Multiobjective Optimization of Earthmoving Operations

This paper presents a framework for optimizing earthmoving operations using computer simulation and genetic algorithms. It provides a multiobjective optimization tool geared towards selection of near-optimum fleet configurations. The optimization aims at minimizing time and cost of earthmoving operations. The proposed framework considers factors that influence earthmoving operations including equipment availability and project indirect cost. The simulation process, in the proposed methodology, utilizes discrete event simulation and object oriented modeling. The optimization process uses a recently developed genetic algorithm to search for a near-optimum fleet configuration employing Pareto optimality to account for multiobjective optimization. The algorithm considers a set of qualitative and quantitative variables that influence the production of earthmoving operations. The developed framework supports time–cost tradeoff analysis and can assist users in considering what if scenarios with respect to fleet configurations. A numerical example is presented to illustrate a number of practical features of the proposed framework and to demonstrate its capabilities in selecting near-optimum fleet configurations.     

Using Elitist Particle Swarm Optimization to Facilitate Bicriterion Time-Cost Trade-Off Analysis

The present study develops a new optimization algorithm to find the complete time-cost profile (Pareto front) over a set of feasible project durations, i.e., it solves the time-cost trade-off problem. To improve existing methods, the proposed algorithm aims to achieve three goals: (1) to obtain the entire Pareto front in a single run; (2) to be insensitive to the scales of time and cost; and (3) to treat all existing types of activity time-cost functions, such as linear, nonlinear, discrete, discontinuous, and a hybrid of the above. The proposed algorithm modifies a population-based search procedure, particle swarm optimization, by adopting an elite archiving scheme to store nondominated solutions and by aptly using members of the archive to direct further search. Through a fast food outlet example, the proposed algorithm is shown effective and efficient in conducting advanced bicriterion time-cost analysis. Future applications of the proposed algorithm are suggested in the conclusion.     

MACROS: Multiobjective Automated Construction Resource Optimization System

This paper presents the development of a practical and automated system for optimizing the utilization of construction resources to simultaneously minimize project cost and duration while maximizing project quality. The system is named the Multiobjective Automated Construction Resource Optimization System (MACROS), and it incorporates four newly developed modules: (1) a multiobjective optimization module to quantify and optimize the impact of resource utilization decisions on construction duration, cost, and quality; (2) a relational database module to facilitate the storage and retrieval of construction scheduling and optimization data; (3) a middleware module to provide seamless integration between the internal modules in MACROS and external commercially available project management software; and (4) a user interface module to facilitate the input of project data and the visualization and ranking of the generated optimal construction plans. An example project of 180 activities is analyzed to illustrate the use of MACROS and demonstrate its unique and practical construction optimization capabilities.     

Electimize: New Evolutionary Algorithm for Optimization with Application in Construction Engineering

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.     

New Mathematical Optimization Model for Construction Site Layout

Layout of temporary construction facilities (objects) is an important activity during the planning process of construction projects. The construction area layout is a complex problem whose solution requires the use of analytical models. Existing popular models employ genetic algorithms that have proven to be useful tools in generating near optimal site layouts. This paper presents an alternative approach based on mathematical optimization that offers several important features and generates a global optimal solution. The construction area consists of an unavailable area that includes existing facilities (sites) and available area in which the objects can be located. The available area is divided into regions that are formulated using binary variables. The locations of the objects are determined by optimizing an objective function subject to a variety of physical and functional constraints. The objective function minimizes the total weighted distance between the objects and the sites as well as among the objects (if desired). The distance can be expressed as Euclidean or Manhattan distance. Constraints that ensure objects do not overlap are developed. The new approach, which considers a continuous space in locating the objects simultaneously, offers such capabilities as accommodating object adjacency constraints, facility proximity constraints, object–region constraints, flexible orientation of objects, visibility constraints, and nonrectangular objects, regions, and construction areas. Application of the model is illustrated using two examples involving single and multiple objects. The proposed model is efficient and easy to apply, and as such should be of interest to construction engineers and practitioners.     

Developing a Resource Supply Chain Planning System for Construction Projects

The high variability of construction environments results in high construction-cost variation, especially in material costs. Inadequate planning may cause material shortages that delay the project schedule or, alternatively, a substantial increase in inventory costs by producing or supplying materials earlier than they are needed at the construction site. In order to solve these problems, this paper studies steel rebar production and supply operations and establishes an optimal model that minimizes the integrated inventory cost of the supply chain. Based on the optimal model, this paper develops a decision-support system to generate a production and supply plan for a supplier and buyers of steel rebar. After utilizing the decision-support system to create the supply and production plan, this paper analyzes the results to study the influence of transaction constraints on inventory cost. This paper also discusses cases of global optimization of the inventory cost for the entire supply chain and compares them with cases of local optimization for individual members.     

Applying Pareto Ranking and Niche Formation to Genetic Algorithm-Based Multiobjective Time–Cost Optimization

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.     

Non-Unit-Based Planning and Scheduling of Repetitive Construction Projects

Repetitive scheduling methods are more effective than traditional critical path methods in the planning and scheduling of repetitive construction projects. Nevertheless, almost all the repetitive scheduling methods developed so far have been based on the premise that a repetitive project is comprised of many identical production units. In this research a non-unit-based algorithm for the planning and scheduling of repetitive projects is developed. Instead of repetitive production units, repetitive or similar activity groups are identified and employed for scheduling. The algorithm takes into consideration: (1) the logical relationship of activity groups in a repetitive project; (2) the usage of various resource crews in an activity group; (3) the maintaining of resource continuity; and (4) the time and cost for the routing of resource crews. A sample case study and a case study of a sewer system project are conducted to validate the algorithm, as well as to demonstrate its application. Results and findings are reported.     

Multiobjective Evolutionary Finance-Based Scheduling: Entire Projects’ Portfolio

A strength Pareto evolutionary algorithm (SPEA) is proposed and was modified by incorporating logic-preserving crossover and mutation operators and employed to devise a set of optimum finance-based schedules of multiple projects being implemented simultaneously by a construction contractor. The problem involves the minimization of the conflicting objectives of financing costs, duration of the group of projects, and the required credit. The modified SPEA was employed to obtain the Pareto-optimal fronts for the two-objective combinations as well as the three objectives. In addition, a fuzzy-based technique was used to help the contractors select the best compromise solution over the Pareto-optimal solutions. The proposed approach has been developed and implemented on projects with different sizes. The results obtained by the modified SPEA, fuzzy-based approach demonstrated its potential and effectiveness in finance-based scheduling of multiple projects.     

Parallel Computing Framework for Optimizing Construction Planning in Large-Scale Projects

Available construction optimization models can be used to generate optimal tradeoffs between construction time and cost, however their application in optimizing large-scale projects is limited due to their extensive and impractical computational time requirements. This paper presents the development of a parallel computing framework in order to circumvent this limitation. The framework incorporates a multi-objective genetic algorithm module that identifies optimal trade-offs between construction time and cost; and a parallel computing module that distributes genetic algorithm computations over a network of processors. The performance of the framework is evaluated using 150 experiments that represent various combinations of project sizes and numbers of processors. The results of this analysis illustrate the robust capabilities of the developed parallel computing framework in terms of its efficiency in reducing the computational time requirements for large-scale construction optimization problems, and its effectiveness in obtaining high quality solutions identical to those generated by a single processor.     

Optimizing Resource Leveling in Construction Projects

Construction schedules, generated by network scheduling techniques, often cause undesirable resource fluctuations that are impractical, inefficient, and costly to implement on construction sites. This paper presents the development of two innovative resource leveling metrics to directly measure and minimize the negative impact of resource fluctuations on construction productivity and cost. The first metric quantifies the total amount of resources that need to be temporarily released during low demand periods and rehired at a later stage during high demand periods. The second measures the total number of idle and nonproductive resource days that are caused by undesirable resource fluctuations. The two new metrics are incorporated in a robust and practical optimization model that is capable of generating optimal and practical schedules that maximize the efficiency of resource utilization. An application example is analyzed to illustrate the use of the model and demonstrate its capabilities. The results of this analysis show that the present model and metrics are capable of outperforming existing metrics and eliminating undesirable resource fluctuations and resource idle time.     

Parallel Genetic Algorithms for Optimizing Resource Utilization in Large-Scale Construction Projects

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.     

Review of Unbalanced Bidding Models in Construction

Unbalanced bidding describes an activity otherwise known as item price loading. It is a practice used to some extent by building contractors to determine the prices that they will allocate to the individual component items within a project. This practice takes advantage of the contractor’s opportunity to manipulate these prices without this affecting their overall bid price for a project. Three types of loading are described, namely those of “front-end loading,” “back-end loading,” and “quantity error exploitation” (otherwise known as “individual rate loading”). Several scientists have expressed an interest in this field, starting with Marvin Gates in 1959. All of these scientific endeavors have entailed the attempt by which to mathematically determine the optimum method of item pricing. These efforts are seen as potentially significant given that it has become recognized that this practice can contribute substantially to a contractor’s profit as well as their risk. It is therefore interesting to note that although some research has found that some forms of unbalanced bidding are being practiced, there is no published research known to the writers that describes any practical application of any of the mathematical models that have been advocated by the aforementioned scientific community. A critical assessment is made of all of the scientific contributions known to the writers that have been in this field. This critical assessment finds flaws with all of the existing scientific proposals. In particular it finds fault with the technique that is incorporated into many of them by which prices are bounded by arbitrary upper and lower limits that have no scientific basis. It also concludes that further research is required to test the practical efficacy of some of these academic models.     

Fast and Accurate Risk Evaluation for Scheduling Large-Scale Construction Projects

This paper presents the development of a novel probabilistic scheduling model that enables fast and accurate risk evaluation for large-scale construction projects. The model is designed to overcome the limitations of existing probabilistic scheduling methods, including the inaccuracy of the program evaluation and review technique (PERT) and the long computational time of the Monte Carlo simulation method. The model consists of three main modules: PERT model; fast and accurate multivariate normal integral method; and a newly developed approximation method. The new approximation method is designed to focus the risk analysis on the most significant paths in the project network by identifying and removing insignificant paths that are either highly correlated or have high probability of completion time. The performance of the new model is analyzed using an application example. The results of this analysis illustrate that the new model was able to reduce the computational time for a large-scale construction project by more than 94% while keeping the error of its probability estimates to less than 3%, compared with Monte Carlo Simulation methods.     

Finance-Based Scheduling: Tool to Maximize Project Profit Using Improved Genetic Algorithms

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.     

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.     

Integrating Construction Operation and Context in Large-Scale Construction Using Hybrid Computer Simulation

This research proposes a hybrid simulation approach based upon the principles of system dynamics (SD) and discrete event simulation (DES), which facilitates a better understanding of complex interactions among various processes in large-scale construction. The significance of the construction context that interacts with construction operations is highlighted, and a hybrid SD-DES approach is proposed as a means to capture the feedback between the two. In particular, this paper focuses on how to seamlessly integrate SD and DES within the framework of a modeling perspective. For the purpose of substantiating the discussion, a pipeline installation process is modeled using the proposed hybrid approach, with specific consideration given to how the approach can serve to address complex interactions between operation and context.     

Dispute Review Boards: Expected Application on Egyptian Large-Scale Construction Projects

The construction industry is heavily affected by the troubles arising out of construction disputes, especially when it comes to large-scale projects, as a direct result of the inherent complexity of such projects. This paper seeks the most suitable dispute-resolution mechanism for large-scale construction projects in Egypt, which is a developing country in the Middle East with an emerging reformed economical policy, a population in excess of 70 million people, and an increasing need for infrastructure and industrial development. This dispute-resolution mechanism was attained through a multistep methodology that (1) started with the study of the Arbitration process in relation to an Egyptian construction project with an initial contract price of 85 million; (2) continued with interviews of five senior experts in the field of construction disputes in Egypt about their views pertaining to the most efficient dispute-resolution methodology for Egyptian megaprojects; (3) developed a tailored questionnaire to assess the perceptions of 35 professionals toward the issue of construction disputes and dispute resolution mechanisms, including DRB; (5) concluded by carrying out a what-if scenario for the arbitration case of the large-scale construction project using DRB instead of arbitration. On basis of the analysis of the methodology, the authors concluded that despite the wide range of current dispute-resolution methodologies, the employment of DRBs in accordance with a set of 13 regulatory guidelines should mitigate the negative effects of disputes in Egyptian large-scale construction projects. Accordingly, this paper is both timely and valuable for all owners, contractors, and professionals who are acquainted with Egyptian megaprojects.     

Investigating the Effectiveness of Certain Priority Rules on Resource Scheduling of Housing Estate Projects

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.