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.     

Genetic Algorithms for Construction Site Layout in Project Planning

Construction site layout is concerned with the existence, positioning, and timing of the temporary facilities that are used to carry out a construction project. Typically these problems are very complicated to formulate and difficult to solve. They are, however, very important to virtually any construction project, since the site layout can significantly affect the cost of the project. This paper describes the general site layout problem from both a theoretical and a practical point of view. It proposes genetic algorithms as a possible solution technique and includes a theoretical example of positioning temporary facilities. This is extended to a practical problem in which the cost of movement is modeled realistically using an augmented genetic algorithm. Some preliminary conclusions are drawn for the application of genetic algorithms to construction site layout problems.     

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.     

Particle Swarm Optimization for Preemptive Scheduling under Break and Resource-Constraints

This paper introduces a particle swarm optimization (PSO)-based methodology to implement preemptive scheduling under break and resource-constraints (PSBRC) for construction projects. The PSBRC under study allows the preemptive activities to be interrupted in off-working time and not to resume immediately in the next working period because all the limited resources are to be reallocated during a break. The potential solution to the PSBRC, i.e., a set of priorities deciding the order to start the activities or restart the interrupted activities, is represented by the multidimensional particle position. Hence PSO is applied to search for the optimal schedule for the PSBRC, in which a parallel scheme is adopted to transform the particle-represented priorities to a schedule. Computational analyses are presented to verify the effectiveness of the proposed methodology. This paper provides an attempt to make use of preemption and break for the resource-constrained construction project with the objective of minimizing project duration.     

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.     

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.     

Permutation-Based Particle Swarm Optimization for Resource-Constrained Project Scheduling

In the light of particle swarm optimization (PSO) which utilizes both local and global experiences during search process, a permutation-based scheme for the resource-constrained project scheduling problem (RCPSP) is presented. In order to handle the permutation-feasibility and precedence-constraint problems when updating the particle-represented sequence or solution for the RCPSP, a hybrid particle-updating mechanism incorporated with a partially mapped crossover of a genetic algorithm and a definition of an activity-move-range is developed. The particle-represented sequence should be transformed to a schedule (including start times and resource assignments for all activities) through a serial method and accordingly evaluated against the objective of minimizing project duration. Experimental analyses are presented to investigate the performances of the permutation-based PSO. The study aims at providing an alternative for solving the RCPSP in the construction field by utilizing the advantages of PSO.     

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 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.     

Particle Swarm Optimization-Supported Simulation for Construction Operations

This study proposes an integration of particle swarm optimization (PSO) and a construction simulation so as to determine efficiently the optimal resource combination for a construction operation. The particle-flying mechanism is utilized to guide the search process for the PSO-supported simulation optimization. A statistics method, i.e., multiple-comparison procedure, is adopted to compare the random output performances resulting from the stochastic simulation model so as to rank the alternatives (i.e., particle-represented resource combinations) during the search process. The indifference zone and confidence interval facilitate consideration of the secondary performance measure (e.g., productivity) when the main performance measures (e.g., cost) of the competing alternatives are close. The experimental analyses demonstrate the effectiveness and efficiency of the proposed simulation optimization. The study aims to providing an alternative combination of optimization methodology and general construction simulation by utilizing PSO and a statistics method so as to improve the efficiency of simulation in planning construction operations.     

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.     

Applying Optimal Control Model to Dynamic Equipment Allocation Problem: Case Study of Concrete-Faced Rockfill Dam Construction Project

This paper presents an optimization method for solving a dynamic equipment allocation problem (DEAP) encountered during the implementation of a concrete-faced rockfill dam construction project. In contrast to prior studies, the uncertainties associated with equipment failures have been explicitly considered in the objective function of our mathematical model that maximizes construction throughput. Specifically, our research assumes that the equipment failure time is characterized by a Weibull distribution, which has been justified by a chi-square goodness-of-fit test. A failure probability–work time equation is also used to characterize the relationship between the equipment failure probability and mean time to work. Furthermore, our model considers multiple types of equipment, and each is capable of doing different jobs. The model is confined by the relationship equations, initial conditions, and constraint conditions. Then, the particle swarm optimization (PSO) technique is applied to search for the optimal solution of the DEAP. Finally, the Shuibuya Hydropower Project was used as a real-world example to demonstrate the practicality and efficiency of the optimization method.     

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.     

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.     

Nondominated Archiving Multicolony Ant Algorithm in Time–Cost Trade-Off Optimization

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.     

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.     

基于粒子群优化算法和博弈论的网络学习控制系统带宽调度

研究了双层网络学习控制系统的带宽调度优化问题.为了合理分配子系统的带宽,引入了网络定价体系和动态带宽调度方法,建立了非合作博弈模型,从而将网络控制系统的网络资源分配问题转换为非合作博弈竞争模型下的Nash均衡点求解问题.在此基础上,采用粒子群优化算法得到此框架下的纳什均衡解,并进一步给出了网络控制系统的时间片调度方法.仿真结果表明了所提方法的有效性.      

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.     

Permutation-Based Elitist Genetic Algorithm for Optimization of Large-Sized Resource-Constrained Project Scheduling

The resource-constrained project scheduling problem (RCPSP) has received the attention of many researchers because its general model can be used in a wide variety of construction planning and scheduling applications. The exact procedures and priority-rule-based heuristics fail to search for the optimum solution to the RCPSP of large-sized project networks in a reasonable amount of time for successful application in practice. This paper presents a permutation-based elitist genetic algorithm for solving the problem in order to fulfill the lack of an efficient optimal solution algorithm for project networks with 60 activities or more as well as to overcome the drawback of the exact solution approaches for large-sized project networks. The proposed algorithm employs the elitist strategy to preserve the best individual solution for the next generation so the improved solution can be obtained. A random number generator that provides and examines precedence feasible individuals is developed. A serial schedule generation scheme for the permutation-based decoding is applied to generate a feasible solution to the problem. Computational experiments using a set of standard test problems are presented to demonstrate the performance and accuracy of the proposed algorithm.     

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.