Optimizing Airport Construction Site Layouts to Minimize Wildlife Hazards

Construction operations in airport expansion projects often attract wildlife species to critical airport traffic areas leading to an increase in the risk of wildlife–aircraft collision accidents. Airport operators and construction planners need to carefully consider and minimize these wildlife hazards during the planning of construction site layouts in order to comply with Federal Aviation Administration recommendations. This paper presents the development of an advanced optimization model for planning airport construction site layouts that is capable of minimizing the hazards of wildlife attractants and minimizing the site layout costs, simultaneously. The model incorporates newly developed concepts and performance criteria that enable (1) quantifying, controlling, and minimizing the hazards of construction-related wildlife attractants near airport traffic areas; and (2) minimizing the travel cost of construction resources and the cost of devices installed to control wildlife on airport construction sites, while complying with all relevant aviation safety constraints. The model is developed using a multiobjective genetic algorithm and an application example is analyzed to demonstrate the use of the model in optimizing airport construction site layouts and its unique capability of generating optimal trade-offs between wildlife control and site layout costs.     

Minimizing Construction-Related Security Risks during Airport Expansion Projects

Airport expansion projects often require the presence of construction personnel, material, and equipment near airport secure areas/facilities, leading to an increase in the level of risk to airport security. Construction planners and airport operators need to carefully study this challenge and implement active measures in order to minimize construction-related security breaches and comply with all relevant Federal Aviation Administration guidelines. This paper presents the development of an advanced multiobjective optimization model for planning airport construction site layouts that is capable of minimizing construction-related security breaches while simultaneously minimizing site layout costs. The model incorporates newly developed criteria and performance metrics that enable evaluating and maximizing the construction-related security level in operating airports. The model is developed using a multiobjective genetic algorithm, and an application example is analyzed to demonstrate the use of the model and its unique capability of generating a wide spectrum of optimal trade-offs between construction-related airport security and site layout costs.     

Trade-off between Safety and Cost in Planning Construction Site Layouts

Planning construction site layouts involves identifying the positions of temporary facilities on site, and accordingly it has a significant impact on the safety and efficiency of construction operations. Although available models are capable of minimizing the travel cost of resources on site, they do not consider safety as an important and separate objective in the optimization of site layouts. This paper presents the development of an expanded site layout planning model that is capable of maximizing construction safety and minimizing the travel cost of resources on site, simultaneously. The model incorporates newly developed concepts and performance criteria that enable the quantification of construction safety and travel cost of resources on site. The present model is developed in three main phases: (1) formulating decision variables and optimization objectives in this site layout planning problem; (2) identifying and satisfying all practical constraints in this optimization problem; and (3) implementing the model as a multiobjective genetic algorithm. An application example is analyzed to illustrate the use of the model and demonstrate its capabilities in optimizing construction site layouts and generating optimal trade-offs between safety and travel cost of resources on site.     

Optimization Research: Enhancing the Robustness of Large-Scale Multiobjective Optimization in Construction

Many construction planning problems require optimizing multiple and conflicting project objectives such as minimizing construction time and cost while maximizing safety, quality, and sustainability. To enable the optimization of these construction problems, a number of research studies focused on developing multiobjective optimization algorithms (MOAs). The robustness of these algorithms needs further research to ensure an efficient and effective optimization of large-scale real-life construction problems. This paper presents a review of current research efforts in the field of construction multiobjective optimization and two case studies that illustrate methods for enhancing the robustness of MOAs. The first case study utilizes a multiobjective genetic algorithm (MOGA) and an analytical optimization algorithm to optimize the planning of postdisaster temporary housing projects. The second case study utilizes a MOGA and parallel computing to optimize the planning of construction resource utilization in large-scale infrastructure projects. The paper also presents practical recommendations based on the main findings of the analyzed case studies to enhance the robustness of multiobjective optimization in construction engineering and management.     

Optimal Construction Site Layout Considering Safety and Environmental Aspects

A good site layout is vital to ensure the safety of the working environment and effective and efficient operations. Site layout planning has significant impacts on productivity, costs, and duration of construction. Construction site layout planning involves identifying, sizing, and positioning temporary and permanent facilities within the boundary of the construction site. Site layout planning can be viewed as a complex optimization problem. Although construction site layout planning is a critical process, systematical analysis of this problem is always difficult because of the existence of a vast number of trades and interrelated planning constraints. The problem has been solved using two distinct approaches: Optimization techniques and heuristics methods. Mathematical optimization procedures have been developed to produce optimal solutions, but they are only applicable for small-size problems. Artificial intelligent techniques have been used practically to handle real-life problems. On the other hand, heuristic methods have been used to produce good but not optimal solutions for large problems. In this paper, an optimization model has been developed for solving the site layout planning problem considering safety and environmental issues and actual distance between facilities. Genetic algorithms are used as an optimization bed for the developed model. In order to validate the performance of the developed model, a real-life construction project was tested. The obtained results proved that satisfactory solutions were obtained.     

Site Layout Planning using Nonstructural Fuzzy Decision Support System

Site layout planning can affect productivity and is crucial to project success. However, as construction is heterogeneous in the nature of its organizations, project designs, time constraints, environmental effects, etc., site layout planning for each project becomes unique. Affected by many uncertainties (variables) and variations, site layout planning is a typical multiobjective problem. To facilitate the decision-making process for these problems, a nonstructural fuzzy decision support system (NSFDSS) is proposed. NSFDSS integrates both experts’ judgment and computer decision modeling, making it suitable for the appraisal of complicated construction problems. The system allows assessments based on pairwise comparisons of alternatives using semantic operators that can provide a reliable assessment result even under the condition of insufficient precise information.     

Genetic Algorithm for Optimizing Supply Locations around Tower Crane

Site layout planning is a complicated issue because of the vast number of trades and interrelated planning constraints. To unfold its complexity, this paper aims to confine the study to a particularly defined area of construction: the structural concrete-frame construction stage of public housing projects. In this study, optimization of the tower crane and supply locations is targeted, as they are the major site facilities for high-rise building construction. A site layout genetic algorithm model is developed and a practical example is presented. The optimization results of the example are very promising and demonstrate the application value of the model.     

Dynamic Layout Planning Using a Hybrid Incremental Solution Method

Efficiently using site space to accommodate resources throughout the duration of a construction project is a critical problem. It is termed the “dynamic layout planning” problem. Solving it involves creating a sequence of layouts that span the entire project duration, given resources, the timing of their presence on site, their changing demand for space over time, constraints on their location, and costs for their relocation. A dynamic layout construction procedure is presented here. Construction resources, represented as rectangles, are subjected to two-dimensional geometric constraints on relative locations. The objective is to allow site space to all resources so that no spatial conflicts arise, while keeping distance-based adjacency and relocation costs minimal. The solution is constructed stepwise for consecutive time frames. For each resource, selected heuristically one at a time, constraint satisfaction is used to compute sets of feasible positions. Subsequently, a linear program is solved to find the optimal position for each resource so as to minimize all costs. The resulting sequence of layouts is suboptimal in terms of the stated global objective, but the algorithm helps the layout planner explore better alternative solutions.     

Genetic Algorithm for Solving Site Layout Problem with Unequal-Size and Constrained Facilities

This paper presents an investigation of the applicability of a genetic approach for solving the construction site layout problem. This problem involves coordinating the use of limited site space to accommodate temporary facilities so that transportation cost of materials is minimized. The layout problem considered in this paper is characterized by affinity weights used to model transportation costs between facilities and by geometric constraints that limit their relative positions on site. The proposed genetic algorithm generates an initial population of layouts through a sequence of mutation operations and evolves the layouts of this population through a sequence of genetic operations aiming at finding an optimal layout. The paper concludes with examples illustrating the strength and limitations of the proposed algorithm in the cases of (1) loosely versus tightly constrained layouts with equal levels of interaction between facilities; (2) loosely versus tightly packed layouts with variable levels of interactions between facilities; and (3) loosely versus tightly constrained layouts. In most problems considered where the total-objects-to-site-area ratio did not exceed 60%, the algorithm returned close to optimal solutions in a reasonable time.     

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.     

Optimal Lighting Arrangements for Nighttime Highway Construction Projects

This paper presents a decision support system for optimizing temporary lighting arrangements in nighttime highway construction projects. The system is developed as a multiobjective genetic algorithm that is capable of: (1) maximizing average illuminance on construction sites; (2) maximizing lighting uniformity in the work zone; (3) minimizing glare to workers and road users; and (4) minimizing lighting costs. The system is designed to support decision makers in their search for practical lighting arrangements that provide various tradeoffs among these four conflicting objectives. Five decision variables are optimized in the present system, namely: number of lighting equipment, equipment positioning, mounting height, aiming angle, and rotation angle. The system is also designed to consider and satisfy all practical constraints that can be encountered in this lighting design problem. An application example is analyzed to illustrate the use of the system and demonstrate its capabilities in generating near optimal and practical lighting arrangements for nighttime highway construction projects.     

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.     

Optimal Planning and Scheduling for Repetitive Construction Projects

This paper presents a multiobjective optimization model for the planning and scheduling of repetitive construction projects. The model enables construction planners to generate and evaluate optimal construction plans that minimize project duration and maximize crew work continuity, simultaneously. The computations in the present model are organized in three major modules: scheduling, optimization, and ranking modules. First, the scheduling module uses a resource-driven scheduling algorithm to develop practical schedules for repetitive construction projects. Second, the optimization module utilizes multiobjective genetic algorithms to search for and identify feasible construction plans that establish optimal tradeoffs between project duration and crew work continuity. Third, the ranking module uses multiattribute utility theory to rank the generated plans in order to facilitate the selection and execution of the best overall plan for the project being considered. An application example is analyzed to illustrate the use of the model demonstrate its new capabilities in optimizing the planning and scheduling of repetitive construction projects.     

Computer Simulation Model: Construction Analysis for Pavement Rehabilitation Strategies

Most state highways in the United States were built during the 1960s and 1970s with an infrastructure investment of more than $1 trillion. They now exceed their 20?year design lives and are seriously deteriorated. The consequences are high maintenance and road user costs because of degraded road surfaces and construction work zone delays. Efficient planning of highway rehabilitation closures is critical. This paper presents a simulation model, Construction Analysis for Pavement Rehabilitation Strategies (CA4PRS), which estimates the maximum amount of highway rehabilitation/reconstruction during various closure timeframes. The model balances project constraints such as scheduling interfaces, pavement materials and design, contractor logistics and resources, and traffic operations. It has been successfully used on several urban freeway rehabilitation projects with high traffic volume, including projects on I-10 and I-710. The CA4PRS helps agencies and contractors plan highway rehabilitation strategies by taking into account long-life pavement performance, construction productivity, traffic delay, and total cost.     

Optimization of Earthmoving Operations in Heavy Civil Engineering Projects

Large scale earthmoving operations require the use of heavy and costly construction equipment. Optimum utilization of equipment is a crucial task for the project management team. It can result in substantial savings in both time and cost of earthmoving operations. This paper presents optimization model for earthmoving operations in heavy civil engineering projects. The developed model is designed to assist general contractor in optimizing planning of earthmoving operations. The model utilizes genetic algorithm, linear programming, and geographic information systems to support its management functions. The model assists in planning earthmoving operations; taking into consideration: (1) availability of resources to contractors; (2) project budget and/or time constraints, if any; (3) scope of work; (4) construction site conditions; (5) soil type; (6) project indirect cost; and (7) equipment characteristics. The model also determines the quantities of earth to be moved from different borrow pits and those to be placed at different landfill sites to meet optimization objective set by the user and to meet project constraints. The model has been implemented in prototype software, using object-oriented programming. Two numerical example projects are presented to validate and demonstrate the use of the developed model in optimizing earthmoving operations.     

Computer-Aided Site Layout Planning

This paper presents an interactive computer-aided site layout model to support site planning in a computer-aided design (CAD) environment and expands upon a model presented earlier by the writers. The developed model performs its task at two levels: Site representation, and site space analysis and allocation. The site representation is carried out using an open architecture supported by object-based concepts. The model offers three tiers of objects: (1) site objects, (2) construction objects, and (3) constraint objects. This structure facilitates the creation of new objects and reuse of domain knowledge, which allows for the gradual expansion and enrichment of the model’s knowledge base. At the space analysis and allocation level, the model introduces a geometric reasoning approach to analyze site space for finding an optimum or near-optimum location for facilities. This feature facilitates easy visualization of the site planning process and encourages user participation. The model is structured in three main modules: Database, Project Module, and Layout Control Module. The functionality of each module, along with their interconnectivity is described. The model is implemented using Visual Basic for Applications in AutoCAD environment and Microsoft Access. A numerical example of an actual site layout is presented to illustrate the functionality of the developed model.     

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.     

Multiobjective Transportation Planning for Waste Hauling

In the transportation planning for some industrial wastes, in addition to hauling cost, environmental impact must frequently be considered. A notable example is transporting waste soil generated by major construction projects. Adequate transportation planning is particularly important for construction in a metropolitan area. In this study, we present a novel two-phase approach to address the multiple-criteria decision problem. The first phase applies the fuzzy analytic hierarchy process to obtain a “composite impedance” for each road sector where transportation costs, environmental impact, and traffic congestion are considered in the evaluation. The second phase employs fuzzy mathematical programming to find the optimal transportation network based on the fuzzy impedance. An illustrative example is provided for the transportation planning for waste soil of the Kaohsiung mass rapid transit system construction project. The optimal solutions using the proposed approach are compared with the solutions using the conventional shortest-path approach where minimizing the transportation cost is the only objective.     

Optimizing Material Procurement and Storage on Construction Sites

Efficient planning of materials procurement and storage on construction sites can lead to significant improvements in construction productivity and project profitability. Existing research studies focus on material procurement and storage layout as two separate planning tasks without considering their critical and mutual interdependencies. This paper presents the development of a new optimization model for construction logistics planning that is capable of simultaneously integrating and optimizing the critical planning decisions of material procurement and material storage on construction sites. The model utilizes genetic algorithms to minimize construction logistics costs that cover material ordering, financing, stock-out, and layout costs. The model incorporates newly developed algorithms to estimate the impact of potential material shortages on-site because of late delivery on project delays and stock-out costs. An application example is analyzed to demonstrate the capabilities of the construction logistics planning model in simultaneously optimizing material procurement decisions and storage layout plans.     

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.