Integer Optimization(정수 최적화)란 무엇입니까?
Integer Optimization 정수 최적화 - Mixed-integer optimization, which focuses on problems where discrete and continuous variables exist simultaneously, is a well-known and challenging area for search algorithms. [1] First, we applied a mixed-integer optimization of time shifts of normal distributed flight times. [2] This thesis contains an extensive study of inner parallel sets in mixed-integer optimization. [3] We formulate this problem by mixed-integer optimization, and derive valid inequalities using the substructure of the problem. [4] Under the framework of supervised machine learning, we explore the new developments in feature selection and the optimal decision tree to predict cycle time by using mixed-integer optimization. [5] This article presents a two-timescale duplex neurodynamic approach to mixed-integer optimization, based on a biconvex optimization problem reformulation with additional bilinear equality or inequality constraints. [6] This poses the following problem: how should one select both the matrix reshape and associated low-rank decomposition scheme in order to compress a neural network so that its implementation is as efficient as possible? We formulate this problem as a mixed-integer optimization over the weights, ranks, and decompositions schemes; and we provide an efficient alternating optimization algorithm involving two simple steps: a step over the weights of the neural network (solved by SGD), and a step over the ranks and decomposition schemes (solved by an SVD). [7] Mathematical methods used: systems analysis, precedent theory, lexicographic ordering, component design, integer optimization, simulation modeling. [8] Integer packing sets appear naturally in Integer Optimization. [9] Using mixed-integer optimization, we evaluate the extent to which renewable energy reduces carbon emissions in the Saudi power sector. [10] Mathematical methods were used: integer optimization, simulation modeling, agent-based modeling. [11]이산 및 연속 변수가 동시에 존재하는 문제에 초점을 맞춘 혼합 정수 최적화는 검색 알고리즘에서 잘 알려져 있고 도전적인 영역입니다. [1] 먼저 정규 분포 비행 시간의 시간 이동에 대한 혼합 정수 최적화를 적용했습니다. [2] 이 논문은 혼합 정수 최적화에서 내부 병렬 집합에 대한 광범위한 연구를 포함합니다. [3] 혼합 정수 최적화를 통해 이 문제를 공식화하고 문제의 하위 구조를 사용하여 유효한 부등식을 도출합니다. [4] 지도 머신 러닝의 프레임워크에서 혼합 정수 최적화를 사용하여 주기 시간을 예측하는 최적의 의사 결정 트리 및 기능 선택의 새로운 개발을 탐색합니다. [5] 이 기사에서는 추가 쌍선형 등식 또는 부등식 제약 조건이 있는 양면 볼록 최적화 문제 재구성을 기반으로 혼합 정수 최적화에 대한 2시간 규모 이중 신경역학적 접근 방식을 제시합니다. [6] 이것은 다음과 같은 문제를 제기합니다. 가능한 한 효율적으로 구현되도록 신경망을 압축하기 위해 행렬 재구성 및 관련 하위 순위 분해 방식을 모두 선택해야 하는 방법은 무엇입니까? 우리는 이 문제를 가중치, 순위 및 분해 방식에 대한 혼합 정수 최적화로 공식화합니다. 그리고 우리는 두 가지 간단한 단계를 포함하는 효율적인 교대 최적화 알고리즘을 제공합니다: 신경망의 가중치에 대한 단계(SGD로 해결) 및 순위 및 분해 계획에 대한 단계(SVD에 의해 해결). [7] 사용된 수학적 방법: 시스템 분석, 선례 이론, 사전 순서, 구성 요소 설계, 정수 최적화, 시뮬레이션 모델링. [8] 정수 패킹 세트는 정수 최적화에서 자연스럽게 나타납니다. [9] 혼합 정수 최적화를 사용하여 재생 에너지가 사우디 전력 부문에서 탄소 배출량을 줄이는 정도를 평가합니다. [10] 정수 최적화, 시뮬레이션 모델링, 에이전트 기반 모델링과 같은 수학적 방법이 사용되었습니다. [11]
non convex mixed 볼록하지 않은 혼합
The algorithm is also tested on a small down-sampled prostate case for which we could computationally afford to obtain the ground-truth by solving the non-convex mixed-integer optimization problem exactly. [1] Many analytical and numerical techniques have been presented as effective tools to solve this highly constrained, nonlinear, non-convex mixed-integer optimization problem. [2] The problem is a non-convex, mixed-integer optimization problem and only depends on the graph structure of the system. [3]알고리즘은 또한 볼록하지 않은 혼합 정수 최적화 문제를 정확하게 해결하여 정답을 계산적으로 얻을 수 있는 작은 다운 샘플링된 전립선 케이스에서 테스트됩니다. [1] nan [2] nan [3]
Mixed Integer Optimization 혼합 정수 최적화
Meta-heuristics algorithms incorporating Tabu search are developed to tackle the proposed non-linear mixed integer optimization model. [1] Here, we integrated modelled hydrologic data, remote sensing products, climate data, and linear mixed integer optimization (MIP) to identify stewardship actions across space and time that can reduce the impact of invasive species. [2] This paper focuses on the analysis of the influence of the VREs’ penetration on the capacity of transmission lines, establishes an analysis model based on mixed integer optimization and power transfer distribution factors (FTDFs), and uses this model to carry out a quantitative study on a typical system. [3] The proposed scheme uses an iterative outer approximation method for coupled mixed integer optimization problems with separable convex objective functions, affine coupling constraints, and compact domain. [4] Taking economic optimization as objective, equipment capacity and operation boundaries as constraints, a bi-level iterative mixed integer optimization planning method for integrated energy planning and operation is proposed. [5] We present a mixed integer optimization model to find optimal policies subject to supplier profit maximization and consumer utility maximization constraints. [6] Considering the congestion of passenger vehicles at the signalized intersections, the problem is modeled as a multi-stage mixed integer optimization problem containing continuous variables (speed trajectory) and discrete ones (signal timing). [7] Our first model is a mixed integer optimization problem, subject to ODE constraints, reminiscent of an optimal control problem. [8] In this paper, an integrated framework that combines i) real-time degradation models used for predicting remaining life distribution of each component, with ii) mixed integer optimization models and solution algorithms used for identifying optimal wind farm maintenance and operations is proposed. [9] Then, a subgame equilibrium problem is solved using a mixed integer optimization model inspired by the fixed-point iteration algorithm. [10] Nine mixed integer optimization models to represent the problem are given and, also, the results of an extensive computational study carried out using a set of instances from the literature. [11] A mixed integer optimization problem is formulated to jointly optimize the route network and the service frequency for each shuttle. [12] A stochastic bilevel mixed integer optimization program is thus developed which is computed in an exact fashion by applying a customized reformulation-decomposition method. [13] Presented is a novel nonlinear mixed integer optimization problem which is valid for a broad class of railway networks. [14] We study a class of mixed integer optimization problems with linear constraints and a multi-linear objective function, the so-called mixed integer linear maximum multiplicative programs (MIL-MMPs). [15] Computing the decision variables of pump scheduling relies over mixed integer optimization (MIO) formulations. [16] The article is devoted to the study of the influence of uncertainty in initial data on the solutions of mixed integer optimization vector problems. [17] This work studies the multistage adaptive stochastic mixed integer optimization problem, where the aim is to find adaptive continuous and integer decision policies that optimize the expected objective. [18] A recent proposal represents BSS as a mixed integer optimization problem so that much larger problems have become feasible in reasonable computation time. [19] To solve the optimal network structure with this new design paradigm, we formulate the network design problem into a nonlinear mixed integer optimization model. [20] The proposed models are organized as tri-level mixed integer optimization problem and column constraint generation algorithm is utilized to make them computationally obedient. [21] This paper addresses the sparse principal component analysis (SPCA) problem for covariance matrices in dimension n aiming to find solutions with sparsity k using mixed integer optimization. [22] The controller uses a model of the system and predictive knowledge of demand and weather information to minimize electrical energy import, while maintaining thermal comfort by solving mixed integer optimization problems online. [23] In order to maximize the firm’s profit, the mixed integer optimization models with time constraints are proposed under carbon trading mechanism. [24] Because of this, the DSM problem is modelled as a mixed integer optimization problem. [25] A non-convex multi-objective mixed integer optimization problem is formulated, addressing the trade-off between maximum load balancing and minimum reconfiguration overhead due to flow migrations, under processing and transmission resource constraints and QoS requirement constraints. [26]제안된 비선형 혼합 정수 최적화 모델을 다루기 위해 Tabu 검색을 통합한 메타 휴리스틱 알고리즘이 개발되었습니다. [1] 여기에서 모델링된 수문학적 데이터, 원격 감지 제품, 기후 데이터 및 선형 혼합 정수 최적화(MIP)를 통합하여 침입 종의 영향을 줄일 수 있는 시공간의 관리 조치를 식별했습니다. [2] nan [3] nan [4] nan [5] nan [6] nan [7] nan [8] nan [9] nan [10] nan [11] nan [12] 따라서 맞춤형 재구성-분해 방법을 적용하여 정확한 방식으로 계산되는 확률론적 이중 수준 혼합 정수 최적화 프로그램이 개발되었습니다. [13] nan [14] nan [15] nan [16] nan [17] nan [18] nan [19] nan [20] nan [21] nan [22] nan [23] nan [24] nan [25] nan [26]
Linear Integer Optimization 선형 정수 최적화
Collective-ACC is a bi-objective non-linear integer optimization. [1] We formally define a non-linear integer optimization problem for max-min residual capacity under indirect multi-mapping. [2] In this paper, we propose models to impute objective function coefficients of linear integer optimization problems from multiple integer observations without any prior knowledge of the cost vector. [3] In this work, the determination of the switching frequency, to be applied to the nearest level modulation (NLM) method used in the SMs, is formulated as a linear integer optimization problem (LIOP). [4] The scheduling problem is formulated as a non-linear integer optimization problem within a genetic algorithm that minimizes the present value of the system cost over a planning horizon. [5] This article is addressed to propose a linear integer optimization model containing a fuzzy parameter that can be used to figure out the solution of a dynamic supplier selection problem considering uncertain demand. [6]Collective-ACC는 양방향 비선형 정수 최적화입니다. [1] 간접 다중 매핑에서 최대-최소 잔여 용량에 대한 비선형 정수 최적화 문제를 공식적으로 정의합니다. [2] nan [3] nan [4] nan [5] nan [6]
integer optimization problem 정수 최적화 문제
In the first stage, a multilayer perceptron (MLP) network with group lasso regularization terms is first trained to construct an integer optimization problem using the proposed algorithm for pre-selection of features and optimization of the hidden layer structure. [1] To pursue higher throughput while guaranteeing delay performance, we formulate a mixed-integer optimization problem of the access scale that contains a nondifferentiable variable derived from a transcendental equation. [2] The method is developed by incorporating the control node selection problem into an open-loop optimal control problem and by approximately solving the resulting mixed-integer optimization problem using a mesh adaptive direct search method. [3] The optimization problem at the core of the MPC formulation consists in an easy-to-solve mixed-integer optimization problem, whose solution is applied in a receding horizon way. [4] It tracks an aggregated power-setpoint that comes from the main grid controller by solving a mixed-integer optimization problem. [5] The complexity of this problem comes from the fact that ideally the i-well's designed flow control performance should be optimized together with its packer placement configuration, which makes it a largely multi-variable, mixed-integer optimization problem often requiring from thousands to millions of iterations to solve. [6] The proposed scheme uses an iterative outer approximation method for coupled mixed integer optimization problems with separable convex objective functions, affine coupling constraints, and compact domain. [7] For instance, MU-MIMO scheduling involves the selection of a user subset and associated rate selection each time-slot for varying channel states (the vector of quantized channels matrices for each of the users) — a complex integer optimization problem that is different for each channel state. [8] Considering the congestion of passenger vehicles at the signalized intersections, the problem is modeled as a multi-stage mixed integer optimization problem containing continuous variables (speed trajectory) and discrete ones (signal timing). [9] Aiming at the mix-integer optimization problem in delayed deep CRJ model, a heuristic evolutionary optimization scheme based on the stepwise differential evolution algorithm is applied to determine the delayed deep CRJ parameters automatically. [10] Our first model is a mixed integer optimization problem, subject to ODE constraints, reminiscent of an optimal control problem. [11] The DSR aiming loss reduction is a complex mixed-integer optimization problem with a quadratic term of power losses in the objective function and a set of linear and non-linear constraints. [12] We formally define a non-linear integer optimization problem for max-min residual capacity under indirect multi-mapping. [13] To achieve this, a mixed-integer optimization problem is formulated, which is furtherly decoupled into two subproblems for ease of handling. [14] An integer optimization problem is derived to fit the current simulation to the latest field measurements. [15] This paper considers a class of two-stage stochastic mixed-integer optimization problems where, for a given first-stage solution, we can determine the optimal values of recourse variables sequentially. [16] A feasible rounding approach is a novel technique to compute good feasible points for mixed-integer optimization problems. [17] The developed tools are formulated as a continuous or mixed-integer optimization problem with many parameters such as injection/production rates and well locations as design variables. [18] Summary of Contribution: Biobjective mixed-integer optimization problems have two linear objectives and a mixed-integer feasible region. [19] This makes two exceedingly nonlinear and mixed-integer optimization problems, which are elucidated by constructing two efficacious algorithms. [20] A mixed integer optimization problem is formulated to jointly optimize the route network and the service frequency for each shuttle. [21] Presented is a novel nonlinear mixed integer optimization problem which is valid for a broad class of railway networks. [22] We study a class of mixed integer optimization problems with linear constraints and a multi-linear objective function, the so-called mixed integer linear maximum multiplicative programs (MIL-MMPs). [23] Accordingly, a four-dimensional, non-linear, non-convex, and mixed-integer optimization problem was formulated, and a cost function was minimized by combining the Haar wavelet (WT) transform and the teaching-learning-based optimization (TLBO) method. [24] The proposed method attempts to simultaneously carry out the localization and data association tasks by formulating a mixed-integer optimization problem, which is approximated as a convex problem that can be efficiently solved with a polynomial complexity. [25] The problem is formulated as a mixed-integer optimization problem to maximize the expected customer showcasing utility through module and product showcasing and product testing. [26] The algorithm is also tested on a small down-sampled prostate case for which we could computationally afford to obtain the ground-truth by solving the non-convex mixed-integer optimization problem exactly. [27] This work studies the multistage adaptive stochastic mixed integer optimization problem, where the aim is to find adaptive continuous and integer decision policies that optimize the expected objective. [28] First, by showing that the optimal attacker strategy is a threshold policy, an optimization problem of the attacker with exponentially growing action space is reduced to a tractable integer optimization problem with a single parameter, then the corresponding defender cost is derived. [29] In particular, we model the problem as a mixed-integer optimization problem to maximize the aggregated spectral efficiency (SE) of downlinks and uplinks, where the quality-of-service constraints and power budget constraints are considered. [30] In this paper, the UC problem is formulated as a mixed-integer optimization problem and solved using novel Adaptive Binary Salp Swarm Algorithm by considering minimum up/down time limits, prohibited operating zones, spinning reserve, valve-point effect, and ramp rate limits. [31] In this paper, we propose models to impute objective function coefficients of linear integer optimization problems from multiple integer observations without any prior knowledge of the cost vector. [32] In this work, the determination of the switching frequency, to be applied to the nearest level modulation (NLM) method used in the SMs, is formulated as a linear integer optimization problem (LIOP). [33] In particular, we combine convex optimization, dynamic programming and Pontryagin’s minimum principle in an iterative scheme to solve the arising mixed-integer optimization problem. [34] This mixed-integer optimization problem with convex penalty terms broadly applies to optimizing pretrained regression tree models. [35] Furthermore, we consider a mixed-integer optimization problem for power allocation and propose a suitable design of the control policy such that the cost incurred at equilibrium is within $\epsilon$ from the optimal cost, providing a non conservative value for $\epsilon$. [36] For solving the studied nonlinear mixed-integer optimization problem, a new improved metaheuristic, called Balanced Mayfly Algorithm (BMA) is proposed. [37] The Potts model describes Ising-model-like interacting spin systems with multivalued spin component, and ground-state search problems of the Potts model can be efficiently mapped onto various integer optimization problems thanks to the rich expression of the multivalued spins. [38] A recent proposal represents BSS as a mixed integer optimization problem so that much larger problems have become feasible in reasonable computation time. [39] While this optimization problem is generally an NP-hard integer optimization problem, we show that the SIR structure leads to a submodular objective function, and provide a computationally attractive greedy algorithm for approximating a solution that has theoretical performance guarantee. [40] Meanwhile, a mixed-integer optimization problem is formulated to determine hyperparameters. [41] To find the marketing decision of such a real-life supply chain model, here, a new variant of ABC is proposed for mixed-integer optimization problems. [42] Our method can be expressed as a mixed-integer optimization problem, which can be to solve by iterative discrete cyclic coordinate descent. [43] Since this is a non-convex and mixed-integer optimization problem, a heuristic joint power and quality of experience (HJPQ) algorithm is proposed in this article, where the UEs’ offloading delay, MIMO channel, transmission power, as well as UAVs’ placement are jointly optimized. [44] Under the proposed approach, the solution of mixed-integer optimization problems is reduced to solving a family of optimization problems where only continuous parameters are used. [45] The formulated problem is a non-convex and mixed-integer optimization problem. [46] We propose a unified framework to address a family of classical mixed-integer optimization problems with logically constrained decision variables, including network design, facility location, unit commitment, sparse portfolio selection, binary quadratic optimization, sparse principal component analysis, and sparse learning problems. [47] The proposed models are organized as tri-level mixed integer optimization problem and column constraint generation algorithm is utilized to make them computationally obedient. [48] In this work, we use the DOMINO framework, a data-driven optimization algorithm initially developed to solve single-leader single-follower bi-level mixed-integer optimization problems, and further develop it to address integrated planning and scheduling formulations with multiple follower lower-level problems, which has not received extensive attention in the open literature. [49] The scheduling problem is formulated as a non-linear integer optimization problem within a genetic algorithm that minimizes the present value of the system cost over a planning horizon. [50]첫 번째 단계에서 그룹 올가미 정규화 항이 있는 다층 퍼셉트론(MLP) 네트워크는 먼저 기능의 사전 선택 및 숨겨진 계층 구조의 최적화를 위해 제안된 알고리즘을 사용하여 정수 최적화 문제를 구성하도록 훈련됩니다. [1] 지연 성능을 보장하면서 더 높은 처리량을 추구하기 위해 우리는 초월 방정식에서 파생된 미분할 수 없는 변수를 포함하는 액세스 규모의 혼합 정수 최적화 문제를 공식화합니다. [2] 이 방법은 제어 노드 선택 문제를 개루프 최적 제어 문제에 통합하고 메쉬 적응형 직접 탐색 방법을 사용하여 결과로 나오는 혼합 정수 최적화 문제를 대략적으로 해결하여 개발되었습니다. [3] MPC 공식의 핵심에 있는 최적화 문제는 풀기 쉬운 혼합 정수 최적화 문제로 구성되며, 이 문제의 솔루션은 수평선이 멀어지는 방식으로 적용됩니다. [4] 혼합 정수 최적화 문제를 해결하여 주 그리드 컨트롤러에서 오는 집계된 전력 설정값을 추적합니다. [5] 이 문제의 복잡성은 이상적으로는 i-well의 설계된 흐름 제어 성능이 패커 배치 구성과 함께 최적화되어야 한다는 사실에서 비롯됩니다. 해결하기 위한 반복. [6] nan [7] nan [8] nan [9] 지연된 심층 CRJ 모델의 혼합 정수 최적화 문제를 목표로 단계적 미분 진화 알고리즘에 기반한 휴리스틱 진화 최적화 기법을 적용하여 지연된 심층 CRJ 매개변수를 자동으로 결정합니다. [10] nan [11] 손실 감소를 목표로 하는 DSR은 목적 함수의 전력 손실에 대한 2차 항과 선형 및 비선형 제약 조건 세트가 있는 복잡한 혼합 정수 최적화 문제입니다. [12] 간접 다중 매핑에서 최대-최소 잔여 용량에 대한 비선형 정수 최적화 문제를 공식적으로 정의합니다. [13] 이를 달성하기 위해 혼합 정수 최적화 문제가 공식화되고 처리 용이성을 위해 두 개의 하위 문제로 추가로 분리됩니다. [14] nan [15] 이 논문은 주어진 1단계 솔루션에 대해 자원 변수의 최적 값을 순차적으로 결정할 수 있는 2단계 확률론적 혼합 정수 최적화 문제의 클래스를 고려합니다. [16] 실현 가능한 반올림 접근 방식은 혼합 정수 최적화 문제에 대한 좋은 실현 가능한 점을 계산하는 새로운 기술입니다. [17] 개발된 도구는 사출/생산 속도 및 유정 위치와 같은 많은 매개변수를 설계 변수로 사용하여 연속 또는 혼합 정수 최적화 문제로 공식화됩니다. [18] 기여 요약: Biobjective 혼합 정수 최적화 문제에는 두 개의 선형 목표와 혼합 정수 가능 영역이 있습니다. [19] 이것은 2개의 효율적인 알고리즘을 구성함으로써 해명되는 2개의 극도로 비선형 및 혼합 정수 최적화 문제를 만듭니다. [20] nan [21] nan [22] nan [23] 따라서 4차원, 비선형, 비볼록, 혼합 정수 최적화 문제를 공식화하고, Haar 웨이블릿(WT) 변환과 TLBO(Teaching-Learning-Based Optimization)를 결합하여 비용 함수를 최소화했습니다. 방법. [24] 제안하는 방법은 다항식 복잡도로 효율적으로 풀 수 있는 볼록 문제로 근사되는 혼합 정수 최적화 문제를 공식화하여 현지화 및 데이터 연관 작업을 동시에 수행하려고 합니다. [25] 문제는 모듈 및 제품 전시 및 제품 테스팅을 통해 기대되는 고객 쇼케이스 효용을 극대화하기 위해 혼합 정수 최적화 문제로 공식화됩니다. [26] 알고리즘은 또한 볼록하지 않은 혼합 정수 최적화 문제를 정확하게 해결하여 정답을 계산적으로 얻을 수 있는 작은 다운 샘플링된 전립선 케이스에서 테스트됩니다. [27] nan [28] nan [29] 특히, 우리는 서비스 품질 제약 및 전력 예산 제약이 고려되는 다운링크 및 업링크의 집계된 스펙트럼 효율성(SE)을 최대화하기 위해 혼합 정수 최적화 문제로 문제를 모델링합니다. [30] 이 논문에서 UC 문제는 혼합 정수 최적화 문제로 공식화되고 최소 업/다운 시간 제한, 금지된 작동 영역, 회전 예비, 밸브 포인트 효과 및 램프 속도 제한을 고려하여 새로운 Adaptive Binary Salp Swarm Algorithm을 사용하여 해결됩니다. . [31] nan [32] nan [33] 특히, 우리는 발생하는 혼합 정수 최적화 문제를 해결하기 위해 볼록 최적화, 동적 계획법 및 Pontryagin의 최소 원리를 반복 방식으로 결합합니다. [34] 볼록 패널티 항이 있는 이 혼합 정수 최적화 문제는 사전 훈련된 회귀 트리 모델 최적화에 광범위하게 적용됩니다. [35] 또한, 우리는 전력 할당에 대한 혼합 정수 최적화 문제를 고려하고 균형에서 발생하는 비용이 최적 비용에서 $\epsilon$ 이내가 되도록 제어 정책의 적절한 설계를 제안하여 $\epsilon$에 대해 보수적이지 않은 값을 제공합니다. . [36] 연구된 비선형 혼합 정수 최적화 문제를 해결하기 위해 BMA(Balanced Mayfly Algorithm)라는 새로운 개선된 메타휴리스틱이 제안되었습니다. [37] nan [38] nan [39] nan [40] nan [41] nan [42] nan [43] nan [44] nan [45] nan [46] nan [47] nan [48] nan [49] nan [50]
integer optimization model 정수 최적화 모델
Meta-heuristics algorithms incorporating Tabu search are developed to tackle the proposed non-linear mixed integer optimization model. [1] In this study, a stochastic multi-objective mixed-integer optimization model is developed to ensure production efficiency in uncertainty conditions and satisfy the requirements of sustainable development. [2] The resulting formulation is a mixed-integer optimization model with quadratic constraints and is solved with a state-of-the-art second-order cone programming solver. [3] We present a mixed integer optimization model to find optimal policies subject to supplier profit maximization and consumer utility maximization constraints. [4] In this paper, an integrated framework that combines i) real-time degradation models used for predicting remaining life distribution of each component, with ii) mixed integer optimization models and solution algorithms used for identifying optimal wind farm maintenance and operations is proposed. [5] Second, we develop a computationally tractable twostage stochastic mixed-integer optimization model to investigate the trading portfolio and risk optimization problem faced by retailers. [6] Firstly, a 0-1 integer optimization model is established. [7] Then, a subgame equilibrium problem is solved using a mixed integer optimization model inspired by the fixed-point iteration algorithm. [8] A binary integer optimization model is developed in order to find the best allocation for ICU beds, considering candidate patients with suspected/confirmed COVID-19. [9] Nine mixed integer optimization models to represent the problem are given and, also, the results of an extensive computational study carried out using a set of instances from the literature. [10] This class of mixed-integer optimization models appears frequently in telecommunication network capacity expansion problems, train scheduling with multiple locomotive options, supply chain and service network design problems. [11] To solve the optimal network structure with this new design paradigm, we formulate the network design problem into a nonlinear mixed integer optimization model. [12] The authors contrast the established approach of using customized integer optimization models to a heuristic that integrates human judgment with Google Maps travel time data to solve vehicle routing problems. [13] We introduce bilevel integer optimization models for airport time slot trading and compute core-stable outcomes, i. [14] We establish a two-stage distributionally robust 0-1 mixed-integer optimization model by considering the uncertainty of the supply chain. [15] This article is addressed to propose a linear integer optimization model containing a fuzzy parameter that can be used to figure out the solution of a dynamic supplier selection problem considering uncertain demand. [16] In order to maximize the firm’s profit, the mixed integer optimization models with time constraints are proposed under carbon trading mechanism. [17]제안된 비선형 혼합 정수 최적화 모델을 다루기 위해 Tabu 검색을 통합한 메타 휴리스틱 알고리즘이 개발되었습니다. [1] 본 연구에서는 불확실성 조건에서 생산 효율성을 보장하고 지속 가능한 개발의 요구 사항을 충족하기 위해 확률론적 다중 목표 혼합 정수 최적화 모델을 개발했습니다. [2] 결과 공식은 2차 제약 조건이 있는 혼합 정수 최적화 모델이며 최첨단 2차 원뿔 프로그래밍 솔버로 해결됩니다. [3] nan [4] nan [5] 둘째, 소매업체가 직면한 거래 포트폴리오 및 위험 최적화 문제를 조사하기 위해 계산 가능한 2단계 확률론적 혼합 정수 최적화 모델을 개발합니다. [6] nan [7] nan [8] nan [9] nan [10] 이러한 종류의 혼합 정수 최적화 모델은 통신 네트워크 용량 확장 문제, 여러 기관차 옵션이 있는 열차 일정, 공급망 및 서비스 네트워크 설계 문제에서 자주 나타납니다. [11] nan [12] nan [13] nan [14] nan [15] nan [16] nan [17]
integer optimization program 정수 최적화 프로그램
This work investigated the performance of several representative piecewise linear (or piecewise affine) relaxation schemes (referred to as McCormick, bm, nf5, and nf6t) and de (which is a new approach proposed based on eigenvector decomposition) that mainly give rise to mixed-integer optimization programs to convexify a bilinear term using predetermined univariate partitioning for instances of uniform and non-uniform partition sizes. [1] A stochastic bilevel mixed integer optimization program is thus developed which is computed in an exact fashion by applying a customized reformulation-decomposition method. [2] This work investigates the performance of several representative piecewise-linear (or piecewise-affine) relaxation schemes (referred to as McCormick, bm, nf5, nf6t, and de (which is a new approach proposed based on eigenvector decomposition) that mainly give rise to mixed-integer optimization programs to convexify a bilinear term using predetermined univariate partitioning for instances of uniform and non-uniform partition sizes. [3]이 작업은 주로 혼합을 발생시키는 몇 가지 대표적인 조각별 선형(또는 조각별 아핀) 이완 계획(McCormick, bm, nf5 및 nf6t라고 함)과 de(고유 벡터 분해에 기반하여 제안된 새로운 접근 방식)의 성능을 조사했습니다. -정수 최적화 프로그램은 균일하고 불균일한 분할 크기의 인스턴스에 대해 미리 결정된 일변량 분할을 사용하여 쌍선형 항을 볼록하게 합니다. [1] 따라서 맞춤형 재구성-분해 방법을 적용하여 정확한 방식으로 계산되는 확률론적 이중 수준 혼합 정수 최적화 프로그램이 개발되었습니다. [2] 이 작업은 주로 균일하고 불균일한 분할 크기의 인스턴스에 대해 미리 결정된 일변량 분할을 사용하여 쌍선형 항을 볼록화하는 혼합 정수 최적화 프로그램. [3]
integer optimization approach 정수 최적화 접근 방식
We present mixed-integer optimization approaches to find optimal distance metrics that generalize the Mahalanobis metric extensively studied in the literature. [1] We propose an integer optimization approach for building sparse regression models with enforced coordination, using data partitioned among leaves in a prediction tree. [2] Using a lifecycle framework with Epstein-Zin (1989) utility and a mixed-integer optimization approach, we compute the optimal age to claim Social Security benefits. [3]우리는 문헌에서 광범위하게 연구된 Mahalanobis 메트릭을 일반화하는 최적의 거리 메트릭을 찾기 위해 혼합 정수 최적화 접근 방식을 제시합니다. [1] 우리는 예측 트리의 잎 사이에 분할된 데이터를 사용하여 강제 조정으로 희소 회귀 모델을 구축하기 위한 정수 최적화 접근 방식을 제안합니다. [2] Epstein-Zin(1989) 유틸리티와 혼합 정수 최적화 접근 방식과 함께 수명 주기 프레임워크를 사용하여 사회 보장 혜택을 청구할 최적의 연령을 계산합니다. [3]
integer optimization framework
To that end, we develop a price-maker mixed-integer optimization framework that maximizes a depreciated battery storage revenue and yields the optimal siting and size of that battery storage. [1] To tackle this challenge, we formulate a novel integer optimization framework to select the antennas of heterogeneous users simultaneously. [2]이를 위해 감가상각된 배터리 스토리지 수익을 극대화하고 해당 배터리 스토리지의 최적 위치와 크기를 산출하는 가격 결정자 혼합 정수 최적화 프레임워크를 개발합니다. [1] nan [2]
integer optimization solver 정수 최적화 솔버
The chapter focuses on the recent advancements in commercial integer optimization solvers as exemplified by the CPLEX software package particularly but not limited to mixed-integer linear programming (MILP) models applied to business intelligence applications. [1] The proposed reformulations are stronger and substantially faster when used with current mixed-integer optimization solvers. [2]이 장에서는 특히 비즈니스 인텔리전스 애플리케이션에 적용된 MILP(혼합 정수 선형 계획법) 모델에 국한되지 않는 CPLEX 소프트웨어 패키지로 예시된 상용 정수 최적화 솔버의 최근 발전에 초점을 맞춥니다. [1] 제안된 재구성은 현재 혼합 정수 최적화 솔버와 함께 사용할 때 더 강력하고 훨씬 빠릅니다. [2]