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Iterated variable neighborhood search for integrated scheduling of additive manufacturing and multi-trip vehicle routing problem

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COMPUTERS & OPERATIONS RESEARCH 2024年 167卷

作者： Sugianto, Willy Chandra Kim, Byung Soo Incheon Natl Univ Dept Ind & Management Engn 119 Acad Ro Incheon 22012 South Korea

This study addresses a novel problem structure for integrated scheduling of additive manufacturing and delivery processes. In the manufacturing process, we consider batch production of parts, simulating Selective Laser Melting technology in parallel additive manufacturing machines with heterogeneous specifications. A batch production, referred to as a build, must consist of parts with homogenous material. During the production run, sequence- dependent material setup times occur in between builds. In the delivery process, parts are delivered in batches using a limited number of homogenous capacitated vehicles. Each vehicle might perform multi-trips. Thus, the route for each trip and departure schedule among trips must be decided. In this regard, batch delivery is independent to batch production. The problem objective function is to minimize the total completion time of all parts. For optimally solving the problem, a mixed-integer linear programming model is devised. To solve larger problems sizes under a lower computation time, an iterated variable neighborhood search algorithm with embedded rule-based heuristics is proposed. The computational experiment denotes that the proposed algorithm with route minimization rule effectively solves small-scale problems, while capacity usage maximization rule performs the best in large-scale problems and outperforms other existing metaheuristics. Further result analysis is also performed to provide managerial insights.

关键词： Scheduling Additive manufacturing Multi-trip vehicle routing problem mixed integer linear programming Variable neighborhood search Heuristic

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An energy management system to schedule the optimal participation to electricity markets and a statistical analysis of the bidding strategies over long time horizons

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RENEWABLE ENERGY 2024年 228卷

作者： La Fata, Alice Brignone, Massimo Procopio, Renato Bracco, Stefano Delfino, Federico Barbero, Giulia Barilli, Riccardo Univ Genoa Elect Elect & Telecommun Engn & Naval Architecture Genoa Italy Renantis Milan Italy

This paper presents a tool that allows to perform an ex-ante estimation of the profitability to participate to the electric energy and service markets for a microgrid or a polygeneration power plant. The tool is the evolution of an Energy Management System (EMS) previously developed by the University of Genoa and Renantis Solutions Srl. Constraints are added to the original EMS to model the Ancillary Service Market (ASM) and Day Ahead Market (DAM) mechanisms in the framework of a planning tool that allows to get estimates of the microgrid/ plant costs and revenues over a long-time horizon. As the market outcome strongly depends on the Transmission System Operator (TSO) acceptance/rejection of bids and offers, a statistical approach is proposed. The optimization process is split in two steps: the first one optimally dispatches the energy units and proposes DAM offers and ASM bid and offers over a specified time horizon. Then, a Monte Carlo method is launched;at each extraction the TSO acceptance/rejection of each ASM bid and offer is simulated and there is a second step of optimization in which the energy sources are optimally re-dispatched to meet the bidding program and so to avoid imbalance power exchange that would increase the energy cost. The cost/revenue is thus computed at each extraction. The result is the probability density function of the costs/revenues that allows to estimate the profitability of the participation to the electricity markets. One test performed on a real microgrid shows that the computational time needed to complete one iteration related to a time horizon of one month is approximately 20 s. Moreover, the comparison of the outcomes with two independent launches to define the set of bids and offers to be accepted in ASM demonstrates that the statistical analysis allows to avoid misleading conclusions on the costs/revenues. Consequently, the fact that the developed EMS allows to perform a statistical analysis in a reasonable time enables the

关键词： Energy Management System Electricity Market Ancillary Services Renewable Energy Sources mixed integer linear programming

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Operating Reserve Quantification Using Prediction Intervals of Wind Power: An Integrated Probabilistic Forecasting and Decision Methodology

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IEEE TRANSACTIONS ON POWER SYSTEMS 2021年第4期36卷 3701-3714页

作者： Zhao, Changfei Wan, Can Song, Yonghua Zhejiang Univ Coll Elect Engn Hangzhou 310027 Peoples R China Univ Macau State Key Lab Internet Things Smart City Taipa Macau Peoples R China

Adequate reserves are urgently needed to hedge against wind power forecasting uncertainties in power systems. Traditional reserve quantification sequentially acquires statistical features of wind power and then determines reserve amounts. This paper establishes a novel integrated probabilistic forecasting and decision (IPFD) methodology to simultaneously optimize the wind power prediction intervals (PIs) and probabilistic reserve quantification. Upward and downward reserve quantities are defined to cover the wind power forecasting uncertainties within the PIs. A cost function evaluating the reserve provision payment and deficit penalty is elaborated to realize cost-benefit trade-offs of reserve decision. Nonparametric wind power PIs are constructed based on extreme learning machine, which minimizes the reserve cost function subject to eligible target coverage probability constraint. The confidence level and quantile proportions associated with wind power PIs can be jointly tuned to reduce the operational cost of reserves. Benefited from extreme learning machine, the IPFD model is reformulated as a mixed integer linear programming problem. A feasible region tightening strategy that shrinks the large constant coefficients and eliminates the redundant binary variables is proposed to accelerate model training. Numerical experiments based on actual wind power data demonstrate the remarkable cost-effective advantages of the IPFD based reserve quantification, as well as the high computational efficiency for online application.

关键词： Wind power generation Forecasting Probabilistic logic Uncertainty Stochastic processes Wind forecasting Upper bound Operating reserve prediction interval probabilistic forecasting wind power machine learning mixed integer linear programming

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OPTIMIZATION AND SENSITIVITY ANALYSES OF A COMBINED COOLING, HEAT AND POWER SYSTEM FOR A RESIDENTIAL BUILDING

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THERMAL SCIENCE 2021年第5期25卷 3969-3986页

作者： Melo, Filipi Maciel Antonio Pina, Eduardo Carvalho, Monica Univ Fed Paraiba Joao Pessoa Paraiba Brazil Univ Zaragoza Zaragoza Spain

In the quest for a better use of energy resources, energy integration, and cogeneration strategies have been employed in the industrial and commercial sectors with considerable benefits realized. However, the residential sector remains underexplored. An optimization procedure should be carried out whenever there is a need to ensure or verify the economic viability of an energy system. This study uses mixed integer linear programming to optimize the energy supply to a residential building, with 20 floors and 40 apartments, located in the city of Joao Pessoa (northeast Brazil). The equipment available includes gas engines, electric and natural gas boilers, heat exchangers, cooling towers, and absorption and mechanical chillers. The optimization establishes the optimal system configuration and operational strategy (operation throughout the year). Economic, technical, and legal aspects were considered in the minimization of the total annual costs associated with the building's energy supply. The energy demands were calculated on an hourly basis, throughout one year, by the EnergyPlus software and corresponded to hot water (83 MWh per year), electricity (171 MWh per year), and cooling (242 MWh per year) demands. The optimal system was entirely reliant on the electric grid to meet the electricity demand directly and to satisfy heating and cooling demands by means of an electric hot water boiler and a mechanical chiller. The optimal solution is tested by varying, within reasonable limits, selected parameters: natural gas and electricity tariffs, the behavior of residents, amortization factor, and relationship between the tariffs of electricity and natural gas.

关键词： optimization mixed integer linear programming energy building residential sensitivity analysis northeast Brazil

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Interpreting Deep-Learned Error-Correcting Codes

Interpreting Deep-Learned Error-Correcting Codes

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IEEE International Symposium on Information Theory (ISIT)

作者： Devroye, N. Mohammadi, N. Mulgund, A. Naik, H. Shekhar, R. Turan, G. Y. Wei, Y. Zefran, M. Univ Illinois Chicago IL 60680 USA ELRN MTA SZTE Res Grp Artificial Intelligence Szeged Hungary

ISBN: (纸本)9781665421607;9781665421591

Deep learning has been used recently to learn error-correcting encoders and decoders which may improve upon previously known codes in certain regimes. The encoders and decoders are learned "black-boxes", and interpreting their behavior is of interest both for further applications and for incorporating this work into coding theory. Understanding these codes provides a compelling case study for Explainable Artificial Intelligence (XAI): since coding theory is a well-developed and quantitative field, the interpretability problems that arise differ from those traditionally considered. We develop post-hoc interpretability techniques to analyze the deep-learned, autoencoder-based encoders of TurboAE-binary codes, using influence heatmaps, mixed integer linear programming (MILP), Fourier analysis, and property testing. We compare the learned, interpretable encoders combined with BCJR decoders to the original black-box code.

关键词： Training Deep learning Neural networks Turbo codes Robustness Decoding mixed integer linear programming

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Scheduling Optimization of Household Equipment using a Wireless Home Automation System 5

Scheduling Optimization of Household Equipment using a Wirel...

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5th International Conference on Communications, Signal Processing, and their Applications (ICCSPA)

作者： Alshehhi, Aaesha Alawadhi, Fatma Baqer, Meera Dhaouadi, Rached Amer Univ Sharjah Dept Elect Engn Sharjah U Arab Emirates

ISBN: (纸本)9781665482370

In this paper, we present a low-cost home automation system that can generate an energy-efficient and cost-reducing electricity consumption schedule. The system is comprised of an energy management system running on a raspberry pi 4 called Home Assistant with wireless smart switches connected to the desired home appliances. The communication between all components in the system is configured to be wireless, which means that the control interface for this system is flexible. The proposed optimization method for generating the schedule is based on mixed integer linear programming. The home energy management system is realized on a raspberry pi. The optimization algorithm is implemented using Python and the Gurobi solver package. The optimized scheduling for the home appliances was obtained for different cases of user time preferences and results prove that the proposed method efficiently reduced the total cost of electricity of a typical household.

关键词： home automation system schedule optimization mixed integer linear programming genetic algorithm nested loops

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Brayton pumped thermal energy storage: Optimal dispatchment in multi-energy districts

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ENERGY CONVERSION AND MANAGEMENT 2024年 314卷

作者： Ghilardi, Alessandra Frate, Guido Francesco Kyprianidis, Konstantinos Tucci, Mauro Ferrari, Lorenzo Univ Pisa Dept Energy Syst Terr & Construct Engn Largo Lucio Lazzarino I-56122 Pisa Italy Malardalen Univ Sch Business Soc & Engn Dept Sustainable Energy Syst Vasteras 72123 Sweden

Energy storage systems play a crucial role in supporting the integration of renewable energy sources. In this framework, Brayton Pumped Thermal Energy Storage is an emerging technology thanks to many positive features, including geographical and raw materials independence, long lifetime, and peculiar sector-coupling capabilities. By storing electric energy as thermal exergy, this technology offers the flexibility to discharge energy directly for heating or cooling applications or convert it back into electricity as needed by the grid. This dual functionality fits well with the multi-energy intrinsic nature of urban districts in which electrical and thermal energy carriers are involved. This paper aims then to evaluate the potential economic benefit due to the usage of a Brayton based Pumped Thermal Energy Storage as multi-energy device instead of a solely electric-to-electric. An urban district with thermal and electric requirements is used as a case study to investigate the technoeconomic performance of the mentioned storage capacity when coupled to photo-voltaic plants to simulate deep-decarbonization scenarios. The system day-ahead optimization, performed through a mixed integer linear programming approach, aims to minimize the operational cost computed over a 24-h horizon. The results highlight that operational yearly cost savings are 5-10 % when using the multi-energy storage functionalities compared to the standard electric-to-electric operation. Despite the cost reduction, allowing only direct heating causes unavoidable thermal curtailment losses in the 6-10 % range. However, these losses can be reduced to 3 % by introducing the additional direct cooling functionality, bringing the best performances from the economic and thermodynamic standpoints.

关键词： Pumped thermal energy storage Sector-coupling Multi -energy storage mixed integer linear programming

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A coordinated generation and voyage planning optimization scheme for all-electric ships under emission policy

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2024年 156卷

作者： Gao, Jingjie Lan, Hai Zhang, Xinan Iu, H. H. C. Hong, Ying-Yi Yin, He Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin 150001 Peoples R China Univ Western Australia Sch Elect Elect & Comp Engn Perth WA 6009 Australia Chung Yuan Christian Univ Dept Elect Engn Chung Li Dist 320 Taoyuan Taiwan

The International Maritime Organization has introduced the concept of Emission Control Area to reduce sulfur emissions from coastal areas. The expensive low-sulfur fuels are required to be used in Emission Control Area, leading to increased sulfide emissions due to bypassing by ship operators. It creates new challenges for navigation and power generation scheduling of all-electric ships. This article presents a new bi-objective joint optimization scheme for ship sailing routes and energy management. It considers emission control strategies to balance the total cost of operation and sulfur dioxide emissions throughout the ship's voyage. Unlike previous optimization problems with fixed routes and sailing time, the influence of emission policies on the operator's navigation decisions is considered. The route options and sailing time are treated as variables embedded in the model. In addition, the optimization accuracy is improved by integrating the model into mixed integer linear programming through a modified piecewise linearization model. The results show that the total operating cost for the same route is reduced by 6.05%, and the SO2 emission is 8.36% after integrating the emission policy influencing factor in the model. The effectiveness of the proposed scheme is validated, and the model's necessity for optimizing ship power generation and voyage within Emission Control Area is also illustrated.

关键词： Emission control area All-electric ships Bi-objective joint optimization Joint voyage planning and energy management mixed integer linear programming

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A novel dynamic batching energy management strategy for multi-microgrid networks with enhanced privacy

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ENERGY AND BUILDINGS 2024年 318卷

作者： Arunkumar, C. P. Albert Paul Selvakumar, K. SRM Inst Sci & Technol Coll Engn & Technol Dept Elect & Elect Engn Chennai Tamil Nadu India

This article presents a novel energy management approach termed Dynamic Batching EMS (DBEMS) designed specifically for day -ahead scheduling within multi-microgrid (MMG) networks. The prime aim of this strategy is to curtail operational costs within the MMG network, fortify the privacy of individual microgrids, and optimize the utilization of available resources, surpassing existing methodologies like centralized, decentralized, and nested management strategies. The emphasis is chiefly on orchestrating cost-effective operations while bolstering data privacy for each microgrid within the complex MMG framework. The efficacy of the proposed strategy is evaluated across both grid -connected and islanded modes, focusing on a network comprising four microgrids. The model formulation is executed utilizing the mixed integer linear programming method. In the islanded mode, optimization is conducted for scenarios both inclusive and exclusive of Battery Energy Storage Systems (BESS). Comparative assessments are made against centralized and nested strategies, considering parameters including operational costs, privacy enhancements, load -shedding, computation time, and computational burden.

关键词： Multi-microgrids Energy management Optimisation Cost-optimization mixed integer linear programming Privacy enhancement Distributed generation

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A bi-objective optimization approach for carbon capture and storage supply chain network combining with pricing policies: Economic and social aspects

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JOURNAL OF CLEANER PRODUCTION 2024年 434卷

作者： Derakhti, Arman Gonzalez, Ernesto D. R. Santibanez Univ Talca Fac Engn Curico Chile Univ Talca Ind Engn Dept Niches Km 1 Curico Chile Univ Talca Fac Engn CES4 0 Niches Km 1 Curico Chile

Carbon capture and storage (CCS) technology and pricing policies are two mitigation strategies that attempt to tackle the carbon emission problem. Nonetheless, the uncertainty of public reaction to CCS infrastructure installation has been noted as a significant issue that may lead to either cancelations, delays, or influence the deployment cost of CCS infrastructure. Although these strategies are solutions to reduce carbon emissions, the entire supply chain itself needs to align with sustainability policy. While some previous studies have addressed mitigation strategies or the combination of social objective and CCS technology, the impact of social objectives on reservoir openness has not been explored. This article contributes to the literature by illustrating the effect of social objectives on reservoir openness and the total cost of the CCS supply chain. Through the development of a two-stage mixed-integer linear programming model, we optimize the supply chain network design. In the first stage, a single-objective model minimizes capture, transportation, and sequestration costs along with CO2 emission costs to the atmosphere, considering various carbon tax prices. In the second stage, a bi-objective model maximizes social acceptance towards CO2 injection wells (reservoirs) to analyze the impact of cultural dimensions. Our findings reveal that maximizing the social objective leads to increased costs compared to the economic optimum, and it significantly influences the openness of reservoirs based on cultural dimensions. Furthermore, we demonstrate that an increase in the CO2 tax enhances the amount of captured carbon and the complexity of the pipeline network in the supply chain. This insight has implications for governments and decision-makers who can encourage industries to adopt CCS technology by setting appropriate carbon tax prices. To validate the model, we apply it to a case study in the European Union cement industry, one of the major carbon emitters among

关键词： Carbon capture and storage mixed integer linear programming Supply chain Social acceptance Carbon tax policy

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