Operations Research Applications Research Articles

Fixed-Ratio Approximation Algorithm for the Minimum Cost Cover of a Digraph by Bounded Number of Cycles

We consider polynomial time approximation for the minimum cost cycle cover problem of an edge-weighted digraph, where feasible covers are restricted to have at most k disjoint cycles. In the literature this problem is referred to as Min-k-SCCP. The problem is closely related to classic Traveling Salesman Problem (TSP) and Vehicle Routing Problem (VRP) and has many important applications in algorithms design and operations research. Unlike its unconstrained variant, the Min-k-SCCP is strongly NP-hard even on undirected graphs and remains intractable in very specific settings. For any metric, the problem can be approximated in polynomial time within ratio 2, while in fixed-dimensional Euclidean spaces it admits Polynomial Time Approximation Schemes (PTAS). In the same time, approximation of the more general asymmetric Min-k-SCCP still remains weakly studied. In this paper, we propose the first fixed-ratio approximation algorithm for this problem, which extends the recent breakthrough Svensson-Tarnawski-Vegh and Traub-Vygen results for the Asymmetric Traveling Salesman Problem.

Research on the design of gamified classroom teaching mode based on fuzzy conjoint analysis method

Operations research is an applied mathematics discipline aimed at providing optimal solutions for complex systems and decision-making processes. However, the theoretical aspects of operations research can often be abstract, making it challenging and potentially tedious for students with a weaker foundational background. To address this issue, game-based learning has been introduced into operations research education in recent years, demonstrating promising results. In this context, gamified learning is seen as a potential solution to enhance student motivation and classroom engagement. Although gamification has been explored in education, its application in operations research, especially within a complex mathematical domain, remains under-researched. These games transform abstract operations research theories into concrete scenarios and problems, allowing students to learn and apply operations research knowledge through interactive play. This study aims to investigate students’ perceptions and attitudes toward game-based operations research classrooms using fuzzy set joint analysis. By employing fuzzy conjoint analysis, we aim to provide a nuanced understanding of student preferences and optimize gamified teaching strategies. In the investigation process, 83 students from School D were selected as the research subjects, and a questionnaire encompassing 16 items was administered. These items covered various aspects of integrating operations research board games into the classroom, including students’ engagement, cooperation, understanding, and application of operations research knowledge during the game process. By utilizing a Likert seven-point scale to collect students’ responses to each item, the data were subsequently converted into fuzzy sets to assess students’ views on game-based operations research classrooms. The results were used to provide targeted optimization for gamified teaching. Group experimental results involving the 83 students showed that gamified teaching in operations research has a positive impact on students’ learning of the subject. This finding holds significant implications for educators and teaching practices, offering valuable insights for the design and implementation of future operations research teaching methods.

A Systematic Mapping Study on Quantum and Quantum-inspired Algorithms in Operations Research

Quantum and quantum-inspired algorithms have not yet been systematically classified in the context of potential Operations Research (OR) applications. Our systematic mapping is designed for quick consultation and shows which algorithms have been significantly explored in the context of OR, as well as which algorithms have been vaguely addressed in the same context. The study provides rapid access to OR professionals, both practitioners and researchers, who are interested in applying and/or further developing these algorithms in their respective contexts. We prepared a replicable protocol as a backbone of this systematic mapping study, specifying research questions, establishing effective search and selection methods, defining quality metrics for assessment, and guiding the analysis of the selected studies. A total of more than 2 000 studies were found, of which 149 were analyzed in detail. Readers can have an interactive hands-on experience with the collected data on an open-source repository with a website. An international standard was used as part of our classification, enabling professionals and researchers from across the world to readily identify which algorithms have been applied in any industry sector. Our effort also culminated in a rich set of takeaways that can help the reader identify potential paths for future work.

البرمجة الضبابية وتحسين أداء الخوارزميات في بيئة متعددة المتغيرات الرتيبة

In many situations of daily life, when we start statistical analysis, we encounter data that we suffer from inaccuracy in its measurement, either due to observation or measurement errors or the lack of appropriate conditions for collecting this data, such as failure times of machines, equipment, electrical devices, medical, engineering and economic data. In many medical studies, the researcher may have a strong conviction that the regression function used to describe the relationship between two variables has a certain form or formula that can be characterized by certain ordering restrictions such as monotonicity; monotonic regression has important applications in statistics, operations research, and signal processing, and these applications are often characterized by a very large value of n. For such large-scale problems, it is of great practical importance to develop algorithms whose complexity does not increase rapidly with increasing n. It is a nonparametric regression technique that preserves order. In this thesis, the proposed fuzzy autoregressive monotonic method, fuzzy autoregressive monotonic least squares (FILSR), fuzzy autoregressive M estimator (FIM), and fuzzy autoregressive maximum likelihood estimator (FIML) were proposed. The best method for estimating the autoregressive function is the fuzzy autoregressive maximum likelihood method with a mean square error of (0.51455).

A Short and General Duality Proof for Wasserstein Distributionally Robust Optimization

Wasserstein distributionally robust optimization has emerged as a recent topic with broader applications in operations research and machine learning. Various proofs have been presented in the literature, each differing in assumptions and levels of generality. In “A Short and General Duality Proof for Wasserstein Distributionally Robust Optimization,” Zhang, Yang, and Gao present a novel elementary proof that not only shortens existing frameworks but also offers surprising generalizations. Leveraging classical Legendre—Fenchel duality, they demonstrate that strong duality is contingent on a certain interchangeability principle. Moreover, they extend this duality result to encompass risk-averse optimization and globalized distributionally robust counterparts.

Variational Quantum Algorithms for Semidefinite Programming

A semidefinite program (SDP) is a particular kind of convex optimization problem with applications in operations research, combinatorial optimization, quantum information science, and beyond. In this work, we propose variational quantum algorithms for approximately solving SDPs. For one class of SDPs, we provide a rigorous analysis of their convergence to approximate locally optimal solutions, under the assumption that they are weakly constrained (i.e., N≫M, where N is the dimension of the input matrices and M is the number of constraints). We also provide algorithms for a more general class of SDPs that requires fewer assumptions. Finally, we numerically simulate our quantum algorithms for applications such as MaxCut, and the results of these simulations provide evidence that convergence still occurs in noisy settings.

Deep learning applications in operations research

Deep learning applications in operations research

The role of drones in disaster response: A literature review of operations research applications

AbstractThe increasing use of drones has led to growing interest in their potential applications in disaster response. In this study, we examine the role of drones in disaster management under three categories of operation types, which are information collection, delivery, and communication network recovery. Focusing on an operations research (OR) perspective, we analyze and summarize the important characteristics of different problem structures and highlight the solution approaches. We also identify gaps and limitations in the current literature and propose future directions for further investigation. Overall, the study provides a comprehensive summary and valuable insights for researchers and practitioners interested in exploring the use of drones for humanitarian purposes and developing OR‐based approaches to support decision‐making.

Using operations research in decision-making and problem-solving among non-governmental organizations in Iraq

This study aims to get to know the extent to of non-governmental organizations apply Operations Research in solving problems and making decisions, and sources of knowledge of these methods that are practiced in NGOs, in addition to identifying the obstruction and encounter problems the use of Operations Research in make-decision in NGOs. We noted that the knowledge of operations research among employees in civil institutions and the extent of their application is below the average, and it seems that there is a need for more effort in defining operations research and its applications and that education and self-interest between workers is one of the main ways to obtain science of operations research in make decision. the study came out with several recommendations, the most important is that it is necessary to work on developing awareness of the importance and advantages of using operations research in making decisions and that workers in civil institutions need training through organizing training courses. More attention must be directed to the operations research in Related colleges, institutes and academic institutions.

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/ .

Code and Data Repository for A FAST Method for Nested Estimation

The goal of this software is to demonstrate the effect of the jackkniFe-bAsed neSted simulaTion (FAST) method proposed in "A FAST Method for Nested Estimation" by Guo Liang, Jun Luo and Kun Zhang. Nested estimation involves estimating an expectation of a function of a conditional expectation, and has many important applications in operations research and machine learning. However, the mean squared error (MSE) of the standard nested simulation (SNS) is only of order Γ − 2 / 3 , where Γ is the total simulation budget. Our proposed FAST method improves the convergence speed to Γ − 4 / 5 . Each folder contains a README.md file for more specific information.

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.