This paper considers a two-stage stochastic programming problem for airport runway scheduling under the uncertainty of arrival time on a single runway. The goal of airport runway scheduling is to schedule a set of air...
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ISBN:
(纸本)9781538651780
This paper considers a two-stage stochastic programming problem for airport runway scheduling under the uncertainty of arrival time on a single runway. The goal of airport runway scheduling is to schedule a set of aircrafts in a given time horizon and minimize a corresponding objective while satisfying separation requirements as well as other practical constraints. In order to boost runway elasticity and throughout, a mess of unpredictable factors, such as weather, pilot behavior and airport surface traffic, should be take into consideration by airport regulator. The arrival scheduling problem at airport can be decomposed into sequential decision problem, where the first stage determines the sequence of aircraft weight class, while the individual flight is assigned to positions in the weight class sequence in the second stage. The main mission of this work is to identify an optimal schedule involving the arrival time of flight is stochastic under different scenarios. A stochastic aircraft landing problem (SALP) formulation based on time-dependent traveling salesman problem (TDTSP) is proposed. Then a sample average approximation (SAA) algorithm is developed to solve this stochastic programming and the efficacy is verified by experimental result.
Quick response through efficient relief distribution strategy after disaster strikes is a vital issue to alleviate the disaster impact in the affected areas, which remains challenging in the field of humanitarian logi...
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Due to the increasing competitiveness of businesses, project planning and scheduling have become a challenging theme in the last years. In this paper, we propose a two-stage stochastic programming model for the resour...
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ISBN:
(纸本)9789897582851
Due to the increasing competitiveness of businesses, project planning and scheduling have become a challenging theme in the last years. In this paper, we propose a two-stage stochastic programming model for the resource constrained project scheduling problem, taking into account the stochasticity of activity durations. In this formulation, assuming that some activity duration scenarios are known, resource allocations are taken in the first stage, while scheduling decisions are postponed in the second stage. The resulting problem is a mixed integer problem with recourse, where binary variables appear in the first stage. In order to efficiently solve the problem, a decomposition algorithm is developed, based on the well-known integer L-shaped method. Detailed computational results are presented for a set of benchmark instances taken from the literature. Copyright 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
The optimal operation of active distribution systems (ADSs) should address the issue of uncertainties caused by high penetration of renewable generation. A two-stage stochastic programming model is proposed for joint ...
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This paper introduces a framework for the quantitative analysis of collaborative service platforms. Services built around platforms require the coordinated collaboration of several independent agents, each from a diff...
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This paper introduces a framework for the quantitative analysis of collaborative service platforms. Services built around platforms require the coordinated collaboration of several independent agents, each from a different field or area of expertise and each providing one or more components to the overall service. One of these agents takes the further role of platform coordinator, whose role consists in assembling the services from components provided by other agents and sharing the revenue between the participants. The agents decide which services to make a contribution to by selecting service portfolios that maximize their expected profit under constraints on risk and capacity. The coordinator selects the revenue-sharing scheme that balances the offers from other agents and ensures the functioning of the platform. We develop two stochastic optimization models with bilevel structure for the analysis of the collaborative service platform provision, analyse the properties of the solutions and provide numerical experiments that show the qualitative difference of the profit/risk trade-off in the multiagent case compared with the classical single agent case.
In the last few years, there has been a growing interest in the disassembly scheduling problem to fulfil the demands of individual disassembled parts over a given planning horizon. An analysis of the literature shows ...
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Transmission expansion planning (TEP) is helping the system operator to decide the optimal solution for building new lines and in the same time to increase the reliability and safety of the existing power system. The ...
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ISBN:
(纸本)9781538639436
Transmission expansion planning (TEP) is helping the system operator to decide the optimal solution for building new lines and in the same time to increase the reliability and safety of the existing power system. The proposed problem is a mixed-integer nonlinear programming problem (MINLP) and it is solved using stochastic programming. stochastic programming is applied when uncertain environment occurs, in this case the uncertain environment refers to the production of renewable energy sources (RES) and its dependence on the short-term weather conditions. stochastic Optimization weights all scenarios considered in this paper in order to obtain an expected total cost. The expected total cost includes the cost associated with the construction of new transmission lines, generation cost and load shedding cost.
When supply chain networks become more complex through the application of modern trends such as outsourcing and global marketing, supply chains become more uncertain. Supply chain planning under uncertainty is a chall...
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ISBN:
(数字)9783319684963
ISBN:
(纸本)9783319684963;9783319684956
When supply chain networks become more complex through the application of modern trends such as outsourcing and global marketing, supply chains become more uncertain. Supply chain planning under uncertainty is a challenge for decision makers. Without considering uncertainties in supply chain planning, global supply chains may suffer enormous economic costs. When probability distributions for uncertain parameters can be estimated, stochastic programming can be used for capturing the characteristics of uncertainties and generating flexible production and transportation plans for global supply chains. This paper presents an outline on how to use stochastic programming for decision support under uncertainty. This includes a high level exposition of how to quantify uncertainties, develop stochastic programming models, generate representative scenarios, apply algorithms for model solving, undertake experimental design and present computational results. Through exemplifying supply chain planning and decision making under uncertainty by using stochastic programming, this paper aims to provide a valuable reference for future research in this area.
In this paper, we consider the application of mathematical optimization models to energy problems. Using the latest information technology, we try to utilize renewable energy whose output is unstable. Such efforts are...
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ISBN:
(纸本)9781728138046
In this paper, we consider the application of mathematical optimization models to energy problems. Using the latest information technology, we try to utilize renewable energy whose output is unstable. Such efforts are collectively called smart communities. stochastic programming deals with optimization under uncertain conditions. Since the output of solar power generation in a smart community is uncertain, application of stochastic programming is required. Considering practical operational constraints, this model becomes a stochastic programming problem involving non-linear recourse, which cannot be solved with typical solvers directly. The problem can be reformulated as a large-scale mixed integer programming problem by piecewise linear approximation to obtain an optimal solution. In our algorithm, we add points for piecewise linear approximation iteratively and increase accuracy of the approximation. In numerical experiments, the effectiveness of the stochastic programming model is shown by comparing it with the deterministic model. Moreover, we calculate a recovery period of investment cost for photovoltaic generation and a storage battery and show usefulness of our model when evaluating a practical operation.
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