Stochastic Mixed(스토캐스틱 혼합)란 무엇입니까?
Stochastic Mixed 스토캐스틱 혼합 - The dual decomposition of stochastic mixed-integer programs can be solved by the projected subgradient algorithm. [1] Under this setting, reliability is defined as the probability of finding a path between sources and sink nodes under random component failures and we show that this measure can be computed by solving a stochastic mixed-integer program. [2] A stochastic mixed-integer conic programming model is then developed for co-optimizing the preparatory schedule of distributed energy storage and distributed generation ahead of hurricane along with post-event decisions of restoring services to customers, while minimizing the expected cost of energy not served as the index to measure the resilience. [3] We build a stochastic mixed-integer program to minimize the operational cost plus expected penalty cost of customers’ waiting time, vehicles’ idleness, and overtime. [4] This formulated optimization problem is a stochastic mixed-integer nonconvex problem and challenging to solve. [5] The problem is first modeled as a stochastic mixed-integer linear mathematical model. [6] Such problems arise, for example, in the split-variable deterministic reformulation of stochastic mixed-integer optimization problems. [7] We formulate the service placement as a stochastic mixed-integer optimization problem and propose an enhanced coarse-grained fixing procedure to facilitate efficient solution finding. [8] We present an asynchronous bundle-trust-region algorithm within the context of Lagrangian dual decomposition for stochastic mixed-integer programs. [9] A deterministic and a stochastic mixed-integer nonlinear program with a target of minimizing the total transponder power consumption are formulated which their solutions provide a long-term and a short-term configuration for delay-sensitive service and delay-tolerant service transponders, respectively. [10] The proposed model is based on stochastic mixed-integer programming with chance constraints. [11] In this paper, an inner-outer 2-layer model system based on stochastic mixed-integer optimization is proposed for ER’s day-ahead EM bidding decision-making. [12] This paper presents a new approach to coupling line balancing and buffer allocation for stochastic mixed-model assembly line. [13] This study proposes a stochastic mixed-integer programming based distributed energy resource allocation method. [14] In this paper, a stochastic mixed-integer linear optimization model is proposed in order to capture horizontal mixing that occurs among the draw columns within the production scheduling optimization. [15]확률적 혼합 정수 프로그램의 이중 분해는 계획된 하위 기울기 알고리즘으로 해결할 수 있습니다. [1] 이 설정에서 신뢰도는 임의의 구성 요소 오류에서 소스와 싱크 노드 사이의 경로를 찾을 확률로 정의되며 이 측정값은 확률론적 혼합 정수 프로그램을 해결하여 계산할 수 있음을 보여줍니다. [2] 그런 다음 허리케인에 앞서 분산 에너지 저장 및 분산 발전의 준비 일정을 공동 최적화하기 위해 확률론적 혼합 정수 원뿔 프로그래밍 모델을 개발하고 고객에게 서비스를 복원하는 이벤트 후 결정과 함께 제공되지 않는 에너지의 예상 비용을 최소화합니다. 회복탄력성을 측정하는 지수. [3] 고객의 대기 시간, 차량의 유휴 시간 및 초과 근무에 대한 운영 비용과 예상 패널티 비용을 최소화하기 위해 확률론적 대수 프로그램을 구축합니다. [4] 이 공식화된 최적화 문제는 확률론적 혼합 정수 비볼록 문제이며 풀기 어렵습니다. [5] 문제는 먼저 확률적 혼합 정수 선형 수학적 모델로 모델링됩니다. [6] 이러한 문제는 예를 들어 확률적 혼합 정수 최적화 문제의 분할 변수 결정론적 재구성에서 발생합니다. [7] 우리는 서비스 배치를 확률론적 혼합 정수 최적화 문제로 공식화하고 효율적인 솔루션 찾기를 용이하게 하기 위해 향상된 거친 수정 절차를 제안합니다. [8] 우리는 확률적 혼합 정수 프로그램에 대한 라그랑주 이중 분해의 맥락에서 비동기 번들 신뢰 영역 알고리즘을 제시합니다. [9] 전체 응답기 전력 소비를 최소화하는 것을 목표로 하는 결정론적 및 확률론적 혼합 정수 비선형 프로그램이 공식화되어 해당 솔루션이 지연에 민감한 서비스 및 지연 허용 서비스 응답기에 대해 각각 장기 및 단기 구성을 제공합니다. [10] 제안된 모델은 우연 제약 조건이 있는 확률적 혼합 정수 프로그래밍을 기반으로 합니다. [11] 본 논문에서는 ER의 당일 EM 입찰 의사결정을 위해 확률적 혼합 정수 최적화를 기반으로 하는 내부-외부 2계층 모델 시스템을 제안한다. [12] 이 논문은 확률적 혼합 모델 조립 라인에 대한 결합 라인 밸런싱 및 버퍼 할당에 대한 새로운 접근 방식을 제시합니다. [13] 본 연구에서는 확률론적 혼합 정수 프로그래밍 기반 분산 에너지 자원 할당 방법을 제안한다. [14] 본 논문에서는 생산 일정 최적화 내에서 드로우 열 사이에서 발생하는 수평 혼합을 포착하기 위해 확률론적 혼합 정수 선형 최적화 모델을 제안합니다. [15]
integer linear programming 정수 선형 계획법
In this regard, this paper constitutes a stochastic mixed integer linear programming (SMILP) model to enhance the resilience of power distribution systems to deal with disastrous events. [1] The model is cast as a stochastic mixed integer linear programming (MILP) bilevel model where the upper level model is profit maximization of the new market balancing group, while the lower level problem models minimization of end user electricity cost. [2] The primary goal of the proposed framework is minimizing the cost of procuring electricity and heat energy carriers in DA and regulation markets, including upward/downward regulations via a stochastic mixed-integer linear programming (MILP) approach. [3] With the objective of maximizing served load and minimizing operation cost, this paper develops a two-stage stochastic mixed-integer linear programming (SMILP) model. [4] A stochastic mixed-integer linear programming (MILP) optimization model is developed to optimize the scheduling of various DERs owned by commercial consumers to maximize the profit of the TVPP. [5] This study presents a stochastic mixed-integer linear programming model developed for polyvinyl chloride pipe manufacturing in China, which is used to evaluate the effects of the life cycle emissions of procurement on the whole supply chain under carbon market uncertainty. [6] The proposed method is based on two-stage stochastic mixed-integer linear programming (MILP), and considers uncertain load consumption and demand response (DR). [7] This article formulates a stochastic mixed integer linear programming (MILP) planning model for energy storage and renewable energy systems to cover the energy demand of stand-alone charging stations for plug-in electric vehicles (PEVs) entirely using green energy generated by renewable energy sources (RESs). [8] The microgrid support management system developed in this paper has a formulation based on a stochastic mixed-integer linear programming problem that depends on knowledge of the stochastic processes that describe the uncertain parameters. [9] This paper aims to develop a stochastic mixed-integer linear programming (MILP) formulation that simultaneously determines the optimal location and size of ESS and IHS in a microgrid (MG) considering the correlation between prevailing uncertainties. [10] For this aim, this study presents a new two-stage stochastic mixed-integer linear programming model (SMILP) to hedge against natural disaster uncertainty. [11] This paper addresses a bi-objective two-stage stochastic mixed-integer linear programming model for a stochastic reliable capacitated facility location in which the optimum numbers, locations and as well as shipment quantity of the product between the network nodes for all scenarios should be determined. [12] We propose a two-stage stochastic mixed-integer linear programming model for the H-SARA problem. [13] The proposed EMS is formulated as a two-stage stochastic mixed-integer linear programming problem (MILP) that guarantees the global optimal solution. [14] In this study, a bi-objective stochastic mixed-integer linear programming model is proposed for designing the supply chain of the clothing industry. [15] Therefore we consider a scenario tree for a stochastic mixed integer linear programming model that incorporates the uncertainty in the hydrogen demand. [16] In this paper, a stochastic mixed integer linear programming model is developed to maximise the conditional value at risk (CVaR) of SC profit given a set of pandemic scenarios. [17] In the proposed method of this paper, a novel modeling strategy is formulated as a multi-period two-stage scenario-based stochastic mixed-integer linear programming (MPTSS-MILP) based on a multi-objective optimization problem (MOOP). [18] The method is based on a stochastic mixed-integer linear programming model with minimal total cost as the objective function to determine the size of the pipeline, the location, the operational plan of pump stations and the location of pressure reduction stations. [19] We propose a new algorithm for solving multistage stochastic mixed integer linear programming (MILP) problems with complete continuous recourse. [20] First, the stochastic mixed-integer linear programming model helps decision makers in determining the right volume of power to be purchased from different sources. [21] The support management system is modeled by stochastic mixed integer linear programming approach. [22] In this regard, a stochastic mixed integer linear programming model is proposed that includes different patient, staff and surgeon preferences: minimization of the total patient waiting time, the tardiness, the number of cancellations, the patient surgery start times, the block overtime, the number of surgeon’s surgery days within the planning horizon and the sum of the idle times of the surgeons. [23] A multi-objective stochastic mixed integer linear programming model is proposed to achieve economic feasibility, as well as environmental and social benefits under multiple uncertainties. [24] This study presents a stochastic mixed-integer linear programming model developed for polyvinyl chloride pipe manufacturing in China, which is used to evaluate the effects of the life cycle emissions of procurement on the whole supply chain under carbon market uncertainty. [25] With the objective of minimizing the cumulative risk of gas leakage scenarios, a stochastic mixed-integer linear programming formulation based on p-median value theorem is proposed. [26] The OR scheduling problem is formulated as a stochastic mixed-integer linear programming (SMILP) model, where a surgery duration follows a probability distribution function. [27] In this regard, this paper presents a multi-objective robust stochastic mixed integer linear programming model for an integrated platelet supply chain considering unidirectional lateral transshipment between hospitals and clinics. [28] In this paper, we explore this challenge and a multi-objective, multi-period, stochastic Mixed Integer Linear Programming (MILP) problem for design and planning a hydrogen SC is developed. [29] In this study, a two-stage stochastic mixed-integer linear programming (MILP) model is formulated to explore the relationship between these two reduction options by minimizing expected total cost (ETC) for CO 2 reduction, considering carbon tax uncertainty. [30] The schedule planning problem is formulated using two-stage stochastic mixed-integer linear programming and solved by inputting the forecast scenarios and the initial operation states of network components. [31] In particular, we present a risk-averse stochastic mixed-integer linear programming (MILP) model to minimize the total expected costs and control the risk of CO2 emissions exceeding a certain budget. [32] This paper proposes a stochastic mixed integer linear programming (MILP) approach aiming at improvement of the reliability level of electric distribution network through a contingency-based energy storage systems (ESSs) incorporation, considering ESSs ancillary services and control sequences in service restoration. [33] The model is of a stochastic mixed integer linear programming (MILP) nature, which uses a linearized AC optimal power flow network model. [34] In this paper, we present a new stochastic mixed-integer linear programming model for the Stochastic Outpatient Procedure Scheduling Problem (SOPSP). [35] The proposed formulation is cast as stochastic mixed-integer linear programming (MILP) model with 24-hour rolling horizon, simulated periodically by updating input data and advancing on an hourly basis for a one year scheduling period. [36] The wind-for-restoration problem is formulated as a stochastic mixed-integer linear programming problem with generated wind energy scenarios. [37] A multi-stage stochastic mixed-integer linear programming model is developed that simultaneously optimizes the electrical demand of a flexible factory and bidding in the German secondary control reserve and day-ahead market. [38] A two-stage stochastic mixed integer linear programming is utilized to minimize the expected system cost while incorporating yield uncertainty in the strategic level decisions related to biomass production and biorefinery investment. [39] The day-ahead operation problem is formulated as a two-stage stochastic mixed integer linear programming (MILP) model that considers workload schedules among server clusters, water consumption, and uncertainties of onsite renewable energy and electricity prices. [40] A stochastic mixed-integer linear programming problem is formulated by considering uncertainties of intermittent DER facilities and day-ahead market price, to find the optimal bidding strategies while maximizing the expected aggregator's profit. [41] The problem is formulated as a stochastic mixed integer linear programming model, taking into account the uncertainties regarding energy and frequency regulation prices. [42]이와 관련하여 이 논문은 재난 상황에 대처하기 위해 배전 시스템의 복원력을 향상시키기 위해 확률적 혼합 정수 선형 계획법(SMILP) 모델을 구성합니다. [1] 이 모델은 상위 수준 모델이 새로운 시장 균형 그룹의 이익 극대화인 반면 하위 수준 문제 모델은 최종 사용자 전기 비용의 최소화인 확률론적 혼합 정수 선형 계획법(MILP) 이중 수준 모델로 캐스팅됩니다. [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] 이 연구에서는 의류 산업의 공급망을 설계하기 위한 이중 목표 확률적 혼합 정수 선형 계획법 모델을 제안합니다. [15] nan [16] nan [17] nan [18] 이 방법은 파이프라인의 크기, 위치, 펌프 스테이션의 운영 계획 및 감압 스테이션의 위치를 결정하는 목적 함수로 최소 총 비용을 사용하는 확률론적 혼합 정수 선형 계획법 모델을 기반으로 합니다. [19] 우리는 완전한 연속 소구로 다단계 확률적 혼합 정수 선형 계획법(MILP) 문제를 해결하기 위한 새로운 알고리즘을 제안합니다. [20] nan [21] 지원 관리 시스템은 확률적 혼합 정수 선형 계획법 접근 방식으로 모델링됩니다. [22] nan [23] 다중 불확실성 하에서 경제적 타당성과 환경적, 사회적 이익을 달성하기 위해 다중 목적 확률적 혼합 정수 선형 계획법 모델을 제안합니다. [24] nan [25] nan [26] nan [27] 이와 관련하여, 본 논문은 병원과 의원 간의 단방향 측면 환적을 고려하여 통합 혈소판 공급망에 대한 다목적 로버스트 확률론적 혼합 정수 선형 계획법 모델을 제시합니다. [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]
integer programming model 정수 프로그래밍 모델
The problem is formulated as a two-stage stochastic mixed-integer programming model, where the binary first-stage decision variables correspond to the assignment of tasks to quay cranes, and the mixed-integer second-stage decision variables are related to the generation of detailed schedules. [1] A new stochastic mixed-integer programming model is formulated with two objectives: to minimise the cost for developing the raw mix and the risk of not meeting production targets. [2] We apply our multi-stage stochastic mixed-integer programming model to the case of controlling the 2018-2020 Ebola Virus Disease (EVD) in the Democratic Republic of the Congo (DRC) using real data. [3] Therefore, a stochastic mixed-integer programming model is formulated that considers workstation activation cost, workload smoothness index between workstations, and total hazard index considering the order of hazardous task removal. [4] The two-stage stochastic mixed-integer programming model proposed in this paper considers various processes of recovering recyclable products, including reuse, refurbishing, remanufacturing, recycling, and selling spare parts. [5] We develop a two-stage stochastic mixed-integer programming model that incorporates two features, including (i) a sustainable-responsive constraint to quantitatively describe the sustainability and responsiveness performance, and (ii) an effective hybrid mitigation strategy that combines the fortification policy with the temporary outsourcing service policy to mitigate disruptions. [6] This study locates shelter sites and allocates the affected population to the established set of shelters in cases of secondary disaster(s) following the main earthquake, via a three-stage stochastic mixed-integer programming model. [7] We provide a two-stage stochastic mixed integer programming model for the three SMFTPO settings, and solve it by means of Sample Average Approximation. [8] A multi-stage multi-period stochastic mixed-integer programming model is developed, with the goal to minimize the overall costs associated with reaching the milestones. [9] To solve this problem, we present a risk-averse two-stage stochastic mixed-integer programming model. [10] Since the number of supplies required to perform a procedure is uncertain, we have developed a robust stochastic mixed-integer programming model to make the inventory allocation decision. [11] The primary and recovery portfolios are determined simultaneously and for both approaches the integrated decision-making, stochastic mixed integer programming models are developed. [12]이 문제는 2단계 확률론적 혼합 정수 프로그래밍 모델로 공식화되며, 여기서 이진 1단계 결정 변수는 안벽 크레인에 대한 작업 할당에 해당하고 혼합 정수 2단계 결정 변수는 생성과 관련됩니다. 자세한 일정. [1] 새로운 확률적 혼합 정수 프로그래밍 모델은 원시 혼합 개발 비용과 생산 목표를 충족하지 못할 위험을 최소화하는 두 가지 목표로 공식화되었습니다. [2] nan [3] nan [4] nan [5] nan [6] 이 연구는 3단계 확률론적 혼합 정수 프로그래밍 모델을 통해 주요 지진에 따른 2차 재해의 경우 대피소 위치를 찾고 영향을 받은 인구를 기존 대피소 세트에 할당합니다. [7] 우리는 세 가지 SMFTPO 설정에 대해 2단계 확률론적 혼합 정수 프로그래밍 모델을 제공하고 샘플 평균 근사를 통해 이를 해결합니다. [8] 이정표에 도달하는 것과 관련된 전체 비용을 최소화하는 것을 목표로 다단계 다중 기간 확률적 혼합 정수 프로그래밍 모델이 개발되었습니다. [9] nan [10] 절차를 수행하는 데 필요한 소모품의 수가 불확실하기 때문에 재고 할당 결정을 내리기 위해 강력한 확률론적 혼합 정수 프로그래밍 모델을 개발했습니다. [11] nan [12]
integer nonlinear programming 정수 비선형 계획법
The container slot allocation problem is investigated in this study using a two-stage stochastic mixed-integer nonlinear programming model. [1] This paper presents a stochastic mixed-integer nonlinear programming model for the optimal energy management system of unbalanced three-phase of alternating current microgrids. [2] 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. [3] More specifically, we formulate a stochastic mixed integer nonlinear programming (MINLP) problem to maximize the expected sum-rate (ESR) of the system. [4] By recognizing those practical uncertainties, a bi-level stochastic mixed-integer nonlinear programming model is proposed to formulate this multi-commodity rebalancing problem. [5] In this paper, we suggest a stochastic mixed integer nonlinear programming model that decide the regional allocation of wind power capacity with the objective of minimizing the needed spinning reserves to compensate for the negative effects of intermittent nature of wind power. [6] First, the optimal operation of the microgrid is formulated as a stochastic mixed-integer nonlinear programming (MINLP) problem, combining the ac power flow and the detailed operational character of the battery. [7] The problem is formulated as a multi-objective stochastic Mixed-Integer Nonlinear Programming (MINLP) model, which can be easily converted into a MILP one. [8] We formulate the problem as a stochastic mixed-integer nonlinear programming (SMINLP) to minimize the system cost, including the server cost, VM cost and wireless transmission cost. [9]이 연구에서는 2단계 확률론적 혼합 정수 비선형 계획법 모델을 사용하여 컨테이너 슬롯 할당 문제를 조사합니다. [1] 본 논문은 교류 마이크로그리드의 불평형 3상 마이크로그리드의 최적 에너지 관리 시스템을 위한 확률론적 혼합 정수 비선형 계획법 모델을 제시한다. [2] nan [3] nan [4] 이러한 실제적인 불확실성을 인식함으로써 이 다중 상품 재조정 문제를 공식화하기 위해 이중 수준 확률론적 혼합 정수 비선형 계획법 모델이 제안됩니다. [5] 본 논문에서는 풍력의 간헐적 특성의 부정적인 영향을 보상하기 위해 필요한 회전 보유량을 최소화하기 위해 풍력 용량의 지역 할당을 결정하는 확률론적 혼합 정수 비선형 계획법 모델을 제안합니다. [6] nan [7] 이 문제는 MILP 모델로 쉽게 변환할 수 있는 다중 목표 확률적 혼합 정수 비선형 계획법(MINLP) 모델로 공식화됩니다. [8] nan [9]
integer linear program 정수 선형 계획
We propose methods for identifying effective appointment scheduling templates using a two-stage stochastic mixed-integer linear program model. [1] We include a reserve market, a day-ahead market and an intraday market in stochastic modeling and develop a multi-stage stochastic Mixed Integer Linear Program. [2] A new two-stage stochastic mixed-integer linear program is formulated to capture the impacts of ROD decisions and uncertainties on system's responses to climatic hazards. [3] We apply this procedure to a well-known example of a residential quarter with photovoltaic systems and storage units, modeled as a two-stage stochastic mixed-integer linear program. [4] The EW-N problem is posed as a two-stage stochastic mixed-integer linear program that minimizes the capital expenditures, operational cost, and water usage of the system. [5] In this paper, a stochastic mixed-integer linear program is formulated to minimize a relay’s tripping time at discrete fault current intervals and considers the cost of tripping a relay as the objective function. [6] Consequently, the optimization problem is formulated as a stochastic mixed integer linear program, which ensures limited convergence to optimality. [7] The resulting model is formulated as a two-stage stochastic mixed-integer bilevel nonlinear program, which can be further reformulated into a tractable single-level stochastic mixed-integer linear program by applying KKT conditions and Glover's linearization method. [8]2단계 확률적 혼합 정수 선형 프로그램 모델을 사용하여 효과적인 약속 일정 템플릿을 식별하는 방법을 제안합니다. [1] 우리는 확률적 모델링에 예비 시장, 하루 선행 시장 및 일중 시장을 포함하고 다단계 확률적 혼합 정수 선형 프로그램을 개발합니다. [2] 새로운 2단계 확률론적 혼합 정수 선형 프로그램은 기후 위험에 대한 시스템의 응답에 대한 ROD 결정 및 불확실성의 영향을 포착하기 위해 공식화되었습니다. [3] 우리는 이 절차를 2단계 확률론적 혼합 정수 선형 프로그램으로 모델링된 태양광 시스템 및 저장 장치가 있는 주거 지역의 잘 알려진 예에 적용합니다. [4] nan [5] nan [6] nan [7] nan [8]
integer non linear
To account for uncertainties, in this study a two-stage stochastic mixed-integer non-linear programing is used to model the optimal design problem of hybrid system for ships. [1] In the operation of microgrids (MGs), the stochastic production of solar/wind, the discrete variables of photovoltaic (PV) inverter's auxiliary service state and diesel generators’ (DGs’) off–on state generally need to be considered, and a stochastic mixed-integer non-linear non-convex programming (MINNP) model is established for the optimal dispatch of MGs. [2] A two-stage stochastic mixed-integer non-linear programming model is established to minimise the fixed cost and the expected operation costs under uncertain demand and return. [3] By jointly considering interference among D2D users, social-aware caching, link scheduling, and routing, an offline finite-queue-aware energy minimization problem is formulated, which is a time-coupling stochastic mixed-integer non-linear programming (MINLP) problem. [4]불확실성을 설명하기 위해 이 연구에서는 2단계 확률론적 혼합 정수 비선형 프로그래밍을 사용하여 선박용 하이브리드 시스템의 최적 설계 문제를 모델링합니다. [1] 마이크로그리드(MG)의 운영에서는 일반적으로 태양광/풍력의 확률적 생산, 태양광(PV) 인버터의 보조 서비스 상태 및 디젤 발전기(DGs') 오프-온 상태의 이산 변수를 고려해야 하며, 확률론적 최적의 MG 디스패치를 위해 혼합 정수 비선형 비볼록 프로그래밍(MINNP) 모델이 설정되었습니다. [2] nan [3] nan [4]
integer programming problem 정수 계획법 문제
The resulting model is a large-scale multi-stage stochastic mixed-integer programming problem. [1] The SCUC is modeled as a two-stage stochastic mixed-integer programming problem and an appropriate model of PEVs is derived to facilitate the integration of these resources into the grid while considering their charging and discharging characteristics as well as efficiency. [2] The integrated planning-and-scheduling problem is formulated as a two-stage stochastic mixed-integer programming problem to determine the optimal capacity of energy facilities and operation strategies. [3] We derive a nested risk measure for a maximization problem and implement it in a scenario-based formulation of a multi-stage stochastic mixed-integer programming problem. [4]결과 모델은 대규모 다단계 확률적 혼합 정수 프로그래밍 문제입니다. [1] SCUC는 2단계 확률론적 혼합 정수 프로그래밍 문제로 모델링되며 충전 및 방전 특성과 효율성을 고려하면서 이러한 자원을 그리드에 쉽게 통합할 수 있도록 적절한 PEV 모델이 도출됩니다. [2] nan [3] nan [4]