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Nonlinear Hybrid Planning with Deep Net Learned Transition Models and Mixed-Integer Linear Programming

Buser Say, Ga Wu, Yu Qing Zhou, Scott Sanner
2017 Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence  
a direct compilation of the deep network transition model to a Mixed-Integer Linear Program (MILP) encoding in a planner we call Hybrid Deep MILP Planning (HD-MILP-PLAN).  ...  But there remains one major problem for the task of control -- how can we plan with deep network learned transition models without resorting to Monte Carlo Tree Search and other black-box transition model  ...  Given a planning horizon H, the second stage of HD-MILP-PLAN finds an optimal plan to the learned-model Π using a Mixed Integer Linear Program (MILP).  ... 
doi:10.24963/ijcai.2017/104 dblp:conf/ijcai/SayWZS17 fatcat:oyjpdhsrx5hypm7vjodt2yewnu

Strong mixed-integer programming formulations for trained neural networks [article]

Ross Anderson, Joey Huchette, Christian Tjandraatmadja, Juan Pablo Vielma
2019 arXiv   pre-print
We present an ideal mixed-integer programming (MIP) formulation for a rectified linear unit (ReLU) appearing in a trained neural network.  ...  We contrast it with an ideal "extended" formulation with a linear number of additional continuous variables, derived through standard techniques.  ...  The authors gratefully acknowledge Yeesian Ng and Ondřej Sýkora for many discussions on the topic of this paper, and for their work on the development of the tf.opt package used in the computational experiments  ... 
arXiv:1811.08359v2 fatcat:s3ecgsxvujapxgcfxc4sqq4bfa

Planning and Operations Research (Dagstuhl Seminar 18071)

J. Christopher Beck, Daniele Magazzeni, Gabriele Röger, Willem-Jan Van Hoeve, Michael Wagner
2018 Dagstuhl Reports  
This report documents the program and the outcomes of Dagstuhl Seminar 18071 "Planning and Operations Research".  ...  The seminar brought together researchers in the areas of Artificial Intelligence (AI) Planning, Constraint Programming, and Operations Research.  ...  Buser Say, Ga Wu, Yu Qing Zhou, and Scott Sanner. Nonlinear hybrid planning with deep net learned transition models and mixed-integer linear programming.  ... 
doi:10.4230/dagrep.8.2.26 dblp:journals/dagstuhl-reports/BeckMRH18 fatcat:lavt5jfujfarfmtwrpbbxan2oq

Recent Techniques Used in Home Energy Management Systems: A Review

Isaías Gomes, Karol Bot, Maria da Graça Ruano, António Ruano
2022 Energies  
In addition, the techniques are divided into four broad categories: traditional techniques, model predictive control, heuristics and metaheuristics, and other techniques.  ...  Power systems are going through a transition period. Consumers want more active participation in electric system management, namely assuming the role of producers–consumers, prosumers in short.  ...  The problems on which HEMS are based can generally be formulated as linear programming problems, non-linear programming, and its variants, mixed-integer linear programming (MILP), and nonlinear integer  ... 
doi:10.3390/en15082866 fatcat:nrhf6bc6frdttbnerk6lrwlcbe

2019 Index IEEE Transactions on Systems, Man, and Cybernetics: Systems Vol. 49

2019 IEEE Transactions on Systems, Man & Cybernetics. Systems  
., +, TSMC Jan. 2019 206-215 Integer programming Fault Identification of Discrete Event Systems Modeled by Petri Nets With Unobservable Transitions.  ...  ., +, TSMC April 2019 687-696 Linear programming Fault Identification of Discrete Event Systems Modeled by Petri Nets With Unobservable Transitions.  ... 
doi:10.1109/tsmc.2019.2956665 fatcat:xhplbanlyne7nl7gp2pbrd62oi

2020 Index IEEE Transactions on Power Systems Vol. 35

2020 IEEE Transactions on Power Systems  
., and Preece, R., Assessing the Impact of VSC-HVDC on the Interdependence of Power System Dynamic Performance in Uncertain Mixed AC/DC Systems; TPWRS Jan. 2020 63-74 Moeini, A., see Rimorov, D., TPWRS  ...  Linearizing Power Flow Model: A Hybrid Physical Model-Driven and Data-Driven Approach.  ...  ., +, TPWRS Sept. 2020 3953-3960 Convolutional neural nets A Unified Online Deep Learning Prediction Model for Small Signal and Transient Stability.  ... 
doi:10.1109/tpwrs.2020.3040894 fatcat:jjw2rnzr2re6fejvariekzr5uy

2020 Index IEEE Transactions on Intelligent Transportation Systems Vol. 21

2020 IEEE transactions on intelligent transportation systems (Print)  
., +, TITS June 2020 2557-2570 Energy-Efficient Train Scheduling and Rolling Stock Circulation Planning in a Metro Line: A Linear Programming Approach.  ...  ., +, TITS Jan. 2020 410-420 Vehicle Turning Behavior Modeling at Conflicting Areas of Mixed-Flow Intersections Based on Deep Learning.  ... 
doi:10.1109/tits.2020.3048827 fatcat:ab6he3jkfjboxg7wa6pagbggs4

The promise of artificial intelligence in chemical engineering: Is it here, finally?

Venkat Venkatasubramanian
2018 AIChE Journal  
Paulson School of Engineering and  ...  This article is based on my Roger Sargent Lecture at Imperial College of Science, Technology, and Medicine (2016), the George Stephanopoulos Retirement Symposium Lecture at MIT (2017), and the Park and  ...  cycle occurred in optimization as well, for mixed-integer linear programming (MILP) and mixed-integer nonlinear programming technologies, and for MPC.  ... 
doi:10.1002/aic.16489 fatcat:a2rbapguynbwjbaup4fnia4h4a

A Review of Models and Algorithms for Surface-Underground Mining Options and Transitions Optimization: Some Lessons Learnt and the Way Forward

Bright Oppong Afum, Eugene Ben-Awuah
2021 Mining  
Understanding the current tools and methodologies used in the mining industry for surface and underground mining options and transitions planning are essential to dealing with complex and deep-seated deposits  ...  In this study, extensive literature review and a gap analysis matrix are used to identify the limitations and opportunities for further research in surface-underground mining options and transitions optimization  ...  Used Model/Algorithm Modification of maximum graph closure method Stochastic Integer Model Mixed Integer Linear Pro- gramming (MILP) Mixed Integer Linear Programming (MILP) Dynamic  ... 
doi:10.3390/mining1010008 fatcat:2q6gl5kelnd6hdnvkb2owk4pk4

Challenges and Opportunities in Dock-Based Bike-Sharing Rebalancing: A Systematic Review

Carlos M. Vallez, Mario Castro, David Contreras
2021 Sustainability  
We also include an exhaustive table that will assist researchers from different disciplines to address the open challenges in the field and to transition towards more sustainable cities.  ...  Although the first attempts to implement a bike-sharing public service date back to 1965 (Amsterdam), their widespread use arrived with the millennium becoming a vibrant research area whose activity has  ...  S. 2019 2019 Asia N/A Heuristics/ metaheuristics Constrained nonlinear mixed-integer programming model.  ... 
doi:10.3390/su13041829 fatcat:nxsbtihplrcndfrk5jnm5xwyn4

2020 Index IEEE Transactions on Industrial Informatics Vol. 16

2020 IEEE Transactions on Industrial Informatics  
Gao, F., Data-Driven Two-Dimensional Deep Correlated Representation Learning for Nonlinear Batch Process Monitoring; TII April 2020 2839-2848 Jiang, S., see Li, Y., 1076-1085 Jiang, X., see Gong, K.,  ...  Sun, D., Motion Planning and Robust Control for the Endovascular Navigation of a Microrobot; TII July 2020 4557-4566 Meng, Q., see Cai, H., TII Jan. 2020 587-594 Meng, Q., see Dong, H., TII Dec. 2020  ...  ., +, TII June 2020 3807-3817 Linear programming A Model for Stochastic Planning of Distribution Network and Autonomous DG Units.  ... 
doi:10.1109/tii.2021.3053362 fatcat:blfvdtsc3fdstnk6qoaazskd3i

2020 Index IEEE Transactions on Automatic Control Vol. 65

2020 IEEE Transactions on Automatic Control  
., +, TAC Oct. 2020 4061-4074 Distributed Mixed-Integer Linear Programming via Cut Generation and Constraint Exchange.  ...  ., +, TAC Dec. 2020 5503-5509 Distributed Mixed-Integer Linear Programming via Cut Generation and Constraint Exchange.  ...  Linear programming A Decentralized Event-Based Approach for Robust Model Predictive Control.  ... 
doi:10.1109/tac.2020.3046985 fatcat:hfiqhyr7sffqtewdmcwzsrugva

A Mathematical Model for Multi-Region, Multi-Source, Multi-Period Generation Expansion Planning in Renewable Energy for Country-Wide Generation-Transmission Planning

Mohammadreza Taghizadeh-Yazdi, Abdolkarim Mohammadi-Balani
2020 Journal of Information Technology Management  
The ultimate purpose is to minimize the total cost by planning, including power plant construction and maintenance costs and transmission costs.  ...  The present study develops a mathematical model for optimal allocation of regional renewable energy to meet a country-wide demand and its other essential aspects.  ...  The authors developed a mixed-integer linear programming model in conjunction with a sampling scheme for the system to learn from historical data on how to regulate the dispatching module and parameters  ... 
doi:10.22059/jitm.2020.298258.2476 doaj:07f716d946994dffb452a7c8b774d872 fatcat:rncht43jbrbrjnirpxyf6lwvuy

A Comprehensive Review on Residential Demand Side Management Strategies in Smart Grid Environment

Sana Iqbal, Mohammad Sarfraz, Mohammad Ayyub, Mohd Tariq, Ripon K. Chakrabortty, Michael J. Ryan, Basem Alamri
2021 Sustainability  
The application of soft computing techniques such as Fuzzy Logic (FL), Artificial Neural Network (ANN), and Evolutionary Computation (EC) is discussed to deal with energy consumption minimization and scheduling  ...  (DSM) and HEMS altogether.  ...  Reference [141] transformed the Mixed-Integer Linear Programming (MILP) problem into a convex programming optimization one for flexible and efficient performance.  ... 
doi:10.3390/su13137170 fatcat:ncqdp7pk7ndq5ffhqmcd3glbym

Machine Learning Algorithms for Urban Land Use Planning: A Review

Vineet Chaturvedi, Walter T. de Vries
2021 Urban Science  
New technologies, such as artificial intelligence (AI) and machine learning (ML) have made it possible to model and predict the nonlinear aspects of urban land dynamics.  ...  Traditional analytical methods of studying the urban land use dynamics associated with urbanization are static and tend to rely on top-down approaches, such as linear and mathematical modeling.  ...  Deep learning algorithms work well with relatively large datasets with supporting infrastructure to train them in reasonable time.  ... 
doi:10.3390/urbansci5030068 fatcat:hozbv3rsq5a7ldfdizytqz2bny
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