An overview of recent research results and future research avenues using simulation studies in project management

This paper gives an overview of three simulation studies in dynamic project scheduling integrating baseline scheduling with risk analysis and project control. This integration is known in the literature as dynamic scheduling. An integrated project control method is presented using a project control simulation approach that combines the three topics into a single decision support system. The method makes use of Monte Carlo simulations and connects schedule risk analysis (SRA) with earned value management (EVM). A corrective action mechanism is added to the simulation model to measure the efficiency of two alternative project control methods. At the end of the paper, a summary of recent and state-of-the-art results is given, and directions for future research based on a new research study are presented

Construction Time-Cost Optimization Modeling Using Ant Colony Optimization

In the field of construction project management, time and cost are the most important factors to be considered in planning every project, and their relationship is complex. The total cost for each project is the sum of the direct and indirect cost. Direct cost commonly represents labor, materials, equipment, etc. Indirect cost generally represents overhead cost such as supervision, administration, consultants, and interests. Direct cost grows at an increasing rate as the project time is reduced from its original planned time. However, indirect cost continues for the life of the project and any reduction in project time means a reduction in indirect cost. Therefore, there is a trade-off between the time and cost for completing construction activities. In this research, modeling of time-cost optimization, generating global optimum solution for time and cost problem, and lowering construction time and cost using ant colony optimization algorith

Simulation and optimization model for the construction of electrical substations

One of the most complex construction projects is electrical substations. An electrical substation is an auxiliary station of an electricity generation, transmission and distribution system where voltage is transformed from high to low or the reverse using transformers. Construction of electrical substation includes civil works and electromechanical works. The scope of civil works includes construction of several buildings/components divided into parallel and overlapped working phases that require variety of resources and are generally quite costly and consume a considerable amount of time. Therefore, construction of substations faces complicated time-cost-resource optimization problems. On another hand, the construction industry is turning out to be progressively competitive throughout the years, whereby the need to persistently discover approaches to enhance construction performance. To address the previously stated afflictions, this dissertation makes the underlying strides and introduces a simulation and optimization model for the execution processes of civil works for an electrical substation based on database excel file for input data entry. The input data include bill of quantities, maximum available resources, production rates, unit cost of resources and indirect cost. The model is built on Anylogic software using discrete event simulation method. The model is divided into three zones working in parallel to each other. Each zone includes a group of buildings related to the same construction area. Each zone-model describes the execution process schedule for each building in the zone, the time consumed, percentage of utilization of equipment and manpower crews, amount of materials consumed and total direct and indirect cost. The model is then optimized to mainly minimize the project duration using parameter variation experiment and genetic algorithm java code implemented using Anylogic platform. The model used allocated resource parameters as decision variables and available resources as constraints. The model is verified on real case studies in Egypt and sensitivity analysis studies are incorporated. The model is also validated using a real case study and proves its efficiency by attaining a reduction in model time units between simulation and optimization experiments of 10.25% and reduction in total cost of 4.7%. Also, by comparing the optimization results by the actual data of the case study, the model attains a reduction in time and cost by 13.6% and 6.3% respectively. An analysis to determine the effect of each resource on reduction in cost is also presented

Application of Risk Analysis and Simulation for Nuclear Refurbishment Projects

In this thesis, a planning methodology is proposed for nuclear refurbishment projects as a means to address project objectives, influential factors, constraints, and their interdependencies to attain a more reliable estimate of project outcomes. As part of this process, the uncertainty and impact of risk events around project outcomes are taken into account. The proposed methodology consists of two stages. The first stage addresses the impact of commonly identified risks (i.e., Type I risks) and uncertainty on the project outcomes. Also, the interdependence among shift schedule, productivity rate, calendar duration, and risk registers within each identified what-if scenario has been taken into account. The confidence in achieving each of the what-if scenarios is determined using Monte Carlo simulation and a 3-dimensional joint confidence limit model. Based on the simulation results, the deterministic values of the selected project outcomes and the mean values of the resultant distributions are driven primarily by uncertainty, and the distribution tails represent the impact of materialized risks. Also, the probability of failure for each project outcome is less than the joint probability of failure for multiple outcomes. In the second stage of the methodology, the resultant distribution tails (attained from the previous stage) are explored by primarily assessing the impact of outliers (i.e., Type II risks) on project outcomes. Although outliers are typically considered rare events with extreme impacts, the scale and complexity of megaprojects such as refurbishment of nuclear reactors leads to a more frequent occurrence of such events. The applied methodology stems from the reliability analysis approach used to partially justify soft error within integrated circuits due to the observed commonalities such as scale and complexity. A combination of probability theory, Critical Path Method, and Monte Carlo simulation is used to assess the true probability of occurrence for such events. Based on the simulation results, the outliers should be acknowledged and incorporated in the risk management plan of large-scale and complex ventures such as megaprojects. The proposed methodology is validated via Delphi and sensitivity analysis, and functional demonstration using information from an actual multi-billion dollar nuclear refurbishment project and a unique full-scale mock-up of the reactor’s fuel channels and feeders

PREDICTIVE MATURITY OF INEXACT AND UNCERTAIN STRONGLY COUPLED NUMERICAL MODELS

The Computer simulations are commonly used to predict the response of complex systems in many branches of engineering and science. These computer simulations involve the theoretical foundation, numerical modeling and supporting experimental data, all of which contain their associated errors. Furthermore, real-world problems are generally complex in nature, in which each phenomenon is described by the respective constituent models representing different physics and/or scales. The interactions between such constituents are typically complex in nature, such that the outputs of a particular constituent may be the inputs for one or more constituents. Thus, the natural question then arises concerning the validity of these complex computer model predictions, especially in cases where these models are executed in support of high-consequence decision making. The overall accuracy and precision of the coupled system is then determined by the accuracy and precision of both the constituents and the coupling interface. Each constituent model has its own uncertainty and bias error. Furthermore, the coupling interface also brings in a similar spectrum of uncertainties and bias errors due to unavoidably inexact and incomplete data transfer between the constituents. This dissertation contributes to the established knowledge of partitioned analysis by investigating the numerical uncertainties, validation and uncertainty quantification of strongly coupled inexact and uncertain models. The importance of this study lies in the urgent need for gaining a better understanding of the simulations of coupled systems, such as those in multi-scale and multi-physics applications, and to identify the limitations due to uncertainty and bias errors in these models

Strategic Technology Maturation and Insertion (STMI): a requirements guided, technology development optimization process

This research presents a Decision Support System (DSS) process solution to a problem faced by Program Managers (PMs) early in a system lifecycle, when potential technologies are evaluated for placement within a system design. The proposed process for evaluation and selection of technologies incorporates computer based Operational Research techniques which automate and optimize key portions of the decision process. This computerized process allows the PM to rapidly form the basis of a Strategic Technology Plan (STP) designed to manage, mature and insert the technologies into the system design baseline and identify potential follow-on incremental system improvements. This process is designated Strategic Technology Maturation and Insertion (STMI). Traditionally, to build this STP, the PM must juggle system performance, schedule, and cost issues and strike a balance of new and old technologies that can be fielded to meet the requirements of the customer. To complicate this juggling skill, the PM is typically confronted with a short time frame to evaluate hundreds of potential technology solutions with thousands of potential interacting combinations within the system design. Picking the best combination of new and established technologies, plus selecting the critical technologies needing maturation investment is a significant challenge. These early lifecycle decisions drive the entire system design, cost and schedule well into production The STMI process explores a formalized and repeatable DSS to allow PMs to systematically tackle the problems with technology evaluation, selection and maturation. It gives PMs a tool to compare and evaluate the entire design space of candidate technology performance, incorporate lifecycle costs as an optimizer for a best value system design, and generate input for a strategic plan to mature critical technologies. Four enabling concepts are described and brought together to form the basis of STMI: Requirements Engineering (RE), Value Engineering (VE), system optimization and Strategic Technology Planning (STP). STMI is then executed in three distinct stages: Pre-process preparation, process operation and optimization, and post-process analysis. A demonstration case study prepares and implements the proposed STMI process in a multi-system (macro) concept down select and a specific (micro) single system design that ties into the macro design level decision

Multi-Objective Multi-Project Construction Scheduling Optimization

In construction industry, contractors usually manage and execute multiple projects simultaneously within their portfolio. This involves sharing of limited resources such as funds, equipment, manpower, and others among different projects, which increases the complexity of the scheduling process. The allocation of scarce resources then becomes a major objective of the problem and several compromises should be made to solve the problem to the desired level of optimality. In such cases, contractors are generally concerned with optimizing a number of different objectives, often conflicting among each other. Thus, the main objective of this research is to develop a multi-objective scheduling optimization model for multiple construction projects considering both financial and resource aspects under a single platform. The model aims to help contractors in devising schedules that obtain optimal/near optimal tradeoffs between different projects’ objectives, namely: duration of multiple projects, total cost, financing cost, maximum required credit, profit, and resource fluctuations. Moreover, the model offers the flexibility in selecting the desired set of objectives to be optimized together. Three management models are built in order to achieve the main objective which involves the development of: (1) a scheduling model that establishes optimal/near optimal schedules for construction projects; (2) a resource model to calculate the resource fluctuations and maximum daily resource demand; and (3) a cash flow model to calculate projects’ financial parameters. The three management models are linked with the designed optimization model, which consequently performs operations of the elitist non-dominated sorting genetic algorithm (NSGA-II) technique, in three main phases: (1) population initialization; (2) fitness evaluation; and (3) generation evolution. The optimization model is implemented and tested using different case studies of different project sizes obtained from literature. Finally, an automated tool using C# language is built with a friendly graphical user interface to facilitate solving multi-objective scheduling optimization problems for contractors and practitioners

Project schedule optimisation utilising genetic algorithms

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This thesis extends the body of research into the application of Genetic Algorithms to the Project Scheduling Problem (PSP). A thorough literature review is conducted in this area as well as in the application of other similar meta-heuristics. The review extends previous similar reviews to include PSP utilizing the Design Structure Matrix (DSM), as well as incorporating recent developments. There is a need within industry for optimisation algorithms that can assist in the identification of optimal schedules when presented with a network that can present a number of possible alternatives. The optimisation requirement may be subtle only performing slight resource levelling or more profound by selecting an optimal mode of execution for a number of activities or evaluating a number of alternative strategies. This research proposes a unique, efficient algorithm using adaptation based on the fitness improvement over successive generations. The algorithm is tested initially using a MATLAB based implementation to solve instances of the travelling salesman problem (TSP). The algorithm is then further developed both within MATLAB and Microsoft Project Visual Basic to optimise both known versions of the Resource Constrained Project Scheduling Problems as well as investigating newly defined variants of the problem class

How Does Refactoring Impact Security When Improving Quality? A Security Aware Refactoring

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155871/1/RefactoringSecurityQMOOD__ICSE____Copy_.pd