Construction Site Layout Planning Research Articles

A simulated annealing optimization algorithm for equal and un-equal area construction site layout problem

Construction Site Layout Planning (CSLP) is an important problem because of its impact on time, cost, productivity, and safety of the projects. In this paper, CSLP is formulated by the Quadratic Assignment Problem (QAP). At first, two case studies including equal and un-equal area facilities are solved by the simulated annealing optimization algorithm. Then, the obtained results are compared with the other papers. The comparisons show that the proposed Simulated Annealing (SA) is as efficient as ACO, PSO, CBO, ECBO, WOA, WOA-CBO, and ACO-PA which have been proposed by other papers for the same problems. As a result, the comparisons show that SA is as capable as other meta-heuristics of solving the combinatorial optimization problems like CSLP, while the hardware properties and computational times have been compared. Besides the comparisons, the design of experiments shows the relationship between each SA parameter and the computational time of the algorithm. Also, the history of convergence of the proposed SA indicates the high speed of reaching the optimal solution and the artificial intelligence of the proposed SA.

Applying rule-based model-checking to construction site layout planning tasks

Abstract Building Information Modeling (BIM) is a widely established method in the architecture, engineering, construction, and facilities management (AEC/FM) industry. Although BIM focuses on processes throughout the lifecycle of the built environment, the applications in the planning phase, e.g. the generation of construction site layouts, have not reached their full potential yet. One important example herein is the allocation and dimensioning of resources (e.g., building materials and equipment) which is typically carried out by humans according to clearly defined rules and best practices. This paper presents model-based rule checking for the planning of construction site layouts. We demonstrate that existing Business Rule Management Systems (BRMS), such as the open-source rule engine Drools, can be used. We combine Drools with the Industry Foundation Classes (IFC) to retrieve data from a building information model and use the information within the rule engine. We define general model requirements and implement a sample set of prototype rules. We also introduce the concept of offset geometry for rules that, for example, demand a known safety distance between temporary construction site elements. The developed approaches are explained and evaluated in field-realistic, practical case studies. Finally, we present a discussion how the application of the developed rule-based system may assist human decision making in tasks such as safe construction sites layout planning.

Construction Site Layout Planning: Application of Multi-Objective Particle Swarm Optimization*

Construction Site Layout Planning (CSLP) comprises determining, sizing and placing of the temporary facilities within the boundaries of a construction site by considering many factors. Traveling distance between facilities and safety risks are two essential factors that need to be minimized while planning site layout of a construction project. Many studies treated CSLP as a single objective optimization problem. They have mainly focused on either diminishing the travel cost of resources on site without considering the safety aspect or vice versa. While a few of the studies have treated the problem as a multi-objective optimization problem, none of them included a risk assessment approach including crane-related constraints. Hence, a user-friendly CSLP model that includes a risk assessment approach for safety constraints is proposed by using a Multi-Objective Particle Swarm Optimization algorithm based on Pareto dominance approach to minimize both the construction safety risks of crane operated projects and the total traveling distance of the resources between temporary facilities.

Charged System Search and Magnetic Charged System Search Algorithms for Construction Site Layout Planning Optimization

Construction site layout planning can be considered as an effort to place different temporary facilities in available site locations such that multiple objectives are satisfied as much as possible. With the extension of high-rise building construction and construction activities besides the lack of available spaces in construction sites, proper utilization of this resource has been highlighted because of its significant positive influences on direct cost, safety, and security of the site which consequently affects the total cost and schedule of the project. Thus the construction site layout planning is considered as one of the essential and important phases in construction projects. Site layout planning problem is an NP-Hard problem from the viewpoint of complexity. In this research, two prominent meta-heuristic algorithms, namely Charged System Search (CSS) and Magnetic Charged System Search (MCSS) are utilized to optimize the site layout planning problem. The obtained results of implementing these two algorithms for two different types of site space modeling are compared with the results of the Particle Swarm Optimization (PSO) algorithm and also those of the previous studies. The results illustrate the capability of the CSS and MCSS algorithms in solving the present problem.

A tri-objective ant colony optimization based model for planning safe construction site layout

Abstract Realizing safety improvements in construction site layout planning (CSLP) is vitally important to construction project safety management. Unlike previous studies in which the safety objective is built without detailed risk factors analysis, this study transforms CSLP into a multi-objective optimization (MOO) problem with designing two safety objective functions due to facility safety relationships (potential risks arising from interaction flows) and geographic safety relationship (potential risks arising from hazardous sources) from the holistic interpretation of interaction relationship connecting temporary facilities. Besides, a supplementary cost reduction objective function was also derived as cost is a critical barrier against safety improvement. Subsequently, a tri-objective ant colony optimization based model was developed to solve MOO problem. Finally, a case study is used to verify the proposed model. The study enriches safety implications by considering onsite safety issues from interaction relationship and enhances site safety of CSLP in the pre-construction stage.

Conflict resolution-motivated strategy towards integrated construction site layout and material logistics planning: A bi-stakeholder perspective

Abstract Efficiency of construction site layout planning (CSLP) and construction material logistics planning (CMLP) is of great significance for a successful project control. Despite considerable research undertaken to separately optimize the site layout plan or material logistics plan, the interdependencies and decision conflicts of the layout planner and the logistics planner are neglected in the construction planning and design phase. The ignorance of the decision conflicts and interdependencies can lead to an overrun cost, a delayed project or a worse quality. This research proposes a decentralized methodology based on a bi-level model and a Bi-PSO algorithm, enabling the layout planner and the logistics planner to optimize the CSLP and the CMLP in an integrated model. Fuzzy random parameters are utilized to deal with uncertainty in the data collection process. By utilizing the methodology, the stakeholders are capable of adjusting the respective decisions based on the changes of others. By iteratively adjusting the CSLP and CMLP solutions, the CSLP-CMLP decision conflicts are dealt with, accordingly, the bi-stakeholder conflict resolution-motivated CSLP-CMLP strategy is planned. To demonstrate the applicability of the proposed methodology, a real case study is conducted to optimize CSLP and CMLP simultaneously. The significance of optimizing an integrated CSLP-CMLP for the layout planner, the logistics planner and the general contractor is also justified. The contributions of this research include the capability of modeling the decision conflicts in a bi-level model, the ability of generating an integrated CSLP-CMLP solution and the practicality of guiding academic researchers and practitioners to optimize construction management from a global viewpoint. The research can be extended to other fields to explore the effects of decentralized decision making on other bi-stakeholder conflict resolution cases.

Genetic Algorithm–Simulation Framework for Decision Making in Construction Site Layout Planning

AbstractSite layout planning is a complicated task in many construction projects because of the diversity of decision variables, conflicting objectives, and the variety of possible solutions. This paper describes a framework that facilitates decision making on site-layout planning problems. The framework consists of three phases: (1) functionality evaluation phase (FEP), which qualitatively evaluates using a new method; (2) cost evaluation phase (CEP), which quantitatively evaluates the goodness of the layouts using simulation; and (3) value evaluation phase (VEP), which selects the most desirable layout from both qualitative and quantitative aspects. This framework also takes advantage of heuristic optimization through genetic algorithm (GA) to search for the most qualified layouts within FEP. The primary contribution of this research is to introduce a novel method for evaluating quality of layouts, which more realistically model the closeness constraints, and consider size and location desirability in t...

Multi-objective Construction Site Layout Planning Using Genetic Algorithms

Efficient layout planning of a construction site is fundamental for successful project undertaking as it enhances both productivity and safety in construction sites. This task usually consists of identifying the temporary facilities needed to support construction operations, determining their size and shape, and optimally positioning them in the unoccupied areas within the site boundaries. The site layout planning problem is a complex combinatorial optimization problem involving multiple objectives and it grows significantly in size as the number of facilities and constraints increases. The existing literature includes a variety of analytical, heuristic, and meta-heuristic techniques for solving the problem but existing studies usually examine a small number of facilities and focus on travel distance minimization, ignoring generally cost related or other decision parameters. The objective of this study is to develop feasible and efficient site layout solutions in a realistic representation scheme taking into consideration not only the total distance traveled but also cost and safety parameters as well. A multi-objective optimization model is developed aiming at minimizing a generalized cost function which results from the construction cost of a facility placed at alternative locations, the transportation cost among locations, and any safety concern in the form of preferred proximity or remoteness of particular facilities to other facilities or work areas. The development integrates the required robust search objective with the optimization capabilities of the genetic algorithms (GAs). The model has been tested on several test cases and the results of a comparative study with existing methods from the literature are presented. The evaluation indicates that the proposed model provides effective and rational solutions, in response to decision parameters and problem constraints, and that it results in more robust layout planning than previous methods both qualitatively and quantitatively.

Bilevel and multi-objective dynamic construction site layout and security planning

Abstract The joint problem of dynamic construction site layout and security planning is fundamental to any construction project. However, it is still largely unexplored. Few research studies investigated the implementation of security measures during the construction phase. This paper proposes a bilevel multi-objective model for the dynamic construction site layout and security planning problem. Specifically, the upper-level programming denotes that the project manager must first choose the construction site layout and security strategies to minimize the layout costs and consequences of a potential attack. The lower-level programming denotes that the attacker will destroy a subset of the facilities to inflict the maximum economic consequence on the construction facilities system. Thereafter, a bilevel multi-objective Particle Swarm Optimization Algorithm (MOBLPSO) is designed to solve this model. Finally, the approach is carried out in the Xiajiang hydropower large-scale construction project to illustrate the effectiveness of the proposed model and algorithm.

Cost–safety trade-off in unequal-area construction site layout planning

Abstract Cost and safety are two key elements when designing a good construction site layout planning (CSLP). Previous research works always considered CSLP from the aspect of reducing cost and treated SCLP as a single objective optimization problem. In the paper, CSLP was designed by a multi-objective optimization (MOO) model using modified Pareto-based ant colony optimization (ACO) algorithm, which could find a Pareto solution (trade-off layout) to fulfill the requirement of reducing cost and improve the site safety level simultaneously. Furthermore, in order to apply MOO model to solve unequal-area problem, the random grids-recognition strategy was employed in the proposed MOO model to solve the unequal-area site layout problems without increasing the computational complexities. A case study of a residential building project is used to validate the proposed MOO model and the results are very positive.

Multi-Objective Dynamic Construction Site Layout Planning in Fuzzy Random Environment

Abstract Construction site layout planning is a fundamental task for any project undertaking. In practice, most of construction site layout planning problems are dynamic, multi-objective and uncertain in nature. To enhance the general practice of dynamic construction site layout planning, this paper proposes a fuzzy random multi-objective decision making model. In this model, two objectives are considered: (1) minimizing the total cost of site layout; and (2) maximizing the distance between the ‘high-risk’ facilities and the ‘high-protection’ facilities to reduce the possibility of safety or environmental accidents. After stating the problem and presenting the mathematical formulation, this paper deals with fuzzy random variables to propose an equivalent crisp model. According to the characteristics of the proposed model, a multi-objective particle swarm optimization algorithm (MOPSO) with permutation-based representation is proposed to solve the problem. The approach is applied to the Longtan hydropower construction project to illustrate the effectiveness of the proposed model and algorithm.

Facility Closeness Relationship Determination in the Construction Site

Construction site layout planning (CSLP) is one of the critical activities in construction planning conducted by the managers on the construction sites. CSLP is significantly influenced by the interaction flows and distance between the facilities. The paper aims to apply fuzzy rule-based system developed in the manufacture industry to determine the facility closeness relationship in the construction site. Facility closeness relationship with consideration of quantitative and qualitative factors can be successfully determined by the procedure based on fuzzy rule-based system.

Construction Site Layout Evaluation by Intuitionistic Fuzzy TOPSIS Model

Effective construction site layout planning (CSLP) shall improve construction operations efficiently and enhance work environment safety. The majority researches works on CSLP focused on developing different algorithms and program to generate site layout alternatives using quantitative and qualitative factors involved in the interaction flows. However, some qualitative factors, such as security and noise control are not taken into consideration. This study proposed intuitionistic fuzzy TOPSIS method to evaluate and select the best site layout from site layout alternatives in terms of the project-specific attributes, which are neglected and hard to quantify in generating site layout alternatives. The results show that the proposed method improves the quality of decision making in evaluation and selection of the best construction site layout.

A decision-making system for construction site layout planning

A decision-making system, which consists of input, design, evaluation and selection, and output stages, is proposed to solve dynamic, multi-objective and unequal-area construction site layout planning (CSLP) problem. In the input stage, the multiple objectives, schedule planning and site condition are determined. In the design stage, two mathematical optimization models max–min ant system (MMAS) and modified Pareto-based ant colony optimization (ACO) algorithm are employed to solve single objective optimization (SOO) and multi-objective optimization (MOO) problem respectively. In the evaluation and selection stage, the intuitionistic fuzzy TOPSIS method is used to evaluate and select the best layout plan among the generated layout alternatives from the design stage. The performance of the proposed decision-making system, which was verified by a residential building project, shall assist the practitioners in the construction industry to deliver construction projects in a more efficient and effective manner, and thus construction costs could be reduced significantly.

Dynamic construction site layout planning using max-min ant system

Construction site layout planning (CSLP) is a dynamic multi-objective optimization (MOO) problem as there are different facilities employed in the different construction phases of a construction project. In this study, a new method using continuous dynamic searching scheme to guide the max-min ant system (MMAS) algorithm, which is one of the ant colony optimization (ACO) algorithms, to solve the dynamic CSLP problem under the two congruent objective functions of minimizing safety concerns and reducing construction cost is proposed. Using weighted sum method the MOO problem can be solved by the proposed MMAS method. An office building case was used to verify the capability of the proposed method to solve dynamic CSLP problem and the results are promising. The approach could be benchmarked by researchers using other advanced optimization algorithms to solve the same problem or expand the applications to other fields.

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.

A hybrid CAD-based construction site layout planning system using genetic algorithms

The efficient layout planning of a construction site is a fundamental task to any project undertaking. In an attempt to enhance the general practice of layout planning of construction sites, the paper introduces a novel approach for producing the sought layouts. This approach integrates the highly sophisticated graphical capabilities of computer-aided design (CAD) platforms with the robust search and optimization capabilities of genetic algorithms (GAs). In this context, GAs are utilized from within the CAD environment to optimize the location of temporary facilities on site. The functional interaction between GAs and CAD and the details of the GA-based layout optimization procedure are presented. A fully automated computer system is further developed to demonstrate the practicality of the chosen approach. In order to evaluate the system's performance, a local construction project with a 24,000m 2 site is used. The automated system produced highly satisfactory results and showed notable flexibility through its CAD-based input/output media.