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.     

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.     

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.     

EvoSite: Evolution-Based Model for Site Layout Planning

Appropriate site layout of temporary facilities is crucial for enhancing the productivity and safety on construction sites. Site layout planning, however, is a complex problem, and researchers have attempted to solve it using a variety of optimization-based and heuristic-based techniques. In this paper, a genetic-algorithm-based model for site layout planning is presented. The advantages of the model stem from three main characteristics: (1) It applies to any user-defined site shape; (2) it accounts for the user preference in the relative closeness among the facilities; and (3) it incorporates a genetic algorithm procedure to search for the optimum layout in a manner that simulates natural evolution. Based on the proposed model, a comprehensive system for site layout planning (EvoSite) is developed. EvoSite uses an intuitive spreadsheet representation of the site and the facilities, and automates the evolution of layout solutions. Details of model development and implementation are described, and an example application is presented to demonstrate the capabilities of the EvoSite system. The advantages, limitations, and future extensions of EvoSite are then discussed.     

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.     

GIS-Based Cost Estimates Integrating with Material Layout Planning

This study is focused on developing an automated site layout system for construction materials. The system, MaterialPlan, including a geographic information system (GIS) based cost estimates system integrated with material layout planning, is a new tool to assist managers in identifying suitable areas to locate construction materials. As tabulation of all project quantities is calculated using GIS, linkages are established between the graphical features of detailed design and the related estimating quantities. Based on information regarding quantities and locations of the materials required in the project, this study identifies the suitable site to store the materials. Using the concept of “searching by elimination,” the system develops a heuristic approach, modeling the process of human decision making to generate potential sites for placing the materials. An objective function called the proximity index is developed to determine the optimal site. In conclusion, MaterialPlan demonstrates that GIS is a promising tool for solving construction layout problems and thus opens up a new way of thinking for the management of spatial information in construction planning and design.     

Conjoining MMAS to GA to Solve Construction Site Layout Planning Problem

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

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.     

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.     

Minimizing Construction-Related Hazards in Airport Expansion Projects

Airport expansion projects often require the presence and movement of construction labor and equipment near critical airport traffic areas. This close proximity between construction activities and airport operations needs to be carefully considered during the planning of construction site layouts in order to minimize and eliminate all potential construction-related hazards to aviation safety. This paper presents the development of a multiobjective optimization model for planning airport construction site layouts that is capable of minimizing construction-related hazards and minimizing site layout costs, simultaneously. The model incorporates newly developed optimization functions and metrics that enable: (1) maximizing the control of hazardous construction debris near airport traffic areas; (2) minimizing site layout costs including the travel cost of construction resources and the cost of debris control measures on airport sites; and (3) satisfying all operational safety constraints required by the federal aviation administration as well as other practical site layout constraints. The model is implemented using a multiobjective genetic algorithm and an application example is analyzed to demonstrate the use of the model and its capabilities in optimizing construction site layouts in airport expansion projects.     

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.     

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.     

Optimal Layout of Sewer Systems: A Deterministic versus a Stochastic Model

The optimization of a new or partially existing urban drainage system may be modeled as a subproblems sequence of layout and optimal design within the discrete search space. The design optimization, incorporating the optimal selection of the pumping stations, intermediate manholes, pipe sections, and installation depths, for a general system fixed layout in plan, is a high level sequential decision problem which may be efficiently solved deterministically through a multilevel dynamic programming model. The optimal general layout may be selected in a deterministic way by means of a simple economical comparison of all plan solutions having optimized designs, for small to medium sized systems (if the specific restrictions of the applications are appropriately exploited) in practicable computer time. For larger dimension networks, where it is clearly impossible to achieve plan optimization with full enumeration (which is a NP complete), stochastic search models can be used. For the subproblem layout, an effective enumeration model is presented; the results of a stochastic model proposed previously, using simulated annealing for an application example, are compared and discussed in detail.     

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.     

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.     

智能调度指挥系统在复杂露天矿生产中的应用

包钢（集团）公司白云鄂博铁矿建矿多年来一直沿用人工调度指挥生产,为了解决人工调度遇到的机构臃肿、人员众多、生产决策人员不能实时了解生产现场情况等问题,自云鄂博铁矿采用智能调度指挥系统,利用最佳路径、线性规划和动态规划子模型,实现了电铲、运矿车自动调度。智能调度指挥系统于2010年1月正式运行,大大降低了生产成本,提高了生产效率。     

Optimal Retailer Load Estimates Using Stochastic Dynamic Programming

In this paper, we describe a stochastic dynamic programming methodology for determining optimal forward load estimates for electric power retailers to their suppliers. This work describes both a model as well as results based on real data for the Electric Reliability Council of Texas market and provides insights useful for planning purposes for electric power retailers in the face of uncertain market prices and end-user loads.     

Time-Cost Optimization of Construction Projects with Generalized Activity Constraints

Time-cost analysis is an important element of project scheduling, especially for lengthy and costly construction projects, as it evaluates alternative schedules and establishes an optimum one considering any project completion deadline. Existing methods for time-cost analysis have not adequately considered typical activity and project characteristics, such as generalized precedence relationships between activities, external time constraints, activity planning constraints, and bonuses/penalties for early/delayed project completion that would provide a more realistic representation of actual construction projects. The present work aims to incorporate such characteristics in the analysis and has developed two solution methods, an exact and an approximate one. The exact method utilizes a linear/integer programming model to provide the optimal project time-cost curve and the minimum cost schedule considering all activity time-cost alternatives together. The approximate method performs a progressive project length reduction providing a near-optimal project time-cost curve but it is faster than the exact method as it examines only certain activities at each stage. In addition, it can be easily incorporated in project scheduling software. Evaluation results indicate that both methods can effectively simulate the structure of construction projects, and their application is expected to provide time and cost savings.     

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.     

Fuzzy Inexact Mixed-Integer Semiinfinite Programming for Municipal Solid Waste Management Planning

Based on the concept of functional intervals, fuzzy inexact mixed-integer semiinfinite programming (FIMISIP) method is developed for municipal solid waste management planning. The method allows the uncertainties in parameters expressed as fuzzy, interval, and functional interval numbers to be directly communicated into the programming problem. The FIMISIP problem is solved by dividing it into two interactive semiinfinite programming (SIP) subproblems. Solutions reflecting the inherent uncertainties can then be generated by combining the SIP solutions into a set of decision intervals. The method is applied to a municipal solid waste management planning system for demonstrating its effectiveness in dealing with uncertain and dynamic complexities. Compared to the previous inexact programming methods, FIMISIP has the advantages as follows: (1) the dynamic complexity can be addressed by introducing the functional-interval parameters associated with time into the programming problem; (2) the FIMISIP solutions provide a set of flexible waste-management schemes to the decision makers; and (3) the FIMISIP solutions are more reliable than those from the previous ILP ones since they can be “really” optimal regardless of how the parameters vary with time within the time period. While this study is a first attempt to solve solid waste management issues under complex uncertainties, the method can be extended to other environmental management planning problems.