Table of Contents
Overview
Definition and Scope
Operations research (OR), also known as operational research, is a branch of applied mathematics that focuses on the development and application of analytical methods to enhance management and decision-making processes. It encompasses a wide array of problem-solving techniques, including mathematical modeling, statistical evaluation, and optimization methods, aimed at improving efficiency and effectiveness within organized systems.[8.1] The origins of operations research can be traced back to World War II, when scientists from various disciplines collaborated to address complex military operational challenges, such as logistics and resource allocation.[33.1] This collaborative effort laid the groundwork for OR as a distinct academic discipline, which has since evolved to address a variety of managerial and administrative problems across different sectors.[33.1] Operations research is characterized by its goal-oriented approach, focusing on finding optimal solutions to specific problems, whether that involves maximizing profits, minimizing costs, or improving time efficiency.[35.1] It is not merely a science in itself but rather the application of scientific principles to solve practical issues faced by organizations.[3.1] The techniques employed in OR include simulation, mathematical optimization, queueing theory, and econometric methods, among others, all of which rely on constructing mathematical models to describe and analyze systems.[35.1]Key Techniques and Methods
Operations Research (OR) employs a variety of mathematical models and analytical techniques to address complex decision-making problems across multiple industries, including manufacturing, transportation, healthcare, finance, and logistics.[19.1] Among the key techniques utilized in OR, linear programming (LP) stands out as one of the most widely applied methods, allowing for the optimization of processes under specific constraints.[20.1] Additionally, non-linear programming, integer programming, network models, and simulation models are also significant, each offering unique insights tailored to specific types of problems.[17.1] The integration of Machine Learning (ML) with traditional OR methods has emerged as a promising approach to enhance decision-making processes. This hybrid methodology leverages the predictive capabilities of ML to identify potential issues based on historical data, thereby enabling timely interventions and recommendations for resource management.[12.1] Furthermore, the synergy between ML and OR techniques can lead to improved optimization strategies, as both fields can benefit from their interaction.[10.1] Operations Research also plays a crucial role in identifying inefficiencies within organizations. Through detailed data analysis and modeling, OR can pinpoint areas where expenses can be reduced, such as in manufacturing processes where bottlenecks may lead to increased costs.[13.1] For instance, hospitals utilize OR techniques to optimize resource allocation, ensuring that staff, equipment, and facilities are effectively managed based on predicted demands.[14.1] The development of mathematical models is fundamental to the effectiveness of OR. Recent advancements have led to the creation of new models and optimization algorithms that address increasingly complex real-world problems, reflecting the growing practical relevance of the field.[18.1] As OR continues to evolve, the incorporation of data analytics and big data is becoming increasingly significant, allowing for real-time analysis and improved decision-making capabilities.[23.1]In this section:
History
Early Developments
The early developments of Operations Research (OR) can be traced back to the period surrounding World War I and the early months of World War II. The discipline emerged from the need to optimize military strategies and resource allocation, particularly with the advent of radar technology, which played a crucial role in its inception.[53.1] The term "Operations Research" itself was coined during World War II as military strategists sought to enhance logistics and resource management.[55.1] The chaotic conditions of the European Campaign during World War II significantly influenced the evolution of OR, leading to the establishment of various consulting firms and societies in both Europe and the United States that were pivotal in its development.[54.1] These organizations utilized personnel and techniques from scientific disciplines to address complex operational problems, marking a shift towards a more systematic approach to decision-making in military and later civilian contexts.[54.1] A key figure in the early history of OR is George Dantzig, who is often referred to as the Father of Linear Programming. His contributions to mathematical optimization, particularly the development of the simplex algorithm, have had a lasting impact on the field.[82.1] Dantzig's work not only advanced theoretical frameworks within OR but also facilitated practical applications in various industries, including manufacturing and transportation.[81.1] His innovative approaches allowed organizations to optimize inventory levels and improve supply chain efficiency, demonstrating the versatility and applicability of OR techniques beyond military uses.[55.1]Evolution During World War II
Operations Research (OR) emerged as a distinct discipline during World War II, primarily in Britain, where it was initiated in 1937 under the leadership of A.P. Rowe at the Bawdsey Research Station. The term "Operations Research" was coined just prior to the war to differentiate the research aimed at integrating radar technology into military operations from other forms of research and development.[51.1] The integration of radar technology significantly influenced the methodologies and techniques developed in OR, as it provided a framework for addressing complex military challenges through scientific analysis.[59.1] The effectiveness of OR in military contexts led to its adoption across various government departments and industries post-war. The principles and methods developed during the conflict were initially focused on optimizing military operations, such as resource allocation and logistics, which were critical for strategic bombing campaigns against the Axis powers.[64.1] The collaboration between scientists and military leaders during this period was pivotal in shaping the foundational principles of OR, as it allowed for the application of mathematical models and statistical techniques to real-world problems.[71.1] As the war progressed, the need for rapid technological adaptation became apparent, and OR played a crucial role in helping military organizations navigate these changes. The methodologies developed included mathematical modeling, simulation, and optimization techniques, which were essential for decision-making in the face of uncertainty.[64.1] By the end of the war, the successful application of OR had demonstrated its value, leading to its expansion into civilian sectors such as manufacturing, transportation, and telecommunications, where similar optimization challenges existed.[63.1]Recent Advancements
Innovative Techniques in Operations Research
Innovative techniques in operations research (OR) have evolved significantly, particularly with the integration of artificial intelligence (AI) and machine learning (ML). These advancements have enhanced the capabilities of OR methodologies, allowing for more sophisticated problem-solving and decision-making processes. The synergy between AI and optimization models has been recognized as a crucial factor in improving efficiency and achieving superior results across various applications.[108.1] One of the key areas where AI has made an impact is in the development of optimization software. Techniques such as Markov decision processes, which form the theoretical foundation for classical reinforcement learning, have been instrumental in advancing OR practices.[105.1] Furthermore, the integration of machine learning with traditional OR methods has been shown to optimize complex problems, including manufacturing scheduling and supply chain management.[107.1] This combination allows for more dynamic and responsive decision-making frameworks that can adapt to changing conditions and data inputs. Moreover, the application of AI within the OR process, referred to as AI4OR, has opened new avenues for enhancing the effectiveness and efficiency of operations research. This integration spans multiple stages, including parameter generation, model formulation, and model optimization, thereby streamlining the overall workflow.[109.1] As a result, organizations can leverage these innovative techniques to improve resource allocation and achieve desired objectives more effectively.[106.1]Applications in Modern Industries
Operations research (OR) has seen significant advancements in its applications across various modern industries, particularly in supply chain management, logistics, and healthcare. These advancements have been driven by the development of sophisticated mathematical models, statistical analyses, and optimization techniques that enhance decision-making processes. In supply chain management, OR techniques are employed to optimize inventory levels and streamline transportation routes. For instance, inventory management models are utilized to ensure timely product availability while minimizing costs associated with stock levels across warehouses. This transformation from a reactive approach to a data-driven engine exemplifies the potential of OR in enhancing operational efficiency and decision-making in supply chains.[94.1] A systematic review of literature from 2005 to 2016 indicates an exponential increase in the application of advanced OR techniques, such as integrated mathematical modeling and simulation-optimization frameworks, in supply chain network optimization.[95.1] These techniques have led to improved decision-making by providing valuable insights that guide strategic and proactive management of supply chains.[94.1] In the logistics sector, optimization theory serves as a cornerstone of operations research, offering mathematical frameworks and algorithms that address complex decision-making problems. The application of these optimization techniques has resulted in substantial cost reductions and efficiency enhancements across various operational challenges.[98.1] Algorithmic optimization techniques have also been highlighted as crucial in solving diverse OR problems, emphasizing the importance of algorithm selection and design in achieving optimal solutions.[97.1] Healthcare has also benefited from recent advancements in OR, particularly through the application of algorithmic techniques to address complex optimization and decision-making challenges. These advancements have facilitated improvements in clinical treatment planning, medical condition monitoring, and healthcare logistics, thereby optimizing resource allocation and enhancing operational efficiency within the sector.[99.1]In this section:
Artificial IntelligenceReinforcement LearningSupply Chain ManagementModel OptimizationInventory Management
Applications
Manufacturing and Production
In the realm of manufacturing and production, Operations Research (OR) techniques, particularly linear programming, have been instrumental in optimizing supply chain processes. Traditional linear programming (LP) techniques serve as a foundational approach to address the complexities of supply chain management, especially as global markets become increasingly interconnected. However, these methods often encounter limitations when faced with the dynamic nature of modern supply chains.[136.1] A practical application of linear programming in manufacturing can be illustrated through a case study focused on optimizing the composition of Christmas food parcels. This study aimed to maximize sales revenue by strategically controlling the items included in the parcels and managing resupplies for any missing stock items.[140.1] Such applications demonstrate how OR methodologies can effectively enhance operational efficiency and decision-making in production environments. Moreover, the integration of advanced methodologies, such as Data-Driven and AI-enhanced methods, has further transformed the landscape of manufacturing research. Techniques like Natural Language Processing (NLP) and computational ethnography allow for real-time data analysis and the observation of behaviors in online spaces, which can inform production strategies.[134.1] This shift towards interdisciplinary approaches and diverse datasets underscores the evolving nature of manufacturing research in the context of big data and artificial intelligence. In addition to linear programming, machine learning and evolutionary algorithms have emerged as vital tools in big data analytics within manufacturing. These AI techniques provide faster, more precise, and scalable outcomes, enabling manufacturers to make informed decisions based on comprehensive data analysis.[135.1] The categorization of big data analytics techniques into machine learning, knowledge-based methods, decision-making algorithms, and optimization theories highlights the multifaceted nature of modern manufacturing challenges and the corresponding OR solutions available to address them.[135.1]Healthcare and Resource Allocation
Operations Research (OR) has become an invaluable tool in the healthcare sector, significantly enhancing decision-making processes and optimizing resource allocation. Its applications are diverse, ranging from improving hospital operations and drug development to optimizing supply chain management and supporting health policy planning.[149.1] The integration of OR techniques allows healthcare organizations to analyze complex problems and devise effective solutions, thereby improving operational efficiency and patient outcomes. One of the key areas where OR has made a substantial impact is in hospital operations. By employing mathematical models and optimization techniques, healthcare providers can streamline processes such as patient flow, staffing, and resource allocation.[148.1] For instance, OR methodologies have been utilized in managing operating rooms and intensive care units, ensuring that resources are allocated efficiently to meet patient needs.[152.1] This not only enhances service delivery but also reduces waiting times and improves overall patient satisfaction. Moreover, OR has been instrumental in demand forecasting and inventory optimization within the pharmaceutical supply chain. By analyzing historical data and employing predictive models, healthcare organizations can better anticipate medication needs, thereby minimizing stockouts and reducing waste.[145.1] This capability is crucial in maintaining the availability of essential drugs and ensuring that healthcare providers can deliver timely care to patients. The collaboration between operations researchers and domain experts is essential for the successful application of OR in healthcare. Such partnerships facilitate the identification and framing of critical problems, the validation of models, and the dissemination of results to decision-makers.[151.1] This collaborative approach not only enhances the relevance of OR analyses but also encourages the integration of practical solutions into healthcare practices.In this section:
Importance In Decision Making
Enhancing Efficiency and Productivity
Operations research (OR) plays a crucial role in enhancing efficiency and productivity across various sectors by providing a systematic framework for addressing complex challenges through mathematical modeling, statistical evaluation, and optimization methods. By focusing on maximizing or minimizing specific objectives, such as increasing profits or reducing costs, OR enables organizations to make informed, data-driven decisions that significantly improve operational efficiency.[196.1] The integration of machine learning (ML) techniques with traditional OR methods further amplifies this effectiveness. ML's ability to extract patterns from large datasets complements OR's precision in decision-making, resulting in a balanced solution that enhances predictive capabilities and optimizes decision processes.[180.1] Moreover, ML techniques can automate problem reduction in combinatorial optimization, thereby streamlining the decision-making process and improving overall efficiency.[181.1] The availability of substantial computing power and extensive project management data has facilitated the development of advanced OR and ML software libraries, which are essential for implementing these integrated approaches.[179.1] This synergy not only increases the success chances of projects by effectively assigning developers to project issues but also allows for a deeper analysis of each step in the decision-making process.[179.1] Furthermore, the quality of data significantly influences the effectiveness of OR models in decision-making. Research indicates that defined dimensions of data quality impact decision-making performance and the time required to reach decisions.[199.1] Organizations that proactively manage data quality can enhance their decision-making capabilities, ensuring that they leverage data optimally to achieve better outcomes.[201.1] The empirical evidence from studies involving agricultural firms in China demonstrates that the use of big data analytics positively affects decision-making quality, highlighting the importance of data analytics capabilities in mediating this relationship.[202.1]Risk Management and Strategic Planning
Operations Research (OR) plays a pivotal role in risk management and strategic planning by providing advanced analytical methods that enhance decision-making processes. The integration of OR techniques allows organizations to optimize their operations, improve efficiency, and manage risks effectively. For instance, OR analysts utilize mathematical models to guide decision-making, which helps organizations make better-informed choices and optimize their operations efficiently.[189.1] In the context of risk management, OR enhances the accuracy of risk assessments through various quantitative tools that support decision analysis. These tools are essential for operational risk management in finance and supply chain risk assessment, allowing organizations to evaluate potential risks and develop strategies to mitigate them.[188.1] Furthermore, understanding the co-movement and spillover effects among different asset classes is crucial for asset allocation and portfolio diversification, which OR facilitates by optimizing decision-making processes.[187.1] Specific applications of OR in risk management include the use of linear programming and simulation tools to model systems and processes. These methods enable organizations to forecast outcomes, assess risks, and optimize resource allocation effectively.[189.1] For example, in the insurance industry, OR techniques are employed to address risk management-related problems, demonstrating the versatility and applicability of OR across various sectors.[186.1]In this section:
Challenges And Limitations
Data Quality and Availability
Operations Research (OR) is heavily reliant on the quality and availability of data, which poses significant challenges for analysts. One of the primary limitations is the dependency on accurate and relevant data to create models and analyze scenarios. Poor-quality or incomplete data can lead to inaccurate results and misleading conclusions, ultimately affecting decision-making processes.[223.1] Furthermore, when basic data undergoes frequent changes, integrating these updates into OR models can be costly and time-consuming, often making a reasonably good solution more desirable than a perfect one that may take longer to achieve.[224.1] The lack of accessible and reliable data is a critical challenge faced by operations research analysts. Without comprehensive data, analysts may struggle to produce accurate analyses and actionable insights, which are essential for effective decision-making.[225.1] The consequences of inadequate data collection can be severe, including distorted findings, wasted resources, and misleading recommendations.[226.1] To mitigate these issues, organizations must implement effective data quality management strategies, such as cleaning, validating, and monitoring data to ensure reliability.[227.1] Common data issues, such as inaccuracies, incomplete records, and duplicates, can undermine the integrity of decision-making processes.[228.1] Organizations often operate with multiple systems and databases, complicating data integration and management. Therefore, establishing a robust data management strategy that aligns with business goals is crucial for overcoming these challenges and fostering a reliable data ecosystem.[228.1] Emerging technologies, particularly artificial intelligence (AI) and machine learning (ML), play a transformative role in enhancing data quality for operations research. The integration of these technologies can improve efficiency, optimize supply chains, and enhance decision-making capabilities.[242.1] AI and ML can also assist in addressing challenges related to data quality, such as feature selection and handling incomplete data, thereby enhancing model accuracy.[244.1] As organizations increasingly leverage these technologies, they can significantly improve their analytical outcomes and decision-making processes.[243.1]Complexity of Real-World Problems
Operations Research (OR) methodologies often encounter significant challenges when addressing the complexity of real-world problems. One of the primary limitations is the assumption of rationality inherent in many OR models, which may not accurately reflect the decision-making processes of individuals or organizations in practice. This discrepancy can lead to models that fail to capture the nuances of human behavior and the unpredictability of real-world scenarios.[234.1] Moreover, the complexity of certain problems can exceed the capabilities of OR techniques. As the intricacy of a problem increases, it becomes increasingly difficult to model and analyze effectively, which can hinder the ability to derive actionable insights.[234.1] For instance, in healthcare, operational research literature frequently presents theoretical frameworks that lack practical documentation on implementation and impact, indicating a gap between model development and real-world application.[233.1] Additionally, the reliance on accurate and comprehensive data poses a significant challenge. Operations research analysts depend heavily on data to identify patterns and trends; however, without access to reliable data, the quality of analyses can suffer, leading to misleading conclusions.[232.1] This data dependency underscores the importance of data quality in the successful application of OR methodologies. Furthermore, the processes involved in developing and implementing OR solutions can be both time-consuming and costly. While OR aims to provide efficient solutions, the initial investment in time and resources can be substantial, which may deter organizations from fully engaging with these methodologies.[239.1]Future Directions
Emerging Trends in Operations Research
Emerging trends in operations research are increasingly characterized by the integration of advanced technologies and methodologies that address contemporary challenges in various sectors. One significant trend is the application of artificial intelligence (AI), which is reshaping industries and driving innovations across multiple fields, thereby influencing the future of operations research.[254.1] Researchers are particularly encouraged to contribute innovative methods and applications that leverage large-scale data from real-life problems, emphasizing the importance of practical relevance in their work.[252.1] Another critical area of focus is the role of blockchain technology in enhancing supply chain operations. Studies have highlighted blockchain's transformative potential in improving efficiency, transparency, and trust among stakeholders within supply chains.[274.1] The technology offers a reliable method for storing and sharing data, which is essential for enhancing security and efficiency in operations.[275.1] However, challenges remain in integrating blockchain into existing operations research frameworks, necessitating further exploration of its implications.[276.1] Additionally, the intersection of sustainability and resilience in operations and supply chain management is gaining attention. Recent frameworks have been developed to guide the integration of these dual demands within manufacturing strategy research and practice, reflecting a growing recognition of their importance in contemporary operations research.[283.1] Game theory also continues to evolve within the context of operations research, particularly as data analytics become more prevalent. The application of game theoretic models is being explored in organizational studies and systems involving individuals and networks, which enhances decision-making in complex scenarios.[278.1] The rapid development of game theory necessitates a balance between theoretical advancements and practical applications, ensuring that the insights gained are relevant to real-world challenges.[279.1]Integration with Artificial Intelligence and Machine Learning
The integration of Artificial Intelligence (AI) and Machine Learning (ML) into Operations Research (OR) is reshaping traditional methodologies and enhancing decision-making processes across various industries. Since the 1990s, there has been a growing interest in combining ML with OR to address complex scheduling problems, as evidenced by early studies that laid the groundwork for this integration.[256.1] This synergy has led to significant advancements in optimization techniques, where ML algorithms, such as linear and logistic regression, are employed to model relationships between variables and optimize decision-making outcomes.[257.1] In today's data-driven environment, industries face increasingly intricate challenges that traditional OR methods, which rely on linear and integer programming, may struggle to address effectively.[258.1] The incorporation of ML not only enhances the accuracy of solutions but also optimizes them, providing a more robust framework for tackling these complex issues.[266.1] For instance, AI-driven heuristics and metaheuristics have been developed to improve algorithms used in mixed-integer programming, thereby facilitating more efficient problem-solving.[263.1] Moreover, the digital transformation accelerated by the COVID-19 pandemic has prompted organizations to enhance their data collection and analysis capabilities, further integrating AI and ML into OR decision-making processes.[265.1] This trend is reflected in the increasing number of scholarly contributions exploring the interplay between OR and ML, indicating a burgeoning field of research that seeks to leverage these technologies for improved operational efficiency.[262.1] The application of AI techniques, such as Long Short-Term Memory (LSTM) networks and Particle Swarm Optimization (PSO), in supply chain management exemplifies how these technologies can enhance resilience and sustainability by addressing challenges like demand forecasting.[267.1] As the integration of AI and ML continues to evolve, it is expected to raise new research questions and transform the methodologies employed in OR, ultimately driving innovation and efficiency across various sectors.[261.1]References
[3] Operations research | Definition, History, Examples, Characteristics ... — Operations research | Definition, History, Examples, Characteristics, & Facts | Britannica Ask the Chatbot Games & Quizzes History & Society Science & Tech Biographies Animals & Nature Geography & Travel Arts & Culture ProCon Money Videos operations research operations research operational research Operations research attempts to provide those who manage organized systems with an objective and quantitative basis for decision; it is normally carried out by teams of scientists and engineers drawn from a variety of disciplines. Thus, operations research is not a science itself but rather the application of science to the solution of managerial and administrative problems, and it focuses on the performance of organized systems taken as a whole rather than on their parts taken separately.
[8] Operations research - Wikipedia — Operations research (British English: operational research) (U.S. Air Force Specialty Code: Operations Analysis), often shortened to the initialism OR, is a branch of applied mathematics that deals with the development and application of analytical methods to improve management and decision-making. Although the term management science is sometimes used similarly, the two fields differ in their scope and emphasis. Operations research (OR) encompasses the development and the use of a wide range of problem-solving techniques and methods applied in the pursuit of improved decision-making and efficiency, such as simulation, mathematical optimization, queueing theory and other stochastic-process models, Markov decision processes, econometric methods, data envelopment analysis, ordinal priority approach, neural networks, expert systems, decision analysis, and the analytic hierarchy process. Nearly all of these techniques involve the construction of mathematical models that attempt to describe the system.
[10] Editorial: Special issue on operations research and machine learning — its cumulative reward. However, there is still room to exploit optimization and operations research (OR) in machine learning, and vice versa. Both machine learning and OR can gain advantages through integration and inter-action. Optimization and OR techniques play a pivotal role in mitigating machine learn-
[12] Combining Machine Learning and Operations Research Methods to Advance ... — Combining Machine Learning and Operations Research Methods to Advance the Project Management Practice. Conference paper; First Online: 11 December 2019; ... Management practice, this paper proposes a hybrid approach that builds on the synergy between contemporary Machine Learning and Operations Research techniques. Based on past data, Machine
[13] Impact of Operations Research on Business — Operations research also helps in pinpointing specific areas where expenses can be reduced. Through detailed data analysis and modeling, businesses can uncover inefficiencies and unnecessary expenditures. For example, in manufacturing, operations research can identify bottlenecks in production processes that lead to increased costs.
[14] Real-World Applications of Operations Research — Here are some key areas where operations research techniques are applied. Hospital Resource Allocation. Operations research helps hospitals determine the optimal allocation of resources such as staff, equipment, and facilities. By analyzing historical data and predicting future demands, hospitals can ensure that resources are utilized effectively.
[17] Operation Research Models - 8 Common Models Explained in Detail ... — Operation Research Models – 8 Common Models Explained in Detail | Operations Management Operational Research (OR) Models, also known as Management Science Models and Decision Science Models, are mathematical and analytical methods used to answer complex questions and make informed decisions in many fields, including business, engineering, healthcare, logistics, and finance. By formulating real-world problems as mathematical equations or algorithms, OR models allow decision-makers to find the best solutions under given constraints, optimizing processes, resources, and outcomes. There are many applications for non-linear programming models, including engineering design, portfolio optimization, financial planning, and resource management. In addition to linear programming and integer programming, non-linear programming, network models, queueing models, simulation models, and more, each type of OR model offers unique insights into specific types of problems. Categories Operations Management Tags Operation Research Models
[18] Development and Optimization of Mathematical Models for Operations Research — The development of mathematical models and their optimization are fundamental for the effective resolution of many problems in operational research. In recent years, increased insights into real-world problems have led to the development of new mathematical models and optimization algorithms, contributing to the development of a research area with increasing practical relevance.
[19] Mathematics in Operations Research: Concepts, Methods, and Examples — Operations Research (OR) is a multidisciplinary field that uses mathematical models, statistical analysis, and optimization techniques to solve complex decision-making problems in various industries such as manufacturing, transportation, healthcare, finance, and logistics. ... we will explore the key mathematical concepts used in operations
[20] Types of Operations Research Models - theintactone — Operations Research (OR) utilizes various models to analyze complex decision-making problems and optimize processes across different industries. These models can be categorized based on their mathematical structure, application, and the nature of the problems they address. Linear Programming Models Linear programming (LP) is one of the most widely used OR techniques.
[23] Top Emerging Trends in Operations Research for 2023 - LinkedIn — Operations research (OR) is a discipline that deals with the application of advanced analytical methods to help make better decisions. As businesses and technologies evolve, so do the trends in OR
[33] Foundations of operations research: From linear programming to data ... — The foundations of operations research (OR) as a distinct academic discipline lie mainly in classical mathematics and statistics. OR emerged during WWII in the UK and then in the US, when scientists from various disciplines worked together to solve complex operational military problems such as logistics, location, scheduling, and resource allocation (Hartcup, 2000).
[35] Characteristics of Operations Research - alterainstitute.com — Operations research (OR) is a methodical framework for addressing challenges and making informed choices by leveraging mathematical modeling, statistical evaluation, and optimization methods. Goal-Oriented Approach: Operations research focuses on finding the most effective solution to a given problem by either maximizing or minimizing specific objectives, such as increasing profits, reducing costs, or improving time efficiency. Despite these challenges, operations research remains a powerful tool for enhancing efficiency, minimizing costs, and making well-informed, data-driven decisions across various industries. Operations research is a highly effective discipline that provides organizations with scientific, data-driven solutions for complex decision-making and problem-solving. Ultimately, operations research remains a vital tool for organizations striving to optimize operations, streamline processes, and make informed, strategic decisions in an increasingly data-driven world.
[51] History of OR: Useful history of operations research — All serious accounts of the origins of O.R. agree that the term was initially applied in Britain just prior to World War II to distinguish research done to integrate radar technology into aerial combat operations from the research and development being done in laboratories and workshops.
[53] The Beginnings of Operations Research: 1934-1941 - JSTOR — This paper, the first in a series on the history of operations research and management science, traces the scattered beginnings of operations research from World War I up to the activities in Britain before and during the early months of World War II. Operations research was born of radar on the eve of World War II. But its advent was forecast
[54] Historical Development of Operations Research. — Focuses on several consulting firms and societies in Europe and the United States significant to the development of operations research. Functions of the Operations Research and Management Science (ORMS); Development of ORMS from the chaotic conditions existing in the European Campaign of World War II; Use of personnel and techniques from scientific disciplines for studying problems.
[55] Operations Research, History, Uses - theintactone — OR involves formulating problems as mathematical models, often using techniques like linear programming, simulation, and queuing theory to identify the best possible solutions. The term “Operations Research” was coined during World War II as military strategists sought to optimize resource allocation and logistics. OR expanded beyond military applications into fields like manufacturing, transportation, and telecommunications, utilizing techniques such as linear programming, game theory, and queuing theory. The development of optimization software and modeling tools facilitated the widespread adoption of OR techniques in business and industry. By analyzing data and modeling logistics, companies can optimize inventory levels, minimize transportation costs, and improve overall supply chain efficiency. Techniques such as the Transportation Problem model help organizations determine the most efficient routes and methods for transporting goods, reducing costs and improving delivery times.
[59] Combat science: the emergence of Operational Research in World War II — Combat science: the emergence of Operational Research in World War II - ScienceDirect Combat science: the emergence of Operational Research in World War II During World War II, the Allies invented a new scientific field – Operational Research (OR) – to help complex military organizations cope with rapid technological change . The UK and the USA were unique among the combatants in World War II in their pursuit of strategic bombing; attempting to weaken the ability of the Axis to wage war through air strikes against military, industrial or civilian targets. OR helped military organizations cope with the uncertainties and controversies surrounding rapid technological change during World War II.
[63] (PDF) Operational Research: Methods and Applications — Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts.
[64] Operations Research, History, Uses - theintactone — OR involves formulating problems as mathematical models, often using techniques like linear programming, simulation, and queuing theory to identify the best possible solutions. The term “Operations Research” was coined during World War II as military strategists sought to optimize resource allocation and logistics. OR expanded beyond military applications into fields like manufacturing, transportation, and telecommunications, utilizing techniques such as linear programming, game theory, and queuing theory. The development of optimization software and modeling tools facilitated the widespread adoption of OR techniques in business and industry. By analyzing data and modeling logistics, companies can optimize inventory levels, minimize transportation costs, and improve overall supply chain efficiency. Techniques such as the Transportation Problem model help organizations determine the most efficient routes and methods for transporting goods, reducing costs and improving delivery times.
[71] Operations Research in World War II | Proceedings - May 1968 Vol. 94/5/783 — It appears, then, that operations research made its formal debut in World War II although some of its methods and principles had been used before that time. For example, F. W. Lanchester, the British aeronautical pioneer, was one of the first men to apply quantitative reasoning to military strategy. His original writings appeared in 1914-15.
[81] George Dantzig: Father of Linear Programming - Onestepguide — George Dantzig, often hailed as the Father of Linear Programming, made significant contributions to the field of operations research through his groundbreaking work in mathematical optimization. From his early life and academic journey to his pivotal role in developing the simplex algorithm, Dantzig's impact on industry and military operations during World War II remains unparalleled. This
[82] George Dantzig - Wikipedia — George Bernard Dantzig (/ ˈ d æ n t s ɪ ɡ /; November 8, 1914 - May 13, 2005) was an American mathematical scientist who made contributions to industrial engineering, operations research, computer science, economics, and statistics.. Dantzig is known for his development of the simplex algorithm, an algorithm for solving linear programming problems, and for his other work with linear
[94] Operations Research in Supply Chain Management — We will discuss core OR techniques specifically tailored for supply chain and operations challenges, from optimising inventory levels to streamlining transportation routes. SCM Application: Inventory management models can be used to optimise stock levels for various products across warehouses, ensuring timely availability while minimising associated costs. The world of operations and supply chain management might seem complex, but with operations research as your partner, you can transform it from a reactive scramble into an efficient, data-driven engine. We've explored the exciting potential of advanced applications like simulation modelling and machine learning, pushing the boundaries of what is possible in supply chain optimisation. Improved decision-making: Data-driven OR models provide valuable insights to guide informed decision-making, leading to more strategic and proactive supply chain management.
[95] The New Generation of Operations Research Methods in Supply Chain ... — Journals Journals Find a Journal Journal Journals Over 380 articles published between 2005 and 2016 in the ISI/Web of Science database have applied advanced O.R. techniques in SCN optimization studies. This paper offers a systematic review of these published contributions by focusing on two categories of O.R. approaches most recently applied for the design of SC systems: integrated mathematical modeling and simulation-optimization (S-O) frameworks. A brief review of the literature on O.R. approaches in the SC context from 2005–2016 demonstrates an exponential increase in the number of published papers and in the variety of the methods and models for SCN optimization and design. Pourhejazy, P.; Kwon, O.K. The New Generation of Operations Research Methods in Supply Chain Optimization: A Review.
[97] Algorithmic Optimization Techniques for Operations Research Problems — Algorithmic Optimization Techniques for Operations Research Problems This paper outlines the core themes covered in our research, including the classification of optimization problems, the utilization of mathematical models, and the development of algorithmic solutions. It highlights the importance of algorithm selection and design in achieving optimal solutions for diverse operations research problems. The paper aims to provide readers with insights into cutting-edge algorithmic techniques, their applications, and their potential impact on addressing complex optimization challenges in operations research. Algorithmic Optimization Techniques for Operations Research Problems serves as theoretical board for researchers, practitioners, and students seeking to understand and apply algorithmic optimization methods to tackle a wide range of operations research problems and make informed decisions in various domains. Algorithmic Optimization Techniques for Operations Research Problems.
[98] PDF — © July 2024 | IJIRT | Volume 11 Issue 2 | ISSN: 2349-6002 IJIRT 166623 INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH IN TECHNOLOGY 1289 Optimization theory and applications in operations research Ashwini Modi Assistant Professor, Atharva College of Engineering, Mumbai-India Abstract—Optimization theory is crucial in operations research, providing mathematical frameworks and algorithms to solve complex decision-making problems efficiently. INTRODUCTION Optimization theory serves as a cornerstone in operations research, providing essential mathematical frameworks and algorithms to solve complex decision-making problems effectively. APPLICATIONS IN OPERATIONS RESEARCH Real-World Applications Optimization theory and algorithms are widely utilized in various sectors, greatly improving operational efficiency and decision-making procedures. These instances demonstrate how optimization theory and algorithms can be used to solve intricate operational problems and result in substantial cost reductions and efficiency enhancements.
[99] Role of metaheuristic algorithms in healthcare: a comprehensive ... — These algorithms have become helpful in healthcare in recent years, providing novel approaches to complex optimization and decision-making problems. Clinical treatment planning, medical condition monitoring, and healthcare logistics and resource optimization are numerous technical challenges impacting the healthcare sector.
[105] Introducing and Integrating Machine Learning in an Operations Research ... — 1. Introduction. Artificial intelligence (AI) and operations research (OR) have long been intertwined because of their synergistic relationship (Bennett and Parrado-Hernández 2006).For example, Markov decision processes form the theoretical foundation for classical reinforcement learning (Bertsekas and Tsitsiklis 1996, Sutton et al. 1999) and deep reinforcement learning relies heavily on the
[106] Artificial Intelligence for Operations Research: Revolutionizing the ... — Decision analysis, Artificial Intelligence, Operations Research, Modeling, Algorithm selection, Optimization, Machine Learning 1 Introduction Operations Research (OR) is an interdisciplinary field that employs advanced analytical techniques and methodologies to support decision-making processes in organizations, aiming to improve efficiency, optimize resource allocation, and achieve desired objectives. C., Chen, M., Cucurull, G., Esiobu, D., Fernandes, J., Fu, J., Fu, W., Fuller, B., Gao, C., Goswami, V., Goyal, N., Hartshorn, A., Hosseini, S., Hou, R., Inan, H., Kardas, M., Kerkez, V., Khabsa, M., Kloumann, I., Korenev, A., Koura, P. Zhou, C., Liu, P., Xu, P., Iyer, S., Sun, J., Mao, Y., Ma, X., Efrat, A., Yu, P., Yu, L., Zhang, S., Ghosh, G., Lewis, M., Zettlemoyer, L., and Levy, O.
[107] Integrating machine learning and operations research methods for ... — Operations research (OR) techniques have been widely used for optimizing problems, such as manufacturing scheduling, supply chain optimization, and resource allocation. ... Integrating machine learning and operations research methods for scheduling problems: a bibliometric analysis and literature review Ayoub OUHADI*, Zakaria YAHOUNI*, Maria DI
[108] Computers & Operations Research | Leveraging the Synergy of AI and ... — Computers & Operations Research | Leveraging the Synergy of AI and Optimization Models for Enhanced Problem Solving and Decision-Making | ScienceDirect.com by Elsevier This Special Issue aims to explore and advance the research on the combination of AI and Optimization Models to harness the complementary strengths of both methodologies and highlight the importance of their integration for achieving superior results in diverse applications This Special Issue aims to explore and advance the research on the combination of AI and Optimization Models to harness the complementary strengths of both methodologies and highlight the importance of their integration for achieving superior results in diverse applications
[109] [2401.03244] Artificial Intelligence for Operations Research ... — Change to arXiv's privacy policy The arXiv Privacy Policy has changed. By continuing to use arxiv.org, you are agreeing to the privacy policy. arXiv:2401.03244 Help | Advanced Search arXiv author ID The rapid advancement of artificial intelligence (AI) techniques has opened up new opportunities to revolutionize various fields, including operations research (OR). This survey paper explores the integration of AI within the OR process (AI4OR) to enhance its effectiveness and efficiency across multiple stages, such as parameter generation, model formulation, and model optimization. Subjects: Optimization and Control (math.OC); Artificial Intelligence (cs.AI) Cite as: arXiv:2401.03244 [math.OC] (or arXiv:2401.03244v2 [math.OC] for this version) From: Bissan Ghaddar [view email] cs.AI Bibliographic and Citation Tools Bibliographic Explorer Toggle Connected Papers Toggle scite.ai Toggle arXiv Operational Status
[134] Emerging Research Methodologies in the Age of Artificial Intelligence ... — Emerging methodologies like Data-Driven and AI-enhanced methods, including Natural Language Processing (NLP), Adaptive Research Designs, Computational Ethnography, Crowdsourced Data Collection, publicly accessed internet data mining, and multimodal research—reflect a shift towards interdisciplinary, diverse datasets and real-time data analysis. In this age of digital data abundance, Public Internet Data Mining stands out as a potent research methodology with broad applications across fields like education, technology, and the social sciences. Unlike traditional methods where data collection might influence participant behavior, public internet data mining allows researchers to observe and analyze behaviors and interactions as they occur naturally in online spaces. Public internet data mining methods in instructional design, educational technology, and online learning research.
[135] Artificial intelligence approaches and mechanisms for big data ... — Artificial Intelligence (AI) techniques such as machine learning and evolutionary algorithms able to provide more precise, faster, and scalable outcomes in big data analytics. In this survey, the existing research on big data analytics techniques is categorized into four major groups, including machine learning, knowledge-based and reasoning methods, decision-making algorithms, and search methods and optimization theory. Athmaja, Hanumanthappa & Kavitha (2017) presented a systematic literature-based review of the big data analytics approaches according to the machine learning mechanisms. As mentioned in the previous sections, machine learning, knowledge-based and reasoning methods, decision-making algorithms, and search methods and optimization theory are four main categories of big data analytics techniques. Based on the claimed results of the investigated articles, the machine learning-based mechanisms focus on improving the accuracy of big data analytics.
[136] Optimizing supply chain networks using mixed integer linear programming ... — 1. Introduction Supply chain management is a critical function for organizations seeking to enhance efficiency and competitiveness. As global markets become more interconnected, optimizing supply chain networks presents both opportunities and challenges. Traditional linear programming (LP) techniques provide a foundation for addressing these challenges but often fall short when decision
[140] A case study on the assembly of food parcel applying linear programming — This article aims to present a practical application of one of these techniques, the linear programming, applied in the making of Christmas food parcel. The article aims to maximize the sales revenue of the food parcel by controlling the items that compose them and performing the possible resupplies if there were missing items in stock.
[145] Machine Learning in Logistics Industry: Benefits and Use Cases — Top Machine Learning Use Cases in Logistics. Machine learning is revolutionizing the logistics and supply chain industry by offering innovative solutions to enhance efficiency, reduce costs, and optimize operations. Here are some top machine learning use cases in logistics, each explored in detail: 1. Demand Forecasting and Inventory Optimization:
[148] Scope of Operations Research: Key Applications and Benefits | iDC — What is the scope of operations research? OR has an extremely broad scope, and its application spans almost every kind of industry: manufacturing, healthcare, logistics, finance, and telecommunications. The techniques are applied to optimize processes, improve decision-making, and conserve resources.
[149] The Crucial Role of Operations Research in Healthcare and ... - Marktine — Operations Research has emerged as an invaluable tool in the healthcare and pharmaceutical industries, revolutionizing the way decisions are made and processes are optimized. Its applications range from enhancing hospital operations and drug development to improving supply chain management and supporting health policy planning.
[151] Creating Impact with Operations Research in Health: Making Room for ... — Working with domain experts can help the operations researcher identify and frame important problems for analysis, understand the salient aspects of the problem, obtain needed data, validate the model, and disseminate the results to decision makers. This means that scholars working on such problems have a double burden: in order to influence decision making, they must publish their work in medical and health journals that decision makers will read; and in order to disseminate their theoretical results, they must publish in OR journals and other journals that publish theoretical work. By making room in academia for practical OR analyses in health, we will be encouraging junior scholars to help solve important problems – and we will be returning to the original spirit of operations research.
[152] Operations Research Applications in Health Care Management — The respective chapters, written by prominent researchers, explain a wealth of both basic and advanced concepts of operations research for the management of operating rooms, intensive care units, supply chain, emergency medical service, human resources, lean health care, and procurement.
[179] PDF — 4.2): (i) we redefine the overall approach by using classification instead of clustering methods and we provide more in depth analysis of each step, (ii) we increase the success chances of the project by properly assigning the available developers to each project issue, (iii) we examine our approach using real data contrary to hypo-thetical data used in and (iv) we evaluate our approach by utilizing the Local Surrogate Models (LIME) explanation method in order to get a solid under-standing of the underlying mechanism of our trained model. Combining Machine Learning and Operations Research Methods 151 5 Discussion Key enablers that are driving the development of the proposed approach are the availability of huge computing power, the existence of big volumes of PM data and knowledge, as well as the accessibility of a range of well-tried and powerful OR and ML software libraries.
[180] Integrating machine learning and operations research methods for ... — Integrating machine learning and operations research methods for scheduling problems: a bibliometric analysis and literature review ... the integration of OR and ML offers a balanced solution, leveraging ML's capability to extract patterns from large datasets and making predictive decisions and OR's precision to enhance decision-making
[181] Editorial: Special issue on operations research and machine learning — making as OR techniques can provide an opportunity to meet these criteria in machine learning. On the other hand, machine learning techniques can contribute to finding the optimal solutions and making the best decision efficiently. Machine learning techniques can auto-mate the process of the problem reduction in combinatorial optimization
[186] Applications of Operations Research in Insurance Risk Management - JETIR — This paper explores the use of operations research in the insurance industry. Our research has attempted to point out the various approaches of operations research that can be used to solve insurance risk management-related problems. Having provided a general overview of all the techniques applied, we then focused on the three most famous OR techniques, which are: Linear Programming, Goal
[187] Operational research insights on risk, resilience & dynamics of ... — Understanding the co-movement and spillover effects among different asset classes is important for asset allocation, portfolio diversification, and cross-market hedging. In the context of operations research, this knowledge enables the optimization of decision-making processes and improves risk management.
[188] Risk analysis and decision theory: A bridge - ScienceDirect — The creation of quantitative tools for decision support is central in operations research and the management sciences. Decision support is often intertwined with a risk analysis or is a part of a decision analysis, with applications ranging from operational risk management in finance (Zhao & Huchzermeier, 2015), to supply chain risk assessment (Fahimnia, Tang, Davarzani, Sarkis, 2015, Heckmann
[189] Key Tools Used by Operations Research Analysts — Operations research analysts play a crucial role in optimizing decision-making processes across various industries. How Operations Research Analysts Use Mathematical Models in Decision-Making Operations research analysts use mathematical models to guide decision-making processes. By leveraging these benefits, analysts can help organizations make better-informed decisions and optimize their operations efficiently. Operations research analysts use several types of simulation tools to model systems and processes. By using various simulation tools, analysts can enhance forecasting, assess risks, optimize resources, and support decision-making processes. This analysis helps in making data-driven decisions, optimizing processes, and improving overall efficiency. How Operations Research Analysts Use Decision Trees for Decision-Making Operations research analysts utilize decision trees to simplify complex decision-making scenarios. Operations research analysts use several critical tools to enhance decision-making.
[196] Characteristics of Operations Research - alterainstitute.com — Operations research (OR) is a methodical framework for addressing challenges and making informed choices by leveraging mathematical modeling, statistical evaluation, and optimization methods. Goal-Oriented Approach: Operations research focuses on finding the most effective solution to a given problem by either maximizing or minimizing specific objectives, such as increasing profits, reducing costs, or improving time efficiency. Despite these challenges, operations research remains a powerful tool for enhancing efficiency, minimizing costs, and making well-informed, data-driven decisions across various industries. Operations research is a highly effective discipline that provides organizations with scientific, data-driven solutions for complex decision-making and problem-solving. Ultimately, operations research remains a vital tool for organizations striving to optimize operations, streamline processes, and make informed, strategic decisions in an increasingly data-driven world.
[199] Data Quality and its Impacts on Decision-Making - Springer — Data Quality and its Impacts on Decision-Making: How Managers can benefit from Good Data | SpringerLink See our privacy policy for more information on the use of your personal data. Data Quality and its Impacts on Decision-Making This is a preview of subscription content, log in via an institution to check access. Access this book Christoph Samitsch investigates whether decision-making efficiency is being influenced by the quality of data and information. Results of the research provide evidence that defined data quality dimensions have an effect on decision-making performance as well as the time it takes to make a decision. Book Title: Data Quality and its Impacts on Decision-Making Book Subtitle: How Managers can benefit from Good Data Access this book
[201] Supporting data quality management in decision-making — Abstract In the complex decision-environments that characterize e-business settings, it is important to permit decision-makers to proactively manage data quality. In this paper we propose a decision-support framework that permits decision-makers to gauge quality both in an objective (context-independent) and in a context-dependent manner.
[202] Evaluating the impact of big data analytics usage on the decision ... — We collected data from 240 agricultural firms in China. The empirical results showed that big data analytics usage had a positive impact on decision-making quality and that data analytics capabilities played a mediating role in the relationship between big data analytics usage and decision-making quality.
[223] Limitations of Operations Research - theintactone — While Operations Research (OR) provides powerful tools for optimizing decision-making and solving complex problems across various industries, it also has its limitations. Data Dependency Operations Research heavily relies on accurate and relevant data to create models and analyze scenarios. Poor-quality or incomplete data can lead to inaccurate results, misleading conclusions, and ultimately
[224] LIMITATIONS OF OPERATIONS RESEARCH - BrainKart — Limitations of Operations Research ... When basic data are subjected to frequent changes, incorporating them into the OR models is a costly proposition. Moreover, a fairly good solution at present may be more desirable than a perfect OR solution available after sometime. The computational time increases depending upon the size of the problem
[225] Challenges Faced by Operations Research Analysts — Home Challenges Faced by Operations Research Analysts Operations research analysts rely heavily on data to identify patterns, trends, and insights that can inform decision-making processes. Without access to reliable and comprehensive data, operations research analysts may struggle to produce accurate analyses and actionable insights. In general, the lack of accessible data is a significant challenge that operations research analysts face in their day-to-day work. By overcoming this challenge, operations research analysts can enhance the effectiveness of their analyses and contribute more effectively to decision-making processes within their organizations. Operations research analysts face significant challenges in their work, particularly when it comes to analyzing large amounts of data. Operations research analysts must navigate these challenges to ensure successful strategy implementation.
[226] What Are Some Data Collection Challenges and How Do ... - Elite Research — The consequences of failing to properly collect data include the inability to answer your research questions, inability to validate the results, distorted findings, wasted resources, misleading recommendations and decisions, and harm to participants.
[227] Data Quality in Operations Research - LinkedIn — Ensure accuracy in Operations Research with effective strategies for maintaining data quality. Clean, validate, and monitor your way to reliable analyses.
[228] Common Data Issues in Organizations: Identifying and Overcoming Challenges — Common Data Issues in Organizations: Identifying and Overcoming Challenges Common Data Issues in Organizations: Identifying and Overcoming Challenges Understanding these common data issues is crucial for any organization seeking to leverage data effectively for decision-making, strategy, and growth. Organizations often face significant data quality challenges, including inaccuracies, incomplete records, and duplicates, which can undermine decision-making and erode trust. Organizations often operate with multiple systems and databases, leading to challenges in integrating data. Effective data management is critical for any organization. Solution: Implement a Data Management Strategy Solution: Align Data Strategy with Business Goals By focusing on data quality, integration, security, skills development, management practices, and alignment with business goals, organizations can create a robust data ecosystem that drives informed decision-making and fosters growth.
[232] Challenges Faced by Operations Research Analysts — Home Challenges Faced by Operations Research Analysts Operations research analysts rely heavily on data to identify patterns, trends, and insights that can inform decision-making processes. Without access to reliable and comprehensive data, operations research analysts may struggle to produce accurate analyses and actionable insights. In general, the lack of accessible data is a significant challenge that operations research analysts face in their day-to-day work. By overcoming this challenge, operations research analysts can enhance the effectiveness of their analyses and contribute more effectively to decision-making processes within their organizations. Operations research analysts face significant challenges in their work, particularly when it comes to analyzing large amounts of data. Operations research analysts must navigate these challenges to ensure successful strategy implementation.
[233] How can operational research make a real difference in healthcare ... — Operational research literature in healthcare is often either intentionally theoretical (Brailsford, Bolt, Connell, Klein & Patel, 2009; Eldabi, 2009) or, if grounded in a practical problem, lacks documentation on the implementation and final impact (Brailsford, Bolt et al., 2009; Fone et al., 2003; Katsaliaki & Mustafee, 2011; van Lent et al., 2012). We have outlined the five key areas we believe, based on our experience, are fundamental to successful implementation of operational research models in healthcare: an internal champion, a critical issue, healthcare cultural insight, Data quality, and expectations management.
[234] Limitations of Operations Research - Shiksha — / Data Science / Data Science Articles / Data Science Basics Articles / Limitations of Operations Research Must Check: Top Operations Research Online Courses and Certifications This article will learn the importance of operations research...read more For example, a business may face a problem that is so complex that it cannot be modelled accurately using operations research. Operations research has several limitations, including the assumption of rationality, incomplete information, model assumptions, complexity, and cost. It also assumes that mathematical models used in operations research are accurate and that the problem being studied can be represented using these models. As the complexity of the problem increases, it becomes more difficult to model and analyze, making it challenging to use operations research to find a solution.
[239] Challenges in Operations Research: Understanding its Limitations — Challenges in Operations Research: Understanding its Limitations • SLM (Self Learning Material) for MBA All Subjects Operations Research (O.R.) has emerged as a powerful tool for decision-making, leveraging mathematical models and systematic analysis to optimize complex processes. While O.R. aims to deliver cost-effective and efficient solutions, the processes involved in developing and implementing these solutions can be both time-consuming and expensive. Conclusion 🔗 How useful was this post? Submit RatingAverage rating 0 / 5. Vote count: 0 No votes so far! Phases and Processes of O.R. Study Optimisation Models Notations and Symbols Other Similar Sites Similar to this, but for other courses. BA LLB Notes ⚖️ BA Notes 📚 DELED Institute 📚 Share This Share
[242] PDF — The Impact of Artificial Intelligence on Operations Management John Peter* Department of Strategic Management, College of Business at Pacific University, Oregon, Mexico E-mail: petjo55@gmail.com Artificial Intelligence (AI) has emerged as a transformative force across various industries, and operations management is no exception. The integration of AI into operations management processes offers significant potential for improving efficiency, enhancing decision-making and driving innovation (Adams et al, 2021). One of the most significant impacts of AI on operations management is its ability to enhance efficiency and productivity. AI is undoubtedly reshaping operations management, offering businesses the tools to enhance efficiency, optimize supply chains, and improve decision-making. AI (artificial intelligence)-assisted planning within emergency management operations.
[243] Interpretable machine learning and explainable artificial intelligence ... — In recent years, the digital transformation accelerated by the COVID-19 pandemic, alongside reduced costs of IT infrastructure, has culminated in an increasing impetus among organizations to collect, store, and analyze data with the objective of enhancing operational research (OR) decision-making processes.
[244] Editorial: Special issue on operations research and machine learning — research (OR) in machine learning, and vice versa. Both machine learning and OR can gain advantages through integration and inter-action. Optimization and OR techniques play a pivotal role in mitigating machine learn-ing challenges. From feature selection to handling incomplete data and imbalance learning, they can enhance model accuracy and
[252] Recent Trends in Operations Research and Game Theoretic Approach in ... — We would especially welcome innovative original contributions to new methods and applications of operations research and game theoretical models involving large-scale data from business or real-life problems.
[254] Future Trends in Operations Research — This blog will delve into the key developments shaping the future of operations research and their implications for practitioners. Applications of Artificial Intelligence in Operations Research Overview of Artificial Intelligence. Artificial Intelligence (AI) is reshaping industries and driving innovations across various fields.
[256] PDF — traditional OR methods, especially in the dynamic and uncertain environments of Industry 4.0. Since the 1990s, there has been an interest in integrating Machine Learning (ML) with Operations Research (OR) to tackle scheduling problems, as evidenced by early efforts (Brown and White, 1991) , (Smith et al., 1996) .
[257] PDF — A. ML Algorithms in Optimization Machine Learning Algorithms used in Optimization Process Linear Regression It models the relationship between variables understanding the impact of each variable on the objective function thereby finding optimal values for decision variables Logistic Regression To classify and optimize categorical outcomes. Machine Learning has significantly enhanced optimization techniques in Operations Research by providing advanced tools and methods for solving complex problems. Machine Learning has significantly advanced network design in Operations Research by providing sophisticated tools and methods for optimizing complex networks. CONCLUSION The integration of Machine Learning (ML) into Operations Research (OR) represents a transformative shift in how complex decision-making and optimization problems are approached and solved.
[258] Machine Learning Meets Operations Research: Solving Complex Problems ... — In today's fast-paced, data-driven world, industries ranging from logistics to healthcare face increasingly complex problems. Traditional optimization methods from operations research (OR) have long been the backbone for solving such challenges. These methods focus on finding the best solutions under defined constraints, often using techniques such as linear programming, integer programming
[261] The Impact of Artificial Intelligence On Business Operations - ResearchGate — This paper explores the transformative impact of Artificial Intelligence (AI) on business operations, aiming to bridge the knowledge gap for the approximately 40% of the population unfamiliar with AI technologies. By elucidating the various applications of AI, such as expert systems, natural language processing, speech recognition, and machine vision, the paper highlights how these technologies are advancing and streamlining business processes. Word Reference: Artificial Intelligence (AI): The simulation of human intelligence processes by machines, especially computer systems. Applications of AI in Business Operations: Artificial intelligence (AI) is transforming business operations across various industries by AI enhances the efficiency of business processes by automating repetitive tasks such as data AI into business processes has improved customer engagement through advanced tools like
[262] The Interplay between Operations Research and Machine Learning — A large number of operations research (O.R.) scholars have been attempting to explore the interplay between O.R. and ML in distinct forms. In fact, the number of contributions in INFORMS journals/publications containing the string "machine learning" has been increasing exponentially in the past few years, as shown in Figure 1.
[263] Artificial Intelligence for Operations Research: Revolutionizing the ... — AI-driven heuristics (Di Liberto et al., 2016), metaheuristics (Talbi, 2009), and learning-based approaches (Gomory, 1960) can be employed to enhance algorithms like branch-and-bound and cutting-plane methods, and improve solving mixed integer programming problems. Many machine-learning-based approaches have been developed to assist node selection (He et al., 2014, Song et al., 2018, Sabharwal et al., 2012) and variable selection (Khalil et al., 2016, Alvarez et al., 2017, Di Liberto et al., 2016, Balcan et al., 2018, Gasse et al., 2019, Gupta et al., 2020, 2022, Zarpellon et al., 2021, Qu et al., 2022, Etheve et al., 2020, Sun et al., 2020) in the B&B algorithm for solving MIP problems. He et al., (2014) introduced an imitation learning method that learns a node selection strategy by observing a small set of solved problems.
[265] Interpretable machine learning and explainable artificial intelligence ... — In recent years, the digital transformation accelerated by the COVID-19 pandemic, alongside reduced costs of IT infrastructure, has culminated in an increasing impetus among organizations to collect, store, and analyze data with the objective of enhancing operational research (OR) decision-making processes.
[266] How Machine Learning is Used with Operations Research? — The combination of machine learning and operation research approaches gives such solutions which are not only accurate but also optimal
[267] Optimizing Supply Chain Resilience Using Advanced Analytics and ... — This paper presents a novel resilient supply chain management (SCM) structure leveraging advanced artificial intelligence (AI) techniques, specifically Long Short-Term Memory (LSTM) networks and Particle Swarm Optimization (PSO). The primary objective is to enhance supply chain efficiency and robustness by integrating these AI methods to address common challenges such as demand forecasting
[274] PDF — This paper explores how blockchain can enhance supply chain transparency by providing traceability, preventing fraud, and fostering accountability. By examining key applications, case studies, and the challenges of implementation, the paper highlights blockchain's potential to revolutionize supply chain management.
[275] PDF — Blockchain technology offers a reliable and transparent method for storing and sharing data, making it a valuable tool for enhancing security and efficiency in various industries. At the same time, advanced data-driven systems are transforming decision-making processes by learning from information patterns.
[276] Operations Research in the Blockchain Technology — Though the operations research has been widely adopted in the blockchain technology, there is a lack of comprehensive survey on the operations research in blockchain-related issues.
[278] Game Theory and Operations Research: Some Musings 50 Years Later — The new game theory in operations research applications lies in the study of organizations and in systems that involve individuals, networks, and institutions. The success of game theory in supplying the language for the study of informa-tion and providing the basic concept of strategy has led to our understanding the limitations implicit in
[279] The Present and Future of Game Theory - SSRN — The use of deep techniques flourishes best when it stays in touch with application. There is a vital symbiotic relationship between good theory and practice. The breakneck speed of development of game theory calls for an appreciation of both the many realities of conflict, coordination and cooperation and the abstract investigation of all of them.
[283] Comparative Analysis of Sustainability and Resilience in Operations and ... — This study contributes to the current discussion on the potential interplay between sustainability and resilience in operations and supply chain management. The developed framework provides guidance for integrating dual demands of sustainability and resilience within manufacturing strategy research and practice.