Knowledge to master for a better foundation (and future)
Tools and general:
Fred W. Glover, Unforeseen Consequences of “Tabu” Choices—A Retrospective, INFORMS Journal on Computing, 2022, Volume 34, Issue 3, Pages 1306-1308.
This retrospective is written upon receiving The IJOC Test of Time Award for the papers “Tabu Search—Part 1 and —Part 2,” a delightful capstone to an unanticipated (and exceptionally fun) adventure.
2023 Edelman Competition (video)
Prakhar Mehrotra et al., (2024) Optimizing Walmart’s Supply Chain from Strategy to Execution. INFORMS Journal on Applied Analytics 54(1):5-19. (2023 Franz Edelman Award) (Keywords: supply chain optimization, network design, simulation, truck routing and loading, mixed-integer programming, metaheuristics)
Dimitris Bertsimas, Alex Paskov (2024) An Exact Solution to Wordle. Operations Research.
Gerard P. Cachon, Karan Girotra, Serguei Netessine (2020) Interesting, Important, and Impactful Operations Management. Manufacturing & Service Operations Management 22(1):214-222.
Omar Besbes, Yonatan Gur, Assaf Zeevi, Optimization in Online Content Recommendation Services: Beyond Click-Through Rates, 18(1), pp. 15–33, Manufacturing & Service Operations Management, Volume 18, Issue 1, Winter 2016.
A new class of online services allows Internet media sites to direct users from articles they are currently reading to other content they may be interested in. This process creates a “browsing path” along which there is potential for repeated interaction between the user and the provider, giving rise to a dynamic optimization problem. A key metric that often underlies this recommendation process is the click-through rate (CTR) of candidate articles. Whereas CTR is a measure of instantaneous click likelihood, we analyze the performance improvement that one may achieve by some lookahead that accounts for the potential future path of users. To that end, by using some data of user path history at major media sites, we introduce and derive a representation of content along two key dimensions: clickability, the likelihood to click to an article when it is recommended; and engageability, the likelihood to click from an article when it hosts a recommendation. We then propose a class of heuristics that leverage both clickability and engageability, and provide theoretical support for favoring such path-focused heuristics over myopic heuristics that focus only on clickability (no lookahead). We conduct a live pilot experiment that measures the performance of a practical proxy of our proposed class, when integrated into the operating system of a worldwide leading provider of content recommendations, allowing us to estimate the aggregate improvement in clicks per visit relative to the CTR-driven current practice. The documented improvement highlights the importance and the practicality of efficiently incorporating the future path of users in real time.
如同如何準備研究所中所言，不一定要念研究所。在網路時代，知識的取得已經非常方便，例如 edX 或 Coursera，所以大學部的基礎知識和自學的動力很重要 (1)。以 The Analytics Edge 而言，是 MIT 商業分析 (Business Analytics) 碩士的必修課，該碩士學費一年七萬美金，所以此線上免費課程價值一萬美金。
El-Ghazali Talbi, Machine Learning into Metaheuristics: A Survey and Taxonomy, ACM Computing Surveys, Volume 54, Issue 6, July 2022, Article No.: 129, pp 1–32, https://doi.org/10.1145/3459664.
During the past few years, research in applying machine learning (ML) to design efficient, effective, and robust metaheuristics has become increasingly popular. Many of those machine learning-supported metaheuristics have generated high-quality results and represent state-of-the-art optimization algorithms. Although various appproaches have been proposed, there is a lack of a comprehensive survey and taxonomy on this research topic. In this article, we will investigate different opportunities for using ML into metaheuristics. We define uniformly the various ways synergies that might be achieved. A detailed taxonomy is proposed according to the concerned search component: target optimization problem and low-level and high-level components of metaheuristics. Our goal is also to motivate researchers in optimization to include ideas from ML into metaheuristics. We identify some open research issues in this topic that need further in-depth investigations.
JEREMY KAHN, YouTube video streaming now using A.I. that mastered chess and Go, Fortune, February 11, 2022.
The artificial intelligence algorithm, called MuZero, was developed by YouTube’s London-based sister company within Alphabet, DeepMind, which is dedicated to advanced A.I. research. When applied to YouTube videos, the system has resulted in a 4% reduction on average in the amount of data the video-sharing service needs to stream to users, with no noticeable loss in video quality.
W.B. Powell, A unified framework for stochastic optimization, European Journal of Operational Research, 2019, Volume 275, Issue 3, 16 June 2019, Pages 795-821. (pdf)
Stochastic optimization is an umbrella term that includes over a dozen fragmented communities, using a patchwork of sometimes overlapping notational systems with algorithmic strategies that are suited to specific classes of problems. This paper reviews the canonical models of these communities, and proposes a universal modeling framework that encompasses all of these competing approaches. At the heart is an objective function that optimizes over policies that is standard in some approaches, but foreign to others. We then identify four meta-classes of policies that encompasses all of the approaches that we have identified in the research literature or industry practice. In the process, we observe that any adaptive learning algorithm, whether it is derivative-based or derivative-free, is a form of policy that can be tuned to optimize either the cumulative reward (similar to multi-armed bandit problems) or final reward (as is used in ranking and selection or stochastic search). We argue that the principles of bandit problems, long a niche community, should become a core dimension of mainstream stochastic optimization.
Prof. Sridhar Tayur, Dr. Davide Venturelli, and David Bernal, 47-779 Quantum Integer Programming (QuIP), Fall 2020. (lecture note)
This course is primarily designed for graduate students (and advanced undergraduates) across CMU campuses interested in integer programming (with non-linear objective functions) and the potential of near-term quantum computing for solving combinatorial optimization problems. By the end of the semester, someone enrolled in this course should be able to:
- Identify the current status of quantum computing and its potential uses for integer programming
- Access and use quantum computing resources (such as DWave Quantum Annealers)
- Set up a given integer program to be solved with quantum computing
- Work in groups collaboratively on a state-of-the-art project regarding applications of quantum computing and integer programming
Martina Fischetti, Jesper Runge Kristoffersen, Thomas Hjort, Michele Monaci, and David Pisinger, Vattenfall Optimizes Offshore Wind Farm Design, INFORMS Journal on Applied Analytics, 2020, Vol. 50, No. 1, pp. 80–94.
In this paper, we describe the use of operations research for offshore wind farm design in Vattenfall, one of the world’s leading companies in the generation of offshore wind energy. We focus on two key aspects that Vattenfall must address in its wind farm design process. The first is determining where to locate the turbines. This aspect is important because the placement of each turbine creates interference on the neighboring turbines, causing a power loss at the overall farm level. The optimizers must minimize this interference based on the wind conditions; however, they must also consider the other costs involved, which depend on factors such as the water depth or soil conditions at each position. The second aspect involves determining how to interconnect the turbines with cables (i.e., cable optimization). This requires Vattenfall to consider both the immediate costs and long-term costs connected with the electrical infrastructure. We developed mixed-integer programming models and matheuristic techniques to solve the two problems as they arise in practical applications. The resulting tools have given Vattenfall a competitive advantage at multiple levels. They facilitate increased revenues and reduced costs of approximately 10 million euros of net present value (NPV) per farm, while ensuring a much faster, more streamlined, and efficient design process. Considering only the sites that Vattenfall has already acquired using our optimization tools, the company experienced NPV gains of more than 150 million euros. This has contributed substantially to its competitiveness in offshore tenders and made green energy cheaper for its end customers. The tools have also been used to design the first wind farms that will be constructed subsidy-free.
Martina Fischetti and David Pisinger, Mathematical Optimization and Algorithms for Offshore Wind Farm Design: An Overview, Business & Information Systems Engineering, 2019, Vol.61, No. 4, pp. 469-485. (Further details)
M. Fischetti, Mixed-integer models and algorithms for wind farm layout optimization. Master’s thesis, University of Padova, 2014. (Stochastic programming for wake effect)
Fischetti M, Fischetti M (2016) Matheuristics. Mart´ı P, Panos P, Resende MG, eds. Handbook of Heuristics (Springer International Publishing, Cham, Switzerland), 1–33.
翁芊儒，組織、人才和技術5年布局， 中鋼如何用AI煉成智慧鋼廠，iThome，2021-03-04
這一場變革，莫約從5年前開始推展。「我們展開數位轉型，是為了提升鋼鐵生產效率、降低成本、縮短交期，來提升產品競爭力。」中鋼技術部門代理副總經理鄭際昭，一句話點出轉型任務最重要的目的。...
Special Issue of INFORMS Journal on Applied Analytics: Interfaces/IJAA 50th Anniversary Special Issue
Fatma Gzara, Samir Elhedhli, Ugur Yildiz, and Gohram Baloch, Data-Driven Modeling and Optimization of the Order Consolidation Problem in E-Warehousing, INFORMS Journal on Optimization, Vol. 2, No. 4, Fall 2020, pp. 273–296. (online pdf)
We analyze data emanating from a major e-commerce warehouse and provided by a third-party warehouse logistics management company to replicate flow diagrams, assess order fulfillment efficiency, identify bottlenecks, and suggest improvement strategies. Without access to actual layouts and process-flow diagrams and purely based on data, we are able to describe the processes in detail and prescribe changes. By investigating the characteristics of orders, the wave-sorting operation, and the order-preparation process, we find that products from different orders are picked in batches for efficiency. Similar products are picked in small containers called totes. Totes are then stored in a buffer area and routed to be emptied of their contents at induction lines. Orders are then consolidated at the put wall, where each order is accumulated in a cubby. This order consolidation process depends on the sequence in which totes are processed and has a huge impact on order-completion time. We, therefore, present a generalization of the parallel machine–scheduling problem that we call the order consolidation problem to determine the tote-processing sequence that minimizes total order completion time. We provide mathematical formulations and devise heuristic and exact solution methods. We propose a fast simulated annealing metaheuristic and a branch-and-price approach in which the subproblems are variants of the single machine-scheduling problem and are solved using dynamic programming. We also devise a new branching rule, compare it against the literature, and test it on randomly generated and industry data. Applied to the data and the warehouse under study, optimizing the order consolidation is found to decrease the completion time of 75.66% of orders and achieve average improvements of up to 28.77% in order consolidation time and 21.92% in cubby usage.