Operations Research Applications Research Articles

A Review of Sports Analytics

Abstract: Due to the lack of review papers published in the field of operations research in sports analytics, the central aim of the authors of this paper is to conduct a bibliometric review of publications on the application of Operations research in sports analytics between 2000 and 2022. Through this bibliometric analysis, this paper aims to identify research gaps for future academicians to enable them to work on the topic. The data (130 papers) used for the paper comprises various central as well as interlinked disciplines and topics like sports scheduling, decision-making, and mathematical models used in sports. To present the data cleanly and allow the reader to visualize it, the authors have used various types of different graphs, tables, and figures. The present bibliometric review would be instrumental in helping academicians and students in identifying areas of research as well as helping tournament organizers and team managers, among others, by enumerating various areas that they can use to optimize decision-making in sports further.

A FAST Method for Nested Estimation

Nested estimation involves estimating an expectation of a function of a conditional expectation and has many important applications in operations research and machine learning. Nested simulation is a classic approach to this estimation, and the convergence rate of the mean squared error (MSE) of nested simulation estimators is only of order [Formula: see text], where Γ is the simulation budget. To accelerate the convergence, in this paper, we establish a jackkniFe-bAsed neSted simulaTion (FAST) method for nested estimation, and a unified theoretical analysis for general functions in the nested estimation shows that the MSE of the proposed method converges at the faster rate of [Formula: see text] or even [Formula: see text]. We also provide an efficient algorithm that ensures the estimator’s MSE decays at its optimal rate in practice. In numerical experiments, we apply the proposed estimator in portfolio risk measurement and Bayesian experimental design in operations research and machine learning areas, respectively, and numerical results are consistent with the theory presented. History: Accepted by Bruno Tuffin, Area Editor for Simulation. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72031006, 72101260, and 72394375]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0118 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0118 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

An adaptive large neighborhood search for the order picking process: the case of a retail distribution company in Italy

Order picking is one of the most important activities in warehouse management. By optimising the picking process, warehouse operations can be managed efficiently in terms of both time and logistics costs. In this work we apply operations research techniques to support the order picking process of an Italian retail distribution company. The picking process poses several challenges from an optimisation point of view, and the related optimization problems are very complex. Therefore, we define an Adaptive Large Neighbourhood Search (ALNS) algorithm that heuristically solves the problem. The proposed metaheuristic is tested on a set of 101 real orders processed by the company within one day. The computational experiments show that the ALNS shows good performances in terms of effectiveness, compared to the optimal solution, as well as allows to implement a better organisation of the order picking process than the one currently adopted by the company.

A study of the application of linear programming to material transportation problems

With the quick development of the world logistics industry, the transportation cost of Chinese enterprises is still very high. There is still a lot of room for development compared with other developed countries. Reducing transportation costs, especially transportation costs, is one of the effective ways for enterprises to improve their income. Due to the rapid development of China's industry, the demand for various materials is increasing day by day. Therefore, China's material transportation is also getting more and more attention. Operations Research is a subject of establishing mathematical models to solve practical problems. It is very necessary to use the relevant theories of operations research to solve material transportation problems. By reading the related literature at home and abroad and analysing the characteristics of material transportation problems and the application of operations research in this field, it is found that the on-table operation method is unable to effectively solve the complex material transportation problems. In order to reduce the transportation cost of enterprises, by conducting theoretical and practical research on the material transportation problem in the field of logistics management, this paper aims to simulate and calculate it by establishing a mathematical model and using Lingo software.

Applications Of Operations Research in Various Fields

Operations research uses techniques from a variety of disciplines, including mathematics, statistics, economics, engineering etc. A mathematical model of the real time problem can be formulated by Operations research techniques. These models help to predict future behaviour of the systems, changes in the system or in the methods of operations. This Paper discusses about the applications of Operations research in different field.

An Operations Research–Based Teaching Unit for Grade 11: The ROAR Experience, Part II

In this paper, we continue describing the project and the experimentation of Ricerca Operativa Applicazioni Reali (ROAR; in English, Real Applications of Operations Research), a three-year project for higher secondary schools, introduced. ROAR is composed of three teaching units, addressed to Grades 10, 11, and 12, respectively, having the main aim to improve students’ interest, motivation, and skills related to Science, Technology, Engineering, and Mathematics disciplines by integrating mathematics and computer science through operations research. In a previous paper, we reported on the design and implementation of the first unit, started in Spring 2021 at the scientific high school IIS Antonietti in Iseo (Brescia, Italy), in a Grade-10 class. Here, we focus on the second unit, carried out in Winter/Spring 2022 with the same students, now in a Grade-11 class. In particular, we describe objectives, prerequisites, topics and methods, the organization of the lectures, digital technologies used, and a challenging final project. Moreover, we analyze the feedback from students and teachers involved in the experimentation. Funding: G. Colajanni and E. Taranto were partially supported by the research projects Programma Ricerca di Ateneo UNICT 2020-22 linea 2, EEEP&DLaD, and OMNIA, respectively, of the University of Catania. The research of G. Colajanni was also partially supported by the Italian Ministry of University and Research and the European Union for the PON project on Research and Innovation 2014–2020 [Grant D.M. 1062/2021]. A. Raffaele was partially supported by the Gruppo Nazionale per il Calcolo Scientifico. These supports are gratefully acknowledged. Supplemental Material: The online appendix is available at https://doi.org/10.1287/ited.2023.0001 .

The α-weighted averaging operator

This paper introduces a new type of mean applicable in various areas of science and practice: the α-weighted averaging operator (AWA). AWA has all the properties required from a linear averaging operator and some additional ones. We discuss the applications of AWA in data aggregation in various areas including uncertainty modeling (summarization, defuzzification), multiple-criteria and multi-expert decision-making and evaluation. We prove that when applied to fuzzy numbers, the α-weighted average converges to the possibilistic mean of a fuzzy number with the increasing number of elements in its support. As such the α-weighted average is a more general aggregation operator than the original possibilistic mean. When fuzzy subsets of the real line represent the information to be aggregated, AWA provides new means for their defuzzification compatible with the possibilistic moments, but applicable to discrete and subnormal fuzzy sets. We also introduce a generalized formulation of the α-weighted averaging operator (GAWA) that can be applied in multiple-criteria and multi-expert evaluation and decision-making problems. We suggest the use of GAWA in operations research theory and applications in the context of data aggregation, multiple-criteria and group evaluation and decision-making.

Technical Note—On Adaptivity in Nonstationary Stochastic Optimization with Bandit Feedback

Optimal Nonstationary Optimization Without Knowing Function Changes Nonstationary stochastic optimization plays a vital role in a number of computer science and operations research applications. It is known how to design and analyze algorithms that optimize a sequence of strongly convex/concave and smooth functions with access to only one-point noisy function values with the underlying function sequence subject to maximum magnitude of function changes. In recent work from Wang titled “Technical Note: On Adaptivity in Nonstationary Stochastic Optimization with Bandit Feedback,” an optimization algorithm is designed and analyzed without assuming the magnitude of function changes is known in advance. Optimality of the designed algorithm is demonstrated.

RECENT ADVANCES IN PLANNING FARM OPERATIONS THROUGH OPTIMIZATION MODELS

Operations Research applications in the agriculture sector have been a research area of high interest for over 50 years. Due to food security and sustainability concerns in the world, a lot of attention has been given to this area by OR researchers and practitioners recently. From distribution planning to performance evaluation, a variety of approaches and methods have been applied to a broad range of agricultural problems. Therefore, many review papers have been published from different points of view to serve both general and specific academic purposes. In this work, we present a review of the optimization approaches for the planning of farming operations which aims to optimize agricultural production systems. We use Scopus database to find relevant studies in three decision areas: crop planning, harvest planning and machinery management. Our review covers 54 papers published between 2002-2022.

EFFICIENT MANAGEMENT OF PHARMACY OPERATIONS THROUGH OPERATIONS RESEARCH TECHNIQUES

This article discusses the use of operations research techniques to improve the management of pharmacy operations. In the context of Pharmacy Management Information Systems, some of the OR techniques that could be used are: Linear Programming (LP), Queueing Theory, Simulation, Decision Analysis, Forecasting, Network Analysis. The aim of applying operations research is to make it easier for pharmacy owners to manage their businesses by streamlining the process through the use of mathematical and quantitative methods. Operations Research (OR) is a problem-solving approach that involves analyzing and optimizing complex systems. In the context of Pharmacy Management Information Systems (PMIS), the study covers a wide range of areas such as sales inventory, stocks inventory, purchases (orders, receipts), payroll, accounting, financial management, sales reports (daily, monthly, yearly), and the pharmacy prescription book (for senior citizens and persons with disabilities). Additionally, a point of sale (POS) system has been built into the application, which can be used in both single and multi-user environments. The Pharmacy Automate or PMIS is a Windows-based operating system that can significantly improve pharmacy business operations by automating the entire process and helping owners keep track of their inventory and other important aspects of the business. The use of OR techniques has proven to be very useful in the more complex fields of pharmacy business operations.

FIXED RATIO POLYNOMIAL TIME APPROXIMATION ALGORITHM FOR THE PRIZE-COLLECTING ASYMMETRIC TRAVELING SALESMAN PROBLEM

We develop the first fixed-ratio approximation algorithm for the well-known Prize-Collecting Asymmetric Traveling Salesman Problem,  which has numerous valuable applications in operations research. An instance of this problem is given by a complete node- and edge-weighted digraph \(G\). Each node of the graph \(G\) can either be visited by the resulting route or skipped, for some penalty, while the arcs of \(G\)  are weighted by non-negative transportation costs that fulfill the triangle inequality constraint. The goal is to find a closed walk that minimizes the total transportation costs augmented by the accumulated penalties. We show that an arbitrary \(\alpha\)-approximation algorithm for the Asymmetric Traveling Salesman Problem induces an \((\alpha+1)\)-approximation for the problem in question. In particular, using the recent \((22+\varepsilon)\)-approximation algorithm of V. Traub and J. Vygen that improves the seminal result of  O. Svensson, J. Tarnavski, and L. Végh,  we obtain \((23+\varepsilon)\)-approximate solutions for the problem.

Conic Mixed-Binary Sets: Convex Hull Characterizations and Applications

A Unifying Framework for the Convexification of Mixed-Integer Conic Binary Sets The paper “Conic Mixed-Binary Sets: Convex Hull Characterizations and Applications,” by Fatma Kilinc-Karzan, Simge Kucukyavuz, Dabeen Lee, and Soroosh Shafieezadeh-Abadeh, develops a unifying framework for convexifying mixed-integer conic binary sets. Many applications in machine-learning and operations research give rise to integer programming models with nonlinear structures and binary variables. The paper develops general methods for generating strong valid inequalities that take into account multiple conic constraints at the same time. The authors demonstrate that their framework applies to conic quadratic programming with binary variables, fractional programming, best subset selection, distributionally robust optimization, and sparse approximation of positive semidefinite matrices.

ENHANCING LIVESTOCK MANAGEMENT EFFICIENCY THROUGH OPERATIONS RESEARCH: ADVANCEMENTS IN FEED FORMULATION, GENETIC OPTIMIZATION, AND DISEASE CONTROL

This research paper explores the applications of Operations Research (OR) techniques in livestock management, specifically focusing on feed formulation and diet optimization, livestock breeding and genetic optimization, and disease control and vaccination scheduling. The study employs a systematic methodology that integrates mathematical modeling, optimization algorithms, and data analysis to address the challenges and objectives in each area. A literature review reveals the effectiveness of OR in optimizing feed formulations, genetic selection strategies, and vaccination scheduling, leading to improved animal nutrition, productivity, and disease control. The findings highlight the value of OR techniques in enhancing decision-making and promoting sustainable and efficient practices in livestock management. However, practical challenges related to data availability and stakeholder acceptance need to be addressed for widespread implementation. This research contributes to the understanding of the applications of OR in livestock management and provides a foundation for future research and practical implementation in the industry.

Thirty years of International Transactions in Operational Research: past, present, and future direction

AbstractThe journal “International Transactions in Operational Research” (ITOR) marks its 30th anniversary this year. This study undertakes the first bibliometrics analysis of ITOR covering the past 30 years (1994–2023). Utilizing metadata from 1835 articles published in the journal, this article evaluates ITOR’s intellectual, conceptual, and social structures. The study highlights several exciting outcomes: First, the literature publication trend shows sustained growth and increased articles’ publication and citation impacts. Second, the analysis of the intellectual structure using co‐citation analysis and text analytics identified a solid collaborative relationship with core sources in operations research and management science and applications. Third, the conceptual evaluation using the co‐occurrence of keywords as the unit of analysis identifies the thematic trends of publications in ITOR. Finally, the social structures, which focused on country collaborations and literature contributions dynamics and impacts, offer insights into future directions to maintaining or strategizing a global focus for the Journal.

Beyond kemeny rank aggregation: A parameterizable-penalty framework for robust ranking aggregation with ties

Rank Aggregation has ubiquitous applications in operations research, artificial intelligence, computational social choice, and various other fields. Interest in this problem has increased due in part to the need to consolidate lists of rankings and scores output by different decision-making processes and algorithms. Although most attention has focused on the variant of this problem induced by the Kemeny-Snell distance, other robust rank aggregation problems have been proposed. This work delves into the rank aggregation problem under the generalized Kendall-tau distance —a parameterizable-penalty distance measure for comparing rankings with ties— which contains Kemeny aggregation as a special case. First, it derives exact and heuristic solution methods. Second, it introduces a social choice property (GXCC) that encloses existing variations of the Condorcet criterion as special cases, thereby expanding this seminal social choice concept beyond Kemeny aggregation for the first time. GXCC offers both computational and theoretical advantages. In particular, GXCC may help to divide the original problem into smaller subproblems, while still ensuring that solving them independently yields the optimal solution to the original problem. Experiments on two benchmark datasets conducted herein show that the featured exact and heuristic solution methods can benefit from GXCC. Finally, this work derives new theoretical insights into the effects of the generalized Kendall-tau distance penalty parameter on the optimal ranking and on the proposed social choice property.

Beyond submodularity: a unified framework of randomized set selection with group fairness constraints

Machine learning algorithms play an important role in a variety of important decision-making processes, including targeted advertisement displays, home loan approvals, and criminal behavior predictions. Given the far-reaching impact of these algorithms, it is crucial that they operate fairly, free from bias or prejudice towards certain groups in the population. Ensuring impartiality in these algorithms is essential for promoting equality and avoiding discrimination. To this end we introduce a unified framework for randomized subset selection that incorporates group fairness constraints. Our problem involves a global utility function and a set of group utility functions for each group, here a group refers to a group of individuals (e.g., people) sharing the same attributes (e.g., gender). Our aim is to generate a distribution across feasible subsets, specifying the selection probability of each feasible set, to maximize the global utility function while meeting a predetermined quota for each group utility function in expectation. Note that there may not necessarily be any direct connections between the global utility function and each group utility function. We demonstrate that this framework unifies and generalizes many significant applications in machine learning and operations research. Our algorithmic results either improves the best known result or provide the first approximation algorithms for new applications.

Numerical Method for Solving Fractional Order Optimal Control Problems with Free and Non-Free Terminal Time

The optimal control theory in mathematics aims to study the finding of control for a dynamic system over time, where an objective function is optimized. It has a broad range of applications in engineering, operations research, and science. The main purpose of this study is to provide numerical algorithms for two cases of optimal control problems of fractional order that involve fractional order derivatives with free and non-free terminal time. In addition to comparing the numerical results for three test problems with exact solutions of these problems, various computer simulations are also introduced.

Book Reviews

In Book Reviews, we review an extensive and diverse range of books. They cover theory and applications in operations research, statistics, management science, econometrics, mathematics, computers, and information systems. In addition, we include books in other fields that emphasize technical applications. The editor will be pleased to receive an email from those willing to review a book with an indication of specific areas of interest. If you are aware of a specific book that you would like to review or that you think should be reviewed, please contact the editor. The following books are reviewed in this issue of INFORMS Journal on Applied Analytics: The Spirit of Green: The Economics of Collisions and Contagions in a Crowded World (author: William D. Nordhaus; reviewer: Amitrajeet A. Batabyal) and Supply Chain Scheduling (authors: Zhi-Long Chen and Nicholas G. Hall; reviewer: Chelliah Sriskandarajah).

Prescriptive analytics applications in sustainable operations research: conceptual framework and future research challenges.

In the broad sphere of Analytics, prescriptive analytics is one of the emerging areas of interest for both academicians and practitioners. As prescriptive analytics has transitioned from its inception to an emerging topic, there is a need to review existing literature in order to ascertain development in this area. There are a very few reviews in the related field but not specifically on the applications of prescriptive analytics in sustainable operations research using content analysis. To address this gap, we performed a review of 147 articles published in peer-reviewed academic journals from 2010 to August 2021. Using content analysis, we have identified the five emerging research themes. Through this study, we aim to contribute to the literature on prescriptive analytics by identifying and proposing emerging research themes and future research directions. Based on our literature review, we propose a conceptual framework for studying the impacts of the adoption of prescriptive analytics and its impact on sustainable supply chain resilience, sustainable supply chain performance and competitive advantage. Finally, the paper acknowledges the managerial implications, theoretical contribution and the limitations of this study.

Nonstationary Reinforcement Learning: The Blessing of (More) Optimism

Motivated by operations research applications, such as inventory control and real-time bidding, we consider undiscounted reinforcement learning in Markov decision processes under model uncertainty and temporal drifts. In this setting, both the latent reward and state transition distributions are allowed to evolve over time, as long as their respective total variations, quantified by suitable metrics, do not exceed certain variation budgets. We first develop the sliding window upper confidence bound for reinforcement learning with confidence-widening (SWUCRL2-CW) algorithm and establish its dynamic regret bound when the variation budgets are known. In addition, we propose the bandit-over-reinforcement learning algorithm to adaptively tune the SWUCRL2-CW algorithm to achieve the same dynamic regret bound but in a parameter-free manner (i.e., without knowing the variation budgets). Finally, we conduct numerical experiments to show that our proposed algorithms achieve superior empirical performance compared with existing algorithms. Notably, under nonstationarity, historical data samples may falsely indicate that state transition rarely happens. This thus presents a significant challenge when one tries to apply the conventional optimism in the face of uncertainty principle to achieve a low dynamic regret bound. We overcome this challenge by proposing a novel confidence-widening technique that incorporates additional optimism into our learning algorithms. To extend our theoretical findings, we demonstrate, in the context of single-item inventory control with lost sales, fixed cost, and zero lead time, how one can leverage special structures on the state transition distributions to achieve improved dynamic regret bound in time-varying demand environments. This paper was accepted by J. George Shanthikumar, data science. Funding: The authors acknowledge support from the Massachusetts Institute of Technology (MIT) Data Science Laboratory and the MIT–IBM partnership in artificial intelligence. W. C. Cheung acknowledges support from the Singapore Ministry of Education [Tier 2 Grant MOE-T2EP20121-0012]. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4704 .