Operations Research, Systems Engineering and Industrial Engineering Commons^™

Optimal Scheduling Of Virtual Power Plants Considering Photothermal Power Stations And Hydrogen Energy Utilization, Yousong Chen, Ruofa Cheng, Yi Liu, Yi Zhang, Zhihao Zuo

Journal of System Simulation

Abstract: To enhance the operational stability and low-carbon performance of virtual power plants (VPPs) with high shares of renewable energy, a coordinated dispatch model integrating concentrated solar power (CSP) plants with power-to-gas (P2G) and carbon capture is developed. An optimal VPP scheduling strategy is proposed, combining a stepped carbon trading mechanism with a compensation coefficient and dynamic hydrogen blending. To address multi-source uncertainties in wind power, CSP power, and loads, an envelope boundary model is used for simulation. Information gap decision theory (IGDT) is applied to provide customized solutions for decision-makers with different risk preferences. A bi-objective optimization model is …

The Main Errors Of The Ultrasonic Sensor In Measuring Water Flow In Open Channels, Anvar Urolovich Djalilov

Chemical Technology, Control and Management

This article analyzes the use of ultrasonic sensors in measuring water flow and the main errors that may occur in this process. In the conducted scientific research, a time-pulse ultrasonic sensor was tested. The absolute, relative and repeatability errors of the sensor during water flow measurement were studied. The absolute error represents the largest difference between the value recorded by the sensor and the real value, affecting the overall accuracy of the measurement system. This error can vary depending on environmental factors, the design and operating principles of the sensor. During the experiment, the performance of this sensor was …

Research On Pac-Bayes-Based A2c Algorithm For Multi-Objective Reinforcement Learning, Xiang Liu, Qiankun Jin

Journal of System Simulation

Abstract: To address the theoretical challenges of exploration and exploitation trade-offs and uncertainty modeling in multi-objective reinforcement learning (MORL), this study developed a learning framework, MO-PAC, based on PAC-Bayes theory. By introducing a multi-objective stochastic Critic network and a dynamic preference mechanism, the framework extended the conventional A2C architecture, enabling adaptive and efficient approximation of complex Pareto fronts. Experimental results demonstrate that in multi-objective MuJoCo environments, MO-PAC outperforms baseline algorithms, achieving approximately 20% improvement in hypervolume and 60% increase in expected utility, while exhibiting superior convergence efficiency and robustness. It verifies both theoretical value and practical performance advantages in …

Safe Human–Robot Collaboration With Risk Tunable Control Barrier Functions, Vipul K. Sharma, Pokuang Zhou, Zhengtong Xu, Yu She, S. Sivaranjani

School of Industrial Engineering Faculty Publications

In this article, we consider the problem of guaranteeing safety constraint satisfaction in human–robot collaboration (HRC) with uncertain human position. We pose this problem as a chance-constrained problem with safety (chance) constraints represented by uncertain control barrier functions, where the probability of safety constraint satisfaction under uncertainty is bounded by a tunable user-defined risk. We solve this stochastic optimization problem using a sampling-based approach and obtain a risk-tunable controller to safely accomplish HRC tasks. We demonstrate the safety and performance of this approach through both simulation and hardware experiments on a 7 degree-of-freedom Franka–Panda manipulator and characterize the tradeoff between …

Optimal Scheduling Of Virtual Power Plant With Coupled Operation Of Ccs-P2g Considering Wind And Photovoltaic Uncertainty, Xurong Jin, Jiang Yin, Guohua Yang, Wei Li, Guobin Wang, Lele Wang, Na Yang, Xuenian Zhou

Journal of System Simulation

Abstract: In order to solve the problem that the uncertainty of wind power and photovoltaic power generation output easily affects the scheduling of virtual power plant, a new optimal scheduling model of virtual power plant is proposed based on information gap decision theory (IGDT) . In order to reduce the carbon emission of the system, carbon capture and storage (CCS) is installed on the combined heat and power units; in order to improve the utilization rate of renewable energy, the power to gas (P2G) device is introduced into the system, and the operation mode of CCS-P2G coupling is proposed; based …

Vibration Simulation And Multivariate Statistical Analysis Method Of Composite Structures, Bo Guo, Ming Tie, Wenhui Fan

Journal of System Simulation

Abstract: To investigate the natural frequency characteristics of composite laminates under parametric uncertainties and the different degree of influence of these parameters on the natural frequency under different boundary conditions and different vibration orders, a two-dimensional anisotropic medium-thick plate material model and a three-dimensional anisotropic cylindrical thin-shell material vibration model are established. Aiming at the uncertainty of structural parameters of these composite materials, the composite material vibration simulation and multivariate statistical analysis software are developed to simulate the structural vibration of composite materials. A multivariate statistical analysis method for natural frequency uncertainty of composite materials is presented. Through principal component …

Application Of Artificial Intelligence Techniques To Improve Leadership Decision Making With Uncertainty, Michael David Parrish

Doctoral Dissertations

"Every good leader is a good manager, but not every good manager is a good leader. The difference between the leader and the manager is critical decision-making. Today’s decision-making environment is characterized as Volatile, Uncertain, Complex, and Ambiguous (VUCA). With the exponential increase in the technical capabilities of systems, the human has become the weakest link in the use of such systems. To remain relevant, good leaders must continuously adapt to new advances in technology and processes.

The research contributions of this work provide several unique and novel solutions for leaders to utilize artificial intelligence tools to improve and optimize …

A Threat Assessment Method In Uncertain Dynamic Environments, Mei Yang, Bingkun Wang, Zhongjie Zhang, Yan Zeng, Jian Huang

Journal of System Simulation

Abstract: A threat assessment method based on priori information and dynamic observation results is studied for the existence of dynamic uncertainty in complex war systems. The data mining is applied to obtain prior knowledge on the battlefield situation and construct an equipment-related confidence matrix. The sensor model is constructed to dynamically update the number of blue-side entities under the current situation by using the Bayesian method and considering both intelligence and observation results. The threat evaluation indicators and their weights are determined, and the TOPSIS method is used to finish the threat assessment. This method can well describe the complex …

Strategic Responses For Unplanned Events, Tidjan Simpson

Harrisburg University Dissertations and Theses

The paper addresses the question, “Can dynamic, effective response plans be made for stakeholders of a manufacturing line dealing with unplanned events at a manufacturing line, irrespective of an individual’s unique subject matter expertise? Prior research in manufacturing-related environments has indicated the existence of a high frequency of unplanned events. When not responded to efficiently, they can result in reduced financial efficiency and employee overwhelm. Through collection and analysis of interviews conducted with stakeholders in the manufacturing environment, a possible means of efficiently addressing unplanned events can be found or synthesized to help stakeholders navigate uncertainty in the manufacturing environment …

Model Reference Adaptive Control For Mobile Manipulators And Beyond, Srivatsan Srinivasan

All Dissertations

In recent years, robotics has expanded into various sectors, including manufacturing, transportation, and household services, making the integration of autonomy a critical area of research. This shift aims to ensure safety and enhance the utility of autonomous systems. Traditionally, robotic applications focused separately on mobility, like automated guided vehicles, and manipulation, such as serial-chain arms in manufacturing. Today, however, we see a merging of these capabilities in the growing field of mobile manipulator robots that combine movement with purposeful interactive functionalities.

A typical mobile manipulator is a robotic arm mounted on a wheeled base. This thesis focuses on advancing control …

Integrating Risk And Vulnerability: Exploring A Unified Model For Supply Chain Resiliency, William G. Cook

USF Tampa Graduate Theses and Dissertations

The world has entered an era of retreating globalization, mounting geo-political tensions, rising protectionism, and increasing focus on the fragility of complex supply chains. The negative impacts of supply chain disruptions have been increasingly documented since the turn of the century. Given the global scale of recent disruptions, supply chain resiliency has become a national imperative. The Global Financial Crisis, the Covid-19 pandemic, and other major disruptive events demonstrate the active role of government in mitigating damage, the enduring effects of regulation, and the resultant re-evaluation of supply chain strategies by the private and public sectors. In this environment, supply …

A Systems Theory-Based Framework For Uncertainty In Complex System Governance, Teresa Anne Crater

Engineering Management & Systems Engineering Theses & Dissertations

Complex systems are all around. A complex system is a system whose collective behavior is more than the behavior exhibited by the individual parts. Such a system is characterized by an exponential increase in information, complexity, ambiguity, emergence, and high levels of uncertainty (Jaradat, 2015). Uncertainty is intrinsic to complex systems. Uncertainty refers to epistemic situations involving imperfect or unknown information. Uncertainty can refer to the chance of certain anticipated outcomes occurring, or it can refer to the unknown events that are not foreseeable as to when or if one might occur. It applies to predictions of future events, to …

Research On Path Optimization Algorithm In Dynamic Routing Environment, Xin Xie, Xiaobing Hu, Hang Zhou

Journal of System Simulation

Abstract: In a real dynamic routing environment, static path optimization (SPO) and traditional dynamic path optimization (DPO) tend to encounter issues such as detours, reversals and high computational complexity due to frequent real-time optimization calculation. To address these problems, a novel restart co-evolutionary path optimization (RCEPO) method based on the ripple-spreading algorithm (RSA) is proposed. This method integrates the path optimization process with the dynamic changes of the routing network environment to enhance the effectiveness of path optimization. Moreover, the path reoptimization calculation is performed only when the dynamic changes in the routing environment exceed the predicted range, thereby reducing …

Study On Robust Chance Constrained Optimization Of Multi-Energy Supply System Based On Wind And Solar Power Combined Output Simulation, Zhe Bao, Wei Li, Xiaofang Zhang, Zongyuan An, Ye Xu

Journal of System Simulation

Abstract: In order to effectively avoid potential imbalance between supply and demand caused by the uncertainty of wind and solar power outputs, and promote the sustained development of the multi-energy supply system, a robust chance-constrained optimization model is developed for identifying optimal operation strategies under complexities and uncertainties through incorporating Copula theory, chance-constrained programming, and robust programming within a general framework. The results show that this model can not only accurately characterize the distribution probability of combined outputs of wind and solar power and formulate the operational strategies under low default risk conditions, but also reduce the proportion of highrisk …

Leveraging Machine Learning And Stochastic Programming To Address Vaccine Hesitancy In Public Health Resource Allocation, Hieu Trung Bui

Graduate Theses and Dissertations

Infectious disease outbreaks highlight the urgent need for effective strategies to distribute vaccines and allocate critical healthcare resources to contain the disease and reduce its negative impacts on the population. Managing these allocations is a significant challenge, especially in marginalized communities facing uncertainty in healthcare demand and logistical constraints. This dissertation addresses these challenges by investigating factors that influence dynamic changes in vaccine hesitancy (VH) and its implications for disease spread and healthcare resource demand. It develops optimization models for vaccine distribution and resource allocation under uncertainty, validated with data from the COVID-19 pandemic in the U.S. The first study …

Synthesis Of An Adaptive Neuro-Fuzzy Control System For Steam Generator Temperature, Isomiddin Xakimovich Siddikov, Dilnoza Maxamadjanovna Umurzakova

Chemical Technology, Control and Management

This paper is devoted to the modeling and synthesis of an adaptive neuro-fuzzy control system for steam generator temperature. Temperature control systems play an important role in industry because accurate and stable control is a prerequisite for the efficient operation of steam systems. Traditional control methods based on mathematical models and fixed parameter controllers may have limitations in providing optimal performance and adapting to changing operating conditions. The paper proposes a synthesized system combining fuzzy logic and adaptation methods to achieve more accurate and stable temperature control. A detailed structural diagram of the system, modeling, and tuning methods are presented. …

Optimal Scheduling Strategy Of Virtual Power Plant With Carbon Emission And Carbon Penalty Considering Uncertainty Of Wind Power And Photovoltaic Power, Jijun Shui, Daogang Peng, Yankan Song, Qiang Zhou

Journal of System Simulation

Abstract: To better meet the development needs of China's new power system, an optimal scheduling strategy of virtual power plant(VPP) with carbon emission and carbon penalty considering the uncertainty of wind power and photovoltaic power is proposed. The mathematical description of photovoltaic(PV), wind turbine(WT), combined heat and power(CHP) unit and energy storage system (ESS) is carried out, and a wind-solar output model considering the uncertainty is established. The scenario generation and reduction method is used to generate the typical scenario. To maximize the overall operation benefit of VPP, considering carbon emission cost and carbon penalty, an optimal scheduling model of …

An Ai-Based Conceptual Framework To Improve Program Management Of Complex Systems, Michael D. Parrish, Steven Corns

Engineering Management and Systems Engineering Faculty Research & Creative Works

With evolving technologies, changing requirements, and limited budgets, governments and industries need to consider new methodologies to help streamline program lifecycle management, from cradle to grave, to ensure projects are delivered on time, on budget, and to the expected performance standards. Traditional approaches fail to adequately address the added complexities of System of Systems programs such as integration, interoperability, and variable lifecycle of subcomponents. The objective of this study is to assess and address the research question - can a new acquisition approach be designed to address and improve program lifecycle management of complex systems? A comparison study, using the …

Design Space Visualization And Exploration For Many Goal Problems Under Uncertainity, Niharika Balaji

Theses and Dissertations

ABSTRACT

Designing a complex engineered system is challenging due to many conflicting goals, uncertainties, and multiple interactions. Traditional optimization approaches often yield single-point solutions, which may not be suitable for early design stages due to their susceptibility to changes in conditions and uncertainties. To address this challenge, a satisficing approach is employed. This approach enables designers to effectively navigate the design space and identify satisficing solutions that balance conflicting goals in the face of uncertainties and changes in conditions. From a systems design perspective, we view design as an iterative process that involves making informed decisions based on available information …

Research On Period Emergency Supply Distribution Optimization Under Uncertainty, Li Zhang, Mingling He, Qiushuang Yin, Ning Li, Le'an Yu

Journal of System Simulation

Abstract: Aiming at the uncertainty and multi-periodicity of emergency supply distribution, a novel period vehicle routing problem(PVRP) multi-objective optimization model is built and a three-step optimization method is proposed. A triangular fuzzy number is used to eliminate the uncertainty. An AHP approach is used to transform the multi-objective function into the single objective function. An improved ACO algorithm is proposed to solve the single objective optimization problem. By classical data set, the time effectiveness of proposed method on emergency supply distribution problem is verified. The computational advantage in convergence speed is proved by the comparative analysis of the proposed …

Optimal Global Supply Chain And Warehouse Planning Under Uncertainty, Avnish Kishor Malde

All Dissertations

A manufacturing company's inbound supply chain consists of various processes such as procurement, consolidation, and warehousing. Each of these processes is the focus of a different chapter in this dissertation.

The manufacturer depends on its suppliers to provide the raw materials and parts required to manufacture a finished product. These suppliers can be located locally or overseas with respect to the manufacturer's geographic location. The ordering and transportation lead times are shorter if the supplier is located locally. Just In Time (JIT) or Just In Sequence (JIS) inventory management methods could be practiced by the manufacturer to procure the raw …

Optimizing The Performance Of Analytical Chemistry Instrumentation, Srividya Sekar

Industrial, Manufacturing, and Systems Engineering Dissertations - Archive

Surrogate Optimization and Global Optimization approaches to optimize underlying functions have been studied and used extensively in the field of Operations Research. However, there are very few instances where these approaches have been applied and tested in applications with uncertainty. Additionally, extensive focus and effort have been put into developing highly complex metamodels rather than globally optimizing these metamodels. In this study, we propose a Mixed Integer Quadratically Constrained Program (MIQCP) based approach that globally optimizes a Quintic Multivariate Adaptive Regression Splines (QMARS) metamodel. The QMARS-MIQCP based optimization is applied to a global optimization framework called QMARS-MIQCP-OPT to optimize several …

Selected Interdiction Games With Uncertain, Risk-Averse, And Simultaneous Play Considerations, Di H. Nguyen

All Dissertations

This dissertation examines two network interdiction problems: a shortest-path interdiction problem under uncertainty and a network interdiction problem in a simultaneous game. Both problems happen in two stages over a directed network, and involve a leader and a follower who have opposing interests.

In the first problem, the leader acts first to lengthen a subset of arcs, and a follower acts second to select a shortest path across the network. The cost for a follower’s arc consists of a base cost if the arc is not interdicted, plus an additional cost that is incurred if the arc is interdicted. The …

Uncertainty Simulation Method Based On Deep Bayesian Networks Learning, Nie Kai, Kejun Zeng, Qinghai Meng

Journal of System Simulation

Abstract: There are lots of uncertain elements in battlefields situation assessment and the uncertainty simulation would enhance the ability of situation assessment. A deep variational autoencoder bayesian networks (BN) model with memory module is proposed aiming at the problem of being unable to represent the uncertainties exactly caused by the various combat objects and more uncertain elements. Based on the deep BN learning, the situation assessment model is designed from the deep generative model. The principle of deep generative model mixing with the memory module is discussed and the leaning and reasoning process of the model is explained. The proposed …

Simulation-Based Optimization: Implications Of Complex Adaptive Systems And Deep Uncertainty, Andreas Tolk

VMASC Publications

Within the modeling and simulation community, simulation-based optimization has often been successfully used to improve productivity and business processes. However, the increased importance of using simulation to better understand complex adaptive systems and address operations research questions characterized by deep uncertainty, such as the need for policy support within socio-technical systems, leads to the necessity to revisit the way simulation can be applied in this new area. Similar observations can be made for complex adaptive systems that constantly change their behavior, which is reflected in a continually changing solution space. Deep uncertainty describes problems with inadequate or incomplete information about …

Uncertainty Of The Ultrasonic Method For Determining The Strength Of Concrete, Ortagoli Sharipovich Hakimov, Zamira Khudoyberdiyevna Ernazarova

Chemical Technology, Control and Management

The issues of measuring the strength of concrete by the method of surface sounding by ultrasound are considered. A measurement model and formulas for estimating the total standard measurement uncertainty, information about the standards used, measurement tools and methods, and environmental parameters are given. Information about the estimation of uncertainty in the form of "eight steps", such as: measurement task; mathematical model of measurement; analysis of input values; observation results; correlations; uncertainty budget; expanded uncertainty; measurement result. It has been established that the uncertainty of type B of the ultrasonic method for determining the strength of concrete is much less …

Active Learning Intelligent Soft Sensor Based On Probability Selection, Xuezhi Dai, Weili Xiong

Journal of System Simulation

Abstract: Aiming at lack of tag samples and high cost of sampling tags in complex industrial processes, an active learning algorithm based on probability selection is proposed. Firstly, unlabeled samples are performed subspace integration by using the principal component analysis. Then, the information of unlabeled samples is evaluated by the uncertainty, which is calculated based on the out put of all sub learners. And the most valuable samples are selected to mark manually. Finally, the function of unlabeled samples and labeled samples are analyzed, and the termination conditions are designed by introducing the performance index of training set. Through simulations …

Methodology For Determination And Assessment Of Uncertainty Sources Of Textile Materials Total Thermal Resistance, Abdurauf Abdurashidovich Abdukayumov, Ortagoli Sharipovich Hakimov

Chemical Technology, Control and Management

The problems of improving of methods for determination of the total thermal resistance of textile materials are considered. A comparative analysis of the applied methods based on stationary and non-stationary heat exchange is performed. A higher accuracy of non-stationary methods based on measuring the cooling time of a heat cell with subsequent determination of the cooling rate and the total thermal resistance of the material under study is noted. A method is proposed for estimating the variance of the main values required for calculating the total thermal resistance of materials. The results of the study of the relationship between sources …

Computational Modeling For Decision-Making Under Climate Change Uncertainty: Reservoir Simulation Game, Julianne Quinn

All ECSTATIC Materials

Almost every decision you make is under uncertainty. Will I need a rain jacket in the afternoon? Will they say yes if I ask them out? Is 1 hour enough time to finish this assignment? Oftentimes, we can use computational modeling to simulate different scenarios of what might happen in the future to inform what decisions are best on average, or what decisions minimize the worst case outcome. For example, you could decide what player to draft for your Fantasy Football team by simulating player performance. In this activity, we will simulate how much water to release from a dam …

Communicating Uncertain Information From Deep Learning Models In Human Machine Teams, Harishankar V. Subramanian, Casey I. Canfield, Daniel Burton Shank, Luke Andrews, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

The role of human-machine teams in society is increasing, as big data and computing power explode. One popular approach to AI is deep learning, which is useful for classification, feature identification, and predictive modeling. However, deep learning models often suffer from inadequate transparency and poor explainability. One aspect of human systems integration is the design of interfaces that support human decision-making. AI models have multiple types of uncertainty embedded, which may be difficult for users to understand. Humans that use these tools need to understand how much they should trust the AI. This study evaluates one simple approach for communicating …