# operations-research Published articles for operations-research. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## A framework for sequential decisions in daily life and beyond DevFeed: [A framework for sequential decisions in daily life and beyond]() Original publisher: [Read original article]() Author: Nisarg Suthar Published: 2026-08-04T07:16:01Z Content type: article Language: en Sources: [Data Science at Microsoft]() Topics: [decision-making]() Tags: [data-modeling](), [data-science](), [decision-making](), [model](), [operations-research](), [sequential-decision](), [structured](), [supply-chain]() ### AI overview The article introduces a Universal Modeling Framework proposed by Warren Powell for reasoning about sequential decisions under uncertainty and incomplete information. It explains how mathematical modeling can clarify decision problems and support better choices across areas such as finance, healthcare, data-center infrastructure, and supply chains. ### Source excerpt Photo by Sophia Kunkel on Unsplash Decision-making occupies a significant portion of our mental uptime. Whether we work in finance, energy, transportation, healthcare, e-commerce, foreign policy, or global supply chains, we are constantly required to make choices in the presence of uncertainty and incomplete information. As new information arrives, decisions must be revised, refined, and sometimes completely reconsidered. Every decision carries consequences -- some rewarding, others costly. The ability to consistently make better choices is often a defining factor behind successful outcomes. Yet effective decision-making remains notoriously difficult. Describing a problem as "mind-bogglingly complex" is really just a by-product of a failure to think about the problem in a structured way.-- Dr. Warren Powell This article introduces a Universal Modeling Framework for reasoning about sequential decision problems proposed by Dr. Warren Powell, an operations researcher at Princeton University. The same framework can be applied across a remarkably diverse set of problems: buying or selling financial assets, evaluating a new user experience, selecting candidate drugs for clinical trials, investing in data-center infrastructure, or managing large-scale supply chains. Here we shall undertake an approach that focusses on identifying the core elements of a decision-making process. Central to our approach is the creation of a simple mathematical model that eliminates the ambiguity of describing problems in language. Modeling is an art, guided by a mathematical framework, and results in a well-defined problem that we can put on a computer to solve. Even when the ultimate goal is not to automate the decision, the act of modeling itself often leads to deeper understanding and better choices. A dynamic model for sequential ecisions A sequential decision process can be represented as follows: Where: Sₜ is the state variable capturing our state of knowledge at time t. For example, inve ## Why Data Quality Matters More Than Mathematical Complexity in Optimization Models DevFeed: [Why Data Quality Matters More Than Mathematical Complexity in Optimization Models]() Original publisher: [Read original article]() Published: 2019-06-08T08:00:06Z Content type: opinion Language: en Sources: [Jeremy Kun]() Topics: [data](), [Optimization](), [datacenter](), [Google](), [math](), [Code]() Tags: [data](), [datacenter](), [debugging](), [google](), [operations-research](), [optimization](), [transparency]() ### AI overview The article argues that obtaining reliable, maintained data is often harder and more consequential than solving the mathematical optimization problem itself. Incomplete or poor-quality data can create technical debt, edge cases, debugging work, and systems that are difficult to explain or audit. ### Source excerpt At Google, our organization designs, owns, and maintains a number of optimization models that automate the planning of Google's datacenter growth and health. As is pretty standard in supply chain optimization and planning, these models are often integer linear programs. It's a core competency of operations research, after all. One might think, "Large optimization problems? That sounds hard!" But it's actually far from the hardest part of the job. In fact, it's one of the few exciting parts of the job. ## Linear Programming and Healthy Diets -- Part 1 DevFeed: [Linear Programming and Healthy Diets -- Part 1]() Original publisher: [Read original article]() Published: 2014-06-02T09:00:27Z Content type: tutorial Language: en Sources: [Jeremy Kun]() Topics: [Optimization](), [Combinatorial optimization](), [Mathematics](), [Programming](), [Computer science]() Tags: [combinatorial-optimization](), [duality](), [linear-programming](), [mathematics](), [nutrition](), [operations-research](), [optimization](), [primal](), [programming](), [real-world]() ### AI overview This introductory tutorial explains linear programming as a central problem in combinatorial optimization. It models choosing quantities of oranges, milk, and broccoli to minimize food costs while meeting nutritional constraints, including recommended levels of water, calcium, and vitamin C. ### Source excerpt Optimization is by far one of the richest ways to apply computer science and mathematics to the real world. Everybody is looking to optimize something: companies want to maximize profits, factories want to maximize efficiency, investors want to minimize risk, the list just goes on and on. The mathematical tools for optimization are also some of the richest mathematical techniques. They form the cornerstone of an entire industry known as operations research, and advances in this field literally change the world.