Search results

The purpose of this paper is to present a newly developed multi-objective optimization method for the time, cost and work interruptions for repetitive scheduling while considering uncertainties associated with different input parameters.

The design of the developed method is based on integrating six modules: uncertainty and defuzzification module using fuzzy set theory, schedule calculations module using the integration of linear scheduling method (LSM) and critical chain project management (CCPM), cost calculations module that considers direct and indirect costs, delay penalty, and work interruptions cost, multi-objective optimization module using Evolver © 7.5.2 as a genetic algorithm (GA) software, module for identifying multiple critical sequences and schedule buffers, and reporting module.

For duration optimization that utilizes fuzzy inputs without interruptions or adding buffers, duration and cost generated by the developed method are found to be 90 and 99 percent of those reported in the literature, respectively. For cost optimization that utilizes fuzzy inputs without interruptions, project duration generated by the developed method is found to be 93 percent of that reported in the literature after adding buffers. The developed method accelerates the generation of optimum schedules.

Unlike methods reported in the literature, the proposed method is the first multi-objective optimization method that integrates LSM and the CCPM. This method considers uncertainties of productivity rates, quantities and availability of resources while utilizing multi-objective GA function to minimize project duration, cost and work interruptions simultaneously. Schedule buffers are assigned whether optimized schedule allows for interruptions or not. This method considers delay and work interruption penalties, and bonus payments.

The purpose of this paper is to identify optimum crew formations at unit execution level of repetitive projects that minimize project duration, project cost, crew work interruptions and interruption costs, simultaneously.

The model consists of four modules. The first module quantifies uncertainties associated with the crew productivity rate and quantity of work using the fuzzy set theory. The second module identifies feasible boundaries for activity relaxation. The third module computes direct cost, indirect cost and interruption costs, including idle crew cost as well as mobilization and demobilization costs. The fourth module identifies near-optimum crew formation using a newly developed multi-objective optimization model.

The developed model was able to provide improvements of 0.2, 16.86 and 12.98% for minimization of project cost, crew work interruptions and interruption costs from US$1,505,960, 8.3 days and US$8,300, as recently reported in the literature, to US$1,502,979, 6.9 days and US$7,222, respectively, without impacting the optimized project duration.

The novelty of this paper lies in its activity-relaxation free float that considers the effect of postponing early finish dates of repetitive activities on crew work interruptions. The introduced new float allows for calculating the required crew productivity rate that minimizes crew work interruptions without delaying successor activities and without impacting the optimized project duration. It safeguards against assignment of unnecessary costly resources.

The purpose of this paper is to present a new method for project tracking and control of integrated offsite and onsite activities in modular construction considering practical characteristics associated with this type of construction.

The design embraces building information modelling and integrates last planner system (LPS), linear scheduling method (LSM) and critical chain project management (CCPM) to develop tracking and control procedures for modular construction projects. The developed method accounts for constraints of resources continuity and uncertainties associated with activity duration. Features of proposed method are illustrated in a case example for tracking and control of modular projects.

Comparison between developed schedule and Monte Carlo simulation showed that baseline duration generated from simulation exceeds that produced by developed method by 12% and 10% for schedules with 50% and 90% confidence level, respectively. These percentages decrease based on interventions of members of project team in the LPS sessions. The case example results indicate that project is delayed 5% and experienced cost overrun of 2.5%.

Developed method integrated LPS, LSM and CCPM while using metrics for reliability assessment of linear schedules, namely, critical percent plan complete (PPC_cr) and buffer index (BI). PPC_cr and BI measure percentage of plan completion for critical activities and buffer consumption, respectively. The developed method provides a systematic procedure for forecasting look-ahead schedules using forecasting correction factor Δ_t and a newly developed tracking and control procedure that uses PPC_cr and BI. Quantitative cost analysis is also provided to forecast and monitor project costs to prove the robustness of proposed framework.

Construction projects are complex projects taking place in dynamic environments, which necessitates accounting for different uncertainties during the planning stage. There is a significant lack of management tools for repetitive projects accounting for uncertainties in the construction environment. The purpose of this paper is to present an algorithm for the optimized scheduling of repetitive construction projects under uncertainty.

Fuzzy set theory is utilized to model uncertainties associated with various input parameters. The developed algorithm has two main components: optimization component and buffering component. The optimization component presents a dynamic programming approach that processes fuzzy numbers. The buffering component converts the optimized fuzzy schedule into a deterministic schedule and inserts time buffers to protect the schedule against anticipated delays. Agreement Index (AI) is used to capture the user’s desired level of confidence in the produced schedule while sizing buffers. The algorithm is capable of optimizing for cost or time objectives. An example project drawn from literature is analysed to demonstrate the capabilities of the developed algorithm and to allow comparison of results to those previously generated.

Testing the algorithm revealed several findings. Fuzzy numbers can be utilized to capture uncertainty in various inputs without the need for historical data. The modified algorithm is capable of optimizing schedules, for different objectives, under uncertainty. Finally AI can be used to capture users’ desired confidence in the final schedule.

Project planners can utilize this algorithm to optimize repetitive projects schedules, while modelling uncertainty in different input parameters, without the need for relevant historical data.

To mitigate the problems in sensor-based facility management (FM) such as lack of detailed visual information about a built facility and the maintenance of large scale sensor deployments, an integrated data source for the facility’s life cycle should be used. Building information modeling (BIM) provides a useful visual model and database that can be used as a repository for all data captured or made during the facility’s life cycle. It can be used for modeling the sensing-based system for data collection, serving as a source of all information for smart objects such as the sensors used for that purpose. Although few studies have been conducted in integrating BIM with sensor-based monitoring system, providing an integrated platform using BIM for improving the communication between FMs and Internet of Things (IoT) companies in cases encountered failed sensors has received the least attention in the technical literature. Therefore, the purpose of this paper is to conceptualize and develop a BIM-based system architecture for fault detection and alert generation for malfunctioning FM sensors in smart IoT environments during the operational phase of a building to ensure minimal disruption to monitoring services.

This paper describes an attempt to examine the applicability of BIM for an efficient sensor failure management system in smart IoT environments during the operational phase of a building. For this purpose, a seven-story office building with four typical types of FM-related sensors with all associated parameters was modeled in a commercial BIM platform. An integrated workflow was developed in Dynamo, a visual programming tool, to integrate the associated sensors maintenance-related information to a cloud-based tool to provide a fast and efficient communication platform between the building facility manager and IoT companies for intelligent sensor management.

The information within BIM allows better and more effective decision-making for building facility managers. Integrating building and sensors information within BIM to a cloud-based system can facilitate better communication between the building facility manager and IoT company for an effective IoT system maintenance. Using a developed integrated workflow (including three specifically designed modules) in Dynamo, a visual programming tool, the system was able to automatically extract and send all essential information such as the type of failed sensors as well as their model and location to IoT companies in the event of sensor failure using a cloud database that is effective for the timely maintenance and replacement of sensors. The system developed in this study was implemented, and its capabilities were illustrated through a case study. The use of the developed system can help facility managers in taking timely actions in the event of any sensor failure and/or malfunction to ensure minimal disruption to monitoring services.

However, there are some limitations in this work which are as follows: while the present study demonstrates the feasibility of using BIM in the maintenance planning of monitoring systems in the building, the developed workflow can be expanded by integrating some type of sensors like an occupancy sensor to the developed workflow to automatically record and identify the number of occupants (visitors) to prioritize the maintenance work; and the developed workflow can be integrated with the sensors’ data and some machine learning techniques to automatically identify the sensors’ malfunction and update the BIM model accordingly.

Transferring the related information such as the room location, occupancy status, number of occupants, type and model of the sensor, sensor ID and required action from the BIM model to the cloud would be extremely helpful to the IoT companies to actually visualize workspaces in advance, and to plan for timely and effective decision-making without any physical inspection, and to support maintenance planning decisions, such as prioritizing maintenance works by considering different factors such as the importance of spaces and number of occupancies. The developed framework is also beneficial for preventive maintenance works. The system can be set up according to the maintenance and time-based expiration schedules, automatically sharing alerts with FMs and IoT maintenance contractors in advance about the IoT parts replacement. For effective predictive maintenance planning, machine learning techniques can be integrated into the developed workflow to efficiently predict the future condition of individual IoT components such as data loggers and sensors, etc. as well as MEP components.

Lack of detailed visual information about a built facility can be a reason behind the inefficient management of a facility. Detecting and repairing failed sensors at the earliest possible time is critical to ensure the functional continuity of the monitoring systems. On the other hand, the maintenance of large-scale sensor deployments becomes a significant challenge. Despite its importance, few studies have been conducted in integrating BIM with a sensor-based monitoring system, providing an integrated platform using BIM for improving the communication between facility managers and IoT companies in cases encountered failed sensors. In this paper, a cloud-based BIM platform was developed for the maintenance and timely replacement of sensors which are critical to ensure minimal disruption to monitoring services in sensor-based FM.

The complexity and criticality of healthcare services highlight the importance of maintenance management function in healthcare facilities. The purpose of this paper is to review the literature on maintenance management of healthcare facilities and hospital buildings to provide an organized literature review and identify gaps from the perspective of research and practice.

The paper categorizes the literature and adopts a review hierarchy according to maintenance management functions in hospital buildings. It explores the impact of those functions on the performance of maintenance activities in hospitals. Furthermore, it examines the role of information technology and automated decision support systems in facilitating hospital maintenance management functions and performance.

Literature on maintenance management in healthcare facilities and hospital buildings has so far been very limited. Recently published literature focusing on healthcare facilities management and its maintenance management functions is classified into various areas and sub-areas. The paper highlights gaps in the literature and suggests avenues for future research and improvements.

The paper contains a comprehensive listing of publications and their classifications according to various attributes. It will be useful for researchers, maintenance managers, practitioners and stakeholders concerned with facility management of hospital buildings.

This paper aims to present an integrated method for optimized project duration and costs, considering the size and cost of crews assigned to project activities' execution modes.

The proposed method utilizes fuzzy set theory (FSs) for modeling uncertainties associated with activities' duration and cost and genetic algorithm (GA) for optimizing project schedule. The method has four main modules that support two optimization methods: modeling uncertainty and defuzzification module; scheduling module; cost calculations module; and decision-support module. The first optimization method uses the elitist non-dominated sorting genetic algorithm (NSGA-II), while the second uses a dynamic weighted optimization genetic algorithm. The developed scheduling and optimization methods are coded in python as a stand-alone automated computerized tool to facilitate the developed method's application.

The developed method is applied to a numerical example to demonstrate its use and illustrate its capabilities. The method was validated using a multi-layered comparative analysis that involves performance evaluation, statistical comparisons and stability evaluation. Results indicated that NSGA-II outperformed the weighted optimization method, resulting in a better global optimum solution, which avoided local minima entrapment. Moreover, the developed method was constructed under a deterministic scenario to evaluate its performance in finding optimal solutions against the previously developed literature methods. Results showed the developed method's superiority in finding a better optimal set of solutions in a reasonable processing time.

The novelty of the proposed method lies in its capacity to consider resource planning and project scheduling under uncertainty simultaneously while accounting for activity splitting.

This paper presents a newly developed algorithm for selecting and locating mobile cranes on construction sites. The algorithm is incorporated into a computer system that integrates a selection module and three databases, dedicated respectively, for cranes, rigging equipment, and projects’ information. This paper focuses primarily on the selection module and its algorithm to support an efficient search for most suitable crane configurations and their associated lift settings. Data pertinent to crane lift configurations and settings are retrieved from the databases and processed to determine the near optimum selection of a crane configuration. The developed selection module features powerful graphics capabilities and a practical user‐friendly interface, designed to facilitate the considerations of user imposed lift and site constraints. The selection algorithm has been implemented within the crane selection module using MS‐Visual Basic programming language. A case example is presented in order to demonstrate the use of the developed selection module and to illustrate its essential features.

Compiling bid proposals for construction projects is a process that depends on extensive computation as well as on experience‐based judgement. Despite the proliferation of estimating tools, bid proposals focus primarily on the computational component and leave the more difficult aspects of risk assessment and mark‐up estimation to estimators' judgement. This may lead to unrealistic estimates that do not account for the operational environment of projects. Such estimates often result in either losing bids or inflicting undesirable cost overruns. In an effort to circumvent such drawbacks, this paper presents a structured system for cost estimation and bid preparation. Unlike current tools, the proposed system supports a holistic bid‐preparation process, accounting for a number of quantitative as well as qualitative factors that are used in practice for bid preparation. The system incorporates three principal features: 1 Integrated cost and schedule computation 2 Adequate risk assessment and mark‐up estimation 3 Optimum bid unbalancing and cash flow optimization. The developments made in the integrated system are described along with a PC‐based prototype Estimator, developed to automate the process. An example application is presented to illustrate the capabilities and essential features of the prototype and demonstrate its practicality.

The objective of this paper is to develop a tracking and control system that automates the process of data collection from construction sites for fast and accurate measurement of work progress.

The proposed system integrates different data acquisition hardware and software technologies including barcoding, radio frequency identification (RFID), laser distance and ranging (LADAR), digital images, and a tablet PC as integrating media.

The paper briefly highlights the advantages and limitations associated with each technology, and presents a methodology that best utilizes these technologies in an integrated system. At the core of the developed system is its database, which is designed to organize and store data collected from construction sites in a way that supports the developed methodology in progress reporting.

The accuracy and timeliness of these reports are crucial for management teams to take corrective actions, if needed, so as to assist in delivering projects on time and within budget.

The paper presents the layout of an IT platform designed to facilitate automated data acquisition from construction sites to support efficient time and cost tracking and control of construction projects. The system presented is capable of capturing text, numerical and graphical data to report efficiently on the progress of a project.