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IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY 2025年 6卷 1591-1609页

作者： Amrallah, Amr Tsukamoto, Yu Murakami, Takahide Ikami, Akio Shinbo, Hiroyuki Amano, Yoshiaki KDDI Res Inc Wireless Technol Div Saitama 3568502 Japan

The evolution toward user-centric radio access networks (RANs) necessitates innovative management strategies to handle growing network demands efficiently. This study presents a novel framework for the disaggregated and distributed deployment of near-real-time RAN intelligent controllers (near-RT RICs) tailored for large-scale user-centric networks. By leveraging a mixed-integer nonlinear programming (MINLP) approach, we aim to optimize both control latency and deployment costs, which are critical considerations in next-generation network management. Our proposed divide-and-conquer (D&C) and heuristic-greedy (HG)-based algorithms significantly reduce computational complexity while maintaining near-optimal performance. Simulation results indicated that the distributed framework achieved a 78% reduction in control latency with only 7% increase in deployment cost compared with the traditional centralized approach. Additionally, it achieved 99% reduction in runtime performance with an objective function value that was just 2% lower than the optimal value. These findings confirm the framework's scalability and effectiveness, validating its suitability for operational telecom environments.

关键词： Open RAN Optimization Costs Heuristic algorithms Computer architecture programming Delays 6G mobile communication 3GPP Time factors Cell-free massive MIMO large-scale network mixed-integer nonlinear programming near-real-time RIC O-RAN RAN optimization user-centric RAN

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Design of hybrid reactive-extractive distillation for separation of azeotropic ternary mixture using a systematic optimization framework

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SEPARATION AND PURIFICATION TECHNOLOGY 2025年 369卷

作者： Liu, Chao Karagoz, Ali Alqusair, Omar Ma, Yingjie Xiao, Xin Li, Jie Univ Manchester Ctr Proc Integrat Dept Chem Engn Manchester M13 9PL England MIT Dept Chem Engn Cambridge MA 02139 USA Chinese Acad Sci Inst Proc Engn Beijing 100045 Peoples R China Kind Saud Univ Coll Engn Dept Chem Engn Riyadh 11421 Saudi Arabia

Hybrid reactive-extractive distillation (RED) has been considered as a promising approach for the separation of ternary azeotropic mixtures that are widely existing in the chemical industry, due to its high energy efficiency. The optimal design of RED is quite challenging, primarily due to the problem's inherent complexity involving both reactive and extractive distillation processes with recycle streams. Mathematical modeling of RED using rigorous models introduces highly nonlinear and nonconvex terms, which are difficult to optimize. This work proposes a systematic optimization framework to address these challenges by integrating advanced modeling, simulation, and optimization techniques. First, we develop a novel mixed-integer nonlinear programming (MINLP) optimization problem in which the pseudo-transient continuation technique is employed to construct a pseudo-transient continuation reactive distillation model in an equation-oriented environment to represent column sections, condensers, and reboilers. Our previously proposed feasible path-based branch and bound algorithm is then employed to solve this difficult MINLP problem. Given the presence of recycle streams within the process, an open-loop MINLP optimization problem is first solved to achieve an optimal design based on product specifications, followed by a closed-loop nonlinear programming optimization problem for a fixed column structure to reduce the computational effort. The design of triple-column reactive-extractive distillation (TCRED) and double-column reactive-extractive distillation (DCRED) processes in three examples demonstrates that the proposed framework enables efficient RED process design, which can reduce total annualized cost by 3.5% to 25.1% compared to existing studies.

关键词： Reactive distillation Extractive distillation Azeotropic separation Process intensification Feasible path-based algorithm mixed-integer nonlinear programming

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A Superstructure-Based Lignin Valorization Process Optimization Model for Lignocellulosic Biorefineries through Biological Upgrading

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ACS SUSTAINABLE CHEMISTRY & ENGINEERING 2024年第18期12卷 6913-6926页

作者： Wu, Yajie Restrepo-Florez, Juan Manuel Vasco-Correa, Juliana Penn State Univ Dept Agr & Biol Engn University Pk PA 16802 USA Univ Florida Dept Chem Engn Gainesville FL 32611 USA

This study presents an optimization framework to identify economically viable pathways for lignin valorization in biorefineries that employ biological upgrading. The economic potential for converting lignin from hardwood, softwood, and herbaceous plants into valuable bioproducts is evaluated. The research indicates that the production of 2-pyrone-4,6-dicarboxylic acid (PDC) from hardwood is the most economically promising, with an estimated net present value of $771.41 million and an internal rate of return of 19.73% through dilute acid pretreatment, base-catalyzed depolymerization, and PDC fermentation. Capital costs represent a large portion of the total expenses across all scenarios. Revenue from woody feedstocks is largely derived from lignin-based products, while for herbaceous plants, coproducts (fermentable sugars) are the main revenue contributors. The analysis provides insights for the development of lignin valorization biorefineries and guides the chemical industry toward a more sustainable use of renewable carbon sources.

关键词： lignin fermentation biorefinery mixed-integer nonlinear programming superstructure-basedoptimization

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A statistical learning approach to pasture, rangeland, forage (PRF) insurance coverage selection

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APPLIED ECONOMIC PERSPECTIVES AND POLICY 2024年第4期46卷 1429-1449页

作者： Zapata, Samuel D. Villavicencio, Xavier Xicay, Anderson Texas A&M Univ Dept Agr Econ Mcallen TX 78504 USA Texas A&M AgriLife Extens Serv Dept Agr Econ Weslaco TX USA

This study uses statistical learning methods to identify robust coverage alternatives for the Pasture, Rangeland, Forage (PRF) insurance program. Shrinkage and ensemble learning techniques are adapted to the context of the PRF coverage selection process. The out-of-sample performance of the proposed methods is evaluated on 116 representative grids throughout Texas during 2018-2022. Ensemble learning methods generated more stable coverage choices compared with the other selection strategies considered. Depending on the target return, a reduction in the prediction error between 5% and 14% was observed. Furthermore, the proposed coverages can provide a broader protection than current coverage choices made by farmers.

关键词： ensemble learning mixed-integer nonlinear programming portfolio analysis rainfall index shrinkage Texas

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Relaxation approaches for nonlinear sparse optimization problems

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OPTIMIZATION 2024年第10期73卷 3237-3258页

作者： Steffensen, Sonja Rhein Westfal TH Aachen Inst Geometrie & Prakt Math Aachen Germany

In many recent applications, sparse solutions of the optimization problems are favoured over non-sparse solutions with comparable objective values. A standard method to induce the sparsity of the solution is based on the use of the l(0) norm in the objective. However, if the underlying optimization problem is nonlinear, the solution of the nonlinear (sparse) l(0)optimization problem is difficult. Therefore, it is often approximated using the convex l(1)-norm although this can lead to suboptimal solutions for the sparsity of the solution. In this paper, we follow another direction. We present exact reformulations (with respect to the l(0) norm) and their relaxations leading to standard nonlinear but nonconvex programmes. Wediscuss and relate the relations between the different reformulations with repect to the original problem. We accompany our theoretical results by some numerical tests using randomly generated datasets.

关键词： Sparse optimization nonlinear programming complementarity constraints mixed-integer nonlinear programming

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Optimum Operation of Microgrid Considering the Effects of Energy Storage and Load Response Program to Increase Resilience

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INTERNATIONAL JOURNAL OF ENGINEERING 2025年第5期38卷 1147-1163页

作者： Valizadeh, M. Mansoor, S. Siyah Sarvenoee, A. Khosravi Kohzadipour, M. Ilam Univ Dept Elect Engn Ilam Iran

Resilience is one of the important issues in power networks. This article analyzes resilience during severe incidents and in normal and critical situations. A mixed-integer nonlinear programming (MINLP) model is presented to investigate the issue. The optimization is done in two levels. In the first level, the optimal size of the energy storage system and its optimal discharge depth are determined for the optimal planning of the microgrid under normal operating conditions. In the second level, after a fault occurs and the microgrid is disconnected from the upstream network and operates in island mode, an energy storage system (ESS) and load response program reduces the system's vulnerability and minimizes the cost of load shedding. This model aims to observe the impact of ESS and demand response programs (DRP) on system performance in numerical conditions and during extreme weather events as a strategy to increase grid resilience. Under normal operating conditions, the return on investment for ESS occurs within one year, and the economic profit of the microgrid with the addition of DRP is about $160,000. Additionally, in critical conditions, the ESS supplies 40% of the loads shut down during the fault, which increases to 66% with the addition of the load response program.

关键词： Resiliency Microgrid Optimal Operation Load Response Program Energy Storage System mixed-integer nonlinear programming Demand Response Program

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A novel economic scheduling of multi-product deterministic demand for co-production air separation system with liquid air energy storage

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RENEWABLE ENERGY 2023年第1期209卷 533-545页

作者： Kong, Fulin Liu, Yuxin Shen, Minghai Tong, Lige Yin, Shaowu Wang, Li Ding, Yulong Univ Sci & Technol Beijing Sch Energy & Environm Engn Beijing 100083 Peoples R China Univ Sci & Technol Beijing Beijing Key Lab Energy Saving & Emiss Reduct Met I Beijing 100083 Peoples R China Univ Birmingham Birmingham Ctr Energy Storage Birmingham England Univ Birmingham Sch Chem Engn Birmingham England

Air separation units (ASUs) integrated with liquid air energy storage (LAES) have the potential to balance grid demand and improve production profits. This study aims to enhance the demand response of a novel coproduction air separation system (NCASS) by maximizing the energy arbitrage of a LAES system. We developed a mixed-integer nonlinear programming model to minimize operating costs by using a proxy model to represent the ASU production space and a black box model to represent the material and energy flow balance of LAES. The model is based on rigorous mathematical programming and, considers material and energy flow balance, analyzes the production and delivery processes and constraints for each product, and matches equipment start-up and shutdown time constraints. The case study shows that the model achieves a high level of matter and energy matching between the distillation process and the LAES process, maximizing the LAES peakshaving capacity and improving system economics. During the 24 h schedule period, the total electricity consumption of NCASS increases by 18.94% during the valley electricity period and decreases by 20.59% during the flat and peak electricity period. The economic benefits increase by 9.51 k$, accounting for 14.29% of the total costs. This study contributes to the wider application of NCAS with LAES technology for sustainable energy management.

关键词： Energy-saving Liquid air energy storage Economic scheduling Multiple product Time-of-day tariff mixed-integer nonlinear programming

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Additive manufacturing capacity allocation problem over a network

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IISE TRANSACTIONS 2023年第8期55卷 807-820页

作者： Cokyasar, Taner Jin, Mingzhou Univ Tennessee Dept Ind & Syst Engn Knoxville TN 37996 USA Argonne Natl Lab Transportat & Power Syst Div Lemont IL USA Tarsus Univ Dept Ind Engn Takbas Mersin Turkey

The use of Additive Manufacturing (AM) for low demand volumes, such as spare parts, has recently attracted considerable attention from researchers and practitioners. This study defines the AM Capacity Allocation Problem (AMCAP) to design an AM supply network and choose between printing upon demand and sourcing through an alternative option for each part in a given set. A mixed-integer nonlinear program was developed to minimize the production, transportation, alternative sourcing, and lead time costs. We developed a cut generation algorithm to find optimal solutions in finite iterations by exploring the convexity of the nonlinear waiting time for AM products at each AM facility. Numerical experiments show the effectiveness of the proposed algorithm for the AMCAP. A case study was conducted to demonstrate that the optimal AM deployment can save almost 20% of costs over situations that do not use any AM. The case also shows that AM can realize its maximum benefits when it works in conjunction with an alternative option, e.g., inventory holding, and its capacity is strategically deployed. Since AM is a new technology and is rapidly evolving, this study includes a sensitivity analysis to see the effects of improved AM technology features, such as machine cost and build speed. When the build speed increases, the total cost decreases quickly, but the number of AM machines will increase first then decrease later when more parts are assigned to the AM option.

关键词： Additive manufacturing capacity planning network optimization mixed-integer nonlinear programming

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A generic optimization framework for resilient systems

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OPTIMIZATION METHODS & SOFTWARE 2023年第2期38卷 356-385页

作者： Pfetsch, Marc E. Schmitt, Andreas TU Discrete Optimizat Grp Dept Math Darmstadt Germany Tech Univ Darmstadt Dept Math Discrete Optimizat Grp Dolivostr 15 D-64293 Darmstadt Germany

This paper addresses the optimal design of resilient systems, in which components can fail. The system can react to failures and its behaviour is described by general mixed integer nonlinear programs, which allows for applications to many (technical) systems. This then leads to a three-level optimization problem. The upper level designs the system minimizing a cost function, the middle level represents worst-case failures of components, i.e. interdicts the system, and the lowest level operates the remaining system. We describe new inequalities that characterize the set of resilient solutions and allow to reformulate the problem. The reformulation can then be solved using a nested branch-and-cut approach. We discuss several improvements, for instance, by taking symmetry into account and strengthening cuts. We demonstrate the effectiveness of our implementation on the optimal design of water networks, robust trusses, and gas networks, in comparison to an approach in which the failure scenarios are directly included into the model.

关键词： Multi-level optimization mixed-integer nonlinear programming interdiction problems gas and water network design truss topology optimization

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Energy-efficient task scheduling for mobile edge computing with virtual machine I/O interference

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2023年第1期148卷 538-549页

作者： Lu, Baoshan Fang, Junli Hong, Xuemin Shi, Jianghong Guangxi Normal Univ Sch Elect & Informat Engn Guangxi Key Lab Brain Inspired Comp & Intelligent Guilin 541004 Peoples R China Xiamen Univ Dept Informat & Commun Engn Xiamen 361005 Fujian Peoples R China

Mobile edge computing (MEC) is expected to support the computation-intensive and delay-sensitive applications of mobile internet users. In this paper, we investigate the resource allocation of MEC with the effect of I/O interference among parallel virtual machines (VMs) while satisfying the quality of service (QoS) of tasks. Different from existing works, we propose a flexible task scheduling approach that combining parallel and sequential computing to minimize the computing energy consumption of MEC server. We formulate the task scheduling problem as a mixed-integer nonlinear programming (MINLP) and decompose it as a CPU resource allocation subproblem, a computing time slot subproblem, and a VM selection subproblem. We show the first subproblem is a convex problem and propose a CPU frequency allocation (CFA) algorithm based on the Karush-Kuhn-Tucker (KKT) conditions to obtain the optimal CPU frequency resource allocation. For the time slot allocation and VM selection subproblems, we propose the three step allocation (TSA) and urgency based adjusting (UBA) algorithms to obtain the near-optimal solutions, respectively. Simulation results show that compared with several time slot allocations and VM selections, the proposed TSA and UBA algorithms can save up to 21.7% and 95.8% of energy consumption, respectively.& COPY;2023 Elsevier B.V. All rights reserved.

关键词： Mobile edge computing Virtual machine I O interference Task scheduling mixed-integer nonlinear programming Karush-Kuhn-Tucker

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