Introduction to Operations Research 2024 Release By Frederick Hillier, Gerald Lieberman
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- Digital PDF
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- Education
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- eBook[PDF]
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About this ebook
The Introduction to Operations Research (2024 Release) by Frederick Hillier and Gerald Lieberman covers 20 core chapters and multiple appendices that span both deterministic and stochastic operations research methods. A key update in the 2024 release is a heavier emphasis on analytics, including descriptive, predictive (leveraging machine learning and AI), and prescriptive models, alongside integration with modern modeling languages like Gurobi and AMPL. [1, 2]
The breakdown of the specific topics covered across the text includes:
Deterministic Models & Optimization
- Introduction & The Modeling Approach: The origins of Operations Research (OR), the scientific method applied to organizational operations, and defining/modeling complex problems.
- Linear Programming (LP): Core theories, formulations, and foundational concepts of LP.
- The Simplex Method: Detailed algorithmic procedures and theories for solving LP models, including duality and sensitivity analysis.
- Transportation and Assignment Problems: Specialized linear programming applications for logistics and resource distribution.
- Network Optimization Models: Algorithms for handling shortest-path, minimum spanning tree, and maximum flow problems.
- Dynamic Programming: Breakdown of multi-stage decision processes and recursive optimization.
- Integer Programming: Solving optimization problems where some or all variables must be whole numbers.
- Nonlinear Programming: Optimization techniques used when objective functions or constraints are non-linear.
- Metaheuristics: Advanced algorithmic frameworks designed to find high-quality solutions for complex combinatoric problems. [1, 2, 3, 4, 5]
Probabilistic & Stochastic Models
- Game Theory: Mathematical modeling of strategic interaction among rational decision-makers. [1, 2]
- Decision Analysis: Frameworks for making choices under uncertainty, utilizing decision trees and utility theory. [1, 2]
- Markov Chains: Analyzing stochastic processes that transition from one state to another based purely on current state information. [1]
- Queueing Theory: The mathematical study of waiting lines, congestion, and service capacity planning. [1, 2]
- Inventory Theory: Mathematical models for managing stock levels, order quantities, and supply chain logistics under certain or uncertain demand. [1, 2]
- Markov Decision Processes: Sequential decision-making frameworks where outcomes are partly random and partly under the control of a decision-maker. [1]
- Simulation: Computer-based modeling techniques used to replicate and analyze the behavior of real-world stochastic systems. [1, 2]
Extended & Supplementary Topics (Appendices & Digital Materials)
Depending on the specific course package or digital McGraw-Hill Connect version, the text also includes: [1]
- Spreadsheet modeling and spreadsheet simulations using Excel
- Project management via PERT/CPM
- Forecasting models, probability theory updates, and reliability engineering [1]
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