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Computational Intelligence for Energy, Transport and Environmental Sustainability (CIETES), IEEE Symposia on

作者： Maqsood Hussain Shah Ji Li Mingming Liu Insight Centre of Data Analytics and the School of Electronic Engineering Dublin City University Dublin Ireland Department of Mechanical Engineering University of Birmingham UK

ISBN: (数字)9798331508258

ISBN: (纸本)9798331508265

In the rapidly evolving landscape of urban transportation, shared e-mobility services have emerged as a sustainable solution to meet growing demand for flexible, eco-friendly travel. However, the existing literature lacks a comprehensive multi-modal optimization framework with focus on user preferences and real-world constraints. This paper presents a multimodal optimization framework for shared e-mobility, with a particular focus on e-mobility hubs (e-hubs) with micromobility. We propose and evaluate two approaches: a mixed-integer linear programming (MILP) solution, complemented by a heuristic graph reduction technique to manage computational complexity in scenarios with limited e-hubs, achieving a computational advantage of 93%, 72%, and 47% for 20, 50, and 100 e-hubs, respectively. Additionally, the modified Dijkstra’s algorithm offers a more scalable, real-time alternative for larger e-hub networks, with median execution times consistently around 53 ms, regardless of the number of e-hubs. Thorough experimental evaluation on real-world map and simulated traffic data of Dublin City Centre reveals that both methods seamlessly adapt to practical considerations and constraints such as multi-modality, user-preferences and state of charge for different e-mobility tools. While MILP offers greater flexibility for incorporating additional objectives and constraints, the modified Dijkstra’s algorithm is better suited for large-scale, real-time applications due to its computational efficiency.

关键词： Scalability Conferences Urban areas Green products Transportation Real-time systems mixed integer linear programming State of charge Optimization Micromobility

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Analyzing the Impact of Uncertain Parameters in Reverse Supply Chain Network Using Fuzzy Alpha-Cut Approach

Analyzing the Impact of Uncertain Parameters in Reverse Supp...

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International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Mahmoud Elashwah N. Afia W. Abbas T. Ismail Basic and Applied Sciences department Faculty of Engineering and Technology Arab Academy for Science Technology and Maritime Transport Design and Production Engineering department Ain Shams University

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

Optimizing reverse supply chains (RSC) design is crucial for sustainability, impacting the collection and processing of used products. This study introduces a fuzzy mixed-integer linear programming (FMILP) model employing the fuzzy alpha-cut method to determine optimal locations for collection points under uncertainty. The model considers uncertainty in the quantity of used products at customer locations and the selling prices at collection points and retailers. Six scenarios were analyzed, covering individual and combined uncertainties. Each scenario utilized triangular fuzzy numbers with alpha-cuts to assess robustness at medium (a=0.5) and high ( $a=1$ ) confidence levels, providing upper and lower bounds of uncertainty. Results indicate that combined uncertainties significantly affect RSC performance compared to individual uncertainties. A comparison with exact solutions highlights the influence of uncertainty parameters on decision-making in reverse supply chains.

关键词： Adaptation models Uncertainty Costs Profitability Supply chains Stochastic processes programming Robustness mixed integer linear programming Sustainable development

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Optimized Design of a Low Carbon Energy Hub: Minimizing Costs and Emissions with Carbon Tax

Optimized Design of a Low Carbon Energy Hub: Minimizing Cost...

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Smart Grid Conference (SGC)

作者： H. Maleki M.S. Sepasian M.R. Aghamohammadi M. Marzband Power and system Department Shahid Beheshti University Teran Iran Faculty of Engineering King Abdulaziz University Jeddah Saudi Arabia

ISBN: (数字)9798331511333

ISBN: (纸本)9798331511340

This paper presents a mathematical model for the optimal design of a low-carbon energy hub (EH) aimed at minimizing investment, operational, replacement and emission costs. The model is formulated as a mixed integer linear programming (MILP) problem and solved using the CBC solver within the PYOMO environment. Results identify the optimal capacities for EH components, and the impact of a carbon tax is evaluated. Findings indicate that with a carbon tax increase to $250/ton CO2, it becomes optimal to use of carbon capture technology and significantly reducing carbon emissions from 4,970 to 711 tons. The results underscore the importance of carbon tax in enabling cleaner EH configurations and the role of component flexibility in enhancing operational efficiency.

关键词： Costs Water storage Hydrogen Production Mathematical models mixed integer linear programming Smart grids Carbon capture and storage Natural gas Carbon tax

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Platelet inventory management with demand and supply uncertainty and variable pricing considerations

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TRANSFUSION AND APHERESIS SCIENCE 2023年第5期62卷 103770-103770页

作者： Mallari, Cristina Beatrice C. Alegre, Mary Christiene Courtney B. Chuang, Kevin Go Gallardo, Grina Kimberly C. Sy, Aaron William P. Juan, Jayne Lois G. San De La Salle Univ Dept Ind & Syst Engn Manila 1004 Philippines

Managing the inventory of blood is a crucial operation in hospitals owing to its significance in medical treatment. At the same time, blood is characterized by unique attributes, such as perishability and the unpredictable nature of its supply and demand. While models have been developed to optimize the said process, gaps in literature exist in terms of considering the possibility of variable pricing and extensively accounting for uncertainties in the supply chain. In this light, the present study proposes a stochastic multi-period mixed integer linear programming cost minimization model that determines the optimal inventory plan for a hospital purchasing platelets, assuming that prices fluctuate along with the blood center's supply. To implement uncertain supply and demand, the model considers a discrete set of scenarios for each parameter. A study was performed, and the results indicate a promising direction as inventory costs decreased relative to models without the new considerations.

关键词： Blood supply chain Inventory model Variable pricing Stochastic optimization mixed integer linear programming

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Digital twin-driven real-time planning, monitoring, and controlling in food supply chains

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TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE 2023年第1期195卷

作者： Maheshwari, Pratik Kamble, Sachin Belhadi, Amine Venkatesh, Mani Abedin, Mohammad Zoynul Indian Inst Management Mumbai 400087 India EDHEC Business Sch F-59057 Roubaix France Int Univ Rabat Rabat Business Sch Sale Morocco Montpellier Business Sch MBS Montpellier France Swansea Univ Sch Management Dept Accounting & Finance Bay CampusFabian Way Swansea SA1 8EN Wales

There needs to be more clarity about when and how the digital twin approach could benefit the food supply chains. In this study, we develop and solve an integrated problem of procurement, production, and distribution strategies (PPDs) in a medium-scale food processing company. Using the digital twin approach, the model considers the industrial symbiosis opportunities between the supplier, manufacturer, and customer using interval and sequence variables operating in a constrained environment using mixed-integer linear programming (MILP) and agent-based simulation (ABS) methodology. The study optimizes the make-span and lead time, simultaneously achieving a higher level of digitalization. The analysis demonstrates how digital twin accelerates supply chain productivity by improving makespan time, data redundancy (DR), optimal scheduling plan (OSP), overall operations effectiveness (OOE), overall equipment effectiveness (OEE), and capacity utilization. Our findings provide compelling evidence that the seamless integration PPDs enormously enhance production flexibility, resulting in an excellent service level of 94 %. Managers leverage real-time simulation to accurately estimate the replenishment point with minimal lead time, ensuring optimized ***, our results demonstrate that implementing PPDs has yielded considerable benefits. Specifically, we observed a remarkable 65 % utilization of the pasteurizer and aging vessel and an impressive 97 % utilization of the freezer. Moreover, by applying the DT model, the present model found a notable 6 % reduction in backlog, further streamlining operations and enhancing efficiency.

关键词： Digital twin Food supply chain mixed integer linear programming Agent-based simulation Digital supply chain Anylogic Industry 4.0

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Preemptive Electric Vehicle Charging Strategy in Active Power Distribution Networks

Preemptive Electric Vehicle Charging Strategy in Active Powe...

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IEEE General Meeting Power& Energy Society

作者： Laiz Souto Philip C. Taylor Sohail Damree School of Electrical Electronic & Mechanical Engineering University of Bristol Bristol UK

ISBN: (数字)9798350381832

ISBN: (纸本)9798350381849

Electric vehicle technologies, notably electric vehicle charging and vehicle-to-grid, represent a critical flexibility resource in smart grids and smart cities, especially within active power distribution networks. During emergencies, they can provide back-up power supply and/or mobility services for vehicle users, enabled to take proactive action to avoid supply interruption or leave the affected area. In this context, this article presents a preemptive strategy for electric vehicle charging in active power distribution networks, considering electric vehicle technology users’ behaviors and the operating constraints of the grid. It incorporates sources of information typically available in a smart grid / smart city context, such as energy management system data, weather forecasts, traffic surveillance and geographic information systems, and users’ behavioral patterns. The operating requirements are given by thermal and voltage constraints, whereas the user behaviors affect power exchanges with the grid, including electric vehicle charging, discharging, and load demand profiles. The methodology is written as a mixed-integer linear programming formulation aimed at minimizing the energy not supplied. Furthermore, it is demonstrated on a real-based low voltage network in the United Kingdom over a range of scenarios.

关键词： Vehicle-to-grid Smart cities Surveillance Weather forecasting Voltage Traffic control Electric vehicle charging mixed integer linear programming Thermal loading State of charge

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Deep Learning Based Hybrid Assisted Stochastic Unit Commitment with Transportable Energy Storage

Deep Learning Based Hybrid Assisted Stochastic Unit Commitme...

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IEEE Industry Applications Society Annual Meeting (IAS)

作者： Jun-Shen Chia Wen-Shan Tan Ze-Yang Ding Yuan-Kang Wu School of Engineering Advance Engineering Platform and Centre for Net-Zero Technology Monash University Malaysia Bandar Sunway Selangor Malaysia National Chung-Cheng University Chia-Yi Taiwan

ISBN: (数字)9798350372717

ISBN: (纸本)9798350372724

The computation load of Transportable Energy Storage System with Stochastic Unit Commitment (TESS-SUC), when modelled as mixed-integer linear programming (MILP) problem, is very demanding, especially with complex transportation topology and large power system. This paper proposes a Deep Learning (DL) based model to reduce the optimization time of TESS-SUC while retaining close-to-optimal solutions. Hybrid Assistance (HA), a novel approach to use DL to assist the optimization of MILP problem, is proposed. HA ensures a reduction in computation time, retainment of optimality of solution and avoids the violation of constraints made by DL's prediction. Simulations on the IEEE 118-bus system with a Transportable Energy Storage (TES) showed that 94.52 % of computation time reduction can be achieved while retaining the optimality of solution with about 0.06 % gap from the optimal solution, without any violations of constraints. Results also showed that assisting the solver with the binary commitment decision variables is the most effective in reducing computation time.

关键词： Deep learning Computational modeling Stochastic processes Transportation mixed integer linear programming Topology Time factors Optimization Energy storage Load modeling

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A New Swap-Based Algorithm for Solving Multidimensional Multi-Way Number Partitioning Problem

A New Swap-Based Algorithm for Solving Multidimensional Mult...

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International Conference on Wavelet Active Media Technology and Information Processing (ICWAMTIP)

作者： Bozhan Zhang University of Electronic Science and Technology of China

ISBN: (数字)9798331519254

ISBN: (纸本)9798331519261

The Multidimensional Multi-Way Number Partitioning Problem (MDMWNPP) is a variant problem of the Number Partitioning Problem (NPP). A special case of the Number Partitioning Problem (NPP) known as the Two-Way Number Partitioning Problem (TWNPP) is one of the original 21 problems that Richard Karp proved to be NP-complete. The existing algorithms usually solve the mixed-integer linear programming (MILP) model of the MDMWNPP by solver or consider the problem from the perspective of metaheuristics. This paper proposes a swap-based heuristic algorithm to solve the MDMWNPP from another point of view. At the same time, the paper provides theoretical supports for the swap process and applies the theory to the new algorithm. The new algorithm has achieved the best results on average when compared with the previous algorithms, and it can also provide solutions that previous algorithms were unable to obtain within a limited time. In terms of experimental performance, there is an improvement of approximately 30%-40% on the previous benchmark sets, while the time efficiency has been improved by one or two orders of magnitude.

关键词： Heuristic algorithms Metaheuristics Information processing Media Benchmark testing Approximation algorithms Distance measurement Partitioning algorithms mixed integer linear programming

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A Study of Practical Cases Regarding the Synergy of Electricity and Heat in Industrial Parks

A Study of Practical Cases Regarding the Synergy of Electric...

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Electrical Engineering and Automation (IFEEA), International Forum on

作者： Zhehan Zhong Jinxia Zhang Jixue Wang Xiaolei Li Shuo Yang Zhenyu Gai Bing Liu Zhipeng Zhao Zheng Li Electric Power Planning & Engineering Institute Beijing China Department of Energy and Power Engineering Tsinghua University Beijing China

ISBN: (数字)9798331516611

ISBN: (纸本)9798331516628

This study focuses on the synergistic utilization of electricity and heat in industrial parks, integrating photovoltaic (PV), wind power, innovative C02 heat pumps, electric and heat storage. Firstly, a model for renewable energy based electricity and heat supply has been established, utilizing the economic index as the 0 bj ective function, with energy and power balances serving as constraints, and incorporating actual data from Shandong Province of China as boundary conditions. Secondly, the model is solved by mixed integer linear programming (MILP) in Gurobi to determine the minimum net present value of cost and the optimal scales of its components. Finally, a comparative analysis reveals that compared to configuring electric or heat storage alone, the coordinated configuration of both of them can significantly improve economic performance. The ratio of heat and electric storage investment primarily influences the allocation of electric and heat storage capacities, while it has little impact on the capacity configuration of PV, wind, and heat pump systems.

关键词： Costs Heat pumps Electricity Heat engines Wind power generation Boundary conditions mixed integer linear programming Resource management Resistance heating Investment

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Optimization Configuration of Agricultural Comprehensive Energy System Considering Generalized Comprehensive Demand Response and System Flexibility

Optimization Configuration of Agricultural Comprehensive Ene...

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IEEE International Conference on Power and Energy Applications (ICPEA)

作者： Dong Han Mengmeng Ren Zhen Yang Hengxin Tian Pengcheng Wang Yujuan Zhang State Grid Shandong Electric Power Company Dongying Power Supply Company Shandong China State Grid Shandong Electric Power Company Guangrao County Power Supply Company Shandong China

ISBN: (数字)9798350356113

ISBN: (纸本)9798350356120

To further tap into the potential of the user side, improve the economic efficiency of the integrated energy system, and achieve source load friendly interaction, a dual layer optimization configuration model for the integrated energy system considering generalized comprehensive demand response is proposed. Firstly, the generalized comprehensive demand response mechanism is introduced in the upper layer model, and the loads participating in the demand response are divided into three categories: transferable, reducible, and replaceable, so that they can participate in the system operation. The improved particle swarm optimization algorithm is used to solve the problem with the total system cost as the objective function. The optimal scheduling result is simulated in the lower layer model, and the mixed integer linear programming method is used to solve the problem with the system operation cost as the objective function. Finally, the optimized configuration result and corresponding optimized operation plan are obtained. Finally, the model was validated through case analysis to reduce system configuration and operating costs, minimize peak valley differences of various loads, and alleviate energy supply pressure.

关键词： Analytical models Costs Systems operation Optimal scheduling linear programming Demand response mixed integer linear programming Particle swarm optimization Load modeling Energy storage

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