Operations Research Research Articles

Numerical Data Processing by The Implementation of Trees and Graphs

Trees and Graphs play a vital role in transport and logistics. In tree, decision tree is one of the important, not only implemented for data processing, but also considered for Numerical data analysis. The decision tree is a flow chart-like structure, in which each internal node represents a ‘test’ on an attribute, which has a node known as root being at the top, which further divides the given data into branches depending upon the conditions. Every branch consists of a rule, and each leaf node is its outcome. A support tool with a tree-like structure that models probable outcomes, cost of resources, utilities, and possible consequences. Decision trees are also used in operations research along with planning logistics. They can help in determining appropriate plans that will help a company achieve its target. In Graph, Dijkstra's algorithm is an admired algorithm used to find the shortest paths between nodes in a graph, which may represent road networks for example. Dijkstra's algorithm finds the shortest path from a given source node to every other node. It can also be used to find the shortest path to a specific destination node, by concluding the algorithm once the shortest path to the destination node is found. It is also commonly used on graphs where the edge weights are in real numbers. A common application of shortest path algorithms is network routing protocols and also support in route optimization for delivery services, ensuring timely and cost-effective deliveries by finding the best paths for transportation.

Research perspectives of supply chain knowledge management: A bibliometrics study

The purpose of this article is to present a bibliometric analysis of scientific publications on knowledge management (KM) and supply chain (SC), provide an overview of research activities in this field and recognise its most substantial and fundamental aspects. In addition, this study aims to quantitatively analyse KM in SC in other words supply chain knowledge management (SCKM) research trends, forecasts and citations from 1999 to 2021 in Web of Science (WOS). A total of 499 documents related to SCKM research were collected from the following databases: SCI-EXPAND, SSCI, AHCI and ESCI. These documents were carefully reviewed and subjected to bibliometric data analysis techniques. We map the time trend, disciplinary distribution, high-frequency keywords and topics, major authors and influential publications to show emerging topics. Based on the findings of other researchers, many implications emerged that improve one’s understanding of the identity of SCKM as a distinct multi-discipline scientific field, and its publication has grown significantly since 2018. SC papers were published in engineering and Operations Research and Management Science journals, while the coverage of research topics related to KM and SCKM is broader and more interdisciplinary than those of SC papers. High-frequency keywords associated with SCKM research are listed. From the citation of 499 papers with both KM and SC as keywords, we find the most popular one.

Uncertainty in underground mining operations: a bibliometric and systematic literature review analysis

Purpose This study aims to provide a comprehensive review of the existing literature on uncertainty in underground mining operations, using a bibliometric and systematic analysis covering the period from 1975 to 2024. Design/methodology/approach To achieve this, the following questions were addressed using a mixed-method approach involving bibliometrics, text mining and content analysis: How has the field of uncertainty research in underground mining operations evolved? What are the most prominent research topics and trends in uncertainty in underground mining operations? and What are the possible directions for future research on uncertainty in underground mining operations? Findings As a result, bibliometric networks of 327 journal articles from the Scopus database were created and examined, the main research topics were underground mining management; rock mechanics; operational optimization; and stochastic systems. Finally, the inclusive investigation of uncertainty in underground mining operations and its prominent patterns can serve as a basis for real-time direction for new research and as a tool to improve underground mining activities by implementing advanced technology for innovative practices and optimizing operational efficiency. This is fundamental to identify unknown variables that impair the planning, operation, safety and economic viability of underground mines. Originality/value This research is 100% original because there is no review research on the uncertainty present in underground mining operations.

Advanced Applications and Methodologies in Mathematical Optimization and Operations Research: Insights into Linear Programming, Nonlinear Programming, and Decision-Making Frameworks

Mathematical optimization and operations research are pivotal disciplines in solving complex decision-making problems across industries. This research delves into advanced methodologies within these fields, with a focus on linear programming (LP), nonlinear programming (NLP), and their applications in optimizing processes and resource allocation. Linear programming, with its capacity to model and solve large-scale problems, remains a cornerstone for optimization, particularly in logistics, finance, and manufacturing. Nonlinear programming, characterized by its ability to handle complex, real-world systems with non-convex functions, expands the scope of optimization to include dynamic and intricate challenges such as energy management and machine learning. This study investigates state-of-the-art algorithms, including interior-point methods, dual simplex techniques, and gradient-based approaches, to enhance the efficiency and accuracy of solutions. By addressing theoretical advancements and practical implementations, this research bridges the gap between mathematical rigor and real-world applications. The insights gained are expected to contribute significantly to operational efficiency, strategic planning, and decision-making in diverse fields.

Training Graduate Students’ Shaping Skills in an Immersive Virtual Reality Environment

Graduate students need a wide range of professional skills, and shaping is one of the critical skills they must learn. This study trained graduate students to acquire shaping skills in an immersive virtual reality environment using the Portable Operant Research Teaching Lab (PORTL). To date, no known study has (a) evaluated the effectiveness of shaping skills training for graduate students or (b) attempted to teach these skills in a virtual environment. We used a single-case A-B design across participants with three graduate students who learned shaping skills in an immersive virtual reality environment using the PORTL curriculum. The shaping skills comprised creating a teaching plan, setting up for a session, delivering reinforcement, and evaluating a session. For all participants, training resulted in improvement in shaping skills. Participants also maintained the shaping skills for a minimum of two weeks. Further, the effect of the training generalized to a novel confederate learner for all participants. Additionally, participants showed high satisfaction with learning shaping skills in an immersive virtual reality (iVR) environment.

The team orienteering problem with variable time windows

AbstractThe orienteering problem is well‐established within the domain of operational research. This paper introduces the team orienteering problem with variable time windows (TOPVTW) as a new variant of the team orienteering problem with time windows. The distinguishing feature of the TOPVTW is the adaptive time windows, which evolve dynamically as a consequence of the solution. This problem arises within the context of spread processes that must be contained. This propagation dictates the time windows, which should be observed by the teams. In turn, these teams must take actions influencing the spread and, consequently, the time windows. The time windows can affect both the nodes and the paths. Thus, two distinct versions of the problem are delineated, each supported by dedicated mixed integer programming models. An application is illustrated optimizing resources for wildfire suppression. This work serves as an initial stride toward further exploration and analysis within this domain.

Sustainability in Long-Term Surface Mine Planning: A Systematic Review of Operations Research Applications

The mining industry, critical for global resource supply, has historically been linked to significant environmental degradation and social challenges, including habitat disruption, water and soil contamination, as well as fatalities. But recently, the industry has been undergoing a transformative change to meet evolving environmental and societal expectations. Operations Research (OR) provides essential tools and techniques to optimize decision-making processes in this context. This paper presents a comprehensive review of different OR methods and their applications in balancing economic, social, and environmental objectives in surface mine planning. Of the 63 papers reviewed, 45 were published by researchers from three main countries: Canada, the United States, and Australia, with 73% of these papers appearing in journals. This review highlighted that linear programming (LP), dynamic programming (DP), Lagrangian relaxation (LR), stochastic programming, and metaheuristics are among the most widely applied methods in surface mine planning within a sustainable development (SD) context. By evaluating the strengths and limitations of these techniques, this review offers valuable insights for researchers and practitioners aiming to improve production efficiency and sustainability through advanced planning strategies.

Analysis of Gain Enhancement Techniques in Integrated Circuit Design

Abstract. Today, the size of semiconductor devices is still decreasing, which makes the circuit more integrated, faster computing speed, and lower power consumption. But for a single transistor, some performance degrades. One of the important properties is voltage gain, which must be compensated with some special methods in the design. This paper will focus on the discussion and analysis of four methods, which are able to improve the voltage gain. These are cascode amplifier, cascade amplifier, gain boosting, and bootstrapping. In this letter, their respective advantages and disadvantages will be discussed. Furthermore current applications and possible external development of these methods are also important part of this paper. In this part, the discussion will be further expanded and summarized on the basis of previous studies. Hopefully, these studies can help others gain a more comprehensive understanding of these technologies and provide a theoretical basis for future high-performance analog integrated operational amplifier research and design.

Operation Research the Influence of Venture Capital on the Growth of E-Commerce Listed Enterprises

Operation Research the Influence of Venture Capital on the Growth of E-Commerce Listed Enterprises

Teaching the Subject of Operations Research at Military Universities (Not Only) of NATO Countries

Teaching the Subject of Operations Research at Military Universities (Not Only) of NATO Countries

A Novel Noise-Aware Classical Optimizer for Variational Quantum Algorithms

A key component of variational quantum algorithms (VQAs) is the choice of classical optimizer employed to update the parameterization of an ansatz. It is well recognized that quantum algorithms will, for the foreseeable future, necessarily be run on noisy devices with limited fidelities. Thus, the evaluation of an objective function (e.g., the guiding function in the quantum approximate optimization algorithm (QAOA) or the expectation of the electronic Hamiltonian in variational quantum eigensolver (VQE)) required by a classical optimizer is subject not only to stochastic error from estimating an expected value but also to error resulting from intermittent hardware noise. Model-based derivative-free optimization methods have emerged as popular choices of a classical optimizer in the noisy VQA setting, based on empirical studies. However, these optimization methods were not explicitly designed with the consideration of noise. In this work we adapt recent developments from the “noise-aware numerical optimization” literature to these commonly used derivative-free model-based methods. We introduce the key defining characteristics of these novel noise-aware derivative-free model-based methods that separate them from standard model-based methods. We study an implementation of such noise-aware derivative-free model-based methods and compare its performance on demonstrative VQA simulations to classical solvers packaged in scikit-quant. History: Accepted by Giacomo Nannicini, Area Editor for Quantum Computing and Operations Research. Accepted for Special Issue. Funding: This material is based upon work supported by the U.S. Department of Energy, Office of Science, National Quantum Information Science Research Centers and the Office of Advanced Scientific Computing Research, Accelerated Research for Quantum Computing program under contract number DE-AC02-06CH11357. 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.2023.0177 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0177 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Machine Learning-Based Optimization Models for Defining Storage Rules in Maritime Container Yards

This paper proposes an integrated approach to define the best consignment strategy for storing containers in an export yard of a maritime terminal. The storage strategy identifies the rules for grouping homogeneous containers, which are defined simultaneously with the assignment of each group of containers to the available blocks (bay-locations) in the yard. Unlike recent literature, this study focuses specifically on weight classes and their respective limits when establishing the consignment strategy. Another novel aspect of this work is the integration of a data-driven algorithm and operations research. The integrated approach is based on unsupervised learning and optimization models and allows us to solve large instances within a few seconds. Results obtained by spectral clustering are treated as input datasets for the optimization models. Two different formulations are described and compared: the main difference lies in how containers are assigned to bay-locations, shifting from a time-consuming individual container assignment to the assignment of groups of containers, which offers significant advantages in computational efficiency. Experimental tests are organized into three campaigns to evaluate the following: (i) The computational time and solution quality (i.e., space utilization) of the proposed models; (ii) The performance of these models against a benchmark model; (iii) The practical effectiveness of the proposed solution approach.

An approach for regime-switching stochastic control problems with memory and terminal conditions

In this research article, we focus on a stochastic optimal control problem with two types of terminal constraints. These specific conditions provide real-valued and stochastic Lagrange multipliers. Our model evolves according to a Markov regime-switching jump diffusion model with memory. In this context, the memory is represented by a Stochastic Differential Delay Equation. We present two theorems for each constraint within the general formulation of stochastic optimal control theory in a Lagrangian environment. We approach to this task from a theoretical perspective and provide mild technical assumptions, which make our theorems applicable for a broad class of stochastic control problems as well as for a wide range of disciplines such as engineering, biology, operations research, medicine, computer science and economics. In this work, we apply Stochastic Maximum Principle to demonstrate an optimal dividend policy corresponding to a time-delayed wealth process of a company. Moreover, we determine the real-valued Lagrange multiplier of this control problem explicitly.

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.

Sea surface temperature error under tropical cyclone in the western North Pacific.

Tropical cyclone forecasting relies on accurate sea surface temperature, yet the errors in available sea surface temperature products remain unclear. Here, I evaluate the sea surface temperature errors under tropical cyclones in the western North Pacific based on surface drifters across eight widely-used datasets, including satellite, atmospheric and oceanic analysis-or-reanalysis products, spanning from 2014 to 2023. The ensemble mean bias and root-mean-squared-error are firstly quantified. Both satellite and oceanic analysis sea surface temperatures exhibit a cold bias, while atmospheric analysis-or-reanalysis sea surface temperature shows a warm bias. Specifically, atmospheric analysis-or-reanalysis products consistently show a warm bias in the right-rear quadrant of the tropical cyclone coordinate system. Regarding root-mean-squared-error, for most products, the right side consistently exhibits higher errors than the left side. SST bias significantly impacts tropical cyclone intensity change as indicated by empirical relationship. These results provide direct insights for operational tropical cyclone forecasting and research.

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.

Performance evaluation of child welfare departments using Data Envelopment Analysis: A comparative study across US states

Public child welfare agencies play a pivotal role in safeguarding the well-being of children and thus, the future of our society. While the performance of child welfare agencies is of critical importance, limited previous research relying on operations research and advanced analytics appears to exist in the analysis of their performance. We conduct a multi-criteria analysis for benchmarking the performance of the United States child welfare system, using Data Envelopment Analysis (DEA) to evaluate the performance of public child welfare agencies across different US states. We select as outputs various statewide data indicators from the Child and Family Services Review (CFSR), while our inputs include the total annual expenditure by each state on the child welfare system. We use clustering to differentiate agencies based on the presence of the “Alternative Response” policy, which provides for preventive and support options for families, and apply DEA to each homogenous cluster. We identify best-practice agencies and provide benchmarks for the remaining agencies to enhance their performance. Our study offers data-driven directions for child welfare agencies to improve safety and permanency outcomes for children.

Digital operations research models for intelligent machines (industry 4.0) and man-machine (industry 5.0) systems

Digital operations research models for intelligent machines (industry 4.0) and man-machine (industry 5.0) systems

Optimized Noise Suppression for Quantum Circuits

Quantum computation promises to advance a wide range of computational tasks. However, current quantum hardware suffers from noise and is too small for error correction. Thus, accurately utilizing noisy quantum computers strongly relies on noise characterization, mitigation, and suppression. Crucially, these methods must also be efficient in terms of their classical and quantum overhead. Here, we efficiently characterize and mitigate crosstalk noise, which is a severe error source in, for example, cross-resonance based superconducting quantum processors. For crosstalk characterization, we develop a simplified measurement experiment. Furthermore, we analyze the problem of optimal experiment scheduling and solve it for common hardware architectures. After characterization, we mitigate noise in quantum circuits by a noise-aware qubit routing algorithm. Our integer programming algorithm extends previous work on optimized qubit routing by swap insertion. We incorporate the measured crosstalk errors in addition to other, more easily accessible noise data in the objective function. Furthermore, we strengthen the underlying integer linear model by proving a convex hull result about an associated class of polytopes, which has applications beyond this work. We evaluate the proposed method by characterizing crosstalk noise for two chips with up to 127 qubits and leverage the resulting data to improve the approximation ratio of the Quantum Approximate Optimization Algorithm by up to 10% compared with other established noise-aware routing methods. Our work clearly demonstrates the gains of including noise data when mapping abstract quantum circuits to hardware native ones. History: Accepted by Giacomo Nannicini, Area Editor for Quantum Computing and Operations Research. Accepted for Special Issue on Quantum Computing. Funding: This work was supported by Bavarian state government; Bayerische Staatsministerium für Wirtschaft, Landesentwicklung und Energie. 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.2024.0551 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2024.0551 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Efficient Implementation of Interior-Point Methods for Quantum Relative Entropy

Quantum relative entropy (QRE) programming is a recently popular and challenging class of convex optimization problems with significant applications in quantum computing and quantum information theory. We are interested in modern interior-point (IP) methods based on optimal self-concordant barriers for the QRE cone. A range of theoretical and numerical challenges associated with such barrier functions and the QRE cones have hindered the scalability of IP methods. To address these challenges, we propose a series of numerical and linear algebraic techniques and heuristics aimed at enhancing the efficiency of gradient and Hessian computations for the self-concordant barrier function, solving linear systems, and performing matrix-vector products. We also introduce and deliberate about some interesting concepts related to QRE such as symmetric quantum relative entropy. We design a two-phase method for performing facial reduction that can significantly improve the performance of QRE programming. Our new techniques have been implemented in the latest version (DDS 2.2) of the software package Domain-Driven Solver (DDS). In addition to handling QRE constraints, DDS accepts any combination of several other conic and nonconic convex constraints. Our comprehensive numerical experiments encompass several parts, including (1) a comparison of DDS 2.2 with Hypatia for the nearest correlation matrix problem, (2) using DDS 2.2 for combining QRE constraints with various other constraint types, and (3) calculating the key rate for quantum key distribution (QKD) channels and presenting results for several QKD protocols. History: Accepted by Giacomo Nannicini, Area Editor for Quantum Computing and Operations Research. Accepted for Special Issue. Funding: This work was supported by the National Science Foundation [Grant CMMI-2347120] and Discovery Grants from the Natural Sciences and Engineering Research Council of Canada. 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.2024.0570 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2024.0570 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .