resource scheduling problems in large-scale construction projects. SBAP is a hybrid framework that integrates multi agent resource allocation (MARA) in a simulation environment. SBAP deploys a centralized resource allocation approach, referred to as an auction protocol, whereby agents bid on different combinations of resources at the start of a simulation cycle. Agents attempt to improve their individual welfare by acquiring a combination of resources; an auctioneer looks at the entire system and allocates resources to the agents using a combinatorial algorithm to maximize an overall objective function (e.g., maximizing the system's revenue or minimizing total costs). The auction is repeated on a regular basis. Simulation is also employed in large-scale projects to track the availability of resources, capture and release the resources, and satisfy constraints of the problem. This paper demonstrates the architecture of the SBAP framework and discusses implementation of SBAP in a real case study of crane allocation in an industrial project. [ABSTRACT FROM AUTHOR]

In nonlinear construction optimization problems, the capability of current optimization algorithms to find an optimal solution is usually limited by their inability to evaluate the effects of changing the value of each decision variable on reaching the optimal solution. This paper presents fundamental research aimed at developing a novel evolutionary optimization algorithm, named Electimize, that mimics the behavior of electrons flowing, through electric circuit branches with the least electric resistance. In the proposed algorithm, solutions are represented by electric wires and are evaluated on two levels: a global level, using the objective function, and a local level, evaluating the potential of each generated value for every decision variable. The paper presents (1) the research philosophy and scope, (2) the research methodology, and (3) the development of the algorithm. The proposed algorithm has been validated and applied successfully to an NP-hard cash flow optimization problem. The algorithm was able to find a better optimal solution and identified ten alternative optimal solutions for the same problem. This should prove useful in enhancing the optimization of complex large-scale problems. [ABSTRACT FROM AUTHOR]

Formal construction inspection planning is needed to help reduce the incidence of overlooked or inefficient inspections, and to help realize the potential of emerging sensing technologies. Prior publications by the authors have presented requirements for such an approach and a component-based inspection planning implementation as an approach in addressing some of the requirements towards formal construction inspection planning. Implementation of such a component-based approach for typical construction examples, however, leads to the generation of large, rugged search spaces. This paper first describes the characteristics of search spaces that are generated using a component-based approach to illustrate which search mechanisms are appropriate to explore these search spaces. The paper then describes a set of search algorithms and heuristics that were investigated and evaluated for searching construction inspection plans. Specific discussions on how these algorithms performed in searching within inspection planning spaces are provided based on experiments conducted using a testbed characterizing a construction site and a building performance monitoring decision-making example. [ABSTRACT FROM AUTHOR]

Time–cost trade-off analysis is addressed as an important aspect of any construction project planning and control. Nonexistence of a unique solution makes the time–cost trade-off problems very difficult to tackle. As a combinatorial optimization problem one may apply heuristics or mathematical programming techniques to solve time–cost trade-off problems. In this paper, a new multicolony ant algorithm is developed and used to solve the time–cost multiobjective optimization problem. Pareto archiving together with innovative solution exchange strategy are introduced which are highly efficient in developing the Pareto front and set of nondominated solutions in a time–cost optimization problem. An 18-activity time–cost problem is used to evaluate the performance of the proposed algorithm. Results show that the proposed algorithm outperforms the well-known weighted method to develop the nondominated solutions in a combinatorial optimization problem. The paper is more relevant to researchers who are interested in developing new quantitative methods and/or algorithms for managing construction projects. [ABSTRACT FROM AUTHOR]

This paper attempts to use evolutionary algorithms to solve the problem of minimizing construction project duration in deterministic conditions, with in-time changeable and limited accessibility of renewable resources (workforce, machines, and equipment). Particular construction processes (with various levels of complexity) must be conducted in the established technological order and can be executed with different technological and organizational variants (different contractors, technologies, and ways of using resources). Such a description of realization conditions allows the method to also be applied to solving more complex problems that occur in construction practice (e.g., scheduling resources for a whole company, not only for a single project). The method’s versatility distinguishes it from other approaches presented in numerous publications. To assess the solutions generated by the evolutionary algorithm, the writers worked a heuristic algorithm (for the allocation of resources and the calculation of the shortest project duration). The results obtained by means of this methodology seem to be similar to outcomes of other comparable methodologies. The proposed methodology (the model and the computer system) may be of great significance to the construction industry. The paper contains some examples of the practical use of the evolutionary algorithm for project planning with time constraints. [ABSTRACT FROM AUTHOR]

Technical knowledge is a valuable asset for construction companies. The diversity and accumulation of such knowledge on an organizational level contribute to company profitability and growth. This paper discusses a new approach for extracting, consolidating, and then retrieving technical construction knowledge that builds on the contradiction resolution concepts of the theory of inventive problem solving. The approach was used to extract knowledge from a number of lessons learned describing technical construction problems encountered by a major construction company. The approach depends on finding the similarities between technical solutions of problems that belong to different technological domains. These similarities represent the essence of these solutions and are represented using domain-independent terms so that they can be applied to new problems. The outcomes of the knowledge extraction and accumulation process are discussed in the paper to address the feasibility of the proposed approach and its potential benefits and limitations. [ABSTRACT FROM AUTHOR]

This paper presents an innovative model for scheduling, resource planning, and cost optimization of large construction and/or maintenance programs that involve multiple distributed sites. The proposed distributed scheduling model (DSM) employs an information system to store data related to various work sites, activities’ optional construction methods, and available resources. The DSM also incorporates a scheduling algorithm that is resource focused, and maintains crew work continuity under any sequence of distributed sites. To minimize cost and meet project time and resource constraints, the DSM uses genetic algorithms to determine the optimum set of construction methods and the optimum routing order among sites. Details of the DSM formulation are presented in this paper and the benefits to municipalities and contractors are outlined. [ABSTRACT FROM AUTHOR]

Earthmoving plants are essential but costly resources in the construction of heavy civil engineering projects. In addition to proper allocation, ongoing control of this equipment is necessary to ensure and increase the productivity of earthmoving operations. Captured videos from construction sites are potential tools to control earthmoving operations; however, the current practice of manual data extraction from surveillance videos is tedious, costly, and error prone. Cutting-edge computer vision techniques have the potential to automate equipment monitoring tasks. This paper presents research in the evaluation of combinations of existing object recognition and background subtraction algorithms to recognize off-highway dump trucks in noisy video streams containing other active machines. Two detection algorithms, namely, Haar-histogram of oriented gradients (HOG) and Blob-HOG, are presented and evaluated for their ability to recognize dump trucks in videos as measured by both effectiveness and timeliness. The results of this study can help practitioners select a suitable approach to recognize such equipment in videos for real-time applications such as productivity measurement, performance control, and proactive work-zone safety. [ABSTRACT FROM AUTHOR]

Project management tasks, such as productivity monitoring and cost estimation, require data to be fused from multiple data sources. Data fusion approaches incorporated in the existing research studies within the construction management domain support a specific task or a decision (e.g., labor productivity monitoring or defect detection). Hence, most of the previously developed approaches do not necessarily support tasks other than the ones that they were intended for. This paper describes an automated planning approach as a general way to fuse data from multiple sources to support construction productivity monitoring tasks. A prototype system, which incorporates two planning algorithms, was developed to validate the generality of the approach on the basis of representative queries of construction engineers and managers identified in previous research studies. [ABSTRACT FROM AUTHOR]

The paper presents a methodology to schedule resource-constrained construction projects by use of algorithms based on ant colony optimization (ACO); an artificial agent inspired by the collective behavior of natural ant colonies as they optimize their path from an origin (ant nest) to a destination (food source) by use of previously acquired knowledge. Further, to an application of the ACO artificial agent to a resource-unconstrained network topology, the method is applied to a resource-constrained network and utilized in examining the effects of resource availability constraints to critical path calculations and project completion time. [ABSTRACT FROM AUTHOR]

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

Materials that are in the form of one-dimensional stocks such as steel rebars, structural steel sections, and dimensional lumber generate a major fraction of the generated construction waste. Cutting one-dimensional stocks to suit the construction project requirements result in trim or cutting losses, which is the major cause of the one-dimensional construction waste. The optimization problem of minimizing the trim losses is known as the cutting stock problem (CSP). In this paper, three approaches for solving the one-dimensional cutting stock problem are presented. A genetic algorithm (GA) model, a linear programming (LP) model, and an integer programming (IP) model were developed to solve the one-dimensional CSP. Three real life case studies from a steel workshop have been studied. The generated cutting schedules using the GA, LP, and IP approaches are presented and compared to the actual workshop’s cutting schedules. The comparison shows a high potential of savings that could be achieved using such techniques. Additionally, a user friendly Visual Basic computer program that utilizes genetic algorithms for solving the one-dimensional CSP is presented. [ABSTRACT FROM AUTHOR]

In this paper, based on the Darwinian and Lamarckian evolution theories, three hybrid genetic programming (GP) algorithms integrated with different local search operators (LSOs) are implemented to improve the search efficiency of the standard GP. These three LSOs are the genetic algorithm, the linear bisection search, and the Hooke and Jeeves method. A simple encoding method is presented to encode the GP individuals into the expressions that can be recognized by the different LSOs. The implemented hybrid GP algorithms are applied to identify the excitation force acting on the structures from the measured structural response, which is an important type of inverse problem in structural dynamics. Illustrative examples of a frame structure and a multistory building structure demonstrate that, compared with the standard GP, the hybrid GP algorithms have higher search efficiency which can be used as alternate global search and optimization tools for other engineering problem solving. [ABSTRACT FROM AUTHOR]

This paper presents the development of a parallel multiobjective genetic algorithm framework to enable an efficient and effective optimization of resource utilization in large-scale construction projects. The framework incorporates a multiobjective optimization module, a global parallel genetic algorithm module, a coarse-grained parallel genetic algorithm module, and a performance evaluation module. The framework is implemented on a cluster of 50 parallel processors and its performance was evaluated using 183 experiments that tested various combinations of construction project sizes, numbers of parallel processors and genetic algorithm setups. The results of these experiments illustrate the new and unique capabilities of the developed parallel genetic algorithm framework in: (1) Enabling an efficient and effective optimization of large-scale construction projects; (2) achieving significant computational time savings by distributing the genetic algorithm computations over a cluster of parallel processors; and (3) requiring a limited and feasible number of parallel processors/computers that can be readily available in construction engineering and management offices. [ABSTRACT FROM AUTHOR]

Contractor’s ability to procure cash to carry out construction operations represents a crucial factor to run profitable business. Bank overdrafts have always been the major source to finance construction projects. However, it is not uncommon that bankers set a limit on the credit allocated to an established overdraft. Bankers’ interest rates and consequently contractors’ financing costs are basically determined based on the allocated credit limits. Furthermore, project indirect costs are directly proportional to the project duration which is affected by the allocated credit limit. Thus, the credit limit affects project financing costs and indirect costs which in turn affect project profit. However, finance-based scheduling produces financially executable schedules at specified credit limits while maintaining the demand of time minimization. Thus, finance-based scheduling provides a tool to control the credit requirements. This control enables contractors to negotiate lower interest rates which reduce financing costs. Thus, finance-based scheduling enables contractors to reduce project indirect costs and financing costs. This paper utilizes genetic algorithm’s technique to devise finance-based schedules that maximize project profit through minimizing financing costs and indirect costs. [ABSTRACT FROM AUTHOR]

Time–cost optimization (TCO) is one of the greatest challenges in construction project planning and control, since the optimization of either time or cost, would usually be at the expense of the other. Although the TCO problem has been extensively examined, many research studies only focused on minimizing the total cost for an early completion. This does not necessarily convey any reward to the contractor. However, with the increasing popularity of alternative project delivery systems, clients and contractors are more concerned about the combined benefits and opportunities of early completion as well as cost savings. In this paper, a genetic algorithms (GAs)-driven multiobjective model for TCO is proposed. The model integrates the adaptive weight to balance the priority of each objective according to the performance of the previous “generation.” In addition, the model incorporates Pareto ranking as a selection criterion and the niche formation techniques to improve popularity diversity. Based on the proposed framework, a prototype system has been developed in Microsoft Project for testing with a medium-sized project. The results indicate that greater robustness can be attained by the introduction of adaptive weight approach, Pareto ranking, and niche formation to the GA-based multiobjective TCO model. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The radio frequency identification (RFID) technology has proven its potential in locating and tracking construction resources, a critical task in construction project control. However, the main challenge is how to achieve desired levels of locating accuracy. This paper presents an enhanced boundary condition method that incorporates the tag-reader angle and the reader geometric configuration factors to control the accuracy of a locating system that integrates RFID and real time kinematic (RTK) global positioning system (GPS). Controlled laboratory experiments were conducted to assess their effects and create quality control filters. This study demonstrated the relationship between the detecting range and the tag-reader angle and used it to separate valid/invalid boundary points. Spatial dilution of precision (SDOP) was formulated to measure the geometric configuration of readers forming the boundary constraint. Correlating SDOP to the locating error through a polynomial regression model, a mechanism was created to use SDOP to predict and control the locating accuracy. Open field experiments validated that appropriate SDOP, and tag-reader angle filters could be created to achieve varying levels of accuracy. [ABSTRACT FROM AUTHOR]