Enhanced Welding Operator Quality Performance Measurement: Work Experience-Integrated Bayesian Prior Determination

Measurement of operator quality performance has been challenging in the construction fabrication industry. Among various causes, the learning effect is a significant factor, which needs to be incorporated in achieving a reliable operator quality performance analysis. This research aims to enhance a previously developed operator quality performance measurement approach by incorporating the learning effect (i.e., work experience). To achieve this goal, the Plateau learning model is selected to quantitatively represent the relationship between quality performance and work experience through a beta-binomial regression approach. Based on this relationship, an informative prior determination approach, which incorporates operator work experience information, is developed to enhance the previous Bayesian-based operator quality performance measurement. Academically, this research provides a systematic approach to derive Bayesian informative priors through integrating multi-source information. Practically, the proposed approach reliably measures operator quality performance in fabrication quality control processes.Comment: 8 pages, 5 figures, 2 tables, i3CE 201

Site Layout and Construction Plan Optimization Using an Integrated Genetic Algorithm Simulation Framework

Efficiency of a planned site layout is essential for the successful completion of construction projects. Despite considerable research undertaken for optimizing construction site layouts, most models developed for this purpose have neglected the mutual impacts of the site layout and construction operation variables and are unable to thoroughly model these impacts. This paper outlines a framework enabling planners to anticipate site layout variables (i.e., size, location, and orientation of temporary facilities) and construction plan variables (e.g., resources and material delivery plan), and simultaneously optimize them in an integrated model. In this framework, genetic algorithm (GA) and simulation are integrated; GA heuristically searches for the near-optimum solution with minimum costs by generating feasible candidate solutions, and simulation mimics construction processes and measures the project costs by adopting those candidate solutions. The contribution of this framework is the ability to capture the mutual impacts of site layout and construction plans in a unified simulation model and optimize their variables in GA, which subsequently entails developing a more efficient and realistic plan. Applicability of the framework is presented in a steel erection project

A hybrid simulation approach for quantitatively analyzing the impact of facility size on construction projects

Sizing temporary facilities is a crucial task in construction site layout planning due to its significant impact on project productivity and cost. This paper describes a simulation-based approach for modeling the size of facilities that temporarily contain materials in construction projects. Different methods have been introduced for estimating the required size of this kind of facility; however, space limitations, particularly on congested sites, may not allow the planner to allocate the estimated space to the facilities. This study aims at quantitatively analyzing the impact of facility size on the project and modeling the managerial corrective actions to remedy the space shortage in facilities. To this end, a hybrid discrete-continuous simulation technique is adopted. Simulation is superior in modeling dynamic interactions between variables as well as modeling construction processes with inherent uncertainties. The combination of discrete and continuous simulation is used to enhance accuracy and model the project at both operational level (i.e., activity level with higher level of detail) to estimate production rate, and strategic level (i.e., macro level with lower level of detail) to account for some construction planning decisions such as material management variables. The novelty of this study is analyzing the impact of facility size on the project time and cost, while managerial actions taken to resolve space shortages are modeled, and interdependent influencing parameters of the different disciplines, such as site layout, material management, logistics, and construction process planning are integrated in a unified model. The applicability and suitability of the proposed approach is demonstrated in layout planning of a tunneling project site

An integrated simulation model for site layout planning of tunnelling projects

Overlooking site layout in the planning phase of construction projects leads to loss of productivity and incurs extra costs. In tunneling projects, site layout has a significant impact on material flow and tunneling operations, particularly on congested sites. In addition, construction planning decisions can influence the efficiency of the layout. This paper proposes simulation as a decision making tool to model tunnel construction operations and site layout, and capture their mutual influences. To facilitate building the simulation model, even for users with limited simulation knowledge, a special purpose simulation (SPS) tool was customized and developed. This simulation tool provides an integrated environment to model the parameters of different disciplines including site layout, material procurement, tunnel operations and logistics. The developed tool is of great assistance for the planners to make decisions simultaneously on site layout and other construction planning parameters, and find the most cost-efficient plan

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

Site 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 the evaluating function. Also, using simulation for estimating project cost improves the effectiveness of the framework in practice because simulation can model construction processes, uncertainties, resources, and dynamic interactions between various parameters. Applicability of the framework is demonstrated through a case study of the layout planning of a tunneling project

Using Computer Simulations to Plan Construction Projects Accurately

The three main objectives in construction projects are completing the project on time, within budget, and with good quality. Each construction project is unique and unpredictable making it beneficial to model the project before executing it. There are many ways to model a construction project; however, computer models are ideal. It is very costly and time consuming to experiment with the actual system. Therefore, by using a computer simulation, accurate data can be collected from the project without the time and cost drawbacks. The specific construction project researched is based on a real project from Fort Mcmurray Alberta, Canada. The construction project involved the delivery and erection of three different types of steel in a construction site. Once the steel has been delivered, it needs to be stored and then carried by forklift to one of two cranes to be erected. A schedule was provided for which days each type of material was expected to be delivered and erected, however this schedule did not account for the 20% chance that any delivery could be delayed by one day or the 10% chance that deliveries could be delayed by two days. A model project was created on Simphony.NET with the assumptions that work could commence the entire day (24 hours), the site has unlimited storage, and a delay in one delivery does not delay all the deliveries after it. The schedule for the project was then modified to reflect the results of the simulation. The modified schedule showed that several deliveries of materials were delayed. However, due to the model’s assumptions and the time for erection being relatively short, the planned schedule for the erection of the materials was not delayed. By using the data collected from the computer simulation it was possible to more accurately plan the schedule for this  construction project

Hybrid Genetic Algorithm-Simulation Optimization Method for Proactively Planning Layout of Material Yard Laydown

This paper presents a hybrid optimization method combining a genetic algorithm (GA) and simulation for planning the layout of material yard laydown areas. An optimized material yard layout entails efficiency in terms of time and cost for decision makers who seek increased performance in material handling, availability, and accessibility. Laying out materials on yards is mostly performed reactively in current practice, where the planner decides daily where to position the incoming materials, based on the list of material arrival and required materials for consumption, received daily. This policy cannot account for the dynamism of material flow in and out of the yard during a construction project. In contrast, a proactive materials placement policy can be used to address this concern based on incoming and outgoing material schedules for a certain period of time. This paper aims to evaluate the proactive material placement policy and present an integrated framework to determine the optimum layout for placing materials resulting in minimum material haulage time. To this end, a hybrid optimization is implemented through a case study from the steel fabrication industry, where an effective materials handling method could be of great significance. The major contribution of this work is the development of an approach that performs dynamic layout optimization of materials arriving at construction yards, using GA to heuristically search for the solution, and use of simulation to model the material handling process and determine the material haulage time. Results of the analyses show clear merits of proactive material placement over the reactive strategy and demonstrate the importance of GA and simulation integration to obtain more realistic outcomes

Enhanced Welding Operator Quality Performance Measurement: Work Experience-Integrated Bayesian Prior Determination

Measurement of operator quality performance has been challenging in the construction fabrication industry. Among various causes, the learning effect is a significant factor, which needs to be incorporated in achieving a reliable operator quality performance analysis. This research aims to enhance a previously developed operator quality performance measurement approach by incorporating the learning effect (i.e., work experience). To achieve this goal, the Plateau learning model is selected to quantitatively represent the relationship between quality performance and work experience through a beta-binomial regression approach. Based on this relationship, an informative prior determination approach, which incorporates operator work experience information, is developed to enhance the previous Bayesian-based operator quality performance measurement. Academically, this research provides a systematic approach to derive Bayesian informative priors through integrating multi-source information. Practically, the proposed approach reliably measures operator quality performance in fabrication quality control processes.Comment: 8 pages, 5 figures, 2 tables, i3CE 201

Material and facility layout planning in construction projects using simulation

Layout planning for construction projects comprises two tasks: facility layout planning (FLP) and material layout planning (MLP), which has significant impacts on project cost and time. FLP specifies where to position temporary facilities on the site, and MLP determines the position of the material in the storage yard. This study focuses on MLP and describes a simulation-based method to improve material yard layout. In this method, simulation is employed for modeling the material handling process to evaluate material handling time. Due to the broad domain of possible solutions, simulation is integrated with genetic algorithm to heuristically search for a near optimum material layout with the least haulage time. The implementation of the proposed method is demonstrated in a case study which shows the superiority of the developed method over conventional methods. This paper also discusses how the results of this research can contribute to FLP