Project Completion Cost Research Articles

Automation of feature engineering for IoT analytics

This paper presents an approach for automation of interpretable feature selection for Internet Of Things Analytics (IoTA) using machine learning (ML) techniques. Authors have conducted a survey over different people involved in different IoTA based application development tasks. The survey reveals that feature selection is the most time consuming and niche skill demanding part of the entire workflow. This paper shows how feature selection is successfully automated without sacrificing the decision making accuracy and thereby reducing the project completion time and cost of hiring expensive resources. Several pattern recognition principles and state of art (SoA) ML techniques are followed to design the overall approach for the proposed automation. Three data sets are considered to establish the proof-of-concept. Experimental results show that the proposed automation is able to reduce the time for feature selection to 2 days instead of 4 -- 6 months which would have been required in absence of the automation. This reduction in time is achieved without any sacrifice in the accuracy of the decision making process. Proposed method is also compared against Multi Layer Perceptron (MLP) model as most of the state of the art works on IoTA uses MLP based Deep Learning. Moreover the feature selection method is compared against SoA feature reduction technique namely Principal Component Analysis (PCA) and its variants. The results obtained show that the proposed method is effective.

Project Scheduling with Simultaneous Optimization, Time, Net Present Value, and Project Flexibility for Multimode Activities with Constrained Renewable Resources

Project success is assessed based on various criteria, every one of which enjoys a different level of importance for the beneficiaries and decision makers. Time and cost are the most important objectives and criteria for the project success. On the other hand, reducing the risk of finishing activities until the predetermined deadlines should be taken into account. Having formulated the problem as a multi-objective planning problem, the present study aims at minimizing the project completion time as well as maximizing the net present value and project flexibility by taking into account the resource constraints and precedence relations. Here the flexibility of project is calculated by considering a free float for each activity and maximizing the sum of these flotation times. Although most of the researches considered the resources as non-renewable resources, here the resources are considered as renewable ones. Moreover, performing each activity may be possible in various states of using resources (mode) which can change the project completion time and cost. Owing to the complexity of the problem, the Multi Objective Simulated Annealing Meta-heuristic Algorithm is used to solve the proposed model. In doing so, first a feasible answers is proposed and then, using the aforementioned algorithm, it was attempted to find Pareto answers. For accrediting the algorithm, four benchmark problems have been considered. Since the algorithm performed well in finding the optimal answers to the benchmark problems, it was used to find the optimal answer of large scale problems.

The Anatomy of Geotechnical Risk Factors in Transportation Infrastructure Projects

Ground conditions constitute a key risk factor that can ultimately determine the successful performance of construction contracts, with the literature reporting statistics of projects which have significantly exceeded their initial budget due to geotechnical uncertainties. The study explores the nature of geotechnical risk factors in transportation infrastructure projects, which potentially lead to cost overruns. The study provides a kaleidoscopic view of the various routes to managing risks due to the ground, at the preconstruction phases of highway projects, and how a lack thereof, can culminate to determine the trend of high-cost overruns in highway projects. The study findings reveal arguments and widely contested issues in geotechnical practice, which to various degrees, can have a significant financial impact on project completion cost in highway projects. The findings uncover various error traps and gaps in practice such as the lack of deterministic costing methods that better reflect heterogeneous ground conditions; insufficiency of preliminary geotechnical exploration; poor geotechnical risk containment in contracts as well as non-deployment of multi-dimensional geotechnically bespoke contractor selection algorithm. The study submits that these gaps in practice constitute the various trajectories through which geotechnical risk can trigger inefficiency and wastage of financial resources, leading to cost overruns in transportation infrastructure projects.

The Risk Analysis of Andalas University Hospital Construction Project

Construction project activities have various risks during their implementation. Every work item on the project has different levels of risk and can affect the project so that it will have an impact on increasing cost and time of project completion. PT. Adhi Karya Tbk is carrying out the project of Andalas University Hospital, where in its implementation there will be many possibilities of risks that will occur. Therefore, it is necessary to identify and analyze the variables of risks that may occur as well as proposing appropriate risk mitigation measures for minimize the potential risks. Risk identification and risk analysis were done using Risk Breakdown Structure (RBS) and Analytic Hierarchy Process (AHP) method respectively. Based on data compilation, variables of risk are grouped in 4 types, namely risk of engineering consist of 8 variables, risk of production consist of 5 variables, risk of construction consist of 9 variables and risk of financial consist of 2 variable, with total of risk are 24 variables. Risk analysis with AHP method is obtained 5 (five) risk priority based on the highest value on each risk type. The five priority risks were a risks of increasing of material prices (extreme), a risk of increasing of procurement costs (high), workplace accidents risk in the project area (medium), mistaken translating the DED to shop drawing (medium) and a quality of goods and services produced by vendors not meet the specifications in contract (medium). To reduce the possibility and impact the risks posed mitigation measures for the risk priority highest were proposed.

Exact Time–Cost Tradeoff Analysis in Concurrency-Based Scheduling

AbstractA construction project can be accelerated by overlapping critical activities. This paper presents a computational method called exact time–cost tradeoff (ETCT) in concurrency-based scheduling that identifies exact global overlaps between critical activities. It calculates the rework cost slope and amount by integrating the activities’ concurrency attributes, identifies the exact global solution, and computes the probability to complete a project within the project completion time (PCT) and/or the project completion cost (PCC). The study is of value to project schedulers because ETCT identifies the exact global solution (i.e., the set of activities to be overlapped and the exact overlap rates between these activities) expeditiously, and establishes a compressed schedule that satisfies a project’s budget and duration constraints. This is also relevant to researchers in that it clearly defines the computational formula to compute the exact solution in detail and facilitates a comparison of the outcom...

An Advanced Stochastic Time‐Cost Tradeoff Analysis Based on a CPM‐Guided Genetic Algorithm

AbstractThis article presents an advanced stochastic time‐cost tradeoff (ASTCT) method that performs time‐cost tradeoff analysis by identifying optimal set(s) of construction methods for activities, hence reducing the project completion time and cost simultaneously. ASTCT involves a stochastic time‐cost tradeoff analysis method based on a critical path method (CPM)‐guided genetic algorithm (GA). It makes use of CPM schedule data exported from a project management software, and alternative construction methods obtained from estimators (i.e., normal and accelerated durations and costs) for each activity. It simulates schedule networks, identifies an optimal set of GA parameters (i.e., population size, crossover rate, mutation rate, and stopping rule), implements several GA cycles, and computes near‐optimal solution(s) exhaustively. This study is of value to practitioners because ASTCT improves the computation time, reliability, and usability of existing GA‐based time‐cost tradeoff methods. The study is also of relevance to researchers because it facilitates experiments using different GA parameters expeditiously. Two test cases verify the usability and validity of the computational methods.

A fuzzy multi-objective model for solving project network problem with bonus and incremental penalty cost

Project management decisions require considering several conflicting constraints and objectives as well as uncertainties in the information over the planning period. The study aims to design a multi-objective linear programming model for solving project network problem under fuzzy environment. The developed model consists of three objectives such as minimizing total project completion time, minimizing total project completion cost and minimizing the earliest time of an event requiring special attention by taking into account several factors such as crash time, normal time, normal cost, crash cost, indirect cost as well as financial bonus and incremental penalty cost. In the study, a case study based on a real life problem is conducted to illustrate the validity and feasibility of the model. The study contributes to the project network literature by developing the fuzzy goal programming model and allows the project managers simultaneously evaluate financial bonus and incremental penalty cost with respect to total project time. In addition, the results of the sensitivity analysis highlight that the developed model can be used for helping the contractors make effective decisions.

Evaluation of queuing systems for knowledge-based simulation of construction processes

Abstract During the course of a construction project, there are many situations in which formation of waiting lines or queues is inevitable. The effect of resource delays in queues on the overall project completion time and cost has motivated researchers to employ simulation for analysis of queuing systems in order to identify the best operational strategies to reduce the time wasted in queues. Providing proper and timely input data with high spatial and temporal accuracy for queuing systems simulation enhances the reliability of decisions made based upon the simulation output. Hence, the presented paper describes a methodology for collecting and mining of spatio-temporal data corresponding to the interactions of queue entities to extract computer interpretable knowledge for simulation input modeling. The developed framework was validated using empirical datasets collected from a series of experiments. The extracted relevant knowledge from the queuing system entities was used to update corresponding simulation models.

ANALISIS SISTEM PENGENDALIAN BIAYA (COST CONTROL FRAMEWORK) PADA PROYEK PELEBARAN JALAN SP.3 LEMPAKE SAMARINDA - SP.3 SAMBERA KALIMANTAN TIMUR

Edward Prana1, M.Ruslin Anwar2 & Yulvi Zaika31,2 & 3Fakultas Teknik Jurusan Teknik Sipil Universitas Brawijaya MalangAlamat Korespondensi: Jl. MT. Haryono No 167 Malang, Indonesiae-mail: edwarprana@ymail.comABSTRACTIn this study, will be analyzed Cost control framework on one road construction project inEast Kalimantan using Earned Value Concept and Work Break Down Structure. As the object ofstudy is Sp.3 Road Widening Project Lempake (Dublin) - Sp.3 Sambera located in the city ofSamarinda, East Kalimantan. way of acquiring primary data through observation and interviewsdirectly to the project, while secondary data from project documents.Performance costs and time by using Earned Value Concept in terms of the reporting week 25CV and SV value value Positive Negative, indicating that the work has been delayed from the timethe work plan, but the cost is still below the budgeted costs. Resource use efficiency using EarnedValue Fund at week-25 values of 1.11 and SPI CPI of 0.93. Cost performance, while showinggood performance for performance efficiency by less well because of a delay of the planned time.Predicted cost and project completion time estimated the cost for the remaining work on theassumption that the trend of the performance of the project will remain until the end of the project(ETC) amounted to Rp 3, 138,305,144.17 while the estimated cost of up project completion (EAC)is equal to = Rp. 9,871,988,500.07, which means there is no advantage or still under the budget plan(PV) in the amount of Rp. 10,968,876,111.19. While the estimated schedule for the completion ofthe aspects of the job completion time estimates obtained (TE) assuming the performance trend ofthe project remains is 212 days. This means that the project will be delayed approximately 14 daysof 198 days plan.Keywords: Control cost (Cost control framework), Work Break Down Structure, Earned ValueConcept.

Concurrent Construction Scheduling Simulation Algorithm

AbstractA scheduler can accelerate a construction project by setting up overlapping activities. Activities are said to be overlapping if two activities that are normally executed in sequence are performed in parallel, the successor starting before the end of the predecessor. This article presents a novel method that identifies an optimal overlap rate between critical activities to provide a time‐cost trade‐off analysis, hence reducing completion time and cost without allocating additional resources. This method is then coded into Concurrent Construction Scheduling Simulation System (C2S2) that makes use of schedule data exported from Primavera Project Planner (P6), integrates the activities’ concurrency attributes, calculates the rework probability/amount, and computes the variability in project completion time and cost by adjusting the overlap between activities. This study is of value to practitioners because C2S2 allows establishing a compressed schedule that satisfies a limited budget within a specified contract duration. The study is also of relevance to researchers because it identifies the optimal overlaps between activities expeditiously. Two test cases verify the usability and validity of the computational methods implemented in the system.

Use of Support Vector Regression to Improve Computational Efficiency of Stochastic Time-Cost Trade-Off

AbstractStochastic time-cost trade-off has been a popular object of investigation in past decades because there are uncertain factors that can be considered when determining the appropriate trade-off between project completion time and cost. Previous studies, however, have implemented a double loop procedure, which performs optimization in the outer loop and simulation in the inner loop. The double loop procedure is ponderous because it requires an unacceptably long computation time (taking hours or days), even for a small to medium project. The present study proposes an integrated system that converts the double loop to single loops,thereby dramatically reducing computation time. This is done by incorporating a support vector regression model to obtain a decision function, which will be used to replace the time-consuming Monte Carlo simulation to evaluate the objective function values for individual solutions. With the objective function values, a multiobjective particle swarm optimization algorithm is d...

Cost Risk Appraisal: An Application of Project Risk Management Process in Libyan Construction Projects

Projects delay and cost overrun have become general facts in the construction industry. Project cost risk analysis considers the different costs associated with a project and focuses on the uncertainties and risks that may affect these costs. An implementation of PRM (project risk management) process on regional construction project has been carried out to maximize the likelihood of project meeting its objectives within its constraints. Qualitative and quantitative risk analyses have been carried out. The qualitative analysis is presented in a table that shows top ranked risks in Libyan construction projects based on probability-impact grid technique. In quantitative risk analyses, Mont Carlo simulation technique has been conducted to quantify and evaluate the overall level of risk exposure associated with the project completion cost. A project simulation uses a model that translates cost uncertainties into their potential impact on project objectives. A frequency curve model that represents simulation results of project completion costs has been constructed. The frequency curve model shows all possible outcomes of expected project cost at different probabilities. Project manager or decision maker can select the appropriate project budget. If a probability of 0.95 confident project budget is selected that means cost overrun risk can be minimized to a probability of 0.05. It is very helpful for project manager to take decisions based on information that shows project completion cost and its associated probability rather than using single information of estimated cost.

A GA-based fuzzy optimal model for time-cost trade-off in a project with resources consideration

The project managers’ major concern is to complete project cost effectively and timely. But, in real life, the project completion time and cost may vary significantly as an effect of uncertainty such as weather, political and social disturbances, inflation, site congestion, human factors, productivity level, etc. Minimisation of project cost directly depends on proper utilisation of resources. Therefore, a GA-based fuzzy optimal model is proposed in this study incorporating multi-objective approach (MAWA) for project time-cost trade-off. In this study, fuzzy set theory is applied to model the managers’ behaviour in predicting time and resources of activities and α-cut method in fuzzy logic theory is used as a measure of accepted risk level. In addition, the activities of project are crashed and penalty is induced for delay to make such problem realistic. The proposed model leads the managers to choose the Pareto optimal time-cost solutions under different risk levels as well as their associated degree of belief in a more flexible and realistic manner. Finally, this proposed model is used to solve a real problem.

A two-stage stochastic programming approach for project planning with uncertain activity durations

This paper investigates the problem of setting target finish times (due dates) for project activities with random durations. Using two-stage integer linear stochastic programming, target times are determined in the first stage followed by the development of a detailed project schedule in the second stage. The objective is to balance (1) the cost of project completion as a function of activity target times with (2) the expected penalty incurred by deviating from the specified values. It is shown that the results may be significantly different when deviations are considered, compared to when activities are scheduled as early as possible in the traditional way. For example, the optimal target completion time for a project may be greater than the makespan of the early-start schedules under any scenario. To find solutions, an exact algorithm is developed for the case without a budget constraint and is used as a part of a heuristic when crashing is permitted. All computational procedures are demonstrated on a set of 150 benchmark problems consisting of 90 activities each.

Bidding ratios to predict highway project costs

PurposeRatios were constructed using bidding data for highway construction projects in Texas to study whether there are useful patterns in project bids that are indicators of the project completion cost. The use of the ratios to improve predictions of completed project cost was studied.Design/methodology/approachRatios were calculated relating the second lowest bid, mean bid, and maximum bid to the low bid for the highway construction projects. Regression and neural network models were developed to predict the completed cost of the highway projects using bidding data. Models including the bidding ratios, low bid, second lowest bid, mean bid and maximum bid were developed. Natural log transformations were applied to the data to improve model performance.FindingsAnalysis of the bidding ratios indicates some relationship between high values of the bidding ratios and final project costs that deviate significantly from the low bid amount. Addition of the ratios to neural network and regression models to predict the completed project cost were not found to enhance the predictions. The best performing regression model used only the low bid as input. The best performing neural network model used the low bid and second lowest bid as inputs.Originality/valueThe nature of bid ratios that can describe the pattern of bids submitted for a project and the relationship of the ratios to project outcomes were studied. The ratio values may be useful indicators of project outcome that can be used by construction managers.

Optimization of project networks with goal programming and fuzzy linear programming

Abstract Minimizing project completion time and cost continues to be a universally sought and highly desirable goal. Linear Programming has been recently adapted to Project Networks followed more recently by Goal Programming. Recent developments in “Fuzzy” Linear Programming suggests it may also contribute significantly. These techniques are compared when applied to a project network, and recommendations are given as to their applicability.

Risk Evaluation in Resource Allocation

The conventional resource allocation procedures implicitly assume that availability of resources is certain. In real life situations. their availability at times is uncertain. A simple model is required to categorize and quantify the uncertainty due to resource availability and evaluate its impact on project schedule and cost. This thesis proposes a Risk Evaluation Model (REM) which takes a resource justified schedule as input, incorporates the uncertainty associated with availability of resources, and generates alternate sets of values on project completion time, cost and performance probability. It will help select a resource justified schedule which has not only the least duration or cost but also reasonable performance probability.

Optimal project compression with due‐dated events

AbstractThe paper proposes an algorithm for the determination of the solution of the activities to be shortened and the amount by which they are to be shortened in order to minimize the total cost of project completion. This cost involves a linear penalty for tardienss of a set of key events and a linear cost of activity compression from its normal duration. The procedure is a generalization of the work of Fulkerson.

Design and Build: Views from some Major Public Clients in Hong Kong

This paper presents the views of clients in public sector on design and build(D&B) procurement method. Specifically, their reasons of adopting D&B and refusing to adopt it were explored. Data were collected through face-to-face interviews with ten senior staff from four major public organisations in Hong Kong. The findings indicate that public clients held different views on D&B methods based on different grounds. Apart from functional requirements such as project completion time, cost certainty, etc., internal resources and political force would also influence client's adoption of D&B method in the public sector.