Project Scheduling

Earned Value Analysis (EVA), also known as Earned Value Management (EVM), is an important project management tool used to integrate cost, schedule, and scope in evaluating project performance. The technique enables project managers to measure project progress objectively, forecast future performance, and implement corrective actions where necessary. In Sub-Saharan Africa, infrastructure projects continue to experience significant challenges related to delays, cost overruns, poor quality delivery, and weak monitoring systems. This paper reviews literature on the application of EVA and its influence on project performance in developed and developing countries, with particular emphasis on Sub-Saharan Africa. The review reveals that EVA improves project monitoring, cost control, schedule management, accountability, and decision-making. Empirical evidence from the United States, Malaysia, South Africa, Rwanda, Uganda, and Kenya demonstrates that EVA contributes positively to project performance when properly implemented. However, the study also identifies several drawbacks affecting EVA adoption in Sub-Saharan Africa, including inadequate technical expertise, low institutional capacity, limited awareness, fluctuating economic conditions, and weak project management maturity. The paper concludes that strengthening training, institutional support, and technological integration can enhance EVA utilization and improve infrastructure project performance in the region.

Earned Value Analysis (EVA), also known as Earned Value Management (EVM), is an important project management tool used to integrate cost, schedule, and scope in evaluating project performance. The technique enables project managers to measure project progress objectively, forecast future performance, and implement corrective actions where necessary. In Sub-Saharan Africa, infrastructure projects continue to experience significant challenges related to delays, cost overruns, poor quality delivery, and weak monitoring systems. This paper reviews literature on the application of EVA and its influence on project performance in developed and developing countries, with particular emphasis on Sub-Saharan Africa. The review reveals that EVA improves project monitoring, cost control, schedule management, accountability, and decisionmaking. Empirical evidence from the United States,

This companion paper compares Probabilistic Chain Analysis (PCA) with established project risk and governance frameworks, including PMI/APM-style approaches, deterministic scheduling, risk registers, and independent Monte Carlo methods. It shows why these frameworks fail to capture hidden assumption chains that drive cost and schedule overruns, and it explains how PCA makes those chains visible through dependency-aware modelling, Bayesian Networks, and Monte Carlo simulation. The paper focuses on framework comparison, governance, validation, and audit readiness, and it is intended as a companion to the original empirical PCA case study.

Engineering and technology play an important role in strengthening the competitive power of a company and in surviving a severe competition in the world. About 70% of the total R&D investment in Japan comes from the private sector. It is the most important to decide which research projects have to be adopted for a future research out of proposals from divisions and sections in a company. The objective of this paper is to analyze the results of experts' evaluation in selecting submitted proposals for R&D and to model the experts' evaluation. This paper analyzes a research and development of a certain manufacturing company in a heavy metallurgy industry. We employed a principal component model, dual scaling, AHP and fuzzy regression analysis to analyze the results that experts evaluated proposed research projects for single or plural of fiscal years. The experts' evaluation was pursued on the basis of (1) the objective of a research project, (2) its background, (3) its research contents, (4) the expected effect, (5) the possibility of obtaining patents, (6) project schedule, (7) developing cost, etc. The obtained model results in the same selection of projects as the experts did.

Crashing project activities is a typical way to shorten their completion times to meet project due dates, and previous research on quality in time-cost tradeoff problems focused on maximizing the individual activity quality of projects. However, implementing project scheduling that takes into account the potential quality loss cost (PQLC) in time-cost tradeoff problems is a practical approach, since individual activity quality is defined by conformance to project contractor requirements. We propose a mixed integer linear programming model that considers the PQLC for excessive crashing activities. This model will help project planners develop practical project schedules.

Crashing project activities is a typical way to shorten their completion times to meet project due dates, and previous research on quality in time-cost tradeoff problems focused on maximizing the individual activity quality of projects. However, implementing project scheduling that takes into account the potential quality loss cost (PQLC) in time-cost tradeoff problems is a practical approach, since individual activity quality is defined by conformance to project contractor requirements. We propose a mixed integer linear programming model that considers the PQLC for excessive crashing activities. This model will help project planners develop practical project schedules.

Most infrastructure projects are planned deterministically — risks are listed,
probabilities assigned, and expected values summed. This paper identifies the
structural flaw in that approach: it assumes risk independence, treating
cascade amplification as zero. When one risk fires on a real project, it does
not fire alone. It triggers chains.

This paper introduces Probabilistic Chain Analysis (PCA), a quantitative risk
methodology that models project risks as causally dependent chains rather than
independent events. PCA rests on three components: (1) cascade chain mapping
via directed acyclic graphs, (2) Bayesian Network-coupled Monte Carlo simulation
with Latin Hypercube Sampling and Gaussian Copula correlation, and (3) an
Intelligence Log that carries Bayesian-updated posteriors across project cycles,
building institutional risk memory where none currently exists.

The methodology is validated through a calibrated UK highways Survey and
Investigation scenario, constructed from a cross-referenced dataset of 16,000
infrastructure projects spanning eight sectors. Results show cascade lift
factors of 3.3–4.1 on six risk nodes that static registers treat as
independent. A single targeted pre-mobilisation intervention reduced P90 cost
exposure by £250,000 at a management cost of £15,000 — a 16.7x return on
risk management effort. CVaR90 was reduced by £101,025 against the standard
Monte Carlo baseline.

The paper presents the full theoretical foundation, empirical calibration
methodology, live case study, and a six-step practitioner framework for
applying PCA within a standard project risk review cycle. Limitations are
documented with epistemic transparency. PCA is positioned as a practitioner-
accessible bridge between Bayesian risk theory and live infrastructure
project delivery across India, the UK, and international markets.

This paper introduces Probabilistic Chain Analysis (PCA), a quantitative risk
methodology for infrastructure and EPC project delivery that models project risks
as causally dependent chains rather than independent events. Traditional risk
registers and standard Monte Carlo (MC) simulation assume risk independence,
systematically underestimating tail exposure by treating cascade amplification
as zero. PCA addresses this structural limitation through three integrated
components:
(1) cascade chain mapping via directed acyclic graphs (DAGs),

(2) Bayesian Network-coupled Monte Carlo simulation with Latin Hypercube
Sampling and Gaussian Copula correlation, and (3) an Intelligence Log
framework that carries Bayesian-updated posteriors across project cycles,
building institutional risk memory.

The methodology is validated through a calibrated UK highways Survey and
Investigation scenario, constructed from a cross-referenced dataset of 16,000
infrastructure projects spanning eight sectors. Results demonstrate that
cascade lift factors of 3.3–4.1 exist on six risk nodes that static registers
treat as independent. A single targeted pre-mobilisation intervention —
identified through mitigation what-if analysis — reduced P90 cost exposure
by £250,000 at a management cost of £15,000, representing a 16.7x return on
risk management effort. CVaR90 was reduced by £101,025 against the standard
Monte Carlo baseline.

The paper presents the full theoretical foundation, empirical calibration
methodology, case study results, and a six-step practitioner framework for
applying PCA within a standard project risk review cycle. Limitations,
including subjective probability inputs and schedule-cost decoupling, are
documented. PCA is positioned as a practitioner-accessible bridge between
academic Bayesian risk theory and live infrastructure project delivery.

Keywords: probabilistic risk analysis, Bayesian networks, Monte Carlo
simulation, cascade risk, infrastructure project management, cost overrun,
CVaR, EPC, risk register, project controls

Nigeria, like most other developing nations, is experiencing a rapid increase in population with the attendant environmental change which create more centers of daily activities, and the intensity of these centers are of increasing environmental liabilities. Society is currently disposing of tons of waste at designated landfills per day. The sorting facilities are currently saturated, and this is becoming a menace to society. This study seeks to determine the impact of Project Scheduling (PS) and Project Monitoring on the 3Rs of Waste Management. (Reduce, Reuse and Recycle) of some selected waste management companies in Lagos State. Survey research design was adopted for this study. The stratified random sampling technique was employed in selecting 129 staff randomly selected from the population. Data was gotten from primary source by using structured questionnaire. The study considered construct and content validities. To test relevant hypotheses, Multiple Regression analysis was adopted. The study found out that there is a strong positive relationship among Project Scheduling, Project monitoring and the 3Rs of Waste Management (R = 0.703)70.3%; the coefficient of multiple determination (Adj. R2 = 0.486) shows that 48.6% of the change in the 3Rs of Waste Management is accounted for by Project Scheduling and Project Monitoring. This implies that Project Scheduling and Project monitoring has a strong influence on the 3Rs of Waste Management. It was therefore recommended that Proper implementation of the 3Rs of Waste Management should be adopted by Waste Management organisations in other to ensure that Project Scheduling and Project Monitoring are adequately executed in other to enhance the Performance of Waste.

Research on over 18 years of software projects reveals that developing large systems involves substantially more activities and a greater variety of specialized personnel than developing smaller systems. Perhaps the most significant observation is that good quality control is the best overall indicator of a successful project. Schedule delays and cost overruns most often occur when you discover during testing that the application has so many bugs that it doesn't work. Projects using QA teams, formal design and code inspections, and pretest defect tracking always had shorter testing cycles and therefore were more likely to be deliverable on schedule.

• Project staffing with discrete time/resource trade-offs and calendar constraints • A cut-branch-and-price procedure is proposed • Decomposition into subproblems with a specific staffing composition. • Dedicated cuts include personnel information in the workload pricing problem

Resumo-Este artigo aborda o problema da parametrização de Técnicas de Optimização Inspiradas na Biologia (BIT -Biological Inspired Optimization Techniques), também conhecidas como Meta-heurísticas, considerando a importância que estas técnicas têm na resolução de situações de mundo real, sujeitas a perturbações externas. É proposto um módulo de aprendizagem com o objectivo de permitir que um Sistema Multi-Agente (SMA) para Escalonamento seleccione automaticamente uma Metaheurística e escolha a parametrização a usar no processo de optimização. Para o módulo de aprendizagem foi usado o Raciocínio baseado em Casos (RBC), permitindo ao sistema aprender a partir da experiência acumulada na resolução de problemas similares. Através da análise dos resultados obtidos é possível concluir acerca das vantagens da sua utilização. O problema do Escalonamento é caracterizado por uma grande quantidade de incerteza associada ao dinamismo do sistema, revelando-se um aspecto importante de automação em sistemas de produção. Existe uma grande necessidade de desenvolver abordagens de escalonamento que possam ser aplicadas a vários problemas de escalonamento com impacto significativo no desempenho das organizações empresariais. Emerge assim um desafio para a construção de sistemas de suporte ao escalonamento para ambientes de produção onde a adaptação dinâmica e a optimização se tornam cada vez mais importantes. Neste cenário, a adaptação dinâmica surge como a capacidade de um agente monitorizar o seu estado e desempenho, e proactivamente se auto-parameterizar para responder aos estímulos no ambiente .

Construction Project Schedules are the most significant leverage point for improving social and economic outcomes for the industry. This paper aims to explore the effects of increasing and decreasing timespans. A pressing issue is a lack of a method and mathematical equation for owners to determine a socially and economically sensitive construction timeline. Disturbingly, the current academic research on this topic is sparse. Creating a holistic schedulesetting process and algorithm is more important than ever due to several factors, including worker shortages, women's participation, increased project complexity, mental health, the algorithm are more important than ever due to several factors, including worker shortages, women participation, increased project complexity, mental health and quality problems. Pre-tender project time setting has been an ignored area. This resulting arbitrariness, which relies on experience, input process times and economic motivation, does not serve Australia's social and economic goals. It is one root cause of today's construction dysfunction. Installation time compression with increasingly complex construction installation appears to be a blind spot for project owners since they have to determine an appropriate deadline for all project parties. For-profit organisations and other entities seek to take earlier possession to capture more economic value for themselves; however, these firms must be sensitive to the many adverse effects of pre-tender schedule setting. A structured assessment process should be an additional criterion for project feasibility practices. This paper asserts that a solution may be found if portfolio management scheduling practices can be extended to an entire market where the incident project is planned to be constructed.

This study presents a systematic literature review on the implementation of 5D Building Information Modeling (BIM) in construction projects, focusing on the integration between scheduling (4D) and cost estimation (5D). The literature search focused on peer-reviewed articles and supporting academic sources published between 2019 and 2025, yielding a set of relevant studies. The analyzed data include project types, software used (Revit, Navisworks/Synchro, CostX), data exchange formats (IFC/CSV), and the relationship among the Work Breakdown Structure (WBS), Quantity TakeOff (QTO), and cost account code (COA). The synthesis shows that 5D BIM improves cost accuracy through model-linked QTO, accelerates estimation workflows, and shortens the time required to identify the impacts of design changes on both cost and schedule. The main obstacles to 5D BIM adoption include the loss of information attributes during cross-software data exchanges, inconsistencies in WBS/COA standards across project stakeholders, and limitations in user competency. Supporting factors for effective implementation include an open BIM approach, a Common Data Environment (CDE), standardized cost libraries, and structured training governance. The study also highlights replicable practices such as early WBS alignment for 4D-5D synchronization, rule-based QTO validation, and linking work progress to payment mechanisms. These findings are expected to contribute to the development of 5D BIM guidelines and educational curricula, particularly for small-to medium-scale contractors.

Several algorithms for finding a lower bound on the makespan for the Resource Constrained Project Scheduling Problem (RCPSP) were proposed in the literature. However, fast computable lower bounds usually do not provide the best estimations and the methods that obtain better bounds are mainly based on the cooperation between linear and constraint programming and therefore are time-consuming. In this paper, a new pseudo-polynomial algorithm is proposed to find a makespan lower bound for RCPSP with time-dependent resource capacities. Its idea is based on a consecutive evaluation of pairs of resources and their cumulated workload. Using the proposed algorithm, several bounds for the PSPLIB benchmark were improved. The results for industrial applications are also presented where the algorithm could provide good bounds even for very large problem instances.

Heavy commercial vehicles are known for being extremely inefficient compared to other ground vehicles, partly due to high aerodynamic drag. This is a result of their un-streamlined body shape. A large commercial vehicle travelling at 100 km/h consumes approximately 52% of the total fuel to provide the power required to overcome the aerodynamic drag. The primary objective of this study is to determine the aerodynamic impact of various fuel saving devices used in heavy commercial vehicles. To measure the aerodynamic drag produced by the vehicle, an experimental and computational simulation study was undertaken using a 1/10th scale model of a Mack 600R class 8 tractor-trailer. The aerodynamic drag on the base vehicle with external attachments (i.e., front faring, side skirting and gap filling) was measured for a range of vehicle operating speeds and yaw angles. The configurations used were chosen as they required minimal modification of the vehicle and could be implemented on existing commercial trucks. This paper focuses on the validation of the experimental work through computer simulations on a baseline vehicle configuration. As well as this, the simulations will be used to further predict the expected aerodynamic effects and possible drag reductions with various add-ons and configurations. The findings indicate that a significant drag reduction between 20% and 35% can be achieved depending on the modifications and cross wind conditions. It was found that the full-skirting (using the front fairing, side skirting and gap filling) has maximum impact while only front fairing has lowest impact on aerodynamic drag reduction overall.

Risk and uncertainty are factors that construction project managers have been increasingly had to deal with. The expected project completion time is often shorter than the actual completion time. Monte Carlo simulation is a widely used simulation technique in modeling a process that is difficult to predict due to its random variables. This study provides a practical way to use Monte Carlo si mulation to simulate a project completion time using functions available in a spreadsheet application. A project with five activities was simulated 2000 times using minimum, maximum, and expected duration. The mean, mode, and median simulation results were then plugged into their respective precedence diagram networks to compare them. The precedence diagram computations found that mean, mode, and median project completion times were longer than the initially expected completion time. The mean, mode, and median were 50, 53, and 48 days, respectively, which were 8, 11, and 6 days longer, respectively, than the 42 days initially expected. The study showed that the Monte Carlo simulation could assist the project manager in planning a project schedule that deals with risk and uncertainty more realistically.

The Precedence Diagram Method (PDM) is widely used for modeling large and complex construction projects characterized by non-linear execution logic, overlapping activities, and multiple precedence constraints. Despite involving standardized and recurrent operations (e.g., formwork, reinforcement, concreting), construction schedules often exhibit highly complex logic that challenges traditional Critical Path Method (CPM) analysis. In particular, PDM networks with multiple relationships complicate critical path identification and float determination, while current commercial scheduling tools impose restrictive logic and limited support for activity splitting. This study proposes a novel objectoriented algorithm and a supporting software tool that integrates automated multi-activity splitting, unrestricted handling of precedence types, and segment-level criticality identification within a unified scheduling framework. The proposed approach transforms complex PDM networks into equivalent Finish-to-Start-based structures solvable using conventional forward and backward pass algorithms, while strictly preserving the original scheduling logic. Results demonstrate that the proposed method improves analytical transparency, scheduling flexibility, and precision in critical path and float analysis, including the identification of partially critical activity segments. The proposed framework provides project managers with a practical and scalable solution to optimize productivity and reduce project duration in complex construction scheduling scenarios.