Review
Computer Science, Artificial Intelligence
Seung Jae Lee, Byung Soo Kim
Summary: This paper investigates the part-packing and build-scheduling problem in parallel additive manufacturing. A mixed integer linear programming (MILP) model is developed to minimize the makespan. A two-stage meta-heuristic based on genetic algorithm (GA) and particle swarm optimization (PSO) is proposed and compared with other meta-heuristics. Experimental results demonstrate the effectiveness of the two-stage meta-heuristic.
APPLIED SOFT COMPUTING
(2023)
Article
Mathematics
Teeradech Laisupannawong, Boonyarit Intiyot, Chawalit Jeenanunta
Summary: This paper presents the application of a new mixed-integer linear programming model to the short-term scheduling of the pressing process in the fabrication process of multi-layer printed circuit board (PCB) manufacturing. The proposed model shows better size complexity compared to the previous model and outperforms it in terms of computational complexity.
Article
Computer Science, Interdisciplinary Applications
Jakob Snauwaert, Mario Vanhoucke
Summary: In this paper, six extensions to the multi-skilled resource-constrained project scheduling problem (MSRCPSP) are presented by introducing hierarchical levels of skills. The impacts of these hierarchical skills on the MSRCPSP are studied, including efficiency differences, cost differences, quality differences and more. Seven continuous and time-indexed (mixed-)integer linear programming formulations are proposed and analyzed for each of these problems. Computational experiments are conducted using a modular artificial dataset. The results of the different formulations for the resource-constrained project scheduling problems with hierarchical levels of skills are compared to explain their inherent similarities and differences.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Mathematics
Teeradech Laisupannawong, Boonyarit Intiyot, Chawalit Jeenanunta
Summary: This paper discusses the scheduling of the pressing process in PCB manufacturing, presenting a novel MILP optimization model and a heuristic algorithm. Experimental results show that the MILP model can find optimal schedules for small- to medium-sized problems within 2 hours, while the 3P-PCB-PH can find optimal schedules in a shorter computational time.
Article
Engineering, Electrical & Electronic
Kenny Vinente dos Santos, Bruno Colonetti, Erlon Cristian Finardi, Victor M. Zavala
Summary: This paper proposes a dual dynamic integer programming framework for efficiently solving the short-term generation scheduling problem. By introducing multiperiod stages and overlap strategies, the method accelerates the computation and simulations on the IEEE-118 system demonstrate its effectiveness in delivering near-optimal solutions.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2023)
Article
Mathematics
Fatima Pilar, Eliana Costa e Silva, Ana Borges
Summary: This study focuses on scheduling mechanical repairs at a Portuguese firm in the automotive sector. By developing a mathematical model that considers available resources, interventions, and repair time, the aim is to reduce vehicle downtime. The model, based on mixed-integer linear programming, effectively schedules interventions, allocates resources, and determines start times for each vehicle. Real-world instances provided by the company were successfully solved using the AMPL modeling language and Gurobi solver. The results demonstrate significant improvements, with an average 67% reduction in vehicle downtime and the ability to automatically generate accurate repair schedules, enabling faster delivery to customers.
Article
Management
Btissam Er-Rahmadi, Tiejun Ma
Summary: In this paper, a new Mixed Integer Linear Programming (MILP) optimization-based failure detector (FD) algorithm is proposed. The MILP formulation is obtained via piecewise linearization relaxations and aims to find optimal FD parameters that meet the desired system requirements. The proposed approach improves overall FD performance and scalability by considering network conditions and system parameters as constraints and adapting to real-time network changes. The results obtained from testing in a realistic environment show consistent improvement in the performance and scalability of the FD. This paper is the first attempt to combine MILP-based optimization modeling with FD to achieve performance guarantees.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
Article
Engineering, Electrical & Electronic
Ignacio Guisandez, Juan Ignacio Perez-Diaz
Summary: The study compares five mixed integer linear programming formulations for modeling the hydro production function, discussing their accuracy, effectiveness, and speed in solving hydro scheduling problems to help make the most appropriate choice depending on the time horizon. The logarithmic independent branching 6-stencil method is identified as one of the most accurate, the parallelogram method as one of the most effective, and the traditional method based on a single concave piecewise linear flow-power function as the fastest one.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2021)
Article
Engineering, Industrial
Jianping Dou, Jun Li, Dan Xia, Xia Zhao
Summary: The study focuses on the integrated optimization problem of configuration design and scheduling for a reconfigurable manufacturing system (RMS), and proposes a multi-objective particle swarm optimization (MoPSO) method. Comparative results show that MoPSO outperforms epsilon-constraint method and nondominated sorting genetic algorithm II (NSGA-II) in both solution quality and computation efficiency.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2021)
Article
Computer Science, Interdisciplinary Applications
Janis Brammer, Bernhard Lutz, Dirk Neumann
Summary: This study introduces a reinforcement learning approach to minimize work overload situations in the mixed model sequencing problem. By generating sequences in a constructive way and using metaheuristics, the trained policy can quickly create an initial sequence to improve solution quality. Numerical evaluation on benchmark datasets shows superior performance to established methods when demand plan distribution aligns with learning process expectations.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Chemistry, Multidisciplinary
Soukaina Oujana, Lionel Amodeo, Farouk Yalaoui, David Brodart
Summary: This paper discusses a research project that aims to optimize the scheduling of production orders in the packaging field. The problem is modeled as an extended version of the hybrid and flexible flowshop scheduling problem with precedence constraints, parallel machines, and sequence-dependent setups. Two methodologies, mixed-integer linear programming (MILP) and constraint programming (CP), are used to tackle the problem. Resource calendar constraints are added to the models, and a novel heuristic is designed for quick solutions. The proposed problem can be easily modified to suit real-world situations involving similar scheduling characteristics.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Interdisciplinary Applications
Giulia Caselli, Maxence Delorme, Manuel Iori, Carlo Alberto Magni
Summary: This study addresses a real-world scheduling problem and proposes four exact methods to solve it. The methods are evaluated through computational experiments on different types of instances and show competitive advantages on specific subsets. The study also demonstrates the generalizability of the algorithms to related scheduling problems with contiguity constraints.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Computer Science, Interdisciplinary Applications
Willy Chandra Sugianto, Byung Soo Kim
Summary: This study addresses the optimization of the scheduling for integrated additive manufacturing and delivery processes with the objective of minimizing the total completion time. The study proposes a mixed-integer linear programming model and rule-based heuristics to solve the problem, and develops a lower bound to evaluate the algorithm performance. Experimental results show that the proposed methods perform well on different scale problems.
COMPUTERS & OPERATIONS RESEARCH
(2024)
Article
Engineering, Chemical
Florian Joseph Baader, Andre Bardow, Manuel Dahmen
Summary: The increasing volatility of electricity prices highlights the importance of simultaneous scheduling optimization for production processes and their energy systems. In this study, we propose an efficient scheduling formulation that takes into account both process dynamics and binary on/off-decisions in the energy system. By considering three different aspects, we demonstrate the feasibility of achieving fast optimization for real-time scheduling.
Article
Computer Science, Interdisciplinary Applications
Vicky Mak-Hau, Brendan Hill, David Kirszenblat, Bill Moran, Vivian Nguyen, Ana Novak
Summary: This paper addresses a unique combinatorial optimization problem derived from helicopter aircrew training for the Royal Australian Navy. The main objective is to find optimal course scheduling solutions and minimize the total time required to complete the syllabus.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Energy & Fuels
Diego Bairrao, Joao Soares, Jose Almeida, John F. Franco, Zita Vale
Summary: This paper investigates the production of green hydrogen and the current state and prospects of Portugal in energy transition. Through a comprehensive simulation that considers energy generation data, hydrogen production aspects, CO2 emissions indicators, and based costs, the total production of green hydrogen is estimated and compared with European green hydrogen targets and Portugal's transport and energy generation prospects. The results suggest that promoting the conversion of buses and trucks into H2-based fuel is more effective for CO2 reduction, while thermoelectric plants fueled by H2 are the best option considering energy security.
Article
Energy & Fuels
Pedro Faria, Zita Vale
Summary: By empowering consumers and enabling them as active players, demand flexibility requires more precise and sophisticated load modeling. A laboratory testbed was designed and implemented to survey the behavior of loads in different network conditions. Power hardware-in-the-loop was used to validate the load models under various technical network conditions, and a realistic testbed was provided to validate the load models under different voltage and frequency conditions.
Editorial Material
Energy & Fuels
Pedro Faria, Zita Vale
Article
Energy & Fuels
Gabriel Santos, Luis Gomes, Tiago Pinto, Pedro Faria, Zita Vale
Summary: There is a growing complexity, volatility, and unpredictability in the electric sector that hardens the decision-making process. To this end, the use of proper decision support tools and simulation platforms becomes essential. This paper presents the Multi-Agent based Real-Time INfrastructure for Energy (MARTINE) platform that allows real-time simulation and emulation of loads, resources, and infrastructures.
SUSTAINABLE ENERGY GRIDS & NETWORKS
(2023)
Article
Mathematics
Pratik Mochi, Kartik Pandya, Joao Soares, Zita Vale
Summary: To encourage energy saving and adoption of renewable sources, this study integrates socioeconomic and behavioral objectives in an experimental framework for the local energy community. By using behavioral interventions, the experiment aims to determine their impact on consumer participation in the local electricity sector. The findings show that the interaction between socioeconomic and behavioral objectives leads to significant cost savings for energy utility customers, implying policy implications for local energy utilities.
Article
Green & Sustainable Science & Technology
Arash Moradzadeh, Hamed Moayyed, Behnam Mohammadi-Ivatloo, Zita Vale, Carlos Ramos, Reza Ghorbani
Summary: This paper proposes a federated deep learning (FDL) model called for forecasting PV power generation in different regions of Iran. The model is trained using convolutional neural networks (CNN) in each client, and a global supermodel is generated based on features extracted from each client. This method provides data privacy and ideal performance against cyber attacks.
Article
Green & Sustainable Science & Technology
Tayenne Dias de Lima, Joao Soares, Fernando Lezama, John F. Franco, Zita Vale
Summary: This paper proposes a multi-period planning model for EDSs and DERs considering conditional value at risk. The model aims to minimize the cost related to investment, operation, and risk, while addressing uncertainty in demand growth and evaluating risk from the perspectives of planning costs and carbon taxes.
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
(2023)
Article
Computer Science, Information Systems
Catia Silva, Pedro Faria, Ruben Barreto, Zita Vale
Summary: This paper proposes a methodology to deal with the complex management of Local Energy Communities (LEC) with electric vehicles (EVs), and compares two approaches: performance rate and clustering groups. The results show that the clustering method is more effective in achieving the best results.
Article
Computer Science, Information Systems
Hamed Moayyed, Arash Moradzadeh, Amin Mansour-Saatloo, Behnam Mohammadi-Ivatloo, Mehdi Abapour, Zita Vale
Summary: This article presents a novel model for dynamic line rating (DLR) forecasting using a federated learning approach. By generating a global model, the approach accurately predicts the maximum current carrying capacity of transmission lines while ensuring data security and protection from cyberattacks. The proposed model is trained using data from nine different regions in Iran and successfully predicts DLR values for new regions with correlation coefficients of 96%, 94%, and 97% for Boroujen, Nahavand, and Rafsanjan, respectively.
IEEE SYSTEMS JOURNAL
(2023)
Article
Computer Science, Information Systems
Yassine Boukili, Motaz M. Ayiad, Hamed Moayyed, A. Pedro Aguiar, Zita Vale
Summary: One of the primary challenges in solving the State Estimation problem in low voltage networks is the presence of Gross Errors (GE). This paper compares robust M-estimators designed to handle GE and introduces a novel SE method called the Adaptive Maximum Correntropy Criterion (AMCC). The AMCC shows superior accuracy with smaller root-mean-square errors compared to other estimators in a real low voltage network.
Article
Energy & Fuels
Luis Gomes, Antonio Coelho, Zita Vale
Summary: The adoption of smart grids is a global reality that affects energy customers, who can actively participate in a dynamic grid. Customer surveys worldwide and a survey in Portugal demonstrate customers' willingness to actively participate in smart grid initiatives. The results show a majority of participants are willing to plan their energy usage and accept external control of appliances, while identifying cognitive tendencies and the importance of social science studies in achieving efficient customer participation.
Review
Green & Sustainable Science & Technology
Han Shao, Rui Henriques, Hugo Morais, Elisabetta Tedeschi
Summary: The integration of offshore wind energy into the electric grid provides opportunities in terms of environmental sustainability and cost efficiency, but poses challenges to power quality. This survey offers a deeper understanding of disturbance detection and classification tools, exploring root causes, disturbance locations, and algorithmic solutions. It highlights synchronized waveform measurement and discusses evaluation metrics for detection and classification algorithms. Additionally, a novel system-wide monitoring framework is proposed.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2024)
Article
Multidisciplinary Sciences
Lucas Pereira, Vitor Aguiar, Fabio Vasconcelos, Ricardo Martins, Toni Garces, Hugo Morais
Summary: Industrial kitchens are highly energy-intensive businesses, yet there has been little research on improving their energy efficiency. This paper presents the FIKElectricity dataset, which collects electricity data from three Portuguese restaurant kitchens during their daily operation. The public release of this dataset is expected to draw more attention from the research community to this overlooked industrial sector.
Article
Energy & Fuels
Ksenia Syrtseva, Welington de Oliveira, Sophie Demassey, Hugo Morais, Paul Javal, Bhargav Swaminathan
Summary: This paper proposes a chance-constrained Alternating Current Optimal Power Flow (AC-OPF) model to address the operational planning problem caused by the increasing expansion of renewable energy sources. It uses a Difference-of-Convex approach to solve the optimization problem, considering the activation of various flexibility levers. The proposed methodology is tested on a 33 bus distribution network and proves to be effective and feasible.
SUSTAINABLE ENERGY GRIDS & NETWORKS
(2023)
Article
Energy & Fuels
Meysam Khojasteh, Pedro Faria, Zita Vale
Summary: This paper proposes a distributed model for determining the optimal energy trading strategy of community participants in energy communities. The model considers local day-ahead energy market, peer-to-peer contracts, and the power grid for trading energy and compensating for power shortages/surpluses. The robust optimization approach is used to model uncertainty, and the augmented Lagrangian relaxation and alternating direction method of multipliers methods are employed to decrease the solution time. A case study demonstrates that the proposed model significantly reduces the solution time of energy management problem in communities.
SUSTAINABLE ENERGY GRIDS & NETWORKS
(2023)
Article
Thermodynamics
Yong Cheng, Fukai Song, Lei Fu, Saishuai Dai, Zhiming Yuan, Atilla Incecik
Summary: This paper investigates the accessibility of wave energy absorption by a dual-pontoon floating breakwater integrated with hybrid-type wave energy converters (WECs) and proposes a hydraulic-pneumatic complementary energy extraction method. The performance of the system is validated through experiments and comparative analysis.
Article
Thermodynamics
Jing Gao, Chao Wang, Zhanwu Wang, Jin Lin, Runkai Zhang, Xin Wu, Guangyin Xu, Zhenfeng Wang
Summary: This study aims to establish a new integrated method for biomass cogeneration project site selection, with a focus on the application of the model in Henan Province. By integrating Geographic Information System and Multiple Criterion Decision Making methods, the study conducts site selection in two stages, providing a theoretical reference for the construction of biomass cogeneration projects.
Article
Thermodynamics
Mert Temiz, Ibrahim Dincer
Summary: The current study presents a hybrid small modular nuclear reactor and solar-based system for sustainable communities, integrating floating and bifacial photovoltaic arrays with a small modular reactor. The system efficiently generates power, hydrogen, ammonia, freshwater, and heat for residential, agricultural, and aquaculture facilities. Thermodynamic analysis shows high energy and exergy efficiencies, as well as large-scale ammonia production meeting the needs of metropolitan areas. The hybridization of nuclear and solar technologies offers advantages of reliability, environmental friendliness, and cost efficiency compared to renewable-alone and fossil-based systems.
Editorial Material
Thermodynamics
Wojciech Stanek, Wojciech Adamczyk
Article
Thermodynamics
Desheng Xu, Yanfeng Li, Tianmei Du, Hua Zhong, Youbo Huang, Lei Li, Xiangling Duanmu
Summary: This study investigates the optimization of hybrid mechanical-natural ventilation for smoke control in complex metro stations. The results show that atrium fires are more significantly impacted by outdoor temperature variations compared to concourse/platform fires. The gathered high-temperature smoke inside the atrium can reach up to 900 K under a 5 MW train fire energy release. The findings provide crucial engineering insights into integrating weather data and adaptable ventilation protocols for smoke prevention/mitigation.
Article
Thermodynamics
Da Guo, Heping Xie, Mingzhong Gao, Jianan Li, Zhiqiang He, Ling Chen, Cong Li, Le Zhao, Dingming Wang, Yiwei Zhang, Xin Fang, Guikang Liu, Zhongya Zhou, Lin Dai
Summary: This study proposes a new in-situ pressure-preserved coring tool and elaborates its pressure-preserving mechanism. The experimental and field test results demonstrate that this tool has a high pressure-preservation capability and can maintain a stable pressure in deep wells. This study provides a theoretical framework and design standards for the development of similar technologies.
Article
Thermodynamics
Aolin Lai, Qunwei Wang
Summary: This study assesses the impact of China's de-capacity policy on renewable energy development efficiency (REDE) using the Global-MSBM model and the difference-in-differences method. The findings indicate that the policy significantly enhances REDE, promoting technological advancements and marketization. Moreover, regions with stricter environmental regulations experience a higher impact.
Article
Thermodynamics
Mostafa Ghasemi, Hegazy Rezk
Summary: This study utilizes fuzzy modeling and optimization to enhance the performance of microbial fuel cells (MFCs). By simulating and analyzing experimental data sets, the ideal parameter values for increasing power density, COD elimination, and coulombic efficiency were determined. The results demonstrate that the fuzzy model and optimization methods can significantly improve the performance of MFCs.
Article
Thermodynamics
Zhang Ruan, Lianzhong Huang, Kai Wang, Ranqi Ma, Zhongyi Wang, Rui Zhang, Haoyang Zhao, Cong Wang
Summary: This paper proposes a grey box model for fuel consumption prediction of wing-diesel hybrid vessels based on feature construction. By using both parallel and series grey box modeling methods and six machine learning algorithms, twelve combinations of prediction models are established. A feature construction method based on the aerodynamic performance of the wing and the energy relationship of the hybrid system is introduced. The best combination is obtained by considering the root mean square error, and it shows improved accuracy compared to the white box model. The proposed grey box model can accurately predict the daily fuel consumption of wing-diesel hybrid vessels, contributing to operational optimization and the greenization and decarbonization of the shipping industry.
Article
Thermodynamics
Huayi Chang, Nico Heerink, Junbiao Zhang, Ke He
Summary: This study examines the interaction between off-farm employment decisions between couples and household clean energy consumption in rural China, and finds that two-paycheck households are more likely to consume clean energy. The off-farm employment of women is a key factor driving household clean energy consumption to a higher level, with wage-employed wives having a stronger influence on these decisions than self-employed ones.
Article
Thermodynamics
Hanguan Wen, Xiufeng Liu, Ming Yang, Bo Lei, Xu Cheng, Zhe Chen
Summary: Demand-side management is crucial to smart energy systems. This paper proposes a data-driven approach to understand the relationship between energy consumption patterns and household characteristics for better DSM services. The proposed method uses a clustering algorithm to generate optimal customer groups for DSM and a deep learning model for training. The model can predict the possibility of DSM membership for a given household. The results demonstrate the usefulness of weekly energy consumption data and household socio-demographic information for distinguishing consumer groups and the potential for targeted DSM strategies.
Article
Thermodynamics
Xinglan Hou, Xiuping Zhong, Shuaishuai Nie, Yafei Wang, Guigang Tu, Yingrui Ma, Kunyan Liu, Chen Chen
Summary: This study explores the feasibility of utilizing a multi-level horizontal branch well heat recovery system in the Qiabuqia geothermal field. The research systematically investigates the effects of various engineering parameters on production temperature, establishes mathematical models to describe their relationships, and evaluates the economic viability of the system. The findings demonstrate the significant economic feasibility of the multi-level branch well system.
Article
Thermodynamics
Longxin Zhang, Songtao Wang, Site Hu
Summary: This investigation reveals the influence of tip leakage flow on the modern transonic rotor and finds that the increase of tip clearance size leads to a decline in rotor performance. However, an optimal tip clearance size can extend the rotor's stall margin.
Article
Thermodynamics
Kristian Gjoka, Behzad Rismanchi, Robert H. Crawford
Summary: This paper proposes a framework for assessing the performance of 5GDHC systems and demonstrates it through a case study in a university campus in Melbourne, Australia. The results show that 5GDHC systems are a cost-effective and environmentally viable solution in mild climates, and their successful implementation in Australia can create new market opportunities and potential adoption in other countries with similar climatic conditions.
Article
Thermodynamics
Jianwei Li, Guotai Wang, Panpan Yang, Yongshuang Wen, Leian Zhang, Rujun Song, Chengwei Hou
Summary: This study proposes an orientation-adaptive electromagnetic energy harvester by introducing a rotatable bluff body, which allows for self-regulation to cater for changing wind flow direction. Experimental results show that the output power of the energy harvester can be greatly enhanced with increased rotatory inertia of the rotating bluff body, providing a promising solution for harnessing wind-induced vibration energy.
