We present a new progressive hedging algorithm to solve Stochastic Variational Inequalities in the formulation introduced by Rockafellar and Wets in 2017, allowing the generated subproblems to be approximately solved ...
详细信息
We present a new progressive hedging algorithm to solve Stochastic Variational Inequalities in the formulation introduced by Rockafellar and Wets in 2017, allowing the generated subproblems to be approximately solved with an implementable tolerance condition. Our scheme is based on Hybrid Inexact Proximal Point methods and generalizes the exact algorithm developed by Rockafellar and Sun in 2019, providing stronger convergence results. We also show some numerical experiments in two-stage Nash games. (c) 2023 Elsevier B.V. All rights reserved.
Stochastic optimization models based on risk-averse measures are of essential importance in financial management and business operations. This paper studies new algorithms for a popular class of these models, namely, ...
详细信息
Stochastic optimization models based on risk-averse measures are of essential importance in financial management and business operations. This paper studies new algorithms for a popular class of these models, namely, the mean-deviation models in multistage decision making under uncertainty. It is argued that these types of problems enjoy a scenario-decomposable structure, which could be utilized in an efficient progressivehedging procedure. In case that linkage constraints arise in reformulations of the original problem, a Lagrange progressive hedging algorithm could be utilized to solve the reformulated problem. Convergence results of the algorithms are obtained based on the recent development of the Lagrangian form of stochastic variational inequalities. Numerical results are provided to show the effectiveness of the proposed algorithms.
In the field of logistics and transportation, drones and trucks effectively enhance each other's capabilities by offering complementary benefits in terms of speed, cargo capacity, and charging frequency. Thus, eff...
详细信息
In the field of logistics and transportation, drones and trucks effectively enhance each other's capabilities by offering complementary benefits in terms of speed, cargo capacity, and charging frequency. Thus, efficient management of their collaboration is an important task. Although there is a vast literature addressing different aspects of drone-truck combined operations (DTCO), only a few studies incorporate the energy consumption of drones into the optimization model, and the existing ones have made simplified assumptions. This paper proposes an optimization model for DTCO by incorporating a comprehensive energy function affected by the drone speed, cargo weight, wind speed, and wind direction, paying attention to environmental viewpoints. Due to this energy function, the problem is formulated as a mixed-integer nonlinear programming (MINLP) model. To enhance tractability and efficiency, we provide a linear approximation for the MINLP model. Given the stochastic nature of wind conditions throughout the day, we extend the deterministic model as a scenario-based stochastic one. Incorporating uncertainty makes the model more complex and hence, we adopt a modified progressive hedging algorithm (PHA) to efficiently solve the model. Computational results over a variety of instances confirm the effectiveness of the proposed approach.
This study proposes to develop a mathematical model that captures and appropriately optimizes a number of realistic features (e.g., barge/towboat assignments, maintenance, and availability decisions) for the design an...
详细信息
This study proposes to develop a mathematical model that captures and appropriately optimizes a number of realistic features (e.g., barge/towboat assignments, maintenance, and availability decisions) for the design and management of an inland waterway transportation network under stochastic commodity supply and water level fluctuations scenarios. To efficiently solve this challenging N P -hard problem, we propose to develop a highly customized parallelized hybrid decomposition algorithm that combines Sample Average Approximation with an enhanced progressivehedging and Nested Decomposition algorithm. Computational results indicate that the proposed algorithm is capable of producing high quality solutions consistently within a reasonable amount of time. Finally, a real-life case study is constructed by utilizing the inland waterway transportation network along the Mississippi River. Through multiple experimentations, a number of managerial insights are drawn that magnifies the impact of different key input parameters on the overall inland waterway port operations. (c) 2020 Elsevier Ltd. All rights reserved.
The uncertainty related to the massive integration of intermittent energy sources (e.g., wind and solar generation) is one of the biggest challenges for the economic, safe and reliable operation of current power syste...
详细信息
The uncertainty related to the massive integration of intermittent energy sources (e.g., wind and solar generation) is one of the biggest challenges for the economic, safe and reliable operation of current power systems. One way to tackle this challenge is through a stochastic security constraint unit commitment (SSCUC) model. However, the SSCUC is a mixed-integer linear programming problem with high computational and dimensional complexity in large-scale power systems. This feature hinders the reaction times required for decision making to ensure a proper operation of the system. As an alternative, this paper presents a joint strategy to efficiently solve a SSCUC model. The solution strategy combines the use of linear sensitivity factors (LSF) to compute power flows in a quick and reliable way and a method, which dynamically identifies and adds as user cuts those active security constraintsN-1that establish the feasible region of the model. These two components are embedded within a progressive hedging algorithm (PHA), which breaks down the SSCUC problem into computationally more tractable subproblems by relaxing the coupling constraints between scenarios. The numerical results on the IEEE RTS-96 system show that the proposed strategy provides high quality solutions, up to 50 times faster compared to the extensive formulation (EF) of the SSCUC. Additionally, the solution strategy identifies the most affected (overloaded) lines before contingencies, as well as the most critical contingencies in the system. Two metrics that provide valuable information for decision making during transmission system expansion are studied.
This paper presents a novel decentralized bi-level stochastic optimization approach based on the progressive hedging algorithm for multi-agent systems (MAS) in multi-energy microgrids (MEMGs) to enhance network flexib...
详细信息
This paper presents a novel decentralized bi-level stochastic optimization approach based on the progressive hedging algorithm for multi-agent systems (MAS) in multi-energy microgrids (MEMGs) to enhance network flexibility. In the proposed model, suppliers and consumers of three energy carrier of power, heat, and hydrogen are considered. This system further consists of multi-energy storage systems such as plug-in electric vehicle aggregators, thermal energy storage, and hydrogen energy storage with the application of power-to-hydrogen and hydrogen-to-power technologies. Furthermore, the Latin Hypercube Sampling method has been utilized to manage the uncertainties. In addition, a penalty function and a power exchange pricing model are evaluated by the electrical marginal price of each microgrid to determine the agreed power exchange among the MEMGs. The suggested work performs over a MAS with three MEMGs. The total profit of each microgrid is maximized over a 24-h scheduling in three diverse case studies. Ultimately, the proposed decentralized bi-level optimization approach, by converging through seven iterations, indicates an effective performance as a promising solution to a MAS-based framework. Besides, the optimal scheduling of the MEMGs were converged in the same profit for the diverse network topologies. Implementing multi-energy storage systems plays a major role in increasing total profit of MEMGs and improving the reliability performance of MAS-based structure. (c) 2022 Elsevier Ltd. All rights reserved.
Electric cars are projected to become the vehicles of the future. A major barrier for their expansion is range anxiety stemming from the limited range a typical electric vehicle can travel. Electric vehicle batteries&...
详细信息
Electric cars are projected to become the vehicles of the future. A major barrier for their expansion is range anxiety stemming from the limited range a typical electric vehicle can travel. Electric vehicle batteries' performance and capacity are affected by many factors. In particular, the decrease in ambient temperature below a certain threshold will adversely affect the battery's efficiency. This paper develops a two-stage stochastic program model for charging stations' optimal location to facilitate the routing decisions of delivery services that use electric vehicles while considering the variability inherent in climate and customer demand. A novel solution approach based on the progressive hedging algorithm is presented to solve the resulting mathematical model and to provide high-quality solutions within reasonable running times for problems with many scenarios. To evaluate the proposed formulation and solution approach's performance, Fargo city in North Dakota is selected as a testbed. We observe that the location-routing decisions are susceptible to the electric vehicle logistics underlying climate, signifying that decision-makers of the direct current fast charging electric vehicle logistic network for cities that suffer from high-temperature fluctuations would not overlook the effect of climate to design and manage the respective logistic network efficiently.
In this paper, a two-stage collaborative stochastic optimization method for pre-disaster emergency repair station planning and post-disaster recovery strategy of cyber-physical-transportation networks is proposed in t...
详细信息
In this paper, a two-stage collaborative stochastic optimization method for pre-disaster emergency repair station planning and post-disaster recovery strategy of cyber-physical-transportation networks is proposed in this paper to enhance the resilience under extreme scenarios. In the first stage, based on the principle of hierarchical portioning, the responsible area for the emergency repair station is divided by the community theory, and the emergency repair station planning model is developed. In the second stage, the time sequence of repairing the faulty components is first established based on the graph theory. Then, based on the spatial distribution of adjacent time-series faulty components on the transportation network, a faulty component restoration time model is established to accurately reflect the faulty restoration time. On this basis, the functional coupling effects of economic dispatch control and substation automation system failures on load recovery are analyzed to establish a load recovery model. The case studies of the IEEE RTS-79 test system demonstrate that, compared with traditional independent recovery model, the model proposed in this paper performs better in enhancing resilience under extreme scenarios.
暂无评论