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On hub location problems in geographically flexible networks

INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2022年第4期29卷 2226-2249页

作者： Blanco, Victor Puerto, Justo Univ Granada Inst Math Granada 18011 Spain Univ Seville Inst Math Seville 41012 Spain

In this paper, we propose an extension of the uncapacitated hub location problem where the potential positions of the hubs are not fixed in advance. Instead, they are allowed to belong to a region around an initial discrete set of nodes. We give a general framework in which the collection, transportation, and distribution costs are based on norm-based distances and the hub-activation setup costs depend not only on the location of the hub that are opened but also on the size of the region where they are placed. Two alternative mathematical programming formulations are proposed. The first one is a compact formulation while the second one involves a family of constraints of exponential size that we separate efficiently giving rise to a branch-and-cut algorithm. The results of an extensive computational experience are reported showing the advantages of each of the approaches.

关键词： hub location mixed integer nonlinear programming neighborhoods network design

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Solving convex MINLP optimization problems using a sequential cutting plane algorithm

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COMPUTATIONAL OPTIMIZATION AND APPLICATIONS 2006年第1期34卷 63-83页

作者： Still, C Westerlund, T Abo Akad Univ Dept Math FIN-20500 Turku Finland Abo Akad Univ Proc Design Lab FIN-20500 Turku Finland

In this article we look at a new algorithm for solving convex mixed integer nonlinear programming problems. The algorithm uses an integrated approach, where a branch and bound strategy is mixed with solving nonlinear programming problems at each node of the tree. The nonlinear programming problems, at each node, are not solved to optimality, rather one iteration step is taken at each node and then branching is applied. A Sequential Cutting Plane (SCP) algorithm is used for solving the nonlinear programming problems by solving a sequence of linear programming problems. The proposed algorithm generates explicit lower bounds for the nodes in the branch and bound tree, which is a significant improvement over previous algorithms based on QP techniques. Initial numerical results indicate that the described algorithm is a competitive alternative to other existing algorithms for these types of problems.

关键词： convex programming branch and bound cutting plane algorithms mixed integer nonlinear programming

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Derivative-free mixed binary necklace optimization for cyclic-symmetry optimal design problems

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OPTIMIZATION AND ENGINEERING 2023年第1期24卷 353-394页

作者： Tran, Thi Thoi Sinoquet, Delphine Da Veiga, Sebastien Mongeau, Marcel IFP Energies Nouvelles Rueil Malmaison France Safran Tech Magny Les Hameaux France ENAC Toulouse France

This paper presents an adapted trust-region method for computationally expensive black-box optimization problems with mixed binary variables that involve a cyclic symmetry property. mixed binary problems occur in several practical optimal design problems, e.g., aircraft engine turbines, mooring lines of offshore wind turbines, electric engine stators and rotors. The motivating application for this study is the optimal design of helicopter bladed disk turbomachines. The necklace concept is introduced to deal with the cyclic symmetry property, and to avoid costly black-box objective-function evaluations at equivalent solutions. An adapted distance is proposed for the discrete-space exploration step of the optimization method. A convergence analysis is presented for the trust-region derivative-free algorithm, DFOb-d(H), extended to the mixed-binary case and based on the Hamming distance. The convergence proof is extended to the new algorithm, DFOb-d(neck), which is based on the necklace distance. Computational comparison with state-of-the-art black-box optimization methods is performed on a set of analytical problems and on a simplified industrial application.

关键词： mixed integer nonlinear programming Black-box simulation Derivative-free optimization Trust-region method Cyclic symmetry Necklace distance

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A novel deep reinforcement learning based business model arrangement for Korean net-zero residential micro-grid considering whole stakeholders' interests

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ISA TRANSACTIONS 2023年 137卷 471-491页

作者： Tightiz, Lilia Yoo, Joon Gachon Univ Sch Comp 1342 Seongnam Daero Seongnam 13120 Gyeonggi Do South Korea

In this paper, we put forward a deep reinforcement learning (DRL) based energy management system (EMS) solution for a typical Korean net-zero residential micro-grid (NZR-MG). We model NZR-MG EMS to extract a profitable business model that respects whole stakeholders' interests and meets Korean power system regulations and specifications. We deployed the value-based DRL technique, dual deep Q-learning (DDQN), as a solution for our EMS problem since of its simplicity, stability in the learning process, and non-dependency on hyper-parameter selection compared to actor-critic methods. Due to the implementation of mixed-integer nonlinear programming (MINLP) to solve the reward function in this paper, DDQN, despite other DRL methods, provides precise, explicit, and meaningful rewards. In addition to encouraging the agent to choose profitable actions, this approach releases the proposed DRL-based method from the hindrance of redesigning the reward function experimentally in any future extension of the environment elements. Moreover, attaching transfer learning (TL) to the process of training DDQN agent defeat the MINLP imposed latency in training convergence. An extensive benchmark is proposed to test the superiority of the proposed method versus other DRL algorithms.& COPY;2022 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Business model Deep reinforcement learning Demand response Energy management system mixed integer nonlinear programming Net -zero building

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Discrete optimal power flow with prohibited zones, multiple-fuel options, and practical operational rules for control devices

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APPLIED ENERGY 2024年 358卷

作者： Alencar, Marina Valenta Silva, Diego Nunes da Nepomuceno, Leonardo Martins, Andre Christovao Pio Balbo, Antonio Roberto Soler, Edilaine Martins Univ Estadual Paulista UNESP Fac Engn Campus Bauru Sao Paulo Brazil Inst Fed Sao Paulo IFSP Campus Presidente Epitacio Sao Paulo Brazil Univ Estadual Paulista UNESP Fac Ciencias Campus Bauru Sao Paulo Brazil

Although the optimal power flow (OPF) problem has been extensively studied, solving realistic OPF models that accurately represent the operating behavior of power system components remains challenging. This paper proposes a novel model for the AC OPF problem, aiming to minimize the fuel costs of thermal units while taking into account valve -point loading effects (VPLE), prohibited operation zones (POZ), multiple fuel options (MFO), and operational rules associated with the discrete tap ratios of on -load tap changer (OLTC) transformers and with the discrete shunt susceptances of capacitor/reactor banks. These rules are represented using complementarity constraints. We propose a solution approach that integrates several strategies to address the non -smooth features of the objective function related to VPLE, the disjoint constraints and functions tied to POZ and MFO, the discrete characteristics of the reactive control variables, and the complementarity constraints governing operational rules linked to voltage control devices such as OLTC transformers and capacitor/reactor banks. The resulting optimization problem is designed to be compatible with commercial solver packages. Numerical tests on the IEEE 30, 118, and 300 -bus systems aim to examine the cumulative impact of these operational factors on the optimal solution. The solution strategy proposed has demonstrated its effectiveness in solving the proposed OPF problem within reasonable computation times.

关键词： Optimal power flow mixed integer nonlinear programming Complementarity constraints Disjoint constraints Branch-and-bound

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Learning Optimized Risk Scores

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JOURNAL OF MACHINE LEARNING RESEARCH 2019年第1期20卷 1-75页

作者： Ustun, Berk Rudin, Cynthia Harvard Univ Ctr Res Computat & Soc Cambridge MA 02138 USA Duke Univ Dept Stat Sci Dept Elect & Comp Engn Dept Comp Sci Durham NC 27706 USA

Risk scores are simple classification models that let users make quick risk predictions by adding and subtracting a few small numbers. These models are widely used in medicine and criminal justice, but are difficult to learn from data because they need to be calibrated, sparse, use small integer coefficients, and obey application-specific constraints. In this paper, we introduce a machine learning method to learn risk scores. We formulate the risk score problem as a mixed integer nonlinear program, and present a cutting plane algorithm to recover its optimal solution. We improve our algorithm with specialized techniques that generate feasible solutions, narrow the optimality gap, and reduce data-related computation. Our algorithm can train risk scores in a way that scales linearly in the number of samples in a dataset, and that allows practitioners to address application-specific constraints without parameter tuning or post-processing. We benchmark the performance of different methods to learn risk scores on publicly available datasets, comparing risk scores produced by our method to risk scores built using methods that are used in practice. We also discuss the practical benefits of our method through a real-world application where we build a customized risk score for ICU seizure prediction in collaboration with the Massachusetts General Hospital.

关键词： scoring systems classification calibration customization interpretability cutting plane methods discrete optimization mixed integer nonlinear programming

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Integration of Scheduling and Control for Batch Processes Using Multi-Parametric Model Predictive Control

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AICHE JOURNAL 2014年第9期60卷 3169-3183页

作者： Zhuge, Jinjun Ierapetritou, Marianthi G. Rutgers State Univ Dept Chem & Biochem Engn Piscataway NJ 08854 USA

Integration of scheduling and control results in mixed integer nonlinear programming (MINLP) which is computationally expensive. The online implementation of integrated scheduling and control requires repetitively solving the resulting MINLP at each time interval. (Zhuge and Ierapetritou, Ind Eng Chem Res. 2012;51: 8550-8565) To address the online computation burden, we incorporare multi-parametric Model Predictive Control (mp-MPC) in the integration of scheduling and control. The proposed methodology involves the development of an integrated model using continuous-time event-point formulation for the scheduling level and the derived constraints from explicit MPC for the control level. Results of case studies of batch processes prove that the proposed approach guarantees efficient computation and thus facilitates the online implementation. (C) 2014 American Institute of Chemical Engineers

关键词： integration of scheduling and control event-point scheduling formulation multi-parametric Model Predictive Control mixed integer nonlinear programming batch processes

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An intelligent resource allocation strategy with slicing and auction for private edge cloud systems

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2024年 160卷 879-889页

作者： Peng, Yuhuai Wang, Jing Ye, Xiongang Khan, Fazlullah Bashir, Ali Kashif Alshawi, Bandar Liu, Lei Omar, Marwan Northeastern Univ Sch Comp Sci & Engn Shenyang 110819 Peoples R China Univ Nottingham Ningbo China Fac Sci & Engn Sch Comp Sci Ningbo 315104 Zhejiang Peoples R China Manchester Metropolitan Univ Dept Comp Networks & Cybersecur Manchester England Lebanese Amer Univ Dept Comp Sci & Math Beirut Lebanon Woxsen Univ Woxsen Sch Business Hyderabad 502345 India Umm Al Qura Univ Coll Comp Dept Comp & Network Engn Mecca Saudi Arabia Xidian Univ Guangzhou Inst Technol Guangzhou 510555 Peoples R China IIT Informat Technol & Management Chicago IL USA

The convergence of transformative technologies, including the Internet of Things (IoT), Big Data, and Artificial Intelligence (AI), has driven private edge cloud systems to the forefront of research efforts. The access to massive terminals and the emergence of personalized services pose serious challenges for efficient resource management in power private edge cloud systems. To address the challenge of inequitable resource allocation in the private edge cloud, this work proposes an intelligent resource allocation strategy with a slicing and auction approach. By formalizing the resource allocation problem as a mixed integer nonlinear programming (MINLP) puzzle, the method transforms it into a hierarchical allocation challenge for Mobile Network Operators (MNOs), Mobile Virtual Network Operators (MVNOs), and power terminals. The proposed Multi-hop Progressive Auction Algorithm (MPAA) addresses the sliced resource allocation problem between MNOs and MVNOs. Furthermore, a Terminal Resource Allocation Strategy (TRAS) based on improved particle swarm optimization is proposed to solve the spectrum resource allocation problem between MVNOs and power terminals. Extensive simulation results show that the bidding overhead of MPAA is reduced by 6.12% and the average terminal satisfaction of TRAS is improved by about 1.3% compared to conventional methods, thus improving the utilization of wireless resources within the power AIoT.

关键词： Private edge cloud systems Resource allocation Spectrum slicing Hierarchical auction mixed integer nonlinear programming

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Measuring performances of multiple-channel queueing systems with imprecise data: a membership function approach

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2008年第3期59卷 381-387页

作者： Chen, S-P Natl Chung Cheng Univ Dept Business Adm Chiayi Taiwan

This paper proposes a mixed integer nonlinear programming (MINLP) approach to measure the system performances of multiple-channel queueing models with imprecise data. The main idea is to transform a multiple-channel queue with imprecise data to a family of conventional crisp multiple-channel queues by applying the alpha-cut approach in fuzzy theory. On the basis of Phi-cut representation and the extension principle, two pairs of parametric MINLP are formulated to describe the family of crisp multiple-channel queues, via which the membership functions of the performance measures are derived. To demonstrate the validity of the proposed procedure, a real-world case of multiple-channel fuzzy queue is investigated successfully. Since the performance measures are expressed by membership functions rather than by crisp values, the fuzziness of input information is completed conserved. Thus, the results obtained from the proposed approach can represent the system more accurately, and more information is provided for system design in practice.

关键词： fuzzy theory queueing theory mixed integer nonlinear programming performance measures

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An access network design problem with end-to-end QoS constraints

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OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2014年 48卷 36-48页

作者： Park, Chanwoo Lee, Youngho Kim, Youngjin Park, Gigyoung Korea Univ Div Ind Management Engn Seoul 136701 South Korea Seoul Financial Ctr SK Res Inst SUPEX Management Seoul 100768 South Korea

In this paper, we present an access network design problem with end-to-end quality of service (QoS) requirement. The problem can be conceptualized as a two-level hierarchical location-allocation problem on the tree topology with nonlinear side constraints. The objective function of the nonlinear mixed integer programming model minimizes the total cost of switch and fiber cable, while satisfying demand within the prescribed level of QoS. By exploiting the inherent structure of the nonlinear QoS constraints, we develop linearization techniques for finding an optimal solution. Also, we devise an effective exact optimal algorithm within the context of disjunctive constraint generation. We present promising computational results that demonstrate the effectiveness of the proposed solution procedure. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Access network design Hierarchical location-allocation problem mixed integer nonlinear programming Linearization techniques Constraint generation

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