Dynamic site layout planning (DSLP) is a pre-construction managerial task that treats construction site space as a constrained recourse by assigning it to site temporary facilities (i.e. offices, storage areas, and workshops) in a timely manner. The dynamic assignment of site space in DSLP is done to achieve several objectives such as minimizing nonproductive time and cost (i.e. material handling and relocation) and/or maximizing safety. This planning task comprehends dividing the project duration into a set of successive stages that represent major construction phases and different levels of site space needs. Accordingly, temporary facilities are identified in each stage and the site layout is dynamically planned in order to optimize various objectives of travel distance, safety, and/or security. DSLP becomes highly effective in congested construction sites by considering construction dynamic environment
through the assignment of released spaces to new temporary facilities and relocating existing facilities to better locations.
Despite its potential benefits, planners tend to underutilize DSLP in project planning stage because of its complexity especially in large-scale and confined construction projects (Mawdesley et al. 2002). Therefore, automated models of DSLP that utilizes Operations Research (OR), optimization techniques, and visualization were developed in order to help planners and contractors in this complex planning task. Available models of dynamic site layout planning can be classified into five main categories: (1) hybrid linear programming, (2) Genetic Algorithms (GA), (3) ant colony optimization (ACO), (4) geographical information system (GIS); and (5) four-dimensional (4D) visualization.
2.2.1 Hybrid Linear Programming
Zouein and Tommelein (1999) developed a hybrid model that utilizes heuristics, constraint satisfaction, and linear programming (LP) to optimize the layout of temporary facilities in order to minimize transportation and relocation costs. Construction temporary facilities are represented as rectangles with their relocation costs and distance-based travel cost among them defined using dimensionless weights. This hybrid model generates the optimal position and orientation of every temporary facility in each stage in a stepwise approach through three steps: (1) selecting heuristically the facility to be positioned based on a set of ad-hoc rules that consider the importance of the facility in terms of its travel and/or relocation weights; (2) computing a set of feasible positioning decisions for the facility using a constraint satisfaction and propagation algorithm; and (3) finding the optimal option from this set of feasible decisions using linear programming so as to minimize travel and relocation costs.
The solutions generated are path-sensitive because layout decisions are generated in a stepwise approach that is significantly affected by the imposed ad-hoc and heuristics rules (Zouein and Tommelein 1999). Furthermore, the positioning of every facility does not consider its future impacts on subsequent decisions of other facilities in the same stage as well as in next stages.
2.2.2 Genetic Algorithms
Genetic Algorithms are search techniques that mimic the metaphor of natural biological evolution to search for global optimum solutions of complex problems (Goldberg 1989, Deb et al. 2000). GA utilizes a set of biological evolution operations such as inheritance, selection, crossover, and mutation to enhance the quality of a set of solutions through evolution over a number of generations. A solution to a given problem is represented in the form of chromosome string, where each chromosome element (gene) refers to a specific decision variable of the problem. The algorithm starts with a random population of solutions, where the fitness of each solution is evaluated using an objective function. Accordingly, the fittest solutions are chosen through a specific selection mechanism to exchange their information (using crossover and mutation operations) to produce better offspring. This process of selection, crossover, and mutation is repeated for a specific number of iterations (generations) or until a predetermined convergence criteria is satisfied.
Researchers implemented Genetic Algorithms to optimize large and complex problems of dynamic site layout planning. Osman et al (2003) proposed a CAD-based optimization model that integrates the graphical capabilities of CAD software applications with the robust search and optimization tools of GA to generate optimal dynamic layout plans. First, the proposed
model utilizes CAD to perform two main functions: space detection and constraint satisfaction. Space detection is performed before the GA optimization to divide the site into a set of grid locations available for assignment of temporary facilities. Constraints satisfaction is performed during the GA optimization to check the feasibility of every temporary against site and overlap constraints. Second, GA is used to optimize the dynamic layout planning for the whole project duration by solving the layout problem of every construction stage separately considering minimizing resource travel cost and facilities relocation cost. This means that the whole problem of dynamic site layout planning is divided into a set of T static layout problems for T construction stages. These T static layout problems are solved in a stepwise fashion for T iterations, where a different stage is selected to be the initial stage whose optimal layout is generated first. Despite the potential benefits of this approach over the model of Zouein and Tommelein (1999), it has the following limitations: (1) it still may result in non-optimal or infeasible solutions as the local optimal layout of each stage is generated for each stage without the consideration of its impact on the layout quality of other stages; and (2) it is computationally exhaustive for large scale problems of dynamic site layout planning.
Elbeltagi et al. (2004) implemented genetic algorithm in a spreadsheet-based optimization model of dynamic site layout planning that enable the simultaneous minimization of both travel cost and safety as one integrated function. Commercial spreadsheet software is used as the platform of the developed model that comprehends inputting, optimization, and outputting modules. Using spreadsheets, user can define construction site and temporary facilities as irregular shapes using sheet (grid) cells as modeling blocks, as shown in Figure 2.1. The size of each sheet cell is calculated as the greatest common divisor (GCD) of the
areas of all site facilities. Interactions between site facilities are represented using seven levels of closeness relationship weights, as shown in Figure 2.1, to model planner’s operational and safety preferences. Assigning a high value of closeness weight to a pair of site facilities refers to the necessity to place them as close as possible to reflect heavy flow of construction resources. On the hand, assigning a low value of closeness weight refers to the necessity to place the facilities apart from each other to reflect any safety concerns. Using these closeness weights, site layout is optimized utilizing GA to minimize the weighted sum of all travel distances between site facilities. The proposed model Elbeltagi et al. (2004) doesn’t consider the relocation cost of facilities as any changes in their layout decisions over project stages are not reflected in the objective function. Furthermore, the model doesn’t consider the tradeoff between minimizing layout cost and minimizing safety as these two objectives are considered in one dimensionless objective function.
Closeness Relationship Weight
Necessary to be close 103
Better to be close 102
May be close 101
Indifferent 0
May be apart -101
Better to be apart -102
Necessary to be apart -103
Figure 2.1 Representations of Site Geometry and Closeness Relationship (Elbeltagi et al. 2004)
2.2.3 Ant Colony Optimization
Ant Colony Optimization (ACO) is a search technique, developed by Dorigo et al (1996), based on the phenomena that the ants are able to iteratively find the shortest path between their nest and a food source. This is done through pheromone trails that the ants leave behind as a mean of indirect communication. ACO is an iterative technique that mimics the ants behavior in converging to the shortest path (the optimal solution) through the gradual compilation of the pheromone trail. The algorithm starts by randomly generating a set of
“artificial ants” that represent different solutions or paths. Each ant is evaluated against an objective function, which determines the change of pheromone concentration on its path.
Iteratively, the ants are sent into different paths (changing the values of the decision variables) considering the pheromone concentrations from previous iterations. Accordingly, positive feedback through iterations would lead that all ants will choose a single path that represents the optimal solution. AbdelRazig et al (2005) implemented ACO in an optimization model of DSLP that minimizes travel and relocation costs. They modeled dynamic site layout planning as a quadratic assignment problem (QAP) that assumes that the number of predetermined site locations should be equal to the number of required temporary facilities. If the number of site locations is larger than the number of temporary facilities, then a set of “dummy” facilities is added to have equal numbers of positions and facilities.
This modeling approach is significantly time consuming especially in large construction sites that have large number of possible positions compared to the number of utilized temporary facilities.
2.2.4 Geographical Information Systems
Geographical information system (GIS) is a technological tool that integrates hardware, software, and data to help in modeling, storing, analyzing, and displaying various kinds of geographically referenced information (GIS 2009). GIS helps planners and managers to investigate relationships, patterns, and trends between information and produce informative results in the form maps, reports, and charts. Cheng and O’Connor (1994 and 1996) developed a GIS-based model (ArcSite) of dynamic site layout planning that captures layout planning knowledge and support planners in designing construction site layout. First, ArcSite comprehends a knowledge base that includes expert’s knowledge and experience of site layout planning obtained from research literature and companies manuals. The knowledge base component of Arcsite is designed to perform four main operations: data inputting, knowledge acquisition, knowledgebase processing, and reasoning explanation. Second, ArcSite helps planners in designing site layout plans through site spatial analysis and quantification of layout decision. A constructive placement procedure is implemented to select and place site temporary facilities one at a time considering available locations and their qualities. During this iterative process, the system generates the potential locations for every facility using the concept of searching by elimination. The quality of each of these potential locations is evaluated using a proximity index that considers the travel frequency and attract/repel relationships between site facilities. Similar to previous models of dynamic site layout planning, this constructive placement procedure adopted in ArcSite doesn’t consider the implications of early layout decision on the quality of subsequent ones.
2.2.5 4D Visualization
Four-dimensional (4D) visualization models were developed as visual decision support tools that help construction planners in dynamic site layout planning. As shown in Figure 2.2, previous 4D visualization models (Zhang et al. 2001; Wang et al. 2004; Chau et al. 2005-a and 2005-b; Ma et al. 2005) are based on integrating (1) 3D models of constructed structures, construction equipments, and site temporary facilities; and (2) schedule plan of construction activities. The integration between project’s 3D model and the construction schedule is facilitated by bi-directional links that connect each construction activity with its related structure element, resources, and temporary facilities. This integration results in an informative animation of the 3D representation of construction site activities and resources that help planners in detecting any operational of safety violations. This 3D animation enables construction planners to better understand site management process and dynamically assign site space to construction tasks and facilities considering construction schedule and space availability.
3D Model
Construction Schedule Bidirectional
linkage
4D Visualization of Site Management
Figure 2.2 4D Visualization Application in Site layout Management and Planning
(Wang et al. 2004)
Some of existing 4D visualization models of site layout planning provide various decision support capabilities to help construction planners in taking site layout decisions. Zhang et al.
(2001) imbedded in their model a site knowledge management system (SKMS) that utilizes expert systems and artificial neural networks to analyze input information and planner’s layout decisions. SKMS uses and updates layout knowledge and rules to check the consistency of planner’s decision and propose alternative options based on previous stored expertise. Other models of 4D site visualization comprehend different capabilities such as:
(1) dynamic automated calculation of resources that reflects any changes occur in the 3D model (Wang et al. 2004); (2) data warehousing to store, organize and analyze planner’s
layout decision of previous projects (Chau et al. 2005-a and 2005-b); and (3) providing 3D templates of various components of constructed building as well as site facilities and equipments (Ma et al. 2005). Despite the benefits of 4D site visualization models, they don’t provide the sufficient means of optimizing site layout decision and quantifying the impacts of these decisions on various planning objectives such as safety, cost, or security.