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10th International Conference on Probabilistic Graphical Models (PGM)

作者： Maua, Denis Deratani Ribeiro, Heitor Reis Katague, Gustavo Perez Antonucci, Alessandro Univ Sao Paulo Inst Math & Stat Sao Paulo Brazil Ist Dalle Molle Studi Intelligenza Artificiale Lugano Switzerland

Sum-product networks are expressive efficient probabilistic graphical models that allow for tractable marginal inference. Many tasks however require the computation of maximum-a-posteriori configurations, an NP-hard problem for such models. To date there have been very few proposals for computing maximum-a-posteriori configurations in sum-product networks. This is in sharp difference with other probabilistic frameworks such as Bayesian networks and random Markov fields, where the problem is also NP-hard. In this work we propose two approaches to reformulate maximuma-posteriori inference as other combinatorial optimization problems with widely available solvers. The first approach casts the problem as a similar inference problem in Bayesian networks, overcoming some limitations of previous similar translations. In addition to making available the toolset of maximum-a-posteriori inference on Bayesian networks to sum-product networks, our reformulation also provides further insight into the connections of these two classes of models. The second approach casts the problem as a mixed-integer linear program, for which there exists very efficient solvers. This allows such inferences to be enriched with integer-linear constraints, increasing the expressivity of the models. We compare our reformulation approaches in a large collection of problems, and against state-of-the-art approaches. The results show that reformulation approaches are competitive.

关键词： Tractable Probabilistic Models Sum-Product Networks Probabilistic Circuits Maximuma-Posteriori Inference mixed-integer linear programming Bayesian Networks

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On two-machine Flow Shop Scheduling Problem with disjoint setups 15

On two-machine Flow Shop Scheduling Problem with disjoint se...

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IEEE 15th International Conference of System of Systems Engineering (SoSE)

作者： Gnatowski, Andrzej Rudy, Jaroslaw Idzikowski, Radoslaw Wroclaw Univ Sci & Technol Dept Control Syst & Mechatron Wroclaw Poland Wroclaw Univ Sci & Technol Dept Comp Engn Wroclaw Poland

ISBN: (数字)9781728180502

ISBN: (纸本)9781728180502

We consider a variant of the Permutational Flow Shop Scheduling Problem with disjoint setups and makespan minimization. A mathematical model of the problem is presented and several properties on the feasibility of solutions are formulated. An elimination property is proposed, allowing to disregard up to 75% of the solution space. We also show an interesting connection between the number of feasible solutions and Catalan numbers. To solve the problem for a fixed job order, we propose two algorithms: mixed-integer linear programming exact formulation and a greedy heuristic algorithm. An empirical evaluation shows a promising efficiency of the heuristic, providing an optimal or near-optimal solutions for problem instances with low setup and job times time deviation.

关键词： discrete optimization disjoint setups flow shop problem mixed-integer linear programming

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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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Economic design framework of microalga-based biodiesel supply chains under uncertainties in CO2 emission and diesel demand

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COMPUTERS & CHEMICAL ENGINEERING 2021年 155卷

作者： Ahn, Yuchan Kim, Junghwan Korea Inst Ind Technol Green Mat & Proc R&D Grp Ulsan 44413 South Korea Keimyung Univ Dept Chem Engn Daegu 42601 South Korea

To determine the optimal configuration of supply chain network for microalga-based biodiesel (MADSCN) considering uncertain values which influence the optimal strategies of microalga-based biodiesel production, we develop a two-stage stochastic model that considers the CO2 supply and biodiesel demand as uncertain parameters, which simultaneously influence the potential locations for the construction of carbon-capture-and-storage systems to supply CO2 as feedstock, and of biorefineries to produce and distribute the biodiesel as to regions in which it is in demand. The proposed framework is formulated using mixed-integer linear programming to identify the optimal MADSCN configuration that minimizes the overall cost of microalga-based biodiesel production. Variation of both uncertain factors in the stochastic model affect the configurations and operating strategies of MADSCN compared to the deterministic model that fixes the factors. The optimal superstructure of MADSCN was affected more significantly by uncertain biodiesel demand than by uncertain CO2 emission. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： Microalgae mixed-integer linear programming Optimization Uncertainty Carbon dioxide Biodiesel

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Integration of Building Inertia Thermal Energy Storage into Smart Grid Control 3

Integration of Building Inertia Thermal Energy Storage into ...

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3rd International Conference on Smart Energy Systems and Technologies (SEST)

作者： Heinekamp, Jan F. Voss, Marcus Krutzsch, Sabine Sick, Friedrich Strunz, Kai Albayrak, Sahin Tech Univ Berlin Sustainable Elect Networks & Sources Energy SENSE Berlin Germany Tech Univ Berlin Distributed Artificial Intelligence Lab DAI Lab Berlin Germany Hsch Tech & Wirtschaft HTW Berlin Sch Engn Energy & Informat Berlin Germany

ISBN: (纸本)9781728147017

A building's structural mass does provide inherent thermal storage capabilities. Through the application of sector coupling energy resources, e.g., combined heat and power plants or power-to-heat, the building mass can provide flexibility to the electric power system. Within this work, a mathematical model of a building inertia thermal energy storage is proposed to allow integration into optimized smart grid control for real-world applications. It is shown how necessary model parameters can be obtained using multiple linear regression based on measurable building data. Thereby, the nonlinear characteristics of external conditions influencing the storage operation, such as outside temperature and solar irradiation, are transformed into weighted linear factors. The possibility of the ceiling surface temperature serving as a proxy for measuring the current state of energy is demonstrated. This allows real-world implementation using minimal additional hardware. Within a case study, the potential of using building storage in optimized smart grid control with sector coupling assets of a Virtual Power Plant is analyzed. The utilization of building storage compared to conventionally used hot water tanks is presented, and cost benefits for both Virtual Power Plant and building operator are shown.

关键词： Building Inertia Thermal Energy Storage Energy Management mixed-integer linear programming Sector Coupling Smart Grid Virtual Power Plant

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Decision Guidance on Software Feature Selection to Maximize the Benefit to Organizational Processes 22

Decision Guidance on Software Feature Selection to Maximize ...

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22nd International Conference on Enterprise Information Systems (ICEIS)

作者： Boccanera, Fernando Brodsky, Alexander George Mason Univ Comp Sci Dept Fairfax VA 22030 USA

ISBN: (纸本)9789897584237

Many software development projects fail because they do not deliver sufficient business benefit to justify the investment. Existing approaches to estimating business benefit of software adopt unrealistic assumptions which produce imprecise results. This paper focuses on removing this limitation for software projects that automate business workflow processes. For this class of projects, the paper proposes a new approach and a decision-guidance framework to select and schedule software features over a sequence of software releases as to maximize the net present value of the combined cash flow of software development as well as the improved organizational business workflow. The uniqueness of the proposed approach is in precise modelling of the business workflow and the savings achieved by deploying new software functionality.

关键词： Decision Guidance System Decision Support System Software Release Schedule Optimization mixed-integer linear programming

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A Multi-objective Emergency Scheduling Model for Forest Fires with Priority Areas

A Multi-objective Emergency Scheduling Model for Forest Fire...

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IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

作者： Wang, Lubing Wu, Peng Chu, Feng Fuzhou Univ Sch Econ & Management Fuzhou Peoples R China

ISBN: (纸本)9781538672204

With global warming, the probability of forest fires is increasing greatly, and more research attention has been paid to forest fires emergency scheduling. This paper addresses a resource-constrained emergency scheduling problem for dealing with forest fires with priority disaster areas. It aims to determine an optimal fire-fighting scheduling plan for multiple forest fire points to minimize the total transport distance and the fire extinguishing rescue time, simultaneously. To effectively solve this problem, we formulate a multi-objective mixed-integer linear programming model, and an iterative and fuzzy logic decision-making based on epsilon-constraint method is designed to obtain a preferred emergency scheduling scheme. Finally, the computational results on benchmark and randomly generate test instances verify the effectiveness and feasibility of the proposed model and method.

关键词： Forest fires emergency scheduling mixed-integer linear programming multi-objective optimization

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Optimal Energy Bidding for the Italian Balancing Service by EVs: a Sensitivity Analysis

Optimal Energy Bidding for the Italian Balancing Service by ...

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20th IEEE International Conference on Environment and Electrical Engineering (EEEIC) / 4th IEEE Industrial and Commercial Power Systems Europe Conference (I and CPS Europe)

作者： Canevese, Silvia Gatti, Antonio Ric Sul Sistema Energet RSE SpA Energy Syst Dev Dept Milan Italy

ISBN: (纸本)9781728174556

Thanks to their flexible power response, Battery Energy Storage Systems (BESSs) have gained attention as possible new suppliers of Ancillary Services (ASs) to the power system. BESSs in Electric Vehicles (EVs), in particular, could supply ASs during EV recharge. Here, some evaluations of how profitable the Balancing Service (BS) could be for the EV owner are carried out: an optimization problem is set up, returning the energy to be bid for the service on the Italian Balancing Market (BM) to minimize the cost of recharging the desired amount of energy;the bid prices and the bid acceptance/rejection status are assumed as known input values;hourly and quarter-of-an-hourly bids are considered, based, respectively, on the current product resolution on the market and on its foreseen evolution. The simulated case studies are for overnight recharge and involve two kinds of end user, with different values of the maximum recharging power, of the energy to be recharged and of taxes on energy exchanges;both one-directional (V1G) and bi-directional (V2G) recharge are analyzed. Overall annual results show that, if the energy to be recharged is small, moderate savings in the EV electricity bill can be achieved with respect to not supplying the BS;high savings can be achieved if such energy is larger (as in case of a non-domestic user) and V2G recharge is adopted, so that energy selling for the BS is enhanced.

关键词： Balancing Market balancing service battery cycling aging Electric Vehicle cost optimization mixed-integer linear programming V1G V2G

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MODELING REAL-TIME APPLICATION PROCESSOR SCHEDULING FOR FOG COMPUTING

MODELING REAL-TIME APPLICATION PROCESSOR SCHEDULING FOR FOG ...

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Annual Modeling and Simulation Conference (ANNSIM)

作者： Sharifi, Mani Abhari, Abdolreza Taghipour, Sharareh Ryerson Univ Dept Comp Sci Distributed Syst & Multimedia Proc Lab DSMP Lab Toronto ON Canada Ryerson Univ Mech & Ind Engn Dept Reliabil Risk & Maintenance Res Lab RRMR Lab Toronto ON Canada

ISBN: (纸本)9781565553750

This paper presents a model for fog computing by considering the processing nodes of both edge and cloud devices for real-time applications. We use mixed-integer linear programming (MILP) mathematical model to find the optimal task scheduling and compare it with the performance of the FIFO online scheduling strategies for a fog computing sample that consists of n edge processors (EP) and one cloud processor. The MILP mathematical dispatching strategy optimizes the jobs' scheduling on the EPs and cloud processors. Finally, solving the model and simulation of more scenarios is presented to compare the performance of the optimized job scheduling model with two FIFO scenarios for a real-time application on fog computing. The results show that the FIFO process scheduling strategy's performance is between 62.71% to 95.10% of the optimal jobs' scheduling proposed in this work for the real-time fog computing-based applications.

关键词： Fog computing Real-time processors' scheduling Dispatching strategies mixed-integer linear programming Performance measurement

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Optimal Scheduling of a Virtual Power Plant with Demand Response in Short-Term Electricity Market 20

Optimal Scheduling of a Virtual Power Plant with Demand Resp...

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20th IEEE Mediterranean Eletrotechnical Conference (IEEE MELECON)

作者： Rashidizadeh-Kermani, Homa Shafie-Khah, Miadreza Osorio, Gerardo J. Catalao, Joao P. S. Vahedipour-Dahraie, Mostafa Univ Birjand Birjand Iran Univ Vaasa Vaasa Finland Univ Beira Interior C MAST Covilha Portugal Univ Porto Fac Engn Porto Portugal INESC TEC Porto Portugal

ISBN: (数字)9781728152004

ISBN: (纸本)9781728152004

This paper presents an optimal bidding and offering strategy for a virtual power plant (VPP), which participates in day-ahead (DA) and balancing markets. The VPP comprises distributed energy resources, plug-in electric vehicles (PEVs) and flexible demands. The objective of the problem is maximizing the VPP's profit while demand response (DR) providers who aggregated the loads try to supply the required demand under their jurisdiction with minimum costs. The proposed optimization problem is formulated as a bi-level stochastic scheduling programming to address uncertainties in DA and balancing electricity prices, renewable energy source's (RES) and DR relationship. Simulation results demonstrate the applicability and effectiveness of the proposed model to any real markets. Also, numerical results show that the flexibility of responsive loads and PEVs can improve the VPP operator's energy management and increase its expected profit.

关键词： Demand response Electricity market mixed-integer linear programming Virtual power plants Stochastic scheduling

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