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A Proposal of Fair Output Curtailment Method for Photovoltaic Generators and Wind Farms

IEEJ Transactions on Power and Energy 2025年第1期145卷 21-31页

作者： Sugawara, Daichi Saitoh, Hiroumi Taguchi, Koyo Miyazaki, Yuichi Department of Electrical Engineering Tohoku University 6-6-05 Aramaki Aza-Aoba Aoba-ku Sendai980-8579 Japan Power System Eng. Dept. Tohoku Electric Power Network Co. Inc. 1-7-1 Honcho Aoba-ku Sendai980-8551 Japan

In an electric power system featuring an abundance of renewable energy sources (RES), such as photovoltaic generators (PVs) and wind farms (WFs), the need for curtailing RES output arises to maintain supply-demand balance during periods of anticipated excess generation across the electric power system. Ensuring equitable treatment among RES units regarding curtailment and minimizing overall curtailed energy become pivotal. This paper introduces a method to equitably establish upper output limits for each RES. The effectiveness of these methods is substantiated through numerical simulations. It was found that the key to achieving fairness is to uniformly improve the output control performance of RESs. © 2025 The Institute of Electrical Engineers of Japan.

关键词： mixed-integer linear programming

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Stochastic Optimal Scheduling of Flexible Traction Power Supply System for Heavy Haul Railway Considering the Online Degradation of Energy Storage

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WORLD ELECTRIC VEHICLE JOURNAL 2025年第4期16卷 206-206页

作者： Li, Zhe He, Yanlin Peng, Gaoqiang Yin, Jie CHN Energy Shuohuang Railway Dev Co Ltd Cangzhou 062350 Peoples R China Southwest Jiaotong Univ Sch Elect Engn Chengdu 611756 Peoples R China

The heavy-haul flexible traction power supply system (HFTPSS), integrated with an energy storage system (ESS) and power flow controller (PFC), offers significant potential for improving energy efficiency and reducing costs. However, the state of ESS capacity and the uncertainty of traction power significantly affect HFTPSS operation, creating challenges in fully utilizing flexibility to achieve economic system operation. To address this challenge, a classical scenario generation approach combining long short-term memory (LSTM), Latin hypercube sampling (LHS), and fuzzy c-means (FCM) is proposed to quantitatively characterize traction power uncertainty. Based on the generated scenarios, and considering the energy balance and safe operation constraints of HFTPSS, a stochastic optimal energy dispatch model is developed. The model aims to minimize the operational cost for heavy-haul electrified railways (HERs) while accounting for the impact of online ESS capacity degradation on the energy scheduling process. Finally, the effectiveness of the proposed strategy and model is validated using operational data from a real HER system.

关键词： battery degradation flexible traction power supply system mixed-integer linear programming stochastic optimization

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Location-Allocation of a Biorefinery Based on Linguistic mixed integer Nonlinear Mathematical Modeling

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IEEE Access 2025年 13卷 83373-83393页

作者： Fan, Lei Karimi, Negar Reza Feylizadeh, Mohammad Khorshidnia, Mohsen Li, Yongzheng Jiangsu University of Science and Technology School of Economics and Management Jiangsu Zhenjiang212100 China Isfahan University of Technology Department of Industrial and Systems Engineering Isfahan*** Iran Islamic Azad University Shiraz Branch Department of Industrial Engineering Shiraz*** Iran Islamic Azad University Najafabad Branch Department of Industrial Engineering Najafabad*** Iran Jiangsu University of Science of Technology School of Naval Architecture and Ocean Engineering Jiangsu Zhenjiang212100 China

Significant research is currently being conducted to explore renewable energy sources, such as biomass fuel, in response to recent fluctuations in fossil fuel prices and environmental concerns. However, utilizing biomass energy poses challenges due to the widespread distribution of feedstock and the need to transport multiple low-energy-density materials to conversion facilities. In this investigation, a Linguistics mixed integer Nonlinear programming (LMINLP) approach with Z-number computations was employed to analyze the optimal allocation of locations for a bio-refinery supply chain network. This proposed method significantly impacted the product process, time value for plutocrats, installation investment costs, and transportation network. This research aims to construct an LMINLP model to determine the optimal position of a single installation within a bio-refinery. The primary goal was to make well-informed investment decisions regarding technologies, facilities, capacities, and site selections. A firefly algorithm incorporating a piecewise linearization approach was utilized to solve the model and achieve global optimality. The model’s effectiveness was demonstrated through numerical examples, including those from the ethanol industry. © 2013 IEEE.

关键词： mixed-integer linear programming

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Optimization of an integrated harvest, and process planning model in the agri-food industry

JOURNAL OF ENGINEERING RESEARCH

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JOURNAL OF ENGINEERING RESEARCH 2025年第1期13卷 331-350页

作者： Bilgen, Bilge Taskiner, Tugce Dokuz Eylul Univ Dept Ind Engn TR-35397 Izmir Turkiye Dokuz Eylul Univ Grad Sch Nat & Appl Sci Dept Ind Engn TR-35397 Izmir Turkiye

In this paper, an integrated harvest and production planning problem in the olive oil industry is addressed. The aim of the paper is to develop and optimize a mathematical model that integrates both olive harvest and olive oil production process. The objective is to maximize the total profit while determining quantity of olives harvested from several olive groves, quantity of olives purchased from external farmers, quantity of olive oil produced, and by-product management to handle hazardous effects of olive oil production. The problem is formulated as a mixed integer linear programming model (MILP). Maximization of profit consists of two components;total sales revenue and total cost including harvesting, purchasing, fixed and variable processing costs. Constraints on the system include harvest planning, harvest capacity, production planning, and processing constraints. The proposed MILP model incorporates several distinguishing characteristics of the problem such as ripeness of olives, olive oil quality, organic and conventional farming, and by-product management. A numerical experiment based on a real-world case study was presented to verify the effectiveness of the developed model. The results show that simultaneously considering harvesting and production processes can significantly assist the profitability of the olive oil supply chain. A scenario analysis is conducted by extending the base model to explore olive loss in the olive groves which can occur due to the severe climatic conditions.

关键词： Harvest planning Production planning mixed-integer linear programming Olive oil industry Optimization

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A novel and efficient mathematical optimization model for multi-stage assembly flow shop considering post-processing

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JOURNAL OF INDUSTRIAL AND PRODUCTION ENGINEERING 2025年第1期42卷 1-13页

作者： da Silva Santana, Jose Renatho Fuchigami, Helio Yochihiro Fed Univ Goias UFG Fac Sci & Technol Aparecida De Goiania Brazil Fed Univ Sao Carlos UFSCar Dept Prod Engn Rod Washington Luis BR-13565905 Sao Carlos SP Brazil

We addressed the multistage assembly flow shop problem with post-processing and makespan minimization, a production environment commonly encountered in diverse industries such as automotive, dental, medical equipment, and clothing manufacturing. In this context, we presented an innovative mixed-integer linear programming model with a position-based strategy. Our proposed formulation demonstrated remarkable efficiency when compared to the model of the literature. It achieved optimal solutions in 77.16% of instances, with an average optimality gap of 10.59%. This study constitutes a significant contribution to the efficient resolution of a practical and frequently encountered scheduling problem that has received relatively limited attention in existing literature. The findings highlight the crucial role of mathematical optimization models as valuable decision-making tools for scheduling within the addressed production system. [GRAPHICS]

关键词： Scheduling assembly flow shop mixed-integer linear programming mathematical optimization multi-stage shop post-processing

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Toward Native Intelligence: An Efficient and Flexible AI Services Provision Scheme in Multilayer Heterogeneous Networks

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IEEE Internet of Things Journal 2025年第9期12卷 11708-11721页

作者： He, Jingchao Cheng, Nan Sun, Ruijin Zhang, Ruqian Zhou, Conghao Quan, Wei Li, Changle Xidian University State Key Laboratory of ISN School of Telecommunications Engineering Xi’an710071 China AI and Intelligent Operation Center China Mobile Research Institute Xi’an710071 China University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada Beijing Jiaotong University School of Electronic and Information Engineering Beijing100044 China

To fulfill future diverse user requirements, 6G networks are envisioned to provide everyone-centric customized services ubiquitously and precisely. However, the diversity in user requirements and the heterogeneity in network resources challenge conventional network operators in network management and service provision. In this article, we investigate the artificial intelligence (AI) service provision in the multilayer heterogeneous network. To provide ubiquitous intelligence to users with different computing requirements, an intelligence-native network architecture is designed. Based on the proposed architecture and the AI model stitching mechanism, we formulate the joint AI provision and access selection problem as a mixed integer nonlinear programming (MINLP) problem to maximize the average user satisfaction value and user satisfaction rate. Then, a heuristic solution based on Dung Beetle algorithm is proposed to optimize the AI model selection, AI service deployment, user access, and stitching coefficient jointly. Extensive simulations are conducted to evaluate the performance of our proposed architecture and algorithm. © 2014 IEEE.

关键词： mixed-integer linear programming

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Identifying potential routes and airports in Brazil: An integration of the route selection and fleet assignment problems

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CASE STUDIES ON TRANSPORT POLICY 2025年 20卷

作者： Nagano, Marcelo Seido de Jesus, Thiago Dias Rossi, Fernando Luis Univ Sao Paulo Sao Carlos Sch Engn Ave Trabalhador Saocarlense400 BR-13566590 Sao Carlos SP Brazil Fed Inst Educ Sci & Technol Sao Paulo Barretos SP Brazil Univ Sao Paulo Inst Ciencias Matemat & Computacao Trabalhador Sao Carlense Ave400 Centre BR-13566 590 Sao Carlos SP Brazil

The strategic planning of airline networks is critical for maximizing profitability and operational efficiency. Among the key challenges faced by airlines is the Route Selection Problem (RSP)-determining which routes to operate based on demand, economic conditions, and infrastructure-and the Fleet Assignment Problem (FAP)- optimally assigning aircraft to routes to minimize costs. While these problems are typically addressed separately, their integration can yield more robust and profitable solutions. This study presents a novel mathematical model that integrates RSP and FAP using mixed integer linear programming (MILP) to optimize network profitability and fleet utilization. The model was validated through a case study of a fictional regional airline in Southeast Brazil, analyzing 41 potential locations. The results identified 28 profitable routes, including 11 destinations currently without regular flights and one without an airport. By optimizing fleet allocation and route selection, the model provides a data-driven framework for airlines, policymakers, and investors to enhance network efficiency and identify underserved markets. This study demonstrates that an integrated approach to route selection and fleet assignment can significantly improve decision-making in the airline industry, offering a scalable methodology for network expansion and strategic investment.

关键词： Route Selection Problem Fleet Assignment Problem mixed-integer linear programming Airline

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New mathematical model for machine scheduling with variable capacity over time

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INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE AND ENGINEERING MANAGEMENT 2025年第2期20卷 215-224页

作者： Esmaelian, Majid Momeni, Abolfazl Mohammadi, Milad Univ Isfahan Dept Management Hezarjerib StAzadi Sq Esfahan *** Iran Vali Easr Univ Rafsanjan Fac Econ & Adm Sci Dept Management Rafsanjan Iran

In this study, machine scheduling with variable capacity over time (SVCap) is investigated. The machine capacity is the maximum number of jobs that a machine can process at a time which can be either fixed or variable over time. In common machine scheduling problems, it is assumed that one machine can process one job at a time. However, in variable machine capacity, multiple jobs can be processed on a machine simultaneously. Unlike the current research, a mathematical formulation is not developed yet for solving this problem. In order to solve the problem, a novel mixed integer linear programming (MILP) is proposed. In addition, the SVCap is regarded as a special type of resource-constrained project scheduling problem (RCPSP). Thus, the discrete-time (DT) formulation is generalized to solve the SVCap. In these formulations, the total tardiness is minimized as the objective function. Proposed models are implemented on an irrigation scheduling problem in which water resources are allocated to each plot of farmland. The computational performances of proposed formulations are evaluated on problem instances with different sizes. Results show that the proposed formulations solved all problem instances. The results demonstrate that the proposed MILP formulation is more efficient than the generalized DT formulation in both solution quality and runtimes.

关键词： Scheduling variable capacity over time total tardiness mixed-integer linear programming

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A nationwide planning model for argon supply chains with coordinated production and distribution

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DIGITAL CHEMICAL ENGINEERING 2025年 14卷

作者： Neiro, Sergio M. S. Madan, Tarun Maravelias, Christos T. Pinto, Jose M. Uberlandia Fed Univ UFU Sch Chem Engn Joao Naves Avila 2121Bloco 5K BR-38408100 Uberlandia Brazil Linde plc Linde Digital Amer Danbury CT 06810 USA Princeton Univ Dept Chem & Biol Engn Princeton NJ USA Princeton Univ Andlinger Ctr Energy & Environm Princeton NJ USA

In this work, we address a nationwide tactical planning for industrial gas supply chains, particularly argon. The proposed approaches follow as extensions of our previous work (Comp. & Chem. Eng., 161 (2022) 107778) in which a regional argon supply chain problem is addressed;in that work, both production and distribution could be represented in detail. Two different types of deliveries from the Air Separating Units (ASU) to customers, which involve single driver deliveries for short distance trips and sleeper team that require multiple days. The nationwide problem requires simplifications to keep the problem mathematically tractable, primarily the representation of production sites with different tier costs and the aggregation of customers in clusters. The regional problem addressed in our previous work is used as a benchmark case study for benchmarking. We then focus on a real-world problem that represents a nationwide argon supply chain. Despite the size of the models, near optimal solutions could be found in reasonable times. Finally, we highlight important features of the proposed approaches.

关键词： mixed-integer linear programming Industrial gases Supply chain optimization

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Optimizing the e-waste management in India: a sustainable mathematical modeling approach to circular economy

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Quality and Quantity 2025年 1-32页

作者： Ali, Wajahat Faculty of Management Studies Marwadi University Gujarat India

The exponential growth of electronic waste (e-waste) in India, driven by rapid technological advancements and consumer demand, poses significant environmental, economic, and social challenges. This study develops a multi-objective mixed-integer linear programming (MILP) model to optimize e-waste management while aligning with circular economy principles. The proposed model minimizes total cost (₹1154.35), maximizes resource recovery (₹1542.82), and reduces environmental impact (₹1378.90). Using a Goal programming (GP) approach, the study balances these conflicting objectives, ensuring a 26.9% cost reduction, 40.4% increase in resource recovery, and 30.3% lower environmental impact than traditional heuristic methods. A benchmark analysis against three traditional methods—Rule-Based Heuristic, linear programming, and Greedy Cost-Minimization—demonstrates the superiority of the proposed approach. While the Greedy Model achieves the lowest cost (₹978.10), it significantly increases environmental impact (₹2451.98) and limits resource recovery (₹835.21). The Rule-Based Heuristic and linear programming models perform better but fail to optimize all three objectives simultaneously. The MILP-GP model integrates economic, environmental, and operational considerations, proving its effectiveness as a practical and scalable decision-support tool for sustainable e-waste management in India. © The Author(s), under exclusive licence to Springer Nature B.V. 2025.

关键词： Circular economy E-waste management Goal programming Mathematical modeling mixed-integer linear programming

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