检索结果-内蒙古大学图书馆

chance-constrained multi-objective approach for hazardous materials routing and scheduling under demand and service time uncertainty

引用

JOURNAL OF MULTI-CRITERIA DECISION ANALYSIS 2020年第5-6期27卷 318-336页

作者： Moghaddam, Kamran S. Azadian, Farshid Clayton State Univ Coll Business Dept Mkt & Supply Chain Management Morrow GA 30260 USA Embry Riddle Aeronaut Univ David OMaley Coll Business Dept Management Mkt & Operat Daytona Beach FL USA

The transportation of hazardous materials (hazmat) is a challenging problem that often requires a trade-off between conflicting objectives. In practice, the complexity of the problem is exacerbated due to the lack of sufficient and reliable historical data. In this research, a stochastic multi-objective optimization model for hazardous materials (hazmat) vehicle routing and scheduling problem is developed. The goal is to find optimal links and routes to obtain a trade-off between the safe and fast distribution of hazmat through a transport network under customers' demand and service time uncertainty. We utilized a hybrid game theory based compromise programming to develop a solution algorithm to determine the Pareto-optimal solutions, which are based on the total travel distance and total risk imposed on the transportation process. Computational results of a realistic numerical case study demonstrate the effectiveness of the proposed model and the solution algorithm in obtaining Pareto-optimal solutions.

关键词： chance-constrained programming demand uncertainty hazardous material multi-objective optimization service time uncertainty vehicle routing and scheduling problem

来源：评论

学校读者我要写书评

暂无评论

A Global Intermodal Shipment Matching Problem Under Travel Time Uncertainty 11th

A Global Intermodal Shipment Matching Problem Under Travel T...

引用

11th International Conference on Computational Logistics (ICCL)

作者： Guo, Wenjing Atasoy, Bilge van Blokland, Wouter Beelaerts Negenborn, Rudy R. Delft Univ Technol Dept Maritime & Transport Technol Delft Netherlands

ISBN: (纸本)9783030597474;9783030597467

Global intermodal transportation involves the movement of shipments between inland terminals located in different continents by using ships, barges, trains, trucks, or any combination among them through integrated planning at a network level. One of the challenges faced by global operators is the matching of shipment requests with transport services in an integrated global network. The characteristics of the global intermodal shipment matching problem include acceptance and matching decisions, soft time windows, capacitated services, and transshipments between multimodal services. The objective of the problem is to maximize the total profits which consist of revenues, travel costs, transfer costs, storage costs, delay costs, and carbon tax. Travel time uncertainty has significant effects on the feasibility and profitability of matching plans. However, travel time uncertainty has not been considered in global intermodal transport yet leading to significant delays and infeasible transshipments. To fill in this gap, this paper proposes a chance-constrained programming model in which travel times are assumed stochastic. We conduct numerical experiments to validate the performance of the stochastic model in comparison to a deterministic model and a robust model. The experiment results show that the stochastic model outperforms the benchmarks in total profits.

关键词： Global intermodal transportation Shipment matching problem Travel time uncertainty chance-constrained programming

来源：评论

学校读者我要写书评

暂无评论

chance-constrained Equilibrium in Electricity Markets With Asymmetric Forecasts

Chance-Constrained Equilibrium in Electricity Markets With A...

引用

International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)

作者： Dvorkin, Vladimir Kazempour, Jalal Pinson, Pierre Tech Univ Denmark Dept Elect Engn Lyngby Denmark

ISBN: (纸本)9781728128221

We develop a stochastic equilibrium model for an electricity market with asymmetric renewable energy forecasts. In our setting, market participants optimize their profits using public information about a conditional expectation of energy production but use private information about the forecast error distribution. This information is given in the form of samples and incorporated into profit-maximizing optimizations of market participants through chance constraints. We model information asymmetry by varying the sample size of participants' private information. We show that with more information available, the equilibrium gradually converges to the ideal solution provided by the perfect information scenario. Under information scarcity, however, we show that the market converges to the ideal equilibrium if participants are to infer the forecast error distribution from the statistical properties of the data at hand or share their private forecasts.

关键词： chance-constrained programming Equilibrium Forecast asymmetry Information asymmetry Uncertainty

来源：评论

学校读者我要写书评

暂无评论

An interval-stochastic programming based approach for a fully uncertain multi-objective and multi-mode resource investment project scheduling problem with an application to ERP project implementation

引用

EXPERT SYSTEMS WITH APPLICATIONS 2020年 149卷 113189-000页

作者： Subulan, Kemal Dokuz Eylul Univ Fac Engn Dept Ind Engn Izmir Turkey

Most of the real-life project scheduling cases may involve different types of uncertainties simultaneously such as randomness, fuzziness and dynamism. Based on this motivation, the present paper proposes a novel interval programming and chance constrained optimization based hybrid solution approach for a fully uncertain, multi-objective and multi-mode resource investment project scheduling problem (MRIPSP). The classical discrete-time binary integer programming formulation of the problem is extended by incorporating both the interval-valued and interval-stochastic project parameters as well as variables. In addition to the uncertain project parameters/inputs, the completion times of the activities which represent the project schedule and the availabilities of the renewable project resources are also stated as uncertain project variables and represented by interval numbers. Then, the proposed interval-stochastic multi-mode resource investment project scheduling (IS-MRIPSP) model is converted into its crisp equivalent form by using the proposed approach. The proposed approach is also able to consider different types of project scheduling risks and produces more reliable and risk-free solutions according to the project manager's attitude toward risks. Furthermore, in addition to the classical makespan objective, effective and efficient utilization of the renewable project resources, i.e., human resources, is also targeted. The efficiency and reliability factors of the human resources are also taken into consideration. In order to generate balanced project schedules which tradeoffbetween the project time and total human resource costs, compromise programming approach is adapted. Finally, in order to test the validity and practicality of the proposed approach, a real-life application is presented for an enterprise resource planning (ERP) implementation project scheduling problem of an international industrial software company. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Resource investment project scheduling Interval programming chance-constrained programming Human resource allocation ERP project management Risk attitude

来源：评论

学校读者我要写书评

暂无评论

Risk-Aware Optimal Dispatch of Resource Aggregators Integrating NGBoost-Based Probabilistic Renewable Forecasting and Bi-Level Building Flexibility Engagements

引用

Engineering 2025年

作者： Hong Tang Zhe Chen Hangxin Li Shengwei Wang Department of Building Environment and Energy Engineering The Hong Kong Polytechnic University Hong Kong 999077 China Shenzhen Research Institute The Hong Kong Polytechnic University Shenzhen 518057 China Research Institute of Smart Energy The Hong Kong Polytechnic University Hong Kong 999077 China

Pressure has been introduced into power systems owing to the intermittent and uncertain nature of renewable energy. As a result, energy resource aggregators are emerging in the electricity market to realize sustainable and economic advantages through distributed generation, energy storage, and demand response resources. However, resource aggregators face the challenge of dealing with the uncertainty of renewable energy generation and setting appropriate incentives to exploit substantial energy flexibility in the building sector. In this paper, a risk-aware optimal dispatch strategy that integrates probabilistic renewable energy prediction and bi-level building flexibility engagement is proposed. A natural gradient boosting algorithm (NGBoost), which requires no prior knowledge of uncertain variables, was adopted to develop a probabilistic photovoltaic (PV) prediction model. The lack of suitable flexibility incentives is addressed by a novel interactive flexibility engagement scheme that can take into account building users’ willingness and optimize the building flexibility provision. The chance-constrained programming method was applied to manage the supply–demand balance of the resource aggregator and ensure risk-aware decision-making in power dispatch. The case study results show the strong economic and environmental performance of the proposed strategy. The proposed strategy leads to a win–win situation in which profit increases through a load reduction of 13% and a carbon emission reduction of 3% is achieved for different stakeholders, which also shows a trade-off between the economic benefits and the risk of supply shortage.

关键词： Resource aggregators Demand-side flexibility NGBoost probabilistic forecasting Interactive flexibility engagement chance-constrained programming

来源：评论

学校读者我要写书评

暂无评论

A Fuzzy programming Approach to Solve Stochastic Multi-objective Quadratic programming Problems

A Fuzzy Programming Approach to Solve Stochastic Multi-objec...

引用

Computing Conference

作者： Khalifa, Hamiden A. Elgendi, Elshimaa A. Ebraheim, Abdul Hadi N. Cairo Univ Dept OR ISSR Giza Egypt Cairo Univ Dept ORDS FCI Giza Egypt Cairo Univ Dept Math Stat ISSR Giza Egypt

ISBN: (纸本)9783030011741;9783030011734

This article handles the stochastic multi-objective quadratic programming problem (S-MOQP) using a fuzzy programming approach. In S-MOQP problem, both the objective function coefficients matrix and parameters in the constraints both are normally distributed random variables. The S-MOQP problem is converted into the corresponding ordinary multi-objective quadratic programming problem (MOQP). Then, a compromise solution for the MOQP problem is found via a fuzzy programming approach using a linear membership function. The applicability of the proposed algorithm is illustrated by two numerical examples.

关键词： Stochastic multi-objective quadratic programming problem chance-constrained programming Fuzzy programming approach Linear membership function Compromise solution

来源：评论

学校读者我要写书评

暂无评论

An algorithm for binary linear chance-constrained problems using IIS

引用

COMPUTATIONAL OPTIMIZATION AND APPLICATIONS 2019年第3期72卷 589-608页

作者： Canessa, Gianpiero Gallego, Julian A. Ntaimo, Lewis Pagnoncelli, Bernardo K. Univ Adolfo Ibanez Diagonal Torres 2640 Santiago 7941169 Chile AT Kearney Inc 227 West Monroe St Chicago IL 60606 USA Texas A&M Univ 3131 TAMUOff ETB 4008 College Stn TX 77843 USA Univ Adolfo Ibanez Diagonal Torres 2640 Oficina 533-C Santiago 7941169 Chile Northwestern Univ IEMS Dept 2145 Sheridan Rd Tech C216 Evanston IL 60208 USA

We propose an algorithm based on infeasible irreducible subsystems to solve binary linear chance-constrained problems with random technology matrix. By leveraging on the problem structure we are able to generate good quality upper bounds to the optimal value early in the algorithm, and the discrete domain is used to guide us efficiently in the search of solutions. We apply our methodology to individual and joint binary linear chance-constrained problems, demonstrating the ability of our approach to solve those problems. Extensive numerical experiments show that, in some cases, the number of nodes explored by our algorithm is drastically reduced when compared to a commercial solver.

关键词： chance-constrained programming Infeasible irreducible subsystems Integer programming

来源：评论

学校读者我要写书评

暂无评论

Optimal pricing decision for supply chains with risk sensitivity and human estimation

引用

INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2019年第7期10卷 1717-1730页

作者： Ma, Weimin Cheng, Rong Ke, Hua Zhao, Zhang Tongji Univ Sch Econ & Management Tongji Bldg ASiping Rd 1500 Shanghai 200092 Peoples R China Yunnan Univ Sch Business & Tourism Management Kunming Yunnan Peoples R China

In reality, consumption markets are changeable and uncertain in terms of consumer demands, product manufacturing costs, sales costs and so on. When facing these markets, the small and medium manufacturers are usually weak and risk sensitive vis-a-vis their counterpart power retailers, like Walmart, Carrefour, and TESCO. Therefore, human estimations on uncertain market information given by experienced experts and risk sensitivity of the weak manufacturers are of great significance for the optimal pricing strategies. Accordingly, this paper investigates the pricing decision problem in an uncertain supply chain where two risk-sensitive manufacturers distribute substitutable products into the same market through a common dominant and risk-neutral retailer. Uncertainty theory is employed to deal with human-estimation information and chance-constrained programming models are proposed to formulate the pricing decision problem with risk sensitivity. Moreover, numerical experiments are provided to validate the effectiveness of the proposed model. Interestingly, we show that the results critically depend on the comparison between how much the demands and manufacturing costs of two competing products change with the manufacturers' risk-sensitivity levels. Correspondingly, based on the estimated uncertainty distributions of uncertain variables by experts, the weak manufacturers are suggested to adjust their risk-sensitivity levels strategically for their profitability.

关键词： Uncertainty theory Risk-averse manufacturer Human estimation chance-constrained programming Pricing decision

来源：评论

学校读者我要写书评

暂无评论

Modelling linear fractional transportation problem in belief degree-based uncertain environment

引用

JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 2019年第3期31卷 393-408页

作者： Mahmoodirad, Ali Dehghan, Reza Niroomand, Sadegh Islamic Azad Univ Masjed Soleiman Branch Dept Math Masjed Soleiman Iran Firouzabad Inst Higher Educ Dept Ind Engn Firouzabad Fars Iran

A linear fractional transportation problem in uncertain environment is studied in this paper where the uncertain parameters of the problem are of belief degree-based uncertainty. For the first time, this type of uncertainty is considered for the linear fractional transportation problem. Belief degree-based uncertainty is useful for the cases that no historical information of an uncertain event is available. Zigzag type uncertainty distribution is used to show the belief degree-based uncertainty of the parameters of the problem. As solution methodology, the uncertain linear fractional transportation problem is converted to a crisp form using three approaches of expected value model, expected value and chance-constrained model, and chance-constrained model, separately. An extensive computational study on a real illustrative example shows the efficiency of the proposed formulation and the conversion approaches. The sensitivity analysis over the example illustrates the high dependency of the objective function value to the changes of the confidence level values of the chance constraints in the expected value and chance-constrained programming approach and the chance-constrained programming approach.

关键词： Uncertainty theory belief degree linear fractional transportation problem chance-constrained programming

来源：评论

学校读者我要写书评

暂无评论

Steady-State Security Region-Based Voltage/Var Optimization Considering Power Injection Uncertainties in Distribution Grids

引用

IEEE TRANSACTIONS ON SMART GRID 2019年第3期10卷 2904-2911页

作者： Yang, Tiankai Yu, Yixin Tianjin Univ Key Lab Smart Grid Minist Educ Tianjin 300072 Peoples R China Tianjin Univ Inst Elect Engn Tianjin 300072 Peoples R China

Solving the power injection uncertainties of wind power generation, photovoltaic power generation, and demand response is a challenge for voltage/var optimization in distribution grids. An effective solution is to establish a chance-constrained optimization model. However, there is no fast and efficient method to determine the chance constraints, and the established models are difficult to solve. Therefore, this paper establishes a steady-state security region-based chance-constrained model. First, we find that the boundaries of the steady-state security region, which satisfy the voltage and current constraints, can be approximated by the union of a few hyperplanes in complex power injection spaces. Additionally, the expressions for hyperplanes are uniquely determined by the grid topology and are irrelevant to power injections. Then, we set the power injection adjustments as variables and propose a fast generation method for the linear expressions of the chance constraints. Because the variables of constraints and the objective function are identical, the computational burden for optimization is significantly reduced. Meanwhile, using the minimum power loss as the objective function, we present a quadratic optimization model that is easily solved by the quadratic programming method. Optimization results for IEEE 33-bus and PG&E 69-bus distribution grids are presented to verify the effectiveness of the proposed method.

关键词： chance-constrained programming hyperplane expression quadratic optimization steady-state security region power injection uncertainty

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：