The main objective of this study is to develop a two-stage stochastic programming framework for lot-sizing and scheduling the production activities at a kitting facility to support a manufacturing plant. The novelty o...
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The main objective of this study is to develop a two-stage stochastic programming framework for lot-sizing and scheduling the production activities at a kitting facility to support a manufacturing plant. The novelty of the study lies in modeling multiple uncertainties using two-stage stochastic programming to solve a kitting specific production planning problem in a manufacturing setting. The demand for the kits and the yield of the kitting workers are the two sources of uncertainties considered in this study. The first-stage decisions include the baseline production schedule and the workforce requirement, while the second stage makes recourse decisions on overtime production. The proposed decision-making framework is validated on a multi-period, multi-product case study involving a kitting facility supporting a manufacturing plant producing braking equipment. The main conclusion of the study is that uncertainties have significant impacts on kitting planning decisions and that the proposed two-stage stochastic programming model was robust in determining optimal production plans under uncertainty.
To solve the problems of optimal dispatch of electric-thermal-gas multi-energy microgrid system and uncertainty of new energy output and load fluctuation, a two-stage stochastic programming method based on energy hub ...
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ISBN:
(纸本)9781728152813
To solve the problems of optimal dispatch of electric-thermal-gas multi-energy microgrid system and uncertainty of new energy output and load fluctuation, a two-stage stochastic programming method based on energy hub (EH) is proposed, and an optimal dispatch model of microgrid energy is established. The coupling relationship between electricity, heat and natural gas in the integrated energy system is studied, and a "virtual port" is added to the EH to realize the linearization of the model. In the first stage, the objective is to maximize the economic benefit. According to the known data and constraints, the dispatch quantity of energy bought/sold from the main grid and the input power of the EH in the microgrid are determined. In the second stage, considering the fluctuation of load and new energy, stochastic programming is adopted to deal with uncertainties, and adjustment is made on the basis of the first stage, and the combination of deterministic factors and random factors in scheduling optimization is realized, which is suitable for actual scheduling. Finally, taking a typical residential area with multi-energy system as an example, the validity and economy of the twostage stochastic programming method proposed in this paper is verified by comparing the deterministic method with the stochastic method.
This paper proposes a coordinated charging scheduling approach for battery electric buses (BEBs) in a hybrid charging scheme, i.e., both plug-in fast charging and battery-swapping charging modes are incorporated in a ...
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As one of the core operations in disaster management, the relief distribution has been challenged by factors such as occurrence time, nature, the intensity of disasters impact, and the existence of secondary disasters...
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As one of the core operations in disaster management, the relief distribution has been challenged by factors such as occurrence time, nature, the intensity of disasters impact, and the existence of secondary disasters (e.g., aftershock, landslides). The distribution of multi -commodity relief after disaster strikes becomes more significant when secondary disasters occur in the same or close areas. Secondary disasters may take place right after the primary disaster attack. Because of the diverse natures and the destruction of secondary disasters, the temporary relief distribution centers in operation for distributing commodities after the primary disaster can be disrupted. New temporary distribution centers should be constructed to cover the capacity of disrupted distribution centers. In addition, roads may be unavailable to transport the commodity, and the demand can vary significantly because of secondary disasters. To hedge against the disruption of temporary distribution canters and road unavailability, this paper proposes a mixed -integer stochastic programming model under demand and capacity uncertainty. The objective of this model is to minimize the unmet demand at relief collection centers. A computational study is performed to highlight the significance of this study, and the results show that the proposed approach is effective in secondary disasters perspective.
In this paper, we propose a stochastic programming approach to perform optimal and robust offshore flight scheduling from a service level perspective, reducing flight delays. The two-stage stochastic programming is re...
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In this paper, we propose a stochastic programming approach to perform optimal and robust offshore flight scheduling from a service level perspective, reducing flight delays. The two-stage stochastic programming is reduced to a deterministic equivalent linear program and, considering the combinatorial characteristic of scheduling problems, we use Sample Average Approximation to generate scenarios. A Discrete Event Simulation model is developed to compare the stochastic and deterministic approaches. Numerical results indicate that a stochastic approach to offshore flight scheduling can reduce unpredictable delays, which have a major impact on passengers, without significantly increasing aircraft idle time. In addition, the stochastic approach allows dealing with operational downtime windows with uncertainties in duration and occurrence. Copyright (C) 2020 The Auhtors.
In this paper, we apply kernel mean embedding methods to sample-based stochastic optimization and control. Specifically, we use the reduced- set expansion method as a way to discard sampled scenarios. The effect of su...
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In this paper, we apply kernel mean embedding methods to sample-based stochastic optimization and control. Specifically, we use the reduced- set expansion method as a way to discard sampled scenarios. The effect of such constraint removal is improved optimality and decreased conservative-ness. This is achieved by solving a distributional-distance-regularized optimization problem. We demonstrated this optimization formulation is well-motivated in theory, computationally tractable, and effective in numerical algorithms.
Undoubtedly, evolving wholesale electricity markets continue to provide new revenue opportunities for diverse generation, energy storage, and flexible demand technologies. In this paper, we quantitatively explore how ...
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ISBN:
(纸本)9781538682661
Undoubtedly, evolving wholesale electricity markets continue to provide new revenue opportunities for diverse generation, energy storage, and flexible demand technologies. In this paper, we quantitatively explore how price uncertainty impacts optimal market participation strategies and resulting revenues. Specifically, we benchmark 2-stage stochastic programming formulations for self-schedule and bidding market participation modes in a receding horizon model predictive control framework. To generate probabilistic price forecasts, we propose an autoregressive Gaussian process regression model and compare three sampling strategies. As an illustrative example, we study a price-taker generation company with six unique generation units using historical price data from CAISO (California market). We show that self-schedule is sensitive to the error in the forecast mean, whereas bidding requires price forecasts that cover extreme events (e.g., tails of the distribution). We benchmark realized market revenue against optimal bidding with perfect information and find static bid curve, time-varying bid curve, and self-schedule modes recovery 95.29%, 94.85%, and 84.87% of perfect information revenue, respectively.
To make a reasonable decision on ride-sharing platform utilizing time-series historical data, we propose a deep learning-based stochastic programming framework for open driver guidance and rebalancing to further reduc...
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ISBN:
(纸本)9781665414852
To make a reasonable decision on ride-sharing platform utilizing time-series historical data, we propose a deep learning-based stochastic programming framework for open driver guidance and rebalancing to further reduce the rider average wait time. The approach proactively guides open drivers to the designated regions by integrating an innovative deep neural network named LSTM-MDN with a two-stage stochastic programming model, which is capable of yielding high quality guidance solutions by leveraging the rider demand information predicted by LSTM-MDN from time-series historical data. To validate the performance of the proposed framework, we conduct a group of numerical experiments based on the New York taxi trip data sets. The results show that our proposed framework is capable of reducing driver rebalancing distance significantly, which implies that the riders' wait time can be decreased effectively. Most importantly, it turns out that by average, riders' wait time with guidance using our approach is 97% lower than the myopic batch matching algorithm without guidance.
In optimization models based on stochastic programming, we often face the problem of representing expectations in proper form known as scenario generation. With advances in computational power, a number of methods sta...
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In optimization models based on stochastic programming, we often face the problem of representing expectations in proper form known as scenario generation. With advances in computational power, a number of methods starting from simple Monte-Carlo to dedicated approaches such as method of moment-matching and scenario reduction are being used for multistage scenario generation. Recently, various variations of moment-matching approach have been proposed with the aim to reduce computational time for better outputs. In this paper, we describe a methodology to speed up moment-matching based multistage scenario generation by using principal component analysis. Our proposal is to pre-process the data using dimensionality reduction approaches instead of using returns as variables for moment-matching problem directly. We also propose a hybrid multistage extension of heuristic based moment-matching algorithm and compare it with other variants of moment-matching algorithm. Computational results using non-normal and correlated returns show that the proposed approach provides better approximation of returns distribution in lesser time.
A stochastic programming model of the operation of energy plants with the introduction of photovoltaic generation and a storage battery is developed. The uncertainty of the output of the photovoltaic generation is rep...
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ISBN:
(数字)9783030148126
ISBN:
(纸本)9783030148119;9783030148126
A stochastic programming model of the operation of energy plants with the introduction of photovoltaic generation and a storage battery is developed. The uncertainty of the output of the photovoltaic generation is represented by a set of discrete scenarios, and the expected value of the operation cost is minimized. The effectiveness of the stochastic programming model by comparing it with the deterministic model is shown. As an economic evaluation, the recovery period for the initial investment of photovoltaic generation and storage battery is also shown.
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