Optimization Of Operations Research Articles

Path planning and optimization for transmission line barrier operations in complex terrain based on multimachine collaborative control

Abstract To address the challenges of path planning for transmission line barrier operations in complex terrains, this paper proposes a solution based on multimachine collaborative control. A path-planning algorithm that integrates multiple classical algorithms and incorporates multiobjective optimization methods is constructed to handle the complexities of challenging terrains. Auction algorithm and genetic algorithm are adopted to achieve optimal load balance by dynamically adjusting task allocation. Simulation results show that the proposed method effectively enhances task execution efficiency, optimizes path planning, and reduces energy consumption in complex terrains and dynamic environments, while maintaining high-obstacle avoidance success rates and communication stability.

Inteligencia de Negocios para la Toma de Decisiones

Business intelligence (BI) has established itself as an essential tool for strategic and operational decision making in various industries. By collecting, analyzing and visualizing data, BI optimizes internal processes, improves decision-making accuracy and enhances organizational competitiveness. Its application in sectors such as banking, education and commerce has demonstrated significant benefits, such as personalization of customer service, efficient management of resources and optimization of operations. Emerging technologies such as artificial intelligence (AI), machine learning and cloud-based systems expand BI capabilities, enabling real-time analytics and greater scalability. However, their implementation presents challenges related to infrastructure investment, data governance and ethical issues around privacy. Despite this, innovative models such as predictive analytics and cloud platforms stand out for their ability to transform large volumes of data into actionable information. BI not only redefines business management, but also positions itself as a key element to face the challenges of the global market.

DATA-DRIVEN OPTIMIZATION IN PHARMACY OPERATIONS: A PRESCRIPTION FOR ENHANCED PATIENT CARE

The convergence of patient care and business efficiency in community pharmacies through a Comprehensive Datafication Approach marks a transformative journey toward enhanced healthcare services and operational effectiveness. The aim of this study was to examine diverse perspectives on datafication, AI, and emerging technologies in healthcare, with a particular focus on optimizing community pharmacy operations. The implementation of a Comprehensive Datafication Approach in community pharmacies significantly impacts overall operational efficiency and patient care outcomes. The strict criterion mandates studies to report outcomes related to patient care, business efficiency, or operational metrics, ensuring selected literature aligns cohesively with research objectives, providing a robust basis for exploring dimensions associated with optimizing community pharmacy operations through datafication. Drawing from multiple studies, our exploration encompasses the challenges and potentials inherent in the integration of digital technologies. As we delve into the optimization of community pharmacy operations through AI, these diverse perspectives contribute to a nuanced understanding of the challenges and opportunities in data-driven healthcare transformation. The integration of AI necessitates ethical considerations, inclusive practices, and a balance between optimization goals and individual rights, ensuring a holistic approach to healthcare datafication. Peer Review History: Received 2 October 2024; Reviewed 17 November; Accepted 24 December; Available online 15 January 2025 Academic Editor: Dr. Emmanuel O. Olorunsola, Department of Pharmaceutics & Pharmaceutical Technology, University of Uyo, Nigeria, olorunsolaeo@yahoo.com Average Peer review marks at initial stage: 6.5/10 Average Peer review marks at publication stage: 7.0/10

Optimization of Bulk Cargo Terminal Unloading and Outbound Operations Based on a Deep Reinforcement Learning Framework

This study addresses the integrated scheduling problem of dry bulk cargo terminal yards, which includes three components: transportation planning, yard selection optimization, and equipment scheduling. Additionally, the research integrates safety considerations and addresses the complexities of dynamic transportation planning. This work presents two innovations. Firstly, this study develops a sophisticated modeling framework that integrates graph structures for precise yard mapping with mixed-integer programming to enforce operational constraints. This integrated approach facilitates a more accurate and comprehensive representation of yard operations, capturing diverse operational aspects while maintaining model clarity and computational efficiency. Secondly, this study proposes an advanced solution methodology that employs a reinforcement learning technique integrating a Dueling Deep Q-Network and Double Deep Q-Network. This hybrid algorithm significantly enhances optimization performance and accelerates the learning process, thereby improving the efficiency of the solutions. The experimental results demonstrate that the proposed model effectively manages the integrated scheduling of bulk material ingress, storage, and egress within the yard. The operational plans generated by the approach outperform traditional first-come, first-served strategies, showcasing substantial improvements in port operational efficiency and reliability. This comprehensive solution underscores the potential for significant advancements in the overall management and performance of dry bulk cargo ports.

Uncertainty Optimization of Industrial Production Operations Considering the Stochastic Performance of Control Loops

Process optimization is a highly successful method for achieving optimal efficiency in industrial production. The conventional optimization approach presupposes that the operational parameters should align with the optimization settings. However, it fails to consider that, influenced by the stochastic performance of the control loops, the operating parameters may deviate from the optimal operating settings. Consequently, this results in the violation of constraints in the optimization results and affects production safety. Therefore, this paper proposes an uncertainty optimization method that considers the stochastic performance of control loops to accurately determine the optimal operational performance that can be practically achieved in industrial production. Firstly, a multi-optimization variational mode decomposition strategy is developed to precisely extract the smooth random and trend terms of the control loop output data. Secondly, the random grouping smooths out the random terms and accurately characterizes the uncertainty associated with these terms. Subsequently, a moment uncertainty set with mild mean-zero net condition is then defined to construct an improved distribution robust optimization model considering the stochastic performance of control loops. Finally, the validation of the proposed optimization method in the actual hydrocracking process shows that the optimization error of the proposed method is reduced by more than 10%, and the constraint violation rate is reduced by 14%, which fully proves the effectiveness and applicability of the method.

Optimization of Continuous Steel Annealing Operations Using Model Predictive Control at Tata Steel, India

In steel manufacturing, continuous annealing is a crucial heat treatment applied to cold-rolled steel strips to achieve a prescribed temperature, which will ensure quality in terms of mechanical properties. However, controlling this process is challenging because of slow furnace temperature dynamics, significant time delays, frequent changes in the steel mass flow rate, target annealing temperature changes induced by steel grade transitions, and multivariable interactions within the furnace zones. To address these challenges, Tata Steel, India, with consultancy support from the Indian Institute of Technology Bombay, developed a novel model predictive control (MPC) technology-based solution for a continuous annealing furnace, which produces automotive grade steels. The dynamic model was developed using data from both perturbation trials and scraping historical processes. We then converted the model to a discrete-time state-space form and used it to formulate an optimal control problem over a moving time window. The solution generates optimal furnace setpoints by solving this finite-horizon optimal control problem each minute, ensuring smooth temperature transitions during steel grade changes while avoiding operational constraint violations. Tata Steel successfully implemented an MPC-based real-time supervisory optimal control solution, which became fully operational in January 2023. The implementation of the solution has led to a significant improvement in the proportion of annealed products meeting the premium quality band (±5°C of the target temperature), increasing from 30% (manually operated) to 50% (MPC operated), thereby ensuring better uniformity of properties. Furthermore, an 8% reduction in products outside the widest band (±15°C) has prevented the reprocessing of 13,000 tons of material from one line alone, annually. We have seen a consistent 8% reduction in specific fuel consumption per ton of steel. When considering Tata Steel’s current installations and those under commissioning, these improvements translate to savings of US$2.5 million and a reduction of 10,000 tons of CO2 emissions annually.

Monitoring of indoor air quality at a large sailing cruise ship to assess ventilation performance and disease transmission risk.

Large passenger ships are characterised as enclosed and crowded indoor spaces with frequent interactions between travellers, providing conditions that facilitate disease transmission. This study aims to provide an indoor ship CO2 dataset for inferring thermal comfort, ventilation and infectious disease transmission risk evaluation. Indoor air quality (IAQ) monitoring was conducted in nine environments (three cabins, buffet, gym, bar, restaurant, pub and theatre), on board a cruise ship voyaging across the UK and EU, with the study conducted in the framework of the EU HEALTHY SAILING project. CO2 concentrations, temperature and relative humidity (RH) were simultaneously monitored to investigate thermal characteristics and effectiveness of ventilation performance. Results show a slightly higher RH of 68.2±5.3% aboard compared to ASHRAE and ISO recommended targets, with temperature recorded at 22.3±1.4°C. Generally, good IAQ (<1000ppm) was measured with CO2 mainly varying between 400 and 1200ppm. The estimated air change rates (ACH) and ventilation rates (VR) implied sufficient ventilation was provided in most locations, and the theatre (VR: 86Ls-1person-1) and cabins (VR: >20Ls-1person-1) were highly over-ventilated. Dining areas including the pub and restaurant recorded high CO2 concentrations (>2000ppm) potentially due to higher footfall (0.6person m-2 and 0.4person m-2) and limited ACH (2.3h-1 and 0.8h-1), indicating a potential risk of infection; these areas should be prioritised for improvement. The IAQ and probability of infection indicate there is an opportunity for energy saving by lowering hotel load for the theatre and cabins and achieving the minimum acceptable VR (10Ls-1person-1) for occupants' comfort and disease control. Our study produced a first-time dataset from a sailing cruise ship's ventilated areas and provided evidence that can inform guidelines about the optimisation of ventilation operations in large passenger ships, contributing to respiratory health, infection control and energy efficiency aboard.

AI-driven optimization in nuclear reactor core operations: advances in prediction, management, and fault diagnosis for enhanced safety

AI-driven optimization in nuclear reactor core operations: advances in prediction, management, and fault diagnosis for enhanced safety

Invited paper: Network digital twins for optical networks

Network Digital Twins (NDTs) are recognized as important components of network operations automation systems. As analytical utilities within such systems, NDTs provide data-driven information that is useful in many operations optimization applications. The nature and role of NDTs, as well as their generic use cases, are discussed in detail, reflecting contemporary industry work and understanding. The specific nature of NDTs for optical transmission networks – optical NDTs – as well as their specific use cases of interest, are reviewed. Optical NDT function, architecture and constituent models are discussed, largely through the lens of a particular – now product – implementation, with reference to alternatives and other industry sources. Performance, function and utility are validated and demonstrated, including operational use of available applications directly supported by an optical NDT. Continuing areas of research interest and industry work are commented.

Modeling and Analyzing the Spatiotemporal Travel Patterns of Bike Sharing: A Case Study of Citi Bike in New York

As the urban transportation demand continues to grow, the effective management and optimization of bike-sharing systems are of significant importance for urban planning and transportation engineering. This study aims to identify the spatiotemporal distribution of the peak-period departures and arrivals of bike sharing within Manhattan, New York, and to analyze the community clustering patterns and their underlying rules. Additionally, a comparative analysis across multiple time periods was conducted to enhance the research’s practical value. This study utilized GPS trajectory data from the New York City bike-sharing system for 2023. After analyzing the travel patterns throughout the year, we selected August, the month with the highest usage, to study the origin-destination (OD) travel aggregation patterns using flow models and the theoretical constructs of travel networks, measuring and analyzing travel characteristics. Subsequently, community detection algorithms were applied to analyze the clustering patterns and relationships among various neighborhoods. The findings revealed that the use of bike sharing in New York exhibits an overall trend of increasing and then decreasing throughout the year, with significantly higher usage in the spring and summer compared to the fall and winter. Notably, August saw the highest usage levels, with hotspots primarily concentrated in the southwestern part of Manhattan, which is also the economic center of New York City. The OD aggregation patterns across the upper, middle, and lower parts of August show distinct variations. Through community analysis, several strongly associated neighborhood clusters were identified, which exhibited both aggregation and dispersion trends over time. In southern Manhattan, a community with high modularity emerged, showcasing strong interconnections among neighborhoods. These findings provide valuable insights into the usage patterns of bike sharing in New York and the factors influencing them, offering significant implications for the optimization of bike-sharing system operations and planning.

THE IMPACT OF MATHEMATICAL MODELING ON AIR TRAFFIC CONTROL EFFICIENCY: A COMPREHENSIVE STRUCTURED REVIEW

Air travel is crucial for global connectivity, yet it faces increasing challenges due to rising air traffic volumes and complex operational demands. Mathematical modeling has emerged as a transformative solution in air traffic control (ATC), enhancing efficiency and safety across the aviation sector. This review delves into the application and outcomes of mathematical models in ATC by analyzing recent literature that incorporates various computational techniques for optimizing air traffic flow, predicting and mitigating conflicts, and improving decision-making processes. In order to attain this, we thoroughly searched scholarly articles from well-known databases, for instance, Scopus as well as IEEE articles published in the last decade, focusing on studies that demonstrated clear numerical results regarding the effectiveness of mathematical modeling in ATC operations. The flow of the study is based on the PRISMA framework. By analyzing a comprehensive selection using an advanced searching approach on Scopus and IEEE database found (n=22), the final primary data was analyzed. The review highlights significant advancements, such as the development of algorithms capable of optimizing flight paths, reducing airspace congestion, and enhancing fuel efficiency, which collectively contributes to more sustainable aviation practices. The findings are divided into four themes, which are (1) optimization of ATC operations, (2) route and flight scheduling optimization, (3) resilience and robustness in atm systems, and (4) technological advances and their integration in air traffic management (ATM). Conclusively, mathematical models have proven indispensable for maximizing ATC efficiency and are pivotal in guiding strategic planning and policy-making to accommodate future air traffic growth while maintaining high safety standards.

Multi-Objective Optimization for Load Balancing and Trading Scheduling in Networked Microgrids

Renewable energy resources are considered an integral part of networked microgrids. Networked microgrids provide an optimal solution for stable and reliable energy to the end consumers even in case of its islanded mode of operation. There are several explicit analytical mathematical formulations for optimization of operations of networked microgrids including load balancing and trade scheduling of energy. Mostly, the scheduling problem in networked microgrids is the job of distribution operators to use common scheduling tools. In this paper, we present the mathematical formulation of multi-objective cost functions and their optimization by using the multi-objective particle swarm optimization (MOPSO) algorithm. Multi-objective cost functions and duality gap are designed and then the problem is solved by introducing MOPSO which has more than one objective function. The total energy cost function is then optimized in order to get swarm swarm-optimal solution. This algorithm computed the local energy generation and demand of each networked microgrid and made the decision of what energy is needed to buy or sell supported by each iteration of the operation of networked microgrids. We used an optimization formulation and its dual conception in order to propose the problem as a multi-objective formulation. The proposed method can deal with convex and nonconvex cost functions. In the case of non-convex functions, we have used the uncertainty cost functions for renewable sources attached to the networked microgrids.

Maximizing the Total Profit of Combined Systems with a Pumped Storage Hydropower Plant and Renewable Energy Sources Using a Modified Slime Mould Algorithm

This paper examines the effectiveness of a pumped storage hydropower plant (PSHP) when combined with other plants. System 1 examines the contribution of the PSHP to reducing fuel costs for thermal power plants. System 2 examines the optimization of operations for power systems with energy storage and uncertain renewable energies to maximize total profit based on four test system cases: Case 1: neglect the PSHP and consider wind and solar certainty; Case 2: consider the PSHP and wind and solar certainty; Case 3: neglect the PSHP and consider wind and solar uncertainty; and Case 4: consider the PSHP and wind and solar uncertainty. Cases 1 and 2 focus on systems that assume stable power outputs from these renewable energy sources, while Cases 3 and 4 consider the uncertainty surrounding their power output. The presence of a PSHP has a key role in maximizing the system’s total profit. This proves that Case 2, which incorporates a PSHP, achieves a higher total profit than Case 1, which does not include a PSHP. The difference is USD 17,248.60, representing approximately 0.35% for a single day of operation. The total profits for Cases 3 and 4 are USD 5,089,976 and USD 5,100,193.80, respectively. Case 4 surpasses Case 3 by USD 10,217.70, which is about 0.2% of Case 3’s total profit. In particular, the PSHP used in Cases 2 and 4 is a dispatching tool that aims to achieve the highest profit corresponding to the load condition. The PSHP executes its storage function by using low-price electricity at off-peak periods to store water in the reservoir through the pumping mode and discharge water downstream to produce electricity at periods with high electricity prices using the generating mode. As a result, the total profit increases. A modified slime mould algorithm (MSMA) is applied to System 2 after proving its outstanding performance compared to the jellyfish search algorithm (JS), equilibrium optimizer (EO), and slime mould algorithm (SMA) in System 1.

Data-Driven Model Reduction via Non-intrusive Optimization of Projection Operators and Reduced-Order Dynamics

Data-Driven Model Reduction via Non-intrusive Optimization of Projection Operators and Reduced-Order Dynamics

Intelligent evaluation and operation recommendation system of sintering process based on analytic hierarchy process–entropy weight method–technique for order preference by similarity to an ideal solution (AHP–EWM–TOPSIS)

Fluctuations in the sintering process have a significant impact on the overall performance and outcomes of sintering production. It is crucial to gain a comprehensive understanding of the sintering operation in a timely manner in order to minimise process fluctuations and ensure the stability of sinter production. In this paper, a sintering process evaluation model is presented, which is based on the analytic hierarchy process–entropy weight method–technique for order preference by similarity to an ideal solution (AHP–EWM–TOPSIS) and is constructed using data from a steel plant sintering production process. Firstly, the evaluation indices input to the system are selected by combining the principle of the sintering process and actual production requirements. The subjective and objective weights of the indices are then calculated using the AHP–EWM method, and the weights are combined through the principle of minimum discriminatory information to construct the set of weighted indices. Subsequently, the sintering process was evaluated by the TOPSIS model, and the results of this evaluation were compared with the actual fluctuation situation at the sintering site. This was done by combining the model results with the differential level method. It was found that the model can accurately reflect the fluctuation situation that occurs in the sintering process. Furthermore, the comprehensive matching rate reached 95.42%, which demonstrates that the model is capable of accurately evaluating the operation of the sintering process. The statistical analysis of historical data provides a basis for the optimised operation of the sintering production process by summarising the optimal range of each parameter when the sintering operates efficiently. Ultimately, a sintering process evaluation system is constructed through the use of computer technology, thereby facilitating the transformation of sintering production into an intelligent system. The model is capable of rapidly ascertaining the actual status of the sintering process in real time. The established system is designed to facilitate the optimisation of sintering production operations and to promote long-term stability in sintering.

ANÁLISE DE ESTRATÉGIAS PARA DISPONIBILIDADE DE MATERIAIS EM MANUTENÇÃO DE MÁQUINAS DE VIA: UMA REVISÃO SISTEMÁTICA

This systematic research addresses the critical importance of material availability for the effective maintenance of track machines, a fundamental element to ensure safety and efficiency in railway operations. The central problem investigated focuses on the challenges faced in provisioning these materials, including logistical issues, stock errors, and delivery delays, which can compromise timely and efficient maintenance. The aim of this study is to analyze innovative and effective strategies that can optimize the provisioning of materials necessary for track machine maintenance. To achieve this goal, a bibliographic research methodology was adopted, focusing on the qualitative analysis of 19 literatures published between 2018 and 2023. The results reveal a convergence among authors on the need to integrate strategic planning, advanced technologies such as artificial intelligence, and efficient inventory management to overcome provisioning challenges. Prescriptive maintenance strategies and the use of advanced diagnostic technologies are highlighted as key elements for more proactive and less reactive maintenance. The final considerations demonstrate that the research problem has been thoroughly explored, with the proposed objectives being achieved. For future research, evaluating the real impact of emerging technologies on the life cycle of railway maintenance is suggested, aiming to provide effective strategies for the continuous optimization of maintenance operations.

Optimization of Container Terminal Operations Using Response Surface Methodology

Optimization of Container Terminal Operations Using Response Surface Methodology

Enhancing wave energy farm efficiency: Eigen-stacking ensemble framework

The significant wave height (SWH) plays a pivotal role across diverse domains, ranging from the assessment of wave energy potential to the optimization of marine operations and the advancement of ocean engineering techniques. Understanding the spatial and temporal distribution patterns of SWH data among buoy network stations holds utmost importance for various applications. This research introduces a novel methodology for accurate spatiotemporal SWH prediction across multiple stations within buoy monitoring networks. Leveraging eigen SWH time series enables the identification of dataset patterns, feature extraction, and dimensionality reduction. Examining eigen time series provides insights into SWH dynamics, allowing exclusive use for prediction across all stations. By incorporating this eigen SWH time series as the sole input into a stacking ensemble model, we introduce a highly efficient and accurate framework for SWH prediction. Notably, the methodology achieves success in forecasting hourly SWH values along the western United States coastline, utilizing a stacking ensemble technique for enhanced accuracy. Evaluation metrics confirm outstanding performance, with CE and R2 values surpassing 0.95 and 0.93, respectively. Further, a single Long Short-Term Memory (LSTM) model is incorporated as a benchmark to assess the effectiveness of the stacking ensemble model for significant wave height (SWH) prediction, with results demonstrating that the ensemble model outperforms the single LSTM in terms of accuracy and robustness. Consequently, the introduced stacking ensemble model promises autonomy in forecasting spatial-temporal SWH patterns, extending its applicability within ocean engineering and scientific research.

Optimized operation of AC–DC microgrid cluster with modified PLL and socket protocol

AbstractMicrogrids integrating inverter based resources face challenges like stability issues during voltage fluctuations and reliance on diesel generators in islanded mode. This paper presents a novel control strategy for photovoltaic and wind‐integrated microgrids to enhance reliability and efficiency. The strategy utilizes a modified phase‐locked loop with droop control for seamless synchronization in grid‐connected and islanded modes. Central to this approach is the localized autonomous controller with a socket protocol interfaced communication layer, dynamically managing operations based on real‐time conditions such as generation, load, and battery state of charge. By integrating battery storage systems, the strategy reduces diesel generator dependency during grid outages. Comprehensive simulations and controller hardware‐in‐the‐loop testing validate its effectiveness, demonstrating improved stability and decreased reliance on diesel generators. The socket protocol enhances real‐time communication, monitoring, and optimization of microgrid operations, ensuring optimal battery storage system utilization and minimal diesel usage. The proposed strategy addresses critical microgrid challenges, offering significant advancements in enhancing system resilience.

Роль бізнес-аналітики в епоху великих даних: нові можливості для ухвалення управлінських рішень

This article delves into the pivotal role that business analytics plays in the era of Big Data, focusing on how it transforms decision-making processes in contemporary organizations. Big Data analytics has become an essential tool for businesses striving to gain a competitive edge in an increasingly data-driven world. The research outlines the main trends in the application of Big Data technologies, such as the integration of artificial intelligence (AI) and machine learning (ML), predictive analytics, and real-time data processing. These technologies enable organizations to process large datasets more efficiently, uncover hidden patterns, and make datainformed decisions with greater precision. The authors discuss the key benefits of adopting Big Data analytics, particularly in areas like customer behaviour personalization, enhanced risk management, and optimization of business operations. By leveraging predictive analytics, companies can forecast trends, mitigate risks, and tailor products and services to meet customer demands. Additionally, the article highlights the growing importance of real-time analytics, allowing organizations to respond promptly to market changes and operational challenges. However, the article also emphasizes the challenges businesses face when integrating Big Data analytics into their operations. Issues related to data security, privacy, and ethics are becoming increasingly critical, particularly with the expansion of data collection from various sources. The paper suggests that for organizations to succeed in the Big Data era, they must address these ethical concerns and ensure transparency and responsibility in data usage. Moreover, the role of skilled data scientists and analysts is underscored as a crucial factor in the effective implementation of analytics tools. The article concludes by identifying potential directions for future research, particularly in improving data quality and addressing the ethical implications of Big Data usage in sectors like healthcare and finance, where sensitive personal information is often involved. Further investigation into how organizations can better manage the balance between data-driven insights and privacy concerns is recommended. Overall, the research highlights how business analytics, supported by Big Data, offers new opportunities for informed decision-making, operational efficiency, and competitive advantage in the modern business landscape.