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Hybrid energy system optimization model: Electrification of Ontario's residential space and water heating case study

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ENERGY AND CLIMATE CHANGE 2022年 3卷

作者： Sanongboon, P. Pettigrew, T. Canadian Nucl Labs Chalk River ON Canada

Energy systems are becoming more complex as new energy sources are introduced in support of clean energy goals. These hybrid energy systems can be configured for cogeneration to account for multiple energy uses, including not only electricity but also space heating, water heating, and industrial process heat. Variable renewable energy systems are increasingly being added to hybrid systems to mitigate climate change and reduce greenhouse gas (GHG) emissions. This often creates additional challenges to meet energy demands due to variability associated with renewable generation. In support of energy planning for the new clean economy, the Hybrid Energy System Optimization (HESO) model has been developed to study the feasibility and benefits of nuclear-renewable hybrid energy systems. The model is formulated, as a mixed-integer linear programming (MILP) algorithm, to determine the best energy mix by minimizing annual cost. Because electrification will play a significant role in realizing a clean economy, this study explores the potential economic viability of electrification of residential water and space heating in Ontario. Different energy scenarios have been analyzed to understand the challenges associated with electrification and determine which energy sources will significantly reduce greenhouse gas emissions, while also maintaining competitive energy costs. The results show that electrification of residential water heating can be a viable alternative to natural gas heaters;reducing GHG emissions and energy cost. However, electrification of residential space heating is more challenging due to the large seasonal temperature variations that create significant energy demand fluctuations. Additional nuclear and wind generating capacity, as well as storage systems, are all important elements to support Ontario's transition to a low carbon economy through electrification.

关键词： Hybrid energy system mixed-integer linear programming Electrification Greenhouse gas emissions Levelized cost of energy

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An integrated machine learning and quantitative optimization method for designing sustainable bioethanol supply chain networks

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Decision Analytics Journal 2023年 7卷

作者： Momenitabar, Mohsen Dehdari Ebrahimi, Zhila Abdollahi, Atefeh Helmi, Wesam Bengtson, Kelly Ghasemi, Peiman Department of Transportation Logistics and Finance College of Business North Dakota State University Fargo 58105 ND United States Department of Geographical Sciences Center for Geospatial Information Science University of Maryland College Park 20742 MD United States University of Guilan Rasht Iran Upper Great Palin Transportation Institute North Dakota State University Fargo 58105 ND United States University of Vienna Department of Business Decisions and Analytics Kolingasse 14-16 Vienna 1090 Austria

Increasing demand for energy is pushing decision-makers in the Bioethanol Supply Chain Network (BSCN) to adopt second-generation biomass feedstocks to meet sustainability criteria. This study proposes an integrated Machine Learning (ML) and quantitative optimization model to design a Sustainable Bioethanol Supply Chain Network (SBSCN). We use ML methods, such as Random Forest (RF), Extreme Gradient Boosting Method (XGBoost), and Ensemble learning algorithm (Bagging), to project the bioethanol demand. We select the RF method as a superior method to forecast the bioethanol demand as inputs to the model by comparing the performance criteria for these three methods. We then propose a mixed-integer linear programming (MILP) model to meet the sustainability criteria defined by three objective functions. We present a case study to demonstrate the applicability of the proposed approach. The sensitivity analysis confirms that the costs of establishing preprocessing and biorefinery centers constitute 37% of the total costs of the network. More importantly, we find that the square bale harvest method is among the methods that utilized the most switchgrass land area. More interestingly, our model determined that the square bale harvest method led to 18,450 tons of switchgrass loss in the case study. Finally, our results can be utilized by policymakers and investors to develop efficient SBSCNs. © 2023 The Author(s)

关键词： Bioethanol supply chain network Demand projection Machine learning mixed-integer linear programming Sustainability Switchgrass cultivation

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Short-term planning of a work shift for open-pit mines: A case study

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COGENT ENGINEERING 2023年第1期10卷 Article: 2168172页

作者： Silva-Junior, Ademar L. Martins, Aldrin G. Pantuza-Jr, Guido Cota, Luciano P. Souza, Marcone J. F. Inst Tecnol Vale BR-354000000 Ouro Preto MG Brazil Univ Fed Ouro Preto Programa Posgrad Instrumentacao Controle & Automac BR-35400000 Ouro Preto Brazil Inst Tecnol Vale BR-35400000 Ouro Preto Brazil Vale SA BR-35900900 Itabira Brazil Fed Inst Minas Gerais Dept Min BR-36415000 Congonhas MG Brazil Themat Network Mat Engn REDEMAT Grad Program Mat Engn BR-35000000 Ouro Preto Brazil Fed Inst Minas Gerais BR-35057760 Governador Valadares Brazil Univ Fed Ouro Preto Dept Comp BR-35400000 Ouro Preto Brazil

This work deals with the short-term planning problem of a work shift for open-pit mines. The problem involves ore and waste fronts, shovels, heterogeneous truck fleets, and discharge points. The allocation of trucks is dynamic to allow multiple routes to be assigned to each truck. The problem consists of deciding which fronts must be mined and establishing the number of trucks, their routes, and the amount of material transported by them to each discharge point, satisfying a stripping ratio at the desired level. The objectives are to minimize the deviations from the targets for production, chemical grade, and particle size range of each control parameter at each plant and reduce the number of trucks needed for the process. To solve the problem, we developed a mixed-integer linear goal programming model and tested it using real data from an iron ore mine. The results showed that the proposed approach supports decision-makers in the sizing and allocation of truck fleets and in meeting the production and control parameter targets required by the ore processing plants according to the daily scenario, such as low availability of shovels and trucks, flexibility in ore quality, and need for increased production.

关键词： Short-term planning open-pit mining blending truck allocation mixed-integer linear programming goal programming

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Designing railway transit network with first-mile access via ride-sharing autonomous vehicle service

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TRAVEL BEHAVIOUR AND SOCIETY 2024年 35卷

作者： Shan, Ali An, Kun Hoang, Nam Hong Vu, Hai L. Monash Univ Inst Transport Studies Dept Civil Engn Melbourne Australia Tongji Univ Coll Transportat Engn 4800 Caoan Rd Shanghai Peoples R China

The rail transit system is an essential component of a sustainable transport system, but its accessibility remains a barrier owing to inadequate transit network coverage. Ride-sharing autonomous vehicle (RAV) service is anticipated to be a potentially viable option to improve the economics of rail-based transportation systems (RTS) by substantially lowering the upfront and ongoing costs. In this study, we model and evaluate the impact of RAVs on integrated RTS + first-mile autonomous vehicle design where the commuters' waiting time at railway stations is capped to ensure the level of quality service for rail. The proposed framework optimizes the RAV fleet size while allowing their relocations to tackle the vehicle imbalance problem in the network to assist operators in optimally and effectively running their services. To this end, this study intends to help the relevant Government agency in their planning, where the RTS extension design and RAV operations are planned concurrently to maximize the coverage area with improved accessibility. The modeling framework is solved using mixed-integer linear programming. We applied our proposed framework to the Doncaster railway extension in Melbourne, Australia, to demonstrate the applicability of our modeling framework. Our findings show that increasing rideshare capacity enhanced the operational performance of the integrated services, increased the percentage of AVs performing ride-sharing, and resulted in a shorter RTS alignment and that the RAV-only service is always a costlier option. Finally, the RTS alignment is found to be robust against the variations in capped commuters' waiting time, the disutility of commuter delays by the RAV service, and changes in headway between train trips.

关键词： Railway transit network design Ride-sharing Shared mobility Dynamic travel demand Vehicle relocations mixed-integer linear programming

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Reorganization of a medical service network to manage pandemic waves: A real case study

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OPERATIONS RESEARCH FOR HEALTH CARE 2023年 39卷

作者： Ahadian, Sajjad Pishvaee, Mir Saman Jahani, Hamed Iran Univ Sci & Technol Sch Ind Engn Tehran Iran RMIT Univ Sch Accounting Informat Syst & Supply Chain Melbourne Australia

During Covid-19, medical service networks (MSNs) faced new challenges, such as an impressive increase in hospital visits, a shortage of hospital beds and staff, and insufficient information to estimate the number of mild and critical cases. In addition, governments were encountered to implement appropriate quarantine policies. Dealing with these problems became more complex and challenging when a new wave of disease occurred. This study develops a mixed-integer linear programming model for reorganizing an MSN to manage future pandemic waves. The model aims at reallocation medical staff to prevent a shortage of hospital beds. A fuzzy approach is employed to estimate the uncertain number of patients in each period. As a result, direct hospital visits are decreased by 60% on average, and shortages of beds are avoided by adding the fewest beds possible in each period. The model can also optimize several performance ratios, e.g., the ratio of hospitalized patients to the specialized personnel assigned to each hospital, which is decreased by approximately 40% in our case.

关键词： Pandemic control Medical service network Staff management Post-disaster casualty management Fuzzy approach mixed-integer linear programming

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Analysing the impact of collaborations between airlines and maintenance service company under MRO outsourcing mode: Perspective from airline?s operations

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JOURNAL OF AIR TRANSPORT MANAGEMENT 2023年第1期109卷

作者： Qin, Yichen Ng, Kam K. H. Shanghai Maritime Univ Sch Econ & Management Shanghai 201306 Peoples R China Hong Kong Polytech Univ Dept Aeronaut & Aviat Engn Hung Hom Hong Kong Peoples R China

The practice of Aircraft Maintenance Repair and Overhaul (MRO) outsourcing has changed airline companies' decision-making processes in both operational and tactical levels. Specifically, the aircraft heavy maintenance service provided by an independent maintenance service company alleviates airlines' fixed-asset investments and daily operations regarding maintenance. On the other hand, the fluctuations in MROs service capability, availability and service level also affect airlines' fleet management and daily operations if their maintenance demands cannot be met in a timely manner. In this regard, the impact of the collaborations between airlines and maintenance service company is investigated from the perspective of airline's operations, by studying the maintenance planning problem at operational level. Afterwards, to examine and investigate the impact of col-laborations between the two parties have on airline's operational performance, aircraft maintenance routing problems under 'airlines-service company' non-collaborative and collaborative contexts are investigated and formulated. The computational results demonstrate the effectiveness of the MRO collaboration under outsourcing mode, in view of airline company's overall operational revenue and flexibility while carrying out maintenance routing for individual aircraft in its fleet.

关键词： Aircraft maintenance routing Aircraft maintenance Repair and overhaul MRO Outsourcing Collaborative MRO mixed-integer linear programming

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What is the minimal systemic risk in financial exposure networks?

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JOURNAL OF ECONOMIC DYNAMICS & CONTROL 2020年 116卷 103900-103900页

作者： Diem, Christian Pichler, Anton Thurner, Stefan WU Vienna Univ Econ & Business Inst Stat & Math Welthandelspl 1 A-1020 Vienna Austria Complex Sci Hub Vienna Josefstadter Str 39 A-1080 Vienna Austria Univ Oxford Inst New Econ Thinking Manor Rd Oxford OX1 3UQ England Univ Oxford Math Inst Woodstock Rd Oxford OX1 3LP England IIASA Schlosspl 1 A-2361 Laxenburg Austria Med Univ Vienna Sect Sci Complex Syst Spitalgasse 23 A-1090 Vienna Austria Santa Fe Inst 1399 Hyde Pk Rd Santa Fe NM 87501 USA

We quantify how much systemic risk can be eliminated in financial contract networks by rearranging their network topology. By using mixed integer linear programming, financial linkages are optimally organized, whereas the overall economic conditions of banks, such as capital buffers, total interbank assets and liabilities, and average risk-weighted exposure remain unchanged. We apply the new optimization procedure to 10 snapshots of the Austrian interbank market where we focus on the largest 70 banks covering 71% of the market volume. The optimization reduces systemic risk (measured in DebtRank) by about 70%, showing the huge potential that changing the network structure has on the mitigation of financial contagion. Existing capital levels would need to be scaled up by a factor of 3.3 to obtain similar levels of DebtRank. These findings underline the importance of macroprudential rules that focus on the structure of financial networks. The new optimization procedure allows us to benchmark actual networks to networks with minimal systemic risk. We find that simple topological measures, like link density, degree assortativity, or clustering coefficient, fail to explain the large differences in systemic risk between actual and optimal networks. We find that if the most systemically relevant banks are tightly connected, overall systemic risk is higher than if they are unconnected. (C) 2020 The Author(s). Published by Elsevier B.V.

关键词： Systemic risk-efficiency Interbank market Financial networks Contagion Network optimization mixed-integer linear programming DebtRank Network topology measures and Systemic risk

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A production-inventory model to optimize the operation of distributed energy resource networks in a rolling horizon

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Heliyon 2024年第21期10卷 e39900页

作者： Cortés, Pablo Escudero-Santana, Alejandro Barbadilla-Martin, Elena Guadix, José Ingeniería de Organización Escuela Técnica Superior de Ingeniería Universidad de Sevilla Camino de los Descubrimientos s/n Sevilla 41092 Spain

The recent advancements in energy production, storage, and distribution are creating unprecedented opportunities in the field. Major consumers can benefit from the implementation of distributed energy resource networks capable of generating electricity or heating from sources, often renewable ones, in close proximity to the point of use, rather than relying on centralized generation sources from power plants. In this paper, we introduce a pioneering model designed to determine the optimal set of energy commands in a distributed energy resource network, minimizing operational costs in a time horizon. Indeed, we propose an innovative mixed-integer linear programming formulation rooted in the production-inventory models commonly employed in aggregate production planning. The system integrates diverse energy generation sources, storage facilities, and demand points, encompassing both electric and heating commodities. The optimum of the model is achieved for all analyzed instances of the test library (2 scenarios-20 instances) in an exceptionally short time, outperforming other approaches previously presented in the literature. We employed the Gurobi optimizer to solve the model, obtaining rapid responses that ensure real-time decision-making and facilitate effective control of the distributed energy resource network within a three-days’ rolling horizon, as discussed in a simulated real-life application case study. Indeed, the proposed model solves in less than 1 s, enabling near-instantaneous decision-making. This swift solution time surpasses any known references in the field, effectively shifting the bottleneck in DER network operation from the decision-making process to the forecasting of demand and weather conditions. While forecasting typically requires a minimum of 15 min, our approach suggests that a reduction in this forecasting time could further enhance the control system's response time, given the model's ability to deliver optimal solutions almost immediatel

关键词： Distributed energy resource Inventory mixed-integer linear programming Production Renewable energy source Rolling horizon

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A multi-objective production scheduling model and dynamic dispatching rules for unrelated parallel machines with sequence-dependent set-up times

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Decision Analytics Journal 2024年 13卷

作者： Tai, Pham Duc Kongsri, Papimol Soeurn, Prasal Buddhakulsomsiri, Jirachai School of Manufacturing Systems and Mechanical Engineering Sirindhorn International Institute of Technology Thammasat University Pathum Thani Thailand

This study presents a production scheduling for unrelated parallel machines with machine and job sequence-dependent setup times. The system performance measures to minimize include makespan, total tardiness, and number of tardy jobs. The aim of the study is to develop a solution methodology that can solve the problem for the large scale. First, the problem is formulated as a mixed-integer linear programming model. The augmented ɛ-constraint method is applied to find Pareto solutions for small problem instances. The purpose is to demonstrate that Pareto solutions, which balance the trade-offs among three measures of performance, can be found for these instances. Dispatching rule-based heuristics is developed to solve large problem instances. The heuristics feature three dispatching rules that are designed to handle the dependent setup time. In addition, these rules are combined into six variants using a time-based rule-switching mechanism. The heuristics are tested with 18 problem instances, containing 244 to 298 jobs, in two demand scenarios derived from the monthly demand data from an industrial user. Under each demand scenario, a set of heuristics that provides the best performance with respect to the three measures is identified. The heuristics include combinations of the shortest completion time and due date-based rules. Finally, a multi-criteria decision-making analysis is performed to determine the conditions specified by the weight given to each measure, with which one heuristic is preferred over the others. © 2024 The Author(s)

关键词： Dispatching rules mixed-integer linear programming Multiple objectives Rule switching Sequence-dependent setup Unrelated parallel machine

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Design of mosquito repellent molecules via the integration of hyperbox machine learning and computer aided molecular design

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DIGITAL CHEMICAL ENGINEERING 2022年 3卷

作者： Hatamleh, Mohamad Chong, Jia Wen Tan, Raymond R. Aviso, Kathleen B. Janairo, Jose Isagani B. Chemmangattuvalappil, Nishanth G. Univ Nottingham Malaysia Dept Chem & Environm Engn Broga Rd Semenyih 43500 Selangor De Malaysia De La Salle Univ Ctr Engn & Sustainable Dev Res 2401 Taft Ave Manila 0922 Philippines De La Salle Univ Dept Biol 2401 Taft Ave Manila 0922 Philippines

The use of mosquito repellents is an efficient way to prevent mosquito-borne diseases. Despite the accumulation of information about repellents, there remains the challenge of the lack of understanding of their mechanism of action. There is also a need for systematic methods for discovering new alternatives that mitigate the drawbacks of repellents currently in use. To address these research gaps, a computer-aided molecular design (CAMD) framework is developed for the optimal molecular design of mosquito repellents. In this framework, the mosquito repelling attribute of molecules are predicted using a data-driven hyperbox-based machine learning approach in the absence of a mechanistic prediction model. The best set of rules is selected from plausible alternative models developed. For the prediction of important physical properties, a group contribution-based method using reliable models is implemented. Subsequently, the CAMD formulation is developed as a mixed-integer linear programming model to obtain structures with minimum viscosity. Results show that of the structures generated, the hyperbox classifier correctly predicted the repelling ability of all molecules found to be known repellents in literature. The molecules not found in the databases provide key insights on where experimental research to develop new repellents should be targeted. Thus, this newly developed framework can be applied as a systematic technique to screen and narrow down the search space for candidate mosquito repellent molecules before final experimental verification.

关键词： Mosquito repellents Computer-aided molecular design Machine learning Hyperbox classifier Cheminformatics mixed-integer linear programming

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