Integer Nonlinear(정수 비선형)란 무엇입니까?
Integer Nonlinear 정수 비선형 - The measurement results show that the proposed CCM can reduce the integer nonlinearity by up to 70% at 0. [1]측정 결과 제안된 CCM이 0에서 정수 비선형성을 최대 70%까지 감소시킬 수 있음을 보여줍니다. [1]
multi objective mixed 다중 목표 혼합
In this study, the centralized voltage regulation is performed based on the worst voltage variation scenarios of ADNs, where a multi-objective mixed integer nonlinear programming (MINP) model with time-varying decision variables is established. [1] The research proposes a multi-objective mixed integer nonlinear programming model to design a closed-loop supply chain network based on the e-commerce context. [2] First, an integrated Multi-Objective Mixed-Integer Nonlinear Programming (MOMINLP) model is provided to minimize costs and evaluate suppliers simultaneously. [3] A multi-objective mixed-integer nonlinear programming (MINLP) model is developed that takes into account the main characteristics of CSP plant, e. [4] A multi-objective mixed integer nonlinear programming model is established to optimize the allocation of liner routes with multiple ship types on multiple routes. [5] A multi-objective mixed integer nonlinear programming model, including RES, ESS, home appliances and the main grid, is proposed to optimize different and conflicting objectives which are energy cost, user comfort and PAR. [6] We formulate the following superstructure optimization problem as a multi-objective mixed-integer nonlinear fractional programming (MINFP) problem, which is efficiently solved by a tailored global optimization algorithm that integrates parametric algorithm and branch-and-refine algorithm. [7] The problem is formulated as a multi-objective mixed-integer nonlinear optimization to maximize the PVHC and minimize the voltage deviation simultaneously. [8] To demonstrate this method, a conditional generative adversarial network (C-GAN) is used to augment the solutions produced by a genetic algorithm (GA) for a 311-dimensional nonconvex multi-objective mixed-integer nonlinear optimization. [9] The GVRP is developed as a multi-objective mixed integer nonlinear programming (MINLP) model that incorporates a fuel consumption calculation algorithm. [10]이 연구에서 중앙 집중식 전압 조정은 ADN의 최악의 전압 변동 시나리오를 기반으로 수행되며, 여기서 시변 결정 변수가 있는 다중 목표 혼합 정수 비선형 프로그래밍(MINP) 모델이 설정됩니다. [1] 이 연구는 전자 상거래 컨텍스트를 기반으로 폐쇄 루프 공급망 네트워크를 설계하기 위해 다중 목표 혼합 정수 비선형 프로그래밍 모델을 제안합니다. [2] nan [3] nan [4] nan [5] nan [6] nan [7] nan [8] nan [9] nan [10]
mixed integer linear 혼합 정수 선형
The original mixed-integer nonlinear problem of UC scheduling is transformed to be a mixed-integer linear problem to be effectively solved. [1] Although the first version of the model is developed as a mixed-integer nonlinear programming (MINLP) model, this study attempts to convert it to a mixed-integer linear programming (MILP) model using the logarithmic transformation and piecewise linear approximation methods. [2] PaDOA proceeds by alternating between solving large-scale structured mixed-integer linear programming problems and partially decoupled mixed-integer nonlinear programming subproblems that comprise much fewer integer variables. [3] The bidding and offering model is first formulated as a hard-to-solve mixed-integer nonlinear programming (MINLP) problem, which is later converted to its easy-to-solve mixed-integer linear programming (MILP) counterpart. [4] Linearization and approximation techniques are used to obtain an approximate mixed-integer linear programming (MILP) model from the original mixed-integer nonlinear programming model. [5] To solve this problem, through analyzing the characteristics of distributed precast production, we first develop a novel mixed integer nonlinear programming (MINLP) model and then transform it into an effective mixed integer linear programming (MILP) model by linearization techniques. [6] The design task is first posed as an NP-hard mixed-integer nonlinear program (MINLP) that is exactly reformulated as a mixed-integer linear program (MILP) using McCormick linearization. [7] The proposed solution approach of the initial mixed-integer nonlinear programming model relies on McCormick relaxation linearization to obtain a more tractable mixed-integer linear model. [8] In this work, an optimization model Mixed-Integer Linear Programming (MILP) and Mixed-Integer Nonlinear Programming (MINLP) for hydrogen networks was applied to minimize the operating costs. [9] The two sub-models are weighted and integrated into a mixed integer nonlinear programming model, which is further transformed into a mixed integer linear programming model using a novel linearization method. [10]UC 스케줄링의 원래 혼합 정수 비선형 문제는 효과적으로 해결하기 위해 혼합 정수 선형 문제로 변환됩니다. [1] 모델의 첫 번째 버전은 혼합 정수 비선형 계획법(MINLP) 모델로 개발되었지만, 본 연구에서는 대수 변환 및 조각별 선형 근사법을 사용하여 혼합 정수 선형 계획법(MILP) 모델로 변환하려고 합니다. [2] nan [3] nan [4] nan [5] nan [6] nan [7] nan [8] nan [9] nan [10]
non convex mixed 볼록하지 않은 혼합
Additionally, the scheme inherently is a non-convex mixed-integer nonlinear programming framework. [1] We convert the resulting non-convex mixed integer nonlinear programming problem into an equivalent quadratically constrained quadratic programming (QCQP) problem, which we solve via a novel Energy-Efficient Task Offloading (EETO) algorithm. [2] This problem is formulated as a non-convex mixed integer nonlinear programming (MINLP) problem. [3] Due to the non-convexity of the SPUN problem as well as complex coupling among mixed integer variables, it is a non-convex mixed integer nonlinear programming (MINLP) problem. [4] By considering a prescribed risk level of unbalancing, a dynamic programming algorithm sets the operational planning of conventional generators by solving a non-convex mixed-integer nonlinear programming model, so that the operational cost and available operating reserve can be calculated. [5]또한 이 체계는 본질적으로 볼록하지 않은 혼합 정수 비선형 계획법 프레임워크입니다. [1] 결과로 생성된 비볼록 혼합 정수 비선형 계획법 문제를 동등한 2차 제약 2차 계획법(QCQP) 문제로 변환합니다. 이 문제는 새로운 EETO(Energy-Efficient Task Offloading) 알고리즘을 통해 해결합니다. [2] nan [3] nan [4] nan [5]
resource allocation problem 자원 할당 문제
Specifically, we first formulate the joint offloading decision and resource allocation problem as a Mixed Integer NonLinear Programming (MINLP) problem. [1] To tackle these problems, we formulate the joint offloading decision, computing and communication resource allocation problem for satellite assisted V2V communication as a mixed-integer nonlinear programming problem with minimum weighted-sum end-to-end latency, and we decouple it into two subproblems. [2] To maximize the system throughput, the joint resource allocation problem is formulated as a mixed-integer nonlinear programming problem, which is difficult to tackle in general. [3]특히, 먼저 공동 오프로딩 결정 및 리소스 할당 문제를 MINLP(혼합 정수 비선형 프로그래밍) 문제로 공식화합니다. [1] 이러한 문제를 해결하기 위해 위성 지원 V2V 통신을 위한 공동 오프로딩 결정, 컴퓨팅 및 통신 리소스 할당 문제를 최소 가중치 합 종단 간 대기 시간이 있는 혼합 정수 비선형 프로그래밍 문제로 공식화하고 이를 두 개의 하위 문제로 분리합니다. . [2] nan [3]
optimal power flow 최적의 전력 흐름
However, the accurate modelling of SLs within the multi-period optimal power flow (MP-OPF) framework results in complex non-convex mixed-integer nonlinear programming (MINLP) problems. [1] This extension to alternative current optimal power flow (ACOPF) problem results in a mixed integer nonlinear program (MINLP) which is not guaranteed to be solved optimally by existing solution methods and also requires excessive computation times for large real systems. [2] The developed computational model is formulated as a mixed integer nonlinear optimisation problem and solved through the combination of meta-heuristics, stochastic simulation methods (Monte-Carlo simulation), and application of optimal power flow. [3]그러나 MP-OPF(다기간 최적 전력 흐름) 프레임워크 내에서 SL을 정확하게 모델링하면 복잡한 MINLP(비볼록 혼합 정수 비선형 계획법) 문제가 발생합니다. [1] ACOPF(교류 최적 전력 흐름) 문제에 대한 이러한 확장은 MINLP(혼합 정수 비선형 프로그램)를 생성하며, 이는 기존 솔루션 방법으로는 최적으로 해결되지 않으며 대규모 실제 시스템에 대해 과도한 계산 시간을 필요로 합니다. [2] nan [3]
bi objective mixed 이중 목표 혼합
The problem is then formulated as a bi-objective mixed-integer nonlinear program. [1] This paper integrates the periodic inventory routing problem with location decisions for designing healthcare waste management systems and presents a bi-objective mixed-integer nonlinear programming model that minimizes operating costs and risk simultaneously. [2] The obtained bi-objective mixed integer nonlinear programming model is solved for a case study example published in the crop protection literature. [3]그런 다음 문제는 이중 목표 혼합 정수 비선형 프로그램으로 공식화됩니다. [1] 이 논문은 의료 폐기물 관리 시스템을 설계하기 위한 위치 결정과 주기적인 재고 라우팅 문제를 통합하고 운영 비용과 위험을 동시에 최소화하는 이중 목표 혼합 정수 비선형 계획법 모델을 제시합니다. [2] nan [3]
formulated optimization problem 공식화된 최적화 문제
Given that the formulated optimization problem is a non-convex mixed integer nonlinear programming problem, a decomposition into subproblems is performed and a two-stage heuristic algorithm is proposed. [1] The formulated optimization problem corresponds to mixed-integer nonlinear programming (MINLP). [2] As the formulated optimization problem is a mixed integer nonlinear optimization problem which cannot be solved conveniently, we apply the McCormick envelopes and the Lagrangian partial relaxation method to decompose the optimization problem into three subproblems which can be iteratively solved by means of the modified Kuhn–Munkres algorithm and the unidimensional knapsack algorithm. [3]공식화된 최적화 문제가 볼록하지 않은 혼합 정수 비선형 계획법 문제임을 감안하여 하위 문제로 분해를 수행하고 2단계 휴리스틱 알고리즘을 제안합니다. [1] 공식화된 최적화 문제는 혼합 정수 비선형 계획법(MINLP)에 해당합니다. [2] nan [3]
programming model considering 고려하는 프로그래밍 모델
The problem is formulated as a mixed-integer nonlinear programming model considering the service availability structure and hub congestion effects. [1] To represent this problem, a mixed-integer nonlinear programming model considering the daily demand and solar radiation curves was developed. [2] A mixed-integer nonlinear programming model considering vessel velocity is proposed to minimize the total debris collection cost. [3]문제는 서비스 가용성 구조와 허브 혼잡 효과를 고려하여 혼합 정수 비선형 계획법 모델로 공식화됩니다. [1] 이를 표현하기 위해 일조량과 일사량 곡선을 고려한 혼합 정수 비선형 계획법 모델을 개발하였다. [2] nan [3]
multi objective optimization 다중 목표 최적화
A methodology has been proposed for VVWC based on a mixed-integer nonlinear multi-objective optimization problem, which is solved by utilizing goal programing approach in order to guarantee Pareto optimality of the solution. [1] On the premise that the error of each index is less than 5%, a linear weighted multi-objective optimization model based on integer nonlinear programming considering cost and performance is established, and the lubricating oil blending scheme is obtained. [2] This work deals with the formulation of a mixed-integer nonlinear multi-objective optimization (MOO) problem having five objectives to optimize the design of a conventional batch extractive distillation (BED). [3]VVWC에 대한 방법론은 혼합 정수 비선형 다중 목적 최적화 문제를 기반으로 제안되었으며 솔루션의 파레토 최적성을 보장하기 위해 목표 프로그래밍 방식을 사용하여 해결되었습니다. [1] 각 지수의 오차가 5% 미만임을 전제로 비용과 성능을 고려한 정수 비선형 계획법 기반의 선형 가중 다목적 최적화 모델을 수립하고 윤활유 혼합 방식을 구한다. [2] 이 작업은 기존 배치 추출 증류(BED)의 설계를 최적화하기 위한 5가지 목표를 가진 혼합 정수 비선형 다중 목적 최적화(MOO) 문제의 공식화를 다룹니다. [3]
second order cone 2차 원뿔
The mixed-integer nonlinear programming problem is converted to a mixed-integer second-order cone (SOCP) and the SOCP and an improved hybrid intelligent algorithm are applied to solve the ESSs planning model. [1] The original mixed-integer nonlinear programming (MINLP) model has been transformed into a mixed-integer conic equivalent via second-order cone programming, which produces a MI-SOCP approximation. [2] The problem is first modeled as a mixed-integer nonlinear program; then, reformulated as a mixed-integer second-order cone program and can be optimally solved. [3]혼합 정수 비선형 계획법 문제를 혼합 정수 2차 원뿔(SOCP)로 변환하고 SOCP와 개선된 하이브리드 지능형 알고리즘을 적용하여 ESS 계획 모델을 해결합니다. [1] 원래의 혼합 정수 비선형 계획법(MINLP) 모델은 MI-SOCP 근사치를 생성하는 2차 원뿔 계획법을 통해 등가 혼합 정수 원추 계획법으로 변환되었습니다. [2] nan [3]
integer linear programming 정수 선형 계획법
We compare this matheuristic with four algorithms: a Hybrid algorithm based on Integer Linear Programming, a Hybrid algorithm based on Integer Nonlinear Programming, the Parallel Prioritized Genetic Solver, and a greedy algorithm called prioritized-ICPL. [1]우리는 이 수학을 4가지 알고리즘, 즉 정수 선형 계획법에 기반한 하이브리드 알고리즘, 정수 비선형 계획법에 기반한 하이브리드 알고리즘, 병렬 우선 순위 지정 유전자 솔버 및 우선 순위 지정-ICPL이라는 탐욕적인 알고리즘과 비교합니다. [1]
supply chain network 공급망 네트워크
To address efficiency issues in a sustainable closed-loop supply chain network, a stochastic integrated multi-objective mixed integer nonlinear programming model is developed in this paper, in which sustainability outcomes as well as efficiency of facility resource utilization are considered in the design of a sustainable supply chain network. [1]지속 가능한 폐쇄 루프 공급망 네트워크의 효율성 문제를 해결하기 위해 이 백서에서는 확률적 통합 다중 목표 혼합 정수 비선형 계획법 모델을 개발했으며, 여기서 지속 가능성 결과와 시설 자원 활용 효율성을 고려합니다. 지속 가능한 공급망 네트워크. [1]
Mixed Integer Nonlinear 혼합 정수 비선형
Generalized Benders decomposition (GBD) is a globally optimal algorithm for mixed integer nonlinear programming (MINLP) problems, which are NP-hard and can be widely found in the area of wireless resource allocation. [1] The problem is formulated as a convex mixed integer nonlinear optimization problem, where the objective is to reduce the energy use by finding the optimal execution time and execution order of the robot motions. [2] The problem is shown to be an NP-hard mixed integer nonlinear programming problem with a group of hidden non-linear equality constraints. [3] Initially, the total cost of the system is provided and then a mixed integer nonlinear programming problem is formulated aiming to determine the optimal policy i. [4] The mixed integer nonlinear programming (MINLP) based Volt/Var-CVR problem is solved using Equilibrium Optimizer (EO) algorithm. [5] Each reformulated optimization problem becomes a mixed integer nonlinear programming (MINLP) over a time slot. [6] Although the formulated optimization is a mixed integer nonlinear programmming, we convert it to a convex problem and develop a successive convex approximation (SCA) based algorithm to effectively solve it. [7] This problem is formulated as a mixed integer nonlinear programming (MINLP) which are difficult to address in general. [8] Specifically, we first formulate the joint offloading decision and resource allocation problem as a Mixed Integer NonLinear Programming (MINLP) problem. [9] This framework, when combined with the array of developed techniques for discrete problems, illuminates a new pathway to solving mixed integer nonlinear programs with the help of DDs. [10] Thirdly, the mixed integer nonlinear programming model is solved by combining particle swarm optimization, cone programming and monte carlo simulation. [11] In this paper, we tackle this problem by designing a compressive sensing framework that jointly estimates the systems states and network topology via an integrated mixed integer nonlinear program (MINLP) formulation. [12] A problem of jointly designing UAVs’ positions, transmit beamforming, as well as UAV-UE association is formulated in the form of mixed integer nonlinear programming (MINLP) to maximize the sum UEs’ achievable rate subject to limited fronthaul capacity constraints. [13] In this study, the centralized voltage regulation is performed based on the worst voltage variation scenarios of ADNs, where a multi-objective mixed integer nonlinear programming (MINP) model with time-varying decision variables is established. [14] We convert the resulting non-convex mixed integer nonlinear programming problem into an equivalent quadratically constrained quadratic programming (QCQP) problem, which we solve via a novel Energy-Efficient Task Offloading (EETO) algorithm. [15] A Mixed Integer Nonlinear Programming (MINLP) based multi-constraint TAS model is formulated, which explicitly considers the appointment change cost, queuing cost, and morning and evening peak congestion cost. [16] In this paper, we present a novel mixed integer nonlinear flight scheduling and fleet assignment optimization model wherein air travel demand generation and allocation are simultaneously and consistently endogenized. [17] Then, based on this combined superstructure model, a mathematical formulation of multiple objective mixed integer nonlinear programming describing the SPS coupled with desulfurization and denitrification was established. [18] Formulated as an intractable mixed integer nonlinear programming (MINLP), we divide the problem into three phases. [19] Regarding this finding and the typical application scenario where the hydrogen gas is stored in gasholders and used in hydrogen oxygen fuel cells, TEP with water electrolysis is further established as a mixed integer nonlinear programing (MINLP) problem. [20] Then, the IDR-embedded scheduling of the IENGS is formulated as a complicated two-level mixed integer nonlinear programming (TL-MINLP) problem. [21] The research proposes a multi-objective mixed integer nonlinear programming model to design a closed-loop supply chain network based on the e-commerce context. [22] Minimum-cost inspection times, as well as the required number of test units and the maximum number of failures allowed, are derived by solving mixed integer nonlinear programming problems. [23] The developed PFRWE model has improved upon the existing methods for water-energy nexus through incorporation of fuzzy flexible programming, fuzzy possibilistic programming, robust programming and mixed integer nonlinear programming into a general framework. [24] The optimization problem is formulated as a mixed integer nonlinear program (MINLP) problem. [25] However, cooptimization of vehicle speed and transmission gearshift leads to a mixed integer nonlinear program (MINLP), solving which can be computationally very challenging. [26] Given that the formulated optimization problem is a non-convex mixed integer nonlinear programming problem, a decomposition into subproblems is performed and a two-stage heuristic algorithm is proposed. [27] Although the resulting optimization problem is a mixed integer nonlinear programmming, we decompose it into two subproblems and develop an alternating iterative approach to effectively solve them. [28] Mixed integer nonlinear programming and a heuristic method are used to solve the optimization problems in this algorithm. [29] Hydrogen networks synthesis is then formulated as a new mixed integer nonlinear model whose nonlinearities are only attributed to the bilinear and concave terms. [30] Proposed formulation is a mixed integer nonlinear programming problem (MINLP), which takes the existence of inputs, neurons and connections of the network into account by binary variables in addition to continuous weights of existing connections. [31] In this paper, we propose a linearization algorithm for solving a Mixed Integer NonLinear Problem (MINLP) for Intersection Management (IM) of Connected Autonomous Vehicles (CAVs). [32] An optimal solution algorithm is proposed based on the idea of branch-and-price for addressing this complicated mixed integer nonlinear programming problem. [33] A novel canonical duality theory (CDT) is presented for solving general bilevel mixed integer nonlinear optimization governed by linear and quadratic knapsack problems. [34] Firstly, the QoS-oriented joint optimization problem of concurrent scheduling and power control is formulated as a mixed integer nonlinear programming (MINLP) problem, where the energy consumption is reduced and the number of the successfully transmitted flows is enhanced. [35] Hence, a mathematical model mixed integer nonlinear programming developed in this area. [36] A Mixed Integer Nonlinear Programming model is proposed to formulate the studied problem. [37] This practical model leads to a bilevel mixed integer nonlinear program, which is difficult to solve because the lower level problem incorporates nonconvex integer variables. [38] By solving the mixed integer nonlinear programming model, the optimal operation plans of the integration in different periods can be obtained. [39] This paper presents an improved method for the Mixed Integer Nonlinear Programming (MINLP) synthesis of flexible Heat Exchanger Network with a large number of uncertain parameters. [40] In the present work, various objective functions were formulated and optimized using the mixed integer nonlinear programming and the generalized reduced gradient nonlinear method from the solver tool of Microsoft® Excel 2016, respectively. [41] This problem is formulated as a non-convex mixed integer nonlinear programming (MINLP) problem. [42] In this paper, in order to guarantee control system performance and image clarity, the time slot allocation and transmission power for heterogeneous sensors are jointly optimized by formulating a mixed integer nonlinear programming (MINLP) problem. [43] In particular, it is shown in this paper that the resulting optimization algorithm should solve a dynamic non-convex stochastic Mixed Integer Nonlinear Programming problem. [44] The proposed model incorporates the following four constraints, 1) the maximum dose of radiation per evacuee, 2) the limitation of bus capacity, 3) the number of evacuees at demand node (bus pickup stop), 4) evacuees balance at demand and shelter nodes, which is formulated as a mixed integer nonlinear programming (MINLP) problem. [45] Second, to prevent the phenomenon of the solution of the intelligent algorithm falling into the local optimum, this paper innovatively uses the Levy flight to optimize the learning step length of the chicken swarm algorithm to quickly solve the proposed mixed integer nonlinear programming model. [46] Starting from a mixed integer nonlinear programming model for the first train timetabling problem, we linearize and reformulate the model using the auxiliary binary variables. [47] First, the system latency minimization problem is formulated as a mixed integer nonlinear programming problem by optimizing reverse offloading decisions and the communication and computation resources allocation. [48] A mixed integer nonlinear programming problem (MINLP) is set up where the goal is to minimize relay operating times. [49] By adopting Lyapunov optimization technique and fractional programming theory, the non-concave EE maximization is converted into a mixed integer nonlinear optimization (MINO) problem. [50]GBD(Generalized Benders Decomposition)는 NP-hard이고 무선 자원 할당 영역에서 널리 볼 수 있는 MINLP(혼합 정수 비선형 계획법) 문제에 대한 전역 최적 알고리즘입니다. [1] 이 문제는 로봇 동작의 최적 실행 시간과 실행 순서를 찾아 에너지 사용을 줄이는 것이 목적인 볼록 혼합 정수 비선형 최적화 문제로 공식화됩니다. [2] nan [3] nan [4] nan [5] nan [6] 공식화된 최적화는 혼합 정수 비선형 프로그래밍이지만 이를 볼록 문제로 변환하고 SCA(successive convex approximation) 기반 알고리즘을 개발하여 효과적으로 해결합니다. [7] nan [8] 특히, 먼저 공동 오프로딩 결정 및 리소스 할당 문제를 MINLP(혼합 정수 비선형 프로그래밍) 문제로 공식화합니다. [9] nan [10] nan [11] nan [12] nan [13] 이 연구에서 중앙 집중식 전압 조정은 ADN의 최악의 전압 변동 시나리오를 기반으로 수행되며, 여기서 시변 결정 변수가 있는 다중 목표 혼합 정수 비선형 프로그래밍(MINP) 모델이 설정됩니다. [14] 결과로 생성된 비볼록 혼합 정수 비선형 계획법 문제를 동등한 2차 제약 2차 계획법(QCQP) 문제로 변환합니다. 이 문제는 새로운 EETO(Energy-Efficient Task Offloading) 알고리즘을 통해 해결합니다. [15] nan [16] nan [17] nan [18] nan [19] nan [20] nan [21] 이 연구는 전자 상거래 컨텍스트를 기반으로 폐쇄 루프 공급망 네트워크를 설계하기 위해 다중 목표 혼합 정수 비선형 프로그래밍 모델을 제안합니다. [22] nan [23] nan [24] nan [25] nan [26] 공식화된 최적화 문제가 볼록하지 않은 혼합 정수 비선형 계획법 문제임을 감안하여 하위 문제로 분해를 수행하고 2단계 휴리스틱 알고리즘을 제안합니다. [27] 결과 최적화 문제는 혼합 정수 비선형 프로그래밍이지만, 우리는 그것을 두 개의 하위 문제로 분해하고 이를 효과적으로 해결하기 위해 교대 반복 접근 방식을 개발합니다. [28] nan [29] 그런 다음 수소 네트워크 합성은 비선형성이 쌍선형 및 오목형 항에만 기인하는 새로운 혼합 정수 비선형 모델로 공식화됩니다. [30] nan [31] nan [32] nan [33] nan [34] nan [35] nan [36] nan [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]
1 Integer Nonlinear 1 정수 비선형
The optimization models are formulated as a 0–1 Integer Nonlinear Programming problem and solved using the General Algebraic Modeling System without the use of heuristic models which were characteristic of all previous models for the simultaneous determine of the pipe layout and pipe design of sewer networks. [1] For the situation of SEQ, when both DMs’ preferences are unknown, the focal DM’s algorithm is a 0–1 integer nonlinear programming problem while, under the condition that the opponent’s preferences are known, the focal DM’s 0–1 integer programming problem is linear. [2] We formulate a novel 0-1 integer nonlinear optimization model, subsequently linearized to enable efficient computational performance, to select vendors that minimize the maximum formation time for creating agile virtual reverse supply chains. [3] The framework is based on a novel mixed 0–1 integer nonlinear programming model for solving the RP problem with constraints originating from the needs of individuals with disabilities; unlike previous models, it minimizes: (1) the collision risk with obstacles within a path by prioritizing the safer paths; (2) the walking time; (3) the number of turns by constructing smooth paths, and (4) the loss of cultural interest by penalizing multiple crossovers of the same paths, while satisfying user preferences, such as points of interest to visit and a desired tour duration. [4]최적화 모델은 0-1 정수 비선형 계획법 문제로 공식화되고 하수도 네트워크의 파이프 레이아웃과 파이프 설계를 동시에 결정하기 위해 이전의 모든 모델의 특징이었던 휴리스틱 모델을 사용하지 않고 일반 대수 모델링 시스템을 사용하여 해결되었습니다. [1] SEQ의 상황에서 두 DM의 선호도가 알려지지 않은 경우 초점 DM의 알고리즘은 0–1 정수 비선형 계획법 문제인 반면, 상대방의 선호도가 알려진 조건에서 초점 DM의 0–1 정수 계획법 문제는 선형입니다. . [2] nan [3] nan [4]
Mix Integer Nonlinear 혼합 정수 비선형
Four mix integer nonlinear programming optimization models were defined–one without and three with social impact consideration. [1] Four mix integer nonlinear programming (MINLP) optimization models were defined without and with social impact consideration. [2]네 가지 혼합 정수 비선형 계획법 최적화 모델이 정의되었습니다. 하나는 사회적 영향을 고려한 것이고 다른 하나는 사회적 영향을 고려한 것입니다. [1] 네 가지 혼합 정수 비선형 계획법(MINLP) 최적화 모델이 사회적 영향을 고려하지 않고 정의했습니다. [2]
integer nonlinear programming 정수 비선형 계획법
Generalized Benders decomposition (GBD) is a globally optimal algorithm for mixed integer nonlinear programming (MINLP) problems, which are NP-hard and can be widely found in the area of wireless resource allocation. [1] The problem is formulated as a mixed-integer nonlinear programming model considering the service availability structure and hub congestion effects. [2] The problem is formulated as a mixed-integer nonlinear programming (MINLP) problem. [3] Four mix integer nonlinear programming optimization models were defined–one without and three with social impact consideration. [4] The problem is shown to be an NP-hard mixed integer nonlinear programming problem with a group of hidden non-linear equality constraints. [5] The embodied energy is minimized through simultaneous optimization of element sizing and actuator placement, which is formulated as a mixed-integer nonlinear programming problem. [6] It involves symbolic modeling and mixed-integer nonlinear programming (MINLP) in the first phase, and a many-objective evolutionary algorithm (many-OEA)-based sizing refiner in the second phase. [7] Initially, the total cost of the system is provided and then a mixed integer nonlinear programming problem is formulated aiming to determine the optimal policy i. [8] With these models, the joint path, energy and sample size planning (JPESP) problem is formulated as a large-scale mixed-integer nonlinear programming (MINLP) problem, which is nontrivial to solve due to the high-dimensional discontinuous variables related to UGV movement. [9] The mixed integer nonlinear programming (MINLP) based Volt/Var-CVR problem is solved using Equilibrium Optimizer (EO) algorithm. [10] This work presents a mixed-integer nonlinear programming (MINLP) formulation for the multi-period planning of a Closed-Loop Supply Chain (CLSC) with the emphasis placed on remanufacturing conditions. [11] We first build a new mixed-integer nonlinear programming (MINLP) model with the objective of minimizing the risk of project cost overrun, which provides a vehicle to obtain optimal solutions. [12] The optimum solutions in the harsh environment and favorable environment are obtained by a numerical example, and the results show the proposed method is more accurate in grey degree and more robust in site selection than the interval chance-constraint mixed-integer nonlinear programming method. [13] Since the formulated problem is a mixed-integer nonlinear programming problem, we decompose the problem into two subproblems, where one is solved by a multi-cell least beam interference (MLBI) algorithm to mitigate such ICI. [14] This paper focuses on the concept of a subsea satellite well system in deep water oil field, and a mixed-integer nonlinear programming model (MINLP) is proposed to help design the layout to minimize the payback period. [15] We consider a nonconvex mixed-integer nonlinear programming (MINLP) model proposed by Goldberg et al. [16] This paper applies the sine cosine algorithm to the operation planning of wind-penetrated thermoelectric systems considering wind-related uncertainties, which is a mixed-integer nonlinear programming problem often referred to as thermal unit commitment with power integration. [17] The optimization models are formulated as a 0–1 Integer Nonlinear Programming problem and solved using the General Algebraic Modeling System without the use of heuristic models which were characteristic of all previous models for the simultaneous determine of the pipe layout and pipe design of sewer networks. [18] The container slot allocation problem is investigated in this study using a two-stage stochastic mixed-integer nonlinear programming model. [19] Each reformulated optimization problem becomes a mixed integer nonlinear programming (MINLP) over a time slot. [20] The mixed-integer nonlinear programming (MINLP) setting due to the water tower ON/OFF controller greatly increases the computational complexity of the optimisation problem. [21] This paper presents a stochastic mixed-integer nonlinear programming model for the optimal energy management system of unbalanced three-phase of alternating current microgrids. [22] On the premise that the error of each index is less than 5%, a linear weighted multi-objective optimization model based on integer nonlinear programming considering cost and performance is established, and the lubricating oil blending scheme is obtained. [23] This problem is formulated as a mixed integer nonlinear programming (MINLP) which are difficult to address in general. [24] Specifically, we first formulate the joint offloading decision and resource allocation problem as a Mixed Integer NonLinear Programming (MINLP) problem. [25] Third, a bi-objective 0-1 mixed-integer nonlinear programming-based robust design model and its solution procedures are proposed to obtain optimum settings of both qualitative and quantitative variables. [26] The LSP is a discrete–continuous problem and is formulated based on a mixed-integer nonlinear programming approach. [27] Hydro Unit Commitment (HUC) is an important problem of power systems and when it is dealt with via a mathematical programming approach and optimization, it leads to the complicated class of mixed-integer nonlinear programming (MINLP). [28] A joint user grouping, hybrid beam coordination and power control strategy is proposed, which is formulated as a Lyapunov optimization based mixed-integer nonlinear programming (MINLP) with unitmodulus and nonconvex coupling constraints. [29] Thirdly, the mixed integer nonlinear programming model is solved by combining particle swarm optimization, cone programming and monte carlo simulation. [30] A mixed-integer nonlinear programming (MINLP) approach is applied to formulate a model to minimize total time associated with order processing, handling, packaging, shipping, and vehicle maintenance. [31] The OPF problem is formulated as a mixed-integer nonlinear programming (MINLP) model, in which the objective function aims to minimize the fuel generation costs, subject to the physical and operational constraints of the power system. [32] A problem of jointly designing UAVs’ positions, transmit beamforming, as well as UAV-UE association is formulated in the form of mixed integer nonlinear programming (MINLP) to maximize the sum UEs’ achievable rate subject to limited fronthaul capacity constraints. [33] The full-flexibility options model is formulated as a mixed-integer nonlinear programming (MINLP) problem while some combinations of flexible options lead to simpler NLP problems. [34] In this paper, to integrate the decisions of parts scheduling, Material Requirement Planning (MRP), Production Planning (PP) and Transportation Planning (TP) for designing a Cellular Manufacturing System (CMS) under a dynamic environment, a Mixed-Integer Nonlinear Programming (MINLP) mathematical model is formulated. [35] Additionally, the scheme inherently is a non-convex mixed-integer nonlinear programming framework. [36] In this study, the centralized voltage regulation is performed based on the worst voltage variation scenarios of ADNs, where a multi-objective mixed integer nonlinear programming (MINP) model with time-varying decision variables is established. [37] We then develop an iterative two-stage algorithm to obtain sub-optimal solutions to the formulated Mixed-Integer Nonlinear Programming (MINLP) problems. [38] The problem is formulated as a multi-variable mixed-integer nonlinear programming (MINLP) problem and the objective of this research is to achieve the minimum integrated expected cost where decision variables are: reorder point, delivery lot size, number of deliveries, and delivery time thresholds. [39] The formulated problem is a mixed-integer nonlinear programming (MINLP) problem. [40] Although the first version of the model is developed as a mixed-integer nonlinear programming (MINLP) model, this study attempts to convert it to a mixed-integer linear programming (MILP) model using the logarithmic transformation and piecewise linear approximation methods. [41] We convert the resulting non-convex mixed integer nonlinear programming problem into an equivalent quadratically constrained quadratic programming (QCQP) problem, which we solve via a novel Energy-Efficient Task Offloading (EETO) algorithm. [42] Next, according to the presented new strategy, an integer nonlinear programming model adapted to unbalanced passenger flow is established, which can be solved efficiently by the controlled random search (CRS) algorithm. [43] A Mixed Integer Nonlinear Programming (MINLP) based multi-constraint TAS model is formulated, which explicitly considers the appointment change cost, queuing cost, and morning and evening peak congestion cost. [44] PaDOA proceeds by alternating between solving large-scale structured mixed-integer linear programming problems and partially decoupled mixed-integer nonlinear programming subproblems that comprise much fewer integer variables. [45] Then, based on this combined superstructure model, a mathematical formulation of multiple objective mixed integer nonlinear programming describing the SPS coupled with desulfurization and denitrification was established. [46] In this paper, a new mixed-integer nonlinear programming formulation is used to determine types of vehicles to carry raw materials, suppliers to procure, priority of each part in order to process, and cell formation to configure work centers. [47] Formulated as an intractable mixed integer nonlinear programming (MINLP), we divide the problem into three phases. [48] A mixed-integer nonlinear programming model to present the problem was developed with the aim of minimizing the annual operating costs of the power system, which is the sum of the costs of the energy losses and of the installation of the STATCOMs. [49] We leverage the separability of DRESS formulation in deriving an exact mixed-integer nonlinear programming reformulation. [50]GBD(Generalized Benders Decomposition)는 NP-hard이고 무선 자원 할당 영역에서 널리 볼 수 있는 MINLP(혼합 정수 비선형 계획법) 문제에 대한 전역 최적 알고리즘입니다. [1] 문제는 서비스 가용성 구조와 허브 혼잡 효과를 고려하여 혼합 정수 비선형 계획법 모델로 공식화됩니다. [2] 문제는 혼합 정수 비선형 계획법(MINLP) 문제로 공식화됩니다. [3] 네 가지 혼합 정수 비선형 계획법 최적화 모델이 정의되었습니다. 하나는 사회적 영향을 고려한 것이고 다른 하나는 사회적 영향을 고려한 것입니다. [4] nan [5] 혼합 정수 비선형 계획법 문제로 공식화되는 요소 크기 조정 및 액추에이터 배치의 동시 최적화를 통해 내재된 에너지가 최소화됩니다. [6] 첫 번째 단계에는 기호 모델링 및 혼합 정수 비선형 계획법(MINLP)이 포함되고 두 번째 단계에는 다객관적 진화 알고리즘(many-OEA) 기반 크기 조정기가 포함됩니다. [7] nan [8] 이러한 모델을 사용하여 JPESP(Joint Path, Energy 및 Sample Size Planning) 문제는 UGV와 관련된 고차원 불연속 변수로 인해 해결하기 어려운 대규모 MINLP(혼합 정수 비선형 계획법) 문제로 공식화됩니다. 움직임. [9] nan [10] 이 작업은 재제조 조건에 중점을 둔 폐쇄 루프 공급망(CLSC)의 다중 기간 계획을 위한 혼합 정수 비선형 계획법(MINLP) 공식을 제시합니다. [11] 우리는 먼저 최적의 솔루션을 얻을 수 있는 수단을 제공하는 프로젝트 비용 초과의 위험을 최소화하기 위한 목적으로 새로운 혼합 정수 비선형 계획법(MINLP) 모델을 구축합니다. [12] 가혹한 환경과 유리한 환경에서 최적의 해를 수치예제를 통해 구하였으며, 결과는 제안한 방법이 구간 기회-제약 혼합 정수 비선형 계획법보다 계조에서 더 정확하고 부지 선택에서 더 강건함을 보여준다. [13] 공식화된 문제는 혼합 정수 비선형 계획법 문제이므로 문제를 두 개의 하위 문제로 분해합니다. 여기서 하나는 이러한 ICI를 완화하기 위해 다중 셀 최소 빔 간섭(MLBI) 알고리즘으로 해결됩니다. [14] 본 논문은 심해 유전에서 해저 위성 우물 시스템의 개념에 초점을 맞추고 투자 회수 기간을 최소화하도록 레이아웃을 설계하는 데 도움이 되는 혼합 정수 비선형 계획법 모델(MINLP)을 제안합니다. [15] Goldberg et al.이 제안한 비볼록 혼합 정수 비선형 계획법(MINLP) 모델을 고려합니다. [16] 본 논문에서는 풍력 관련 불확실성을 고려한 풍력 투과형 열전 시스템의 운영 계획에 사인 코사인 알고리즘을 적용합니다. 이는 종종 전력 통합을 통한 열 단위 투입이라고 하는 혼합 정수 비선형 계획법 문제입니다. [17] 최적화 모델은 0-1 정수 비선형 계획법 문제로 공식화되고 하수도 네트워크의 파이프 레이아웃과 파이프 설계를 동시에 결정하기 위해 이전의 모든 모델의 특징이었던 휴리스틱 모델을 사용하지 않고 일반 대수 모델링 시스템을 사용하여 해결되었습니다. [18] 이 연구에서는 2단계 확률론적 혼합 정수 비선형 계획법 모델을 사용하여 컨테이너 슬롯 할당 문제를 조사합니다. [19] nan [20] 급수탑 ON/OFF 컨트롤러로 인한 혼합 정수 비선형 계획법(MINLP) 설정은 최적화 문제의 계산 복잡성을 크게 증가시킵니다. [21] 본 논문은 교류 마이크로그리드의 불평형 3상 마이크로그리드의 최적 에너지 관리 시스템을 위한 확률론적 혼합 정수 비선형 계획법 모델을 제시한다. [22] 각 지수의 오차가 5% 미만임을 전제로 비용과 성능을 고려한 정수 비선형 계획법 기반의 선형 가중 다목적 최적화 모델을 수립하고 윤활유 혼합 방식을 구한다. [23] nan [24] 특히, 먼저 공동 오프로딩 결정 및 리소스 할당 문제를 MINLP(혼합 정수 비선형 프로그래밍) 문제로 공식화합니다. [25] 셋째, 이중 목적 0-1 혼합 정수 비선형 계획법 기반 강건 설계 모델과 그 해법 절차를 제안하여 정성적 변수와 양적 변수 모두의 최적 설정을 얻습니다. [26] LSP는 이산-연속 문제이며 혼합 정수 비선형 계획법 접근 방식을 기반으로 공식화됩니다. [27] HUC(Hydro Unit Commitment)는 전력 시스템의 중요한 문제이며 수학적 프로그래밍 접근 방식 및 최적화를 통해 처리될 때 복잡한 MINLP(혼합 정수 비선형 프로그래밍) 클래스로 이어집니다. [28] 공동 사용자 그룹화, 하이브리드 빔 조정 및 전력 제어 전략이 제안되며, 이는 단위 계수 및 비볼록 결합 제약 조건이 있는 Lyapunov 최적화 기반 혼합 정수 비선형 계획법(MINLP)으로 공식화됩니다. [29] nan [30] 혼합 정수 비선형 계획법(MINLP) 접근 방식을 적용하여 주문 처리, 취급, 포장, 배송 및 차량 유지 관리와 관련된 총 시간을 최소화하는 모델을 공식화합니다. [31] OPF 문제는 MINLP(혼합 정수 비선형 계획법) 모델로 공식화되며, 여기서 목적 함수는 전력 시스템의 물리적 및 운영적 제약 조건에 따라 연료 생성 비용을 최소화하는 것을 목표로 합니다. [32] nan [33] 완전 유연성 옵션 모델은 MINLP(혼합 정수 비선형 계획법) 문제로 공식화되는 반면 유연한 옵션의 일부 조합은 더 간단한 NLP 문제로 이어집니다. [34] 본 논문에서는 동적 환경에서 셀룰러 제조 시스템(CMS)을 설계하기 위한 부품 스케줄링, 자재 소요량 계획(MRP), 생산 계획(PP) 및 운송 계획(TP)의 결정을 통합하기 위해 혼합 정수 비선형 계획법 (MINLP) 수학적 모델이 공식화되었습니다. [35] 또한 이 체계는 본질적으로 볼록하지 않은 혼합 정수 비선형 계획법 프레임워크입니다. [36] 이 연구에서 중앙 집중식 전압 조정은 ADN의 최악의 전압 변동 시나리오를 기반으로 수행되며, 여기서 시변 결정 변수가 있는 다중 목표 혼합 정수 비선형 프로그래밍(MINP) 모델이 설정됩니다. [37] 그런 다음 공식화된 혼합 정수 비선형 계획법(MINLP) 문제에 대한 차선책 솔루션을 얻기 위해 반복적인 2단계 알고리즘을 개발합니다. [38] 이 문제는 MINLP(다변수 혼합 정수 비선형 계획법) 문제로 공식화되며, 이 연구의 목적은 의사 결정 변수가 재주문 지점, 배송 로트 크기, 배송 수 및 배송인 경우 최소 통합 예상 비용을 달성하는 것입니다. 시간 임계값. [39] 공식화된 문제는 혼합 정수 비선형 계획법(MINLP) 문제입니다. [40] 모델의 첫 번째 버전은 혼합 정수 비선형 계획법(MINLP) 모델로 개발되었지만, 본 연구에서는 대수 변환 및 조각별 선형 근사법을 사용하여 혼합 정수 선형 계획법(MILP) 모델로 변환하려고 합니다. [41] 결과로 생성된 비볼록 혼합 정수 비선형 계획법 문제를 동등한 2차 제약 2차 계획법(QCQP) 문제로 변환합니다. 이 문제는 새로운 EETO(Energy-Efficient Task Offloading) 알고리즘을 통해 해결합니다. [42] 다음으로, 제시된 새로운 전략에 따라 불균형 승객 흐름에 적합한 정수 비선형 프로그래밍 모델이 설정되며, 이는 CRS(Controlled Random Search) 알고리즘에 의해 효율적으로 해결될 수 있습니다. [43] nan [44] nan [45] nan [46] 이 논문에서는 새로운 혼합 정수 비선형 계획법 공식을 사용하여 원자재를 운반할 차량 유형, 조달할 공급자, 처리할 각 부품의 우선 순위, 작업 센터를 구성하기 위한 셀 형성을 결정합니다. [47] nan [48] 문제를 제시하기 위한 혼합 정수 비선형 계획법 모델은 전력 시스템의 연간 운영 비용(에너지 손실 및 STATCOM 설치 비용의 합)을 최소화하기 위해 개발되었습니다. [49] 정확한 혼합 정수 비선형 계획법 재구성을 유도할 때 DRESS 공식의 분리 가능성을 활용합니다. [50]
integer nonlinear program 정수 비선형 계획
This problem is formulated as a binary mixed-integer nonlinear program that can be solved using the branch-and-bound algorithm. [1] Given the urgent need to develop decision support tools that can prevent the overcrowding of vehicles, this study introduces a dynamic integer nonlinear program to derive the optimal service patterns of individual vehicles that are ready to be dispatched. [2] The resulting OWF problem is a mixed-integer nonlinear program (MINLP). [3] , which links) for placing PRVs can be casted as a difficult mixed-integer nonlinear program (MINLP) where binary variables are used to represent the presence of PRVs on links. [4] This framework, when combined with the array of developed techniques for discrete problems, illuminates a new pathway to solving mixed integer nonlinear programs with the help of DDs. [5] This well-known engineering problem can be casted into a mixed-integer nonlinear program (MINLP) where binary variables are introduced to represent positions of PRVs. [6] In this paper, we tackle this problem by designing a compressive sensing framework that jointly estimates the systems states and network topology via an integrated mixed integer nonlinear program (MINLP) formulation. [7] The contributions of this work are: firstly, modelling a novel RDP formulation as a mixed-integer nonlinear program (MINLP); secondly, incorporating both directional and omnidirectional antenna models into the RDP; and, lastly, introducing an adapted Leaders and Followers (LaF) solution algorithm for the RDP. [8] Searching for a grid topology that offers more efficient operation leads to a mixed-integer nonlinear program (MINLP) which is computationally challenging due to: i) Non-convex power flow equations, (ii) Non-convex converter loss equations, and iii) Binary variables accounting for the operational status of transmission lines. [9] To solve such a MLDO problem, we transform it into a mixed-integer nonlinear program (MINLP) using the Big M reformulation and the simultaneous collocation method. [10] In this paper, we formulate a mixed-integer nonlinear program and develop a hybrid particle swarm optimization algorithm that can find a suitable solution within a reasonable time. [11] The optimization problem is formulated as a nonconvex Mixed-Integer Nonlinear Program (MINLP) and solved to global optimality using ANTIGONE. [12] The overall problem is formulated as a mixed-integer nonlinear program (MINLP). [13] The resulting charging demand is the key input to a mixed-integer nonlinear program that seeks charging station configuration. [14] The optimization problem is formulated as a mixed integer nonlinear program (MINLP) problem. [15] However, cooptimization of vehicle speed and transmission gearshift leads to a mixed integer nonlinear program (MINLP), solving which can be computationally very challenging. [16] The proposed solution is meant as a substitute for the cutting-edge approaches, such as model predictive control, where the problem is a mixed-integer nonlinear program (MINLP), known to be computationally-intensive, and therefore requiring specialized hardware and sophisticated solvers, not suited for residential use. [17] More efficient techniques for solving nonconvex mixed-integer nonlinear programs and better methods of including more accurate, higher-order unit models for industrial problems within network optimisation problems are particularly important, as current methods provide highly simplified unit representations that do not take into account many important practical design considerations that have significant cost implications. [18] The problem is formulated as a mixed-integer nonlinear program and linearization is proposed. [19] The planning problem is formulated as a mixed-integer nonlinear program to maximize the net annual revenue. [20] Then, an integer nonlinear program is formulated for the network-level resource pre-allocation. [21] This practical model leads to a bilevel mixed integer nonlinear program, which is difficult to solve because the lower level problem incorporates nonconvex integer variables. [22] We model the problem as a mixed-integer nonlinear program which is then linearized. [23] The study describes this situation using a mixed-integer nonlinear program with a piecewise approximation algorithm. [24] We formulate a mixed-integer nonlinear program for computing the optimal controller strategy and present two algorithms. [25] The problem is then formulated as a bi-objective mixed-integer nonlinear program. [26] The methodology includes a novel refrigeration cycle superstructure capable of reproducing a wide range of cycle architectures, and an effective solution algorithm (based on the decomposition of the problem on two levels) to tackle the challenging Mixed-Integer Nonlinear Program. [27] Two mixed-integer nonlinear programs are proposed for achieving the stable traffic state on arterials and in grid networks. [28] We first present a mixed-integer nonlinear program with bilinear terms in the objective function and the constraints. [29] First, we formulate the problem as a mixed-integer nonlinear program (MINLP) that can be transformed to MILP for some special cases. [30] To maximize energy efficiency, we propose a heuristic and a mixed-integer nonlinear program. [31] The challenging synthesis problem is formulated as a Mixed Integer NonLinear Program and tackled with a novel sequential algorithm based on the idea of optimizing the independent mass flow rates of the Rankine cycle superstructure with a derivative-free algorithm. [32] To optimally address design and control simultaneously, one must formulate a bi-level mixed-integer nonlinear program with a dynamic optimization problem as the inner problem; this is intractable. [33] Computational experiments show significant optimality gap improvement for integer nonlinear programs over the traditional cutting plane methods employed in the state-of-the-art solvers. [34] The design task is first posed as an NP-hard mixed-integer nonlinear program (MINLP) that is exactly reformulated as a mixed-integer linear program (MILP) using McCormick linearization. [35] The problem is formulated as a mixed-integer nonlinear program (MINLP) with linear constraints and a large number of separable concave terms in the objective function. [36] We build a mixed-integer nonlinear program that captures the nonlinear waiting time of drones at ABSMs. [37] We reveal that the problem under consideration is formally a mixed-integer nonlinear program (MINLP) and propose an improved brute-force search algorithm to find its optimal solutions. [38] The problem is formulated as a mixed integer nonlinear program and solved using a bilevel decomposition algorithm. [39] Considering the uncertainty of the selection of intersection control strategies, the problem is formulated as a two-stage stochastic mixed-integer nonlinear program. [40] Assigning shoes to a minimum number of box types is achieved using a 0–1 program, whereas the loading problem is tackled via a mixed-integer nonlinear program that minimizes the total volume of the container. [41] We formulate our problem as a mixed-integer nonlinear program (MINLP) and we define a heuristic, named Neighbor Exploration and Sequential Fixing (NESF), to facilitate the solution of the problem. [42] We model the dynamic rescheduling and bus holding problem as an integer nonlinear program (INLP) and we prove its NP-hardness. [43] To address the challenge, we propose novel Mixed Integer Nonlinear Programs (MINLPs) that are formulated such that they can be solved using off-the-shelf global optimization solvers. [44] Since the formulated problem is a mixed-integer nonlinear program (MINLP), we adopt an auxiliary variable to convert the original problem into a tractable problem, which is then solved by an efficient algorithm involving the Lagrangian dual method, the subgradient method and the multiple one-dimensional search algorithm. [45] After formulating this problem as a mixed-integer nonlinear program and proving its NP-completeness, we propose a modified guided population archive whale optimization algorithm to solve it. [46] This combinatorial optimization problem is initially formulated as a mixed-integer nonlinear program, which we then extend to account for the variability that is inherent to the demands of gas-fired electricity production and uncertainty in expected future loads. [47] Hence, a Mixed Integer Nonlinear Program (MINLP) that allows the selection of optimal heat transfer media, in TGS, has been formulated, and the performance of three different molten salts (solar salt, Hitec, and Hitec XL) has been analysed. [48] This optimal road maintenance problem is modeled as an integer nonlinear program, where the objective function evaluation is based on the solution of a stochastic variational inequality. [49] In this study, There is a mixed-integer nonlinear program (MINLP) for a two-echelon supply chain that focuses on supplier location, supplier selection and order allocation with green constraints. [50]이 문제는 분기 제한 알고리즘을 사용하여 풀 수 있는 이진 혼합 정수 비선형 프로그램으로 공식화됩니다. [1] 차량의 과밀화를 방지할 수 있는 의사결정 지원 도구의 개발이 시급하다는 점을 감안할 때, 본 연구에서는 출차 준비가 된 개별 차량의 최적 서비스 패턴을 도출하기 위해 동적 정수 비선형 프로그램을 도입합니다. [2] 결과 OWF 문제는 혼합 정수 비선형 프로그램(MINLP)입니다. [3] , PRV를 배치하기 위한 링크)는 이진 변수가 링크에 PRV의 존재를 나타내는 데 사용되는 어려운 혼합 정수 비선형 프로그램(MINLP)으로 캐스트될 수 있습니다. [4] nan [5] 이 잘 알려진 엔지니어링 문제는 이진 변수가 PRV의 위치를 나타내기 위해 도입된 혼합 정수 비선형 프로그램(MINLP)으로 캐스팅될 수 있습니다. [6] nan [7] 이 작업의 기여는 다음과 같습니다. 첫째, 혼합 정수 비선형 프로그램(MINLP)으로 새로운 RDP 공식을 모델링합니다. 둘째, 지향성 및 무지향성 안테나 모델을 모두 RDP에 통합합니다. 마지막으로 RDP를 위한 적응형 리더 및 팔로워(LaF) 솔루션 알고리즘을 도입합니다. [8] 보다 효율적인 작동을 제공하는 그리드 토폴로지를 검색하면 i) 비볼록 전력 흐름 방정식, (ii) 비볼록 변환기 손실 방정식 및 iii)으로 인해 계산이 어려운 혼합 정수 비선형 프로그램(MINLP)이 생성됩니다. 전송 라인의 작동 상태를 설명하는 이진 변수. [9] 이러한 MLDO 문제를 해결하기 위해 Big M 재구성과 동시 배열 방법을 사용하여 혼합 정수 비선형 프로그램(MINLP)으로 변환합니다. [10] 이 논문에서 우리는 혼합 정수 비선형 프로그램을 공식화하고 합리적인 시간 내에 적절한 솔루션을 찾을 수 있는 하이브리드 입자 군집 최적화 알고리즘을 개발합니다. [11] 최적화 문제는 비볼록 혼합 정수 비선형 계획법(MINLP)으로 공식화되고 ANTIGONE을 사용하여 전역 최적으로 해결됩니다. [12] 전체 문제는 혼합 정수 비선형 프로그램(MINLP)으로 공식화됩니다. [13] 결과적인 충전 수요는 충전소 구성을 찾는 혼합 정수 비선형 프로그램에 대한 핵심 입력입니다. [14] nan [15] nan [16] nan [17] nan [18] nan [19] nan [20] nan [21] nan [22] nan [23] nan [24] nan [25] 그런 다음 문제는 이중 목표 혼합 정수 비선형 프로그램으로 공식화됩니다. [26] nan [27] nan [28] nan [29] nan [30] nan [31] nan [32] nan [33] nan [34] nan [35] nan [36] nan [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 nonlinear optimization 정수 비선형 최적화
The problem is formulated as a convex mixed integer nonlinear optimization problem, where the objective is to reduce the energy use by finding the optimal execution time and execution order of the robot motions. [1] This paper extends that work in six important ways: (1) we introduce the concept of in-manhole sensors, as these sensors will reduce the number of manholes requiring on-site testing; (2) we present a realistic tree network depicting the topology of the sewer pipeline network; (3) for simulations, we present a method to create random tree networks exhibiting key attributes of a given community; (4) using the simulations, we empirically demonstrate that the mean and median number of manholes to be opened in a search follows a well-known logarithmic function; (5) we develop procedures for determining the number of sensors to deploy; (6) we formulate the sensor location problem as an integer nonlinear optimization and develop heuristics to solve it. [2] The resulting mixed-integer nonlinear optimization problem is approximately solved using three methods. [3] A novel canonical duality theory (CDT) is presented for solving general bilevel mixed integer nonlinear optimization governed by linear and quadratic knapsack problems. [4] By adopting Lyapunov optimization technique and fractional programming theory, the non-concave EE maximization is converted into a mixed integer nonlinear optimization (MINO) problem. [5] Solving mixed-integer nonlinear optimization problems (MINLPs) to global optimality is extremely challenging. [6] The offloading decisions and joint optimization of radio and computational resources result in a mixed integer nonlinear optimization problem which is NP hard. [7] The model is formulated as a mixed integer nonlinear optimization problem and solved in the genetic algorithm. [8] Because this mixed integer nonlinear optimization problem (MINLP) is NP-hard, we develop a polynomial-time approximation algorithm with a bounded approximation ratio. [9] The SCUC model is a two-stage mixed-integer nonlinear optimization problem so that a sequential linear approximation-based Column & Constraint Generation (SLA-based C&CG) algorithm is proposed. [10] The objective function in the present study is a mixed-integer nonlinear optimization problem. [11] We formulate a novel 0-1 integer nonlinear optimization model, subsequently linearized to enable efficient computational performance, to select vendors that minimize the maximum formation time for creating agile virtual reverse supply chains. [12] This work attempts to combine the strengths of two major technologies that have matured over the last three decades: global mixed-integer nonlinear optimization and branch-and-price. [13] Then, the process was modeled through a superstructure representation that resulted into a mixed-integer nonlinear optimization problem, enabling the optimization of an array of objectives for a given set of input water and energy supply, as well as output water demand restrictions. [14] We formulate a multiobjective mixed integer nonlinear optimization problem that seeks to determine the next maximum acceptable sampling moment of control plants and to minimize the energy usage in the uplinks and downlinks while taking into account the dynamics and intended performance of control plants, the quality of service of RC users, and power and subcarrier constraints. [15] The problem is formulated using the mixed integer nonlinear optimization method where the estimation algorithm is developed using the least error square technique. [16] Based on a bi-directional railway line, a mixed-integer nonlinear optimization model is established, in which the decision variables mainly include the track maintenance intervention type, the end time of track maintenance, arrival/departure time, arrival/departure orders, stopping plans, and train cancellations. [17] This paper presents a mixed-integer nonlinear optimization model for portfolio selection that considers four main performance measures for project management, namely, value maximization, strategic alignment, balance, and future preparedness. [18] The problem is formulated as a multi-objective mixed-integer nonlinear optimization to maximize the PVHC and minimize the voltage deviation simultaneously. [19] A mixed integer nonlinear optimization problem results from the interdependence between these distinct values. [20] For the first one, we propose a mathematical mixed-integer nonlinear optimization formulation, that allows one to compute global optimal solutions. [21] To demonstrate this method, a conditional generative adversarial network (C-GAN) is used to augment the solutions produced by a genetic algorithm (GA) for a 311-dimensional nonconvex multi-objective mixed-integer nonlinear optimization. [22] New mixed integer nonlinear optimization models for the Euclidean Steiner tree problem in d-space (with $$d\ge 3$$ ) will be presented in this work. [23] Firstly, a scheme for multiuser frequency division multiplexing approach in mobile edge computing offloading is proposed, and a mixed-integer nonlinear optimization model for energy consumption minimization is developed. [24] As the formulated optimization problem is a mixed integer nonlinear optimization problem which cannot be solved conveniently, we apply the McCormick envelopes and the Lagrangian partial relaxation method to decompose the optimization problem into three subproblems which can be iteratively solved by means of the modified Kuhn–Munkres algorithm and the unidimensional knapsack algorithm. [25]이 문제는 로봇 동작의 최적 실행 시간과 실행 순서를 찾아 에너지 사용을 줄이는 것이 목적인 볼록 혼합 정수 비선형 최적화 문제로 공식화됩니다. [1] 이 백서는 그 작업을 6가지 중요한 방식으로 확장합니다. (1) 맨홀 내 센서의 개념을 소개합니다. 이러한 센서는 현장 테스트가 필요한 맨홀의 수를 줄여주기 때문입니다. (2) 우리는 하수도 파이프라인 네트워크의 토폴로지를 묘사하는 현실적인 트리 네트워크를 제시합니다. (3) 시뮬레이션을 위해 주어진 커뮤니티의 주요 속성을 나타내는 랜덤 트리 네트워크를 생성하는 방법을 제시합니다. (4) 시뮬레이션을 사용하여 검색에서 열리는 맨홀의 평균과 중앙값 수는 잘 알려진 로그 함수를 따른다는 것을 경험적으로 증명합니다. (5) 배치할 센서의 수를 결정하기 위한 절차를 개발합니다. (6) 센서 위치 문제를 정수 비선형 최적화로 공식화하고 이를 해결하기 위한 휴리스틱을 개발합니다. [2] nan [3] nan [4] nan [5] nan [6] nan [7] nan [8] nan [9] nan [10] nan [11] nan [12] nan [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]
integer nonlinear problem 정수 비선형 문제
Our design maximizes the number of admitted users while satisfying their data transmission demands, which formulates a mixed-integer nonlinear problem. [1] The original mixed-integer nonlinear problem of UC scheduling is transformed to be a mixed-integer linear problem to be effectively solved. [2] In this paper, we propose a linearization algorithm for solving a Mixed Integer NonLinear Problem (MINLP) for Intersection Management (IM) of Connected Autonomous Vehicles (CAVs). [3] With the formulated mixed-integer nonlinear problem, the optimal ESUs and UCs are identified for each cluster. [4] To achieve this goal, we formulate our objective function into a mixed-integer nonlinear problem. [5] Under the maximum energy cost constraints, we formulate the cooperative offloading problem into a mixed-integer nonlinear problem aiming to minimize the total delay of tasks. [6] Considering that the formulation problem is a mixed integer nonlinear problem and it is difficult to obtain the optimal solution, we design an improved Gale-Shapley algorithm based on matching game theory to obtain the feasible solution of the problem. [7] However, the formulated problem is a mixed integer nonlinear problem (MINLP), which is NP-hard. [8] The resulting optimization program is a mixed-integer nonlinear problem. [9] Conversely, the natural gas network optimization is a mixed-integer nonlinear problem (MINLP), due to the highly nonlinear and nonconvex Weymouth equation modeling the gas flow in pipelines. [10] The optimization model is formulated as a mixed-integer nonlinear problem and solved using the Nelder–Mead method. [11] Although the formulated problem is a mixed-integer nonlinear problem, the optimal solution is found by linearizing the non-linear constraints. [12] The formulated problem is a mixed-integer nonlinear problem, which is difficult to solve in the original form. [13]우리의 설계는 혼합 정수 비선형 문제를 공식화하는 데이터 전송 요구를 충족시키면서 허용된 사용자 수를 최대화합니다. [1] UC 스케줄링의 원래 혼합 정수 비선형 문제는 효과적으로 해결하기 위해 혼합 정수 선형 문제로 변환됩니다. [2] nan [3] nan [4] nan [5] nan [6] nan [7] nan [8] nan [9] nan [10] nan [11] nan [12] nan [13]
integer nonlinear model 정수 비선형 모델
Moreover, the exergy analysis method is proposed to evaluate the quality of different energy and develop a mixed-integer nonlinear model of SPS considering the economic and exergy objectives. [1] Hydrogen networks synthesis is then formulated as a new mixed integer nonlinear model whose nonlinearities are only attributed to the bilinear and concave terms. [2] In this paper, a novel bi-level mixed-integer nonlinear model is proposed to evaluate the maximum operation duration of isolated island with fully consideration of the uncertainties of the wind turbines (WTs), the photovoltaics (PVs) and the load. [3] This work presents the results of experimental operation of a solar-driven climate system using mixed-integer nonlinear model predictive control (MPC). [4] Two mixed-integer nonlinear models that consider bundling and separate sales have been proposed and solved through different solution algorithms such that the profit is maximized. [5] Benders decomposition method was used to transform and solve the mixed integer nonlinear model. [6]또한, 경제성과 엑서지 목표를 고려하여 서로 다른 에너지의 품질을 평가하고 SPS의 혼합 정수 비선형 모델을 개발하기 위해 엑서지 분석 방법을 제안합니다. [1] 그런 다음 수소 네트워크 합성은 비선형성이 쌍선형 및 오목형 항에만 기인하는 새로운 혼합 정수 비선형 모델로 공식화됩니다. [2] nan [3] nan [4] nan [5] nan [6]
integer nonlinear mathematical 정수 비선형 수학
In this paper, a novel mixed-integer nonlinear mathematical model is proposed to integrate cellular manufacturing systems into a three-stage supply chain to deal with customers' changing demands, which has been little explored in the literature. [1] The modeling of the inventory problem leads to an integer nonlinear mathematical programming problem. [2] A mixed-integer nonlinear mathematical model was formulated and solved using the Lagrangian relaxation method and a Genetic algorithm. [3] The recipe was optimized by the method of integer nonlinear mathematical programmingfor the optimality criterion of blend No. [4] A mixed integer nonlinear mathematical model is proposed for multi-vehicle routing problem considering product transshipment between vehicles in dynamic situations. [5]이 논문에서는 문헌에서 거의 탐구되지 않은 고객의 변화하는 요구를 처리하기 위해 셀룰러 제조 시스템을 3단계 공급망에 통합하는 새로운 혼합 정수 비선형 수학적 모델을 제안합니다. [1] 인벤토리 문제의 모델링은 정수 비선형 수학 계획법 문제로 이어집니다. [2] nan [3] nan [4] nan [5]
integer nonlinear multus 많은 비선형 정수
A methodology has been proposed for VVWC based on a mixed-integer nonlinear multi-objective optimization problem, which is solved by utilizing goal programing approach in order to guarantee Pareto optimality of the solution. [1] This work deals with the formulation of a mixed-integer nonlinear multi-objective optimization (MOO) problem having five objectives to optimize the design of a conventional batch extractive distillation (BED). [2] Moreover, combined with economic objective function, a mixed-integer nonlinear multi-objective model of SPS is established. [3]VVWC에 대한 방법론은 혼합 정수 비선형 다중 목적 최적화 문제를 기반으로 제안되었으며 솔루션의 파레토 최적성을 보장하기 위해 목표 프로그래밍 방식을 사용하여 해결되었습니다. [1] 이 작업은 기존 배치 추출 증류(BED)의 설계를 최적화하기 위한 5가지 목표를 가진 혼합 정수 비선형 다중 목적 최적화(MOO) 문제의 공식화를 다룹니다. [2] nan [3]
integer nonlinear bilevel
Moreover, we also briefly discuss the area of mixed-integer nonlinear bilevel problems. [1] If so, the model boils down to a mixed-integer nonlinear bilevel problem with robust aspects. [2]또한 혼합 정수 비선형 이중 수준 문제 영역에 대해서도 간략하게 논의합니다. [1] nan [2]
integer nonlinear optimisation
DICOPT solver in general algebraic mathematical system (GAMS) is used to solve the formulated mixed-integer nonlinear optimisation problem. [1] The developed computational model is formulated as a mixed integer nonlinear optimisation problem and solved through the combination of meta-heuristics, stochastic simulation methods (Monte-Carlo simulation), and application of optimal power flow. [2]일반 대수 수학 시스템(GAMS)의 DICOPT 솔버는 공식화된 혼합 정수 비선형 최적화 문제를 해결하는 데 사용됩니다. [1] nan [2]