Leading With Data· a Bicycle Guide

Partial book profile

Operations Research

Frederick S. Hillier Gerald J. Lieberman

A comprehensive textbook that introduces the fundamental mathematical models and algorithmic techniques of operations research, a scientific approach to optimal decision-making in complex organizational systems.

The full book profile is not available yet.

Get the book →

What it’s about

Operations Research by Hillier and Lieberman provides a thorough introduction to the quantitative techniques used to make optimal decisions in business, industry, and government. The book systematically covers both mathematical programming (deterministic models like linear programming, network analysis, and dynamic programming) and probabilistic models (like queueing theory, inventory theory, and simulation). By structuring real-world problems into mathematical models, readers learn to allocate limited resources, manage uncertainty, and find the best possible solutions to complex operational challenges. With numerous examples, from production scheduling and pollution control to project management and decision analysis, this textbook equips students and practitioners with the core tools to improve organizational effectiveness through a scientific, data-driven approach.

The through-line

Who it’s for
A student, analyst, or manager in fields like engineering, business, or mathematical sciences who needs to make complex decisions in organizations. They want to move beyond intuitive or ad-hoc decision-making and acquire a systematic, quantitative toolkit to solve operational problems effectively and find optimal solutions.
The problem
The reader faces complex problems involving the allocation of limited resources, managing uncertain systems (like queues or inventories), and planning large projects, but lacks the formal methods to find the best possible solutions. They feel uncertain, overwhelmed by complexity, and frustrated by inefficient, suboptimal outcomes. They may worry that their decisions are not truly the best they could be, leading to wasted money, time, and resources.
The plan
  1. Learn the fundamentals of mathematical programming, starting with the powerful technique of Linear Programming and its solution algorithm, the Simplex method.
  2. Master various probabilistic models to analyze systems with uncertainty, including queueing theory, inventory theory, and decision analysis.
  3. Explore advanced topics and specialized algorithms to tackle even more complex and large-scale problems.
  4. Understand the full lifecycle of an O.R. study, from problem formulation to implementation.
The payoff
The reader becomes a confident and competent problem-solver, able to model complex systems mathematically. · They can identify and apply the correct O.R. technique to find optimal solutions, leading to significant cost savings and efficiency gains for their organization. · They gain a valuable, in-demand skill set that opens doors to outstanding career opportunities and positions them as a key decision-maker.

See our guide

Related profiles we’ve built

Featured in these guides

Co-cited books