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Personalized Model Identification for Glucose Dynamics From Clinical Data With Incomplete Inputs

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2025年第6期72卷 2001-2012页

作者： Ozaslan, Basak Aiello, Eleonora M. Fushimi, Emilia Doyle III, Francis J. Dassau, Eyal Harvard Univ Harvard John A Paulson Sch Engn & Appl Sci Boston MA 02134 USA Insulet Corp Acton MA USA Univ Trento Trento Italy Natl Univ La Plata UNLP Natl Sci & Tech Res Council CONICET La Plata Argentina Brown Univ Providence RI USA Eli Lilly & Co Indianapolis IN 46285 USA

Objective: A common challenge in model identification with clinical data is incomplete and sometimes imprecise information. In this work, we provide a method to reconstruct the corrupted input data in a clinical dataset and, jointly identify the person-specific parameters of a metabolic model describing meal-insulin-glucose-dynamics for people with type 1 diabetes (T1D). Method: The proposed method is an algorithm that iterates between nonlinear least-squares and mixed-integer quadratic programming to optimize model parameters in conjunction with sparse corrections to the input data. In order to handle long stretches of data, the optimization problem is designed to be i) computationally tractable, and ii) robust against the potential presence of significant inaccuracies corrupting a data portion. Moreover, since the pattern of the inaccuracies is specific to each person, we propose a personalized hyperparameter tuning approach. The method is applied on clinical data from 13 people with T1D. Identified model performance is compared to the performance of model identified with standard least squares (LS) method. Results: Compared to LS, identifying corrections in conjunction with model parameters on training data lead to an improvement in the model prediction capabilities on unseen data with an average 2.2% improvement in MARD for two-hour prediction horizon (p-value = 0.0006). Conclusions: The proposed method is effective in model identification for clinical data with unknown inaccuracies in the inputs. Significance: Personalized models with high accuracy can inform treatment decisions and lead to better glucose control outcomes in people with T1D.

关键词： Data models Glucose Insulin Predictive models Mathematical models Training data Standards Diabetes Adaptation models Postal services Incomplete data mixed-integer optimization model identification type 1 diabetes

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Energy-Efficient Cooperative Routing in Wireless Sensor Networks: A mixed-integer optimization Framework and Explicit Solution

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IEEE TRANSACTIONS ON COMMUNICATIONS 2013年第8期61卷 3424-3437页

作者： Habibi, Jalal Ghrayeb, Ali Aghdam, Amir G. Concordia Univ Dept Elect & Comp Engn Montreal PQ H3G 1M8 Canada

This paper presents an optimization framework for a wireless sensor network whereby, in a given route, the optimal relay selection and power allocation are performed subject to signal-to-noise ratio constraints. The proposed approach determines whether a direct transmission is preferred for a given configuration of nodes, or a cooperative transmission. In the latter case, for each node, data transmission to the destination node is performed in two consecutive phases: broadcasting and relaying. The proposed strategy provides the best set of relays, the optimal broadcasting power and the optimal power values for the cooperative transmission phase. Once the minimum-energy transmission policy is obtained, the optimal routes from every node to a sink node are built-up using cooperative transmission blocks. We also present a low-complexity implementation approach of the proposed framework and provide an explicit solution to the optimization problem at hand by invoking the theory of multi-parametric programming. This technique provides the optimal solution as a function of measurable parameters in an off-line manner, and hence the on-line computational tasks are reduced to finding the parameters and evaluating simple functions. The proposed efficient approach has many potential applications in real-world problems and, to the best of the authors' knowledge, it has not been applied to communication problems before. Simulations are presented to demonstrate the efficacy of the approach.

关键词： Wireless sensor networks cooperative routing energy efficiency mixed-integer optimization relay selection multi-parametric programming explicit solution

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A Framework for the Automated and Optimal Design of Vertical Lift Modules

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2025年第6期55卷 4001-4014页

作者： Mignoni, Nicola Scarabaggio, Paolo Carli, Raffaele Dotoli, Mariagrazia Polytech Bari Dept Elect & Informat Engn I-70126 Bari Italy

To this day, tasks like planning and managing warehouses remain complex. Automated storage and retrieval systems have enhanced warehousing efficiency, yet designing them optimally is still challenging, despite their importance for the efficient operation of the warehouse. This article aims to present a novel framework for automating the optimal design of vertical lift modules (VLMs), focusing on tray types, quantities, and item-tray sector assignments, based on a specified inventory list. The approach accounts for VLM's physical, manufacturing, and ergonomic constraints to ensure a manufacturable system design. To manage computational complexity, the size of the mixed-integer problem is reduced through an exact clustering of items, sectors, and layouts. The proposed framework is tested through numerical simulations using real data from an Italian VLM manufacturer.

关键词： Warehousing Manufacturing Ergonomics Throughput Layout Economics Numerical simulation Hands Focusing Systematic literature review Automatic storage and retrieval systems combinatorial assignment logistics mixed-integer optimization vertical lift modules (VLMs)

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Identification of piecewise constant parameters in nonlinear models

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EUROPEAN JOURNAL OF CONTROL 2025年 81卷

作者： Calafiore, Giuseppe Carlo Fracastoro, Giulia Zino, Lorenzo Politecn Torino Dept Elect & Telecommun Corso Duca Abruzzi 24 I-10129 Turin Italy

Many real-world dynamical systems are characterized by different temporal phases, with sudden changes in the values of the system's parameters in correspondence to variations from one phase to another. Identifying the system's parameters and these switching instants from potentially noisy measurements of the system's states is a relevant problem in several applications. We here propose a novel approach for estimating the time-varying parameters of abroad class of nonlinear dynamical systems from noisy state measurements. We formulate the problem as a mixed-integer quadratic program (MIQP) including a sparsity constraint to enforce the piecewise constant nature of the parameters. Then, we develop a convex relaxation of the problem in the form of a quadratic program (QP). The solution of the relaxed convex QP and/or the sub-optimal solutions of the MIQP returned by the MIQP solvers provide us with computationally-efficient approximations that can be used effectively in those large-dimensional cases in which the solution of the original MIQP is difficult to obtain. After validating our approach in a controlled experiment, we demonstrate its potential on two real-world case studies regarding marketing and epidemiological applications.

关键词： System identification Nonlinear dynamics mixed-integer optimization Convex programming

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Learning-Based Model Predictive Control for the Energy Management of Hybrid Electric Vehicles Including Driving Mode Decisions

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第4期73卷 5113-5127页

作者： Machacek, David Theodor van Dooren, Stijn Huber, Thomas Onder, Christopher Harald Swiss Fed Inst Technol Inst Dynam Syst & Control IDSC CH-8092 Zurich Switzerland Robert Bosch GmbH Powertrain Solut & Syst Engn Grp D-70442 Stuttgart Germany

This paper presents an online-capable controller for the energy management system of a parallel hybrid electric vehicle based on model predictive control. Its task is to minimize the vehicle's fuel consumption along a predicted driving mission by calculating the distribution of the driver's power request between the electrical and the combustive part of the powertrain, and by choosing the driving mode, which depends on the vehicle's clutch state. The inclusion of the clutch state in a model predictive control structure is not trivial because the underlying optimization problem becomes a mixed-integer program as a consequence. Using Pontryagin's Minimum Principle and a simplified vehicle model, it is possible to prove that a drive cycle-dependent critical power request P-crit exists, which uniquely separates the optimal driving mode. Based on this result, a learning algorithm is proposed to determine P-crit during the operation of the vehicle. The learning algorithm is incorporated into a multi-level controller structure and the working principle of the resulting multi-level learning-based model predictive controller is analyzed in detail using three realistic driving missions. A comparison to the solution obtained by Dynamic Programming reveals that the proposed controller achieves close-to-optimal performance.

关键词： Learning control systems predictive control mixed-integer optimization driving modes optimal control

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Frontiers in Operations: Equitable Data-Driven Facility Location and Resource Allocation to Fight the Opioid Epidemic

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M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT 2024年第4期26卷 1229-1244页

作者： Luo, Joyce Stellato, Bartolomeo MIT Operat Res Ctr Cambridge MA 02139 USA Princeton Univ Dept Operat Res & Financial Engn Princeton NJ 08544 USA

Problem definition: The opioid epidemic is a crisis that has plagued the United States for decades. One central issue of the epidemic is inequitable access to treatment for opioid use disorder (OUD), which puts certain populations at a higher risk of opioid overdose. Methodology/results: We integrate a predictive dynamical model and a prescriptive optimization problem to compute high -quality opioid treatment facility and treatment budget allocations for each U.S. state. Our predictive model is a differential equation -based epidemiological model that captures the dynamics of the opioid epidemic. We use a process inspired by neural ordinary differential equations to fit this model to opioid epidemic data for each state and obtain estimates for unknown parameters in the model. We then incorporate this epidemiological model into a corresponding mixed -integer optimization problem (MIP) that aims to minimize the number of opioid overdose deaths and the number of people with OUD. We develop strong relaxations based on McCormick envelopes to efficiently compute approximate solutions to our MIPs that have a mean optimality gap of 3.99%. Our method provides socioeconomically equitable solutions, as it incentivizes investments in areas with higher social vulnerability (from the U.S. Centers for Disease Control's Social Vulnerability Index) and opioid prescribing rates. On average, when allowing for overbudget solutions, our approach decreases the number of people with OUD by 9.03 +/- 1.772%, increases the number of people in treatment by 88.75 +/- 26.223%, and decreases the number of opioid-related deaths by 0.58 +/- 0.111% after 2 years compared with the baseline epidemiological model's predictions. Managerial implications: Our solutions show that policymakers should target adding treatment facilities to counties that have significantly fewer facilities than their population share and are more socially vulnerable. Furthermore, we demonstrate that our optimization approach,

关键词： mixed-integer optimization epidemiological model resource allocation opioid epidemic

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Equivariant Deep Learning of mixed-integer Optimal Control Solutions for Vehicle Decision Making and Motion Planning

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IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY 2024年 1-15页

作者： Reiter, Rudolf Quirynen, Rien Diehl, Moritz Di Cairano, Stefano Univ Freiburg Dept Microsyst Engn IMTEK D-79110 Freiburg Germany Mitsubishi Elect Res Labs MERL Cambridge MA 02139 USA Univ Freiburg Dept Math D-79110 Freiburg Germany Mitsubishi Elect Res Labs Cambridge MA USA

mixed-integer quadratic programs (MIQPs) are a versatile way of formulating vehicle decision making (DM) and motion planning problems, where the prediction model is a hybrid dynamical system that involves both discrete and continuous decision variables. However, even the most advanced MIQP solvers can hardly account for the challenging requirements of automotive-embedded platforms. Thus, we use machine learning to simplify and hence speed up optimization. Our work builds on recent ideas for solving MIQPs in real time by training a neural network (NN) to predict the optimal values of integer variables and solving the remaining problem by online quadratic programming. Specifically, we propose a recurrent permutation equivariant deep set (REDS) that is particularly suited for imitating MIQPs that involve many obstacles, which is often the major source of computational burden in motion planning problems. Our framework comprises also a feasibility projector (FP) that corrects infeasible predictions of integer variables and considerably increases the likelihood of computing a collision-free trajectory. We evaluate the performance, safety, and real-time feasibility of DM for autonomous driving using the proposed approach on realistic multilane traffic scenarios with interactive agents in SUMO simulations.

关键词： Planning Artificial neural networks Deep learning Real-time systems Trajectory Roads Predictive models Geometric deep learning mixed-integer optimization motion planning

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Continuous Switch Model and Heuristics for mixed-integer Nonlinear Problems in Power Systems

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IEEE TRANSACTIONS ON POWER SYSTEMS 2024年第4期39卷 5780-5791页

作者： Agarwal, Aayushya Pandey, Amritanshu Pileggi, Larry Carnegie Mellon Univ Dept Elect & Comp Engn Pittsburgh PA 15213 USA Univ Vermont Elect & Biomed Engn Dept Burlington VT 05405 USA

Many power systems operation and planning computations (e.g., transmission and generation switching and placement) solve a mixed-integer nonlinear problem (MINLP) with binary variables representing the decision to connect devices to the grid. Binary variables with nonlinear AC network constraints make this problem NP-hard. For large real-world networks, obtaining an AC feasible optimum solution for these problems is computationally challenging and often unattainable with state-of-the-art tools today. In this work, we map the MINLP decision problem into a set of equivalent circuits by representing binary variables with a circuit-based continuous switch model. We characterize the continuous switch model by a controlled nonlinear impedance that more closely mimics the physical behavior of a real-world switch. This mapping effectively transforms the MINLP problem into an NLP problem. We mathematically show that this transformation is a tight relaxation of the MINLP problem. For fast and robust convergence, we develop physics-driven homotopy and Newton-Raphson damping methods. To validate this approach, we empirically show robust convergences for large, realistic systems ($>$70,000 buses) in a practical wall-clock time to an AC-feasible optimum. We compare our results and show improvement over industry-standard tools and other binary relaxation methods.

关键词： Switches Integrated circuit modeling Switching circuits optimization Power systems Transforms Power transmission lines mixed-integer optimization optimization continuous switch unit commitment transmission line switching

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Integration of support vector machines and mean-variance optimization for capital allocation

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2025年第3期322卷 1045-1058页

作者： Islip, David Kwon, Roy H. Kim, Seongmoon Univ Toronto Dept Mech & Ind Engn 5 Kings Coll Rd Toronto ON M5S 3G8 Canada Yonsei Univ Sch Business 50 Yonsei Ro Seoul South Korea

This work introduces a novel methodology for portfolio optimization that is the first to integrate support vector machines (SVMs) with cardinality-constrained mean-variance optimization. We propose augmenting cardinality-constrained mean-variance optimization with a preference for portfolios with the property that a low-dimensional hyperplane can separate assets eligible for investment from those ineligible. We present convex mixed-integer quadratic programming models that jointly select a portfolio and a separating hyperplane. This joint selection optimizes a tradeoff between risk-adjusted returns, hyperplane margin, and classification errors made by the hyperplane. The models are amenable to standard commercial branch-and-bound solvers, requiring no custom implementation. We discuss the properties of the proposed optimization models and draw connections between existing portfolio optimization and SVM approaches. We develop a parameter selection strategy to address the selection of big-Ms and provide a financial interpretation of the proposed approach's parameters. The parameter strategy yields valid big-M values, ensures the risk of the resulting portfolio is within a factor of the lowest possible risk, and produces informative hyperplanes for practitioners. The mathematical programming models and the associated parameter selection strategy are amenable to financial backtesting. The models are evaluated in-sample and out-of-sample on two distinct datasets in a rolling horizon backtesting framework. The portfolios resulting from the proposed approach display improved out-of-sample risk-adjusted returns compared to cardinality-constrained mean-variance optimization.

关键词： mixed-integer optimization Portfolio optimization Support vector machine

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Supervised Feature Selection via Collaborative Neurodynamic optimization

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第5期35卷 6878-6892页

作者： Wang, Yadi Wang, Jun Pal, Nikhil R. Henan Univ Sch Comp & Informat Engn Henan Key Lab Big Data Anal & Proc Kaifeng 475004 Peoples R China Henan Univ Sch Comp & Informat Engn Inst Data & Knowledge Engn Kaifeng 475004 Peoples R China City Univ Hong Kong Dept Comp Sci Kowloon Hong Kong Peoples R China City Univ Hong Kong Sch Data Sci Kowloon Hong Kong Peoples R China Indian Stat Inst Ctr Artificial Intelligence & Machine Learning Kolkata 700108 India Indian Stat Inst Elect & Commun Sci Unit Kolkata 700108 India

As a crucial part of machine learning and pattern recognition, feature selection aims at selecting a subset of the most informative features from the set of all available features. In this article, supervised feature selection is at first formulated as a mixed-integer optimization problem with an objective function of weighted feature redundancy and relevancy subject to a cardinality constraint on the number of selected features. It is equivalently reformulated as a bound-constrained mixed-integer optimization problem by augmenting the objective function with a penalty function for realizing the cardinality constraint. With additional bilinear and linear equality constraints for realizing the integrality constraints, it is further reformulated as a bound-constrained biconvex optimization problem with two more penalty terms. Two collaborative neurodynamic optimization (CNO) approaches are proposed for solving the formulated and reformulated feature selection problems. One of the proposed CNO approaches uses a population of discrete-time recurrent neural networks (RNNs), and the other use a pair of continuous-time projection networks operating concurrently on two timescales. Experimental results on 13 benchmark datasets are elaborated to substantiate the superiority of the CNO approaches to several mainstream methods in terms of average classification accuracy with three commonly used classifiers.

关键词： Feature extraction optimization Redundancy Neurodynamics Recurrent neural networks Collaboration Mutual information Biconvex optimization collaborative neurodynamic optimization (CNO) feature selection mixed-integer optimization

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