Subject Overview

An advanced study of planning, loading, allocating, smoothing, and leveling scarce resources across projects and portfolios. The subject integrates scheduling theory, managerial decision-making, optimization, analytics, governance, and contemporary digital tools.

Learning Goal

Enable students to design, analyze, optimize, govern, and communicate resource-feasible project and portfolio schedules using established methods, quantitative analytics, professional software, and responsible AI-enabled decision support.

Syllabus

1. Foundations of Project Resource Management

Learning Objective: Define resources, capacity, availability, utilization, productivity, loading, allocation, smoothing, leveling, over-allocation, and resource-constrained scheduling; distinguish time-, cost-, and resource-driven planning.

Category: foundation

2. Scope, Work and Organizational Structures

Learning Objective: Translate scope into resource requirements using WBS, work packages, activity lists, OBS, responsibility assignment matrices, resource breakdown structures, skills taxonomies, and control accounts.

Category: foundation

3. Schedule and Network Planning Foundations

Learning Objective: Develop precedence networks and apply CPM concepts, dependencies, leads, lags, calendars, float, critical paths, milestones, baselines, and schedule quality checks as prerequisites for resource analysis.

Category: foundation

4. Resource Estimation and Capacity Planning

Learning Objective: Estimate resource quantities, competencies, effort, productivity, and duration using expert judgment, historical data, parametric and analogous methods; build resource calendars and capacity-demand forecasts.

Category: core

5. Resource Loading and Utilization Analysis

Learning Objective: Create time-phased resource assignments, loading tables, histograms, heat maps, S-curves, and utilization profiles; identify bottlenecks, idle capacity, skill gaps, and over-allocation.

Category: core

6. Resource Smoothing and Leveling Methods

Learning Objective: Apply smoothing within available float and leveling beyond float; evaluate priority rules, activity splitting, sequencing, substitution, overtime, shift work, outsourcing, and schedule extension.

Category: core

7. Resource-Constrained Scheduling Theory

Learning Objective: Analyze the resource-constrained project scheduling problem, renewable and non-renewable resources, serial and parallel scheduling schemes, heuristic rules, constraint propagation, and computational complexity.

Category: advanced

8. Optimization and Decision Models

Learning Objective: Formulate resource decisions using linear, integer, and constraint programming, simulation, multi-criteria decision analysis, and metaheuristics; balance duration, cost, utilization, risk, and strategic value.

Category: advanced

9. Time–Cost–Resource Trade-offs

Learning Objective: Evaluate crashing, fast tracking, marginal cost slopes, overtime, hiring, subcontracting, learning effects, and resource substitution; select defensible alternatives through scenario and sensitivity analysis.

Category: application

10. Uncertainty, Risk and Schedule Resilience

Learning Objective: Incorporate uncertain durations, productivity, absenteeism, supply constraints, and rework through contingency reserves, Monte Carlo simulation, probabilistic forecasts, critical chain, buffers, and stress testing.

Category: advanced

11. Multi-Project and Portfolio Resource Management

Learning Objective: Prioritize and allocate shared resources across programs and portfolios using strategic alignment, portfolio capacity planning, pipeline control, bottleneck management, governance gates, and benefits-based decisions.

Category: strategic

12. Monitoring, Control and Resource Analytics

Learning Objective: Integrate actual effort, remaining work, earned value, earned schedule, utilization, throughput, variance, forecast-at-completion, and dashboard indicators to reforecast and control resource-feasible baselines.

Category: application

13. Digital Platforms, Automation and AI

Learning Objective: Use project scheduling, ERP, workforce, and portfolio systems for integrated resource management; assess data interoperability, cloud collaboration, digital twins, predictive analytics, optimization engines, and responsible generative AI support.

Category: contemporary

14. Leadership, Ethics, Governance and Future Practice

Learning Objective: Manage negotiation, conflict, scarce-skill allocation, burnout, inclusion, labor constraints, privacy, accountability, and auditability; examine hybrid work, gig resources, sustainability, human–AI collaboration, and adaptive resource ecosystems.

Category: strategic