检索结果-内蒙古大学图书馆

Two-Stage stochastic Optimization for Customer-Centric Aircraft Routing Considering stochastic Travel Times

TRANSPORTATION RESEARCH RECORD 2025年第4期2679卷 509-523页

作者： Kwon, Yeongjun Lu, Bo Moon, Ilkyeong Seoul Natl Univ Dept Ind Engn Seoul South Korea Seoul Natl Univ Inst Ind Syst Innovat Seoul South Korea Dalian Univ Technol Sch Econ & Management Dalian Peoples R China

This study addresses the problem of creating single aircraft route within strict time constraints, taking into account the uncertainty of travel times. The approach involves a two-phase decision-making process: the initial phase focuses on selecting airports from a pool of candidates by optimizing for maximum customer satisfaction;the subsequent phase involves arranging the sequence of itinerary to these airports, factoring in variable travel times to enhance the overall experience. To tackle this complex issue, we have developed a two-stage stochastic optimization model that incorporates probability-based constraints. This model is then analyzed using the sample average approximation method. To validate the efficacy of our proposed model, we compared its performance with two established benchmark models. Additionally, case studies focused on the U.S. airline network suggest that operators can optimize airline route recommendations by enhancing both customer utility and their own profitability. This approach demonstrates that strategic adjustments in route planning can lead to mutual benefits, effectively balancing customer satisfaction with financial performance.

关键词： single aircraft routing stochastic programming sample average approximation case study

来源：评论

学校读者我要写书评

暂无评论

SUPPORT A FINANCIALLY CONSTRAINED SUPPLIER WITH ORDER FINANCING AND REFUNDABLE NON-REFUNDABLE ADVANCES

引用

JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION 2025年第6期21卷 4441-4473页

作者： Wu, Andy Natl Chung Hsing Univ Dept Mkt Taichung 403 Taiwan

This study considers a supply chain in which a credit-worthy buyer purchases goods from a capital-constrained supplier to satisfy her deterministic demand. The buyer simultaneously implements both cash in advance (CIA) and purchase order financing (POF) as part of their pre-shipment financing (PSF) schemes to fund the supplier's operation. This research classifies CIA based on refundability, considering the increasing use of mitigating services or platforms associated with these PSF schemes. The study provides guidelines for aligning these primary PSF schemes with purchasing decisions, strengthening the buyer's ability to develop suppliers in sectors with small vendors, such as agriculture and dairy farming. We reveal that whether or not a CIA agreement includes so-called refundability strongly affects buyer preferences for CIA and POF. Specifically, we summarize our findings as follows: (1) Pure CIA financing strategy is optimal when CIA is refundable, which implies refundable CIA dominates POF. (2) When CIA is non-refundable, POF is preferred over CIA because POF allows a buyer to share the financing risk with a bank, whereas non-refundable CIA involves high risk. (3) When CIA is non-refundable, we recommend the buyer implement a mixed financing strategy, with POF and CIA as primary and backup financing options, respectively.

关键词： Supply chain financing purchasing strategy stochastic programming stackelberg model

来源：评论

学校读者我要写书评

暂无评论

Economic evaluation of a CHP biomass plant using stochastic dual dynamic programming

引用

ELECTRICAL ENGINEERING 2020年第4期102卷 2605-2615页

作者： Leocadio, Caio Monteiro Gomide Richa, Carlos Salvador Fortes, Marcio Zamboti Ferreira, Vitor Hugo Dias, Bruno Henriques Univ Fed Fluminense UFF Postgrad Program Elect Engn & Telecommun Passo da Patria St 156 BR-24210240 Niteroi RJ Brazil Univ Fed Juiz de Fora UFJF Elect Energy Dept Jose Lourenco Kelmer StUniv Campus BR-36036330 Juiz De Fora MG Brazil

Cogeneration systems have gained prominence in recent years driven by the simultaneous systematic production of heat and electricity, which are involved in industrial and commercial processes. Sugar and alcohol industry has become a reference in the use of cogeneration through the burning of sugarcane bagasse, with an important role in the future policies for generation expansion in Brazil. However, in the economic evaluation of this type of project, some previously ignored variables had a gain of relevance, such as the possibility of selling surplus energy produced by the plant. This paper presents a multiobjective stochastic optimization model using stochastic dynamic dual programming techniques, which seeks to maximize net revenue of a cogeneration project, indicating also the installed power adequate to the fuel availability and the needs of the plant, considering different energy scenarios in the spot market. The results indicate the best configuration for a cogeneration plant, considering the possibility of energy revenue, allowing an easy cost comparison of different alternatives for decision making from the investor's point of view.

关键词： Cogeneration stochastic programming SDDP Biomass Sugarcane

来源：评论

学校读者我要写书评

暂无评论

Multi-Time-Scale Resource Allocation Based on Long-Term Contracts and Real-Time Rental Business Models for Shared Energy Storage Systems

引用

Journal of Modern Power Systems and Clean Energy 2024年第2期12卷 454-465页

作者： Yuxuan Zhuang Zhiyi Li Qipeng Tan Yongqi Li Minhui Wan College of Electrical Engineering Zhejiang UniversityHangzhouChina China Southern Power Grid Power Generation Company Energy Storage Research Institute GuangzhouChina IEEE

The push for renewable energy emphasizes the need for energy storage systems(ESSs)to mitigate the unpre-dictability and variability of these sources,yet challenges such as high investment costs,sporadic utilization,and demand mismatch hinder their broader *** response,shared energy storage systems(SESSs)offer a more cohesive and efficient use of ESS,providing more accessible and cost-effective energy storage solutions to overcome these *** enhance the profitability of SESSs,this paper designs a multi-time-scale resource allocation strategy based on long-term contracts and real-time rental business *** initially construct a life cycle cost model for SESS and introduce a method to estimate the degradation costs of multiple battery groups by cycling numbers and depth of discharge within the ***,we design various long-term contracts from both capacity and energy perspectives,establishing associated models and real-time rental ***,multi-time-scale resource allocation based on the decomposition of user demand is *** analysis validates that the business model based on long-term contracts excels over models operating solely in the real-time market in economic viability and user satisfaction,effectively reducing battery degradation,and leveraging the aggregation effect for SESS can generate an additional increase of 10.7%in net revenue.

关键词： Capacity allocation long-term contracts shared energy storage system stochastic programming

来源：评论

学校读者我要写书评

暂无评论

A stochastic mixed integer linear programming framework for oil sands mine planning and waste management in the presence of grade uncertainty

引用

CIM JOURNAL 2022年第1期13卷 16-37页

作者： Mbadozie, O. Ben-Awuah, E. Maremi, A. Laurentian Univ Sudbury ON Canada Laurentian Univ Min Engn Sudbury ON Canada

The primary purpose of oil sands mine planning and waste management is to provide ore from the mine pit to the processing plant while containing the tailings in an efficient manner in-pit. Incorporating waste management in the mine plan is essential to maximize the economic potential of the mineral reserve and minimize waste management costs. However, spatial variability such as grade uncertainty results in ore tonnage variations, which leads to fluctuations in the quantity of ore to be processed and waste to be managed. This paper investigates the application of a stochastic mixed integer linear programming (SMILP) on oil sands mine planning to integrate bitumen grade uncertainty and waste management. Sequential Gaussian simulation is employed to quantitatively model the spatial variability of bitumen grade in the oil sands deposit. Multiple simulated orebody models are used as inputs for the SMILP model to generate optimal mine plans in the presence of grade uncertainty. The results demonstrate that the SMILP schedule generates 14% and 17% improvements in net present value compared to the E-type and ordinary kriging schedules, respectively. These results indicate that the SMILP model is a robust tool for optimizing stochastic integrated oil sands production schedules and waste management. L'objectif principal de la planification des mines de sables bitumineux et de la gestion des d & eacute;chets est de fournir du minerai de la mine & agrave;l'usine de traitement tout en confinant les r & eacute;sidus de mani & egrave;re efficace dans la fosse. L'int & eacute;gration de la gestion des d & eacute;chets dans le plan minier est essentielle pour maximiser le potentiel & eacute;conomique de la r & eacute;serve min & eacute;rale et de minimiser les co & ucirc;ts de gestion des d & eacute;chets. Cependant, la variabilit & eacute;spatiale comme l'incertitude de la teneur se traduit par des variations du tonnage de minerai, ce qui entra & icirc;ne des fluctuations de la qu

关键词： Grade uncertainty Ordinary kriging Production scheduling optimization Sequential Gaussian simulation stochastic programming Uncertainty-based oil sands mine planning Waste management gaussien s & eacute quentiel simulation gestion des d & eacute chets incertitude de teneur krigeage ordinaire optimisation de l'ordonnancement de production programmation stochastique planification des mines de sables bitumineux fond & eacute e sur l'incertitude, simulation gaussienne s & eacute quentielle

来源：评论

学校读者我要写书评

暂无评论

Integrated energy scheduling under uncertainty for sustainable ports

引用

TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2025年 197卷

作者： Gao, Yinping Yang, Linying Wang, Miaomiao Zhen, Lu Shanghai Maritime Univ Coll Transport & Commun Shanghai 201306 Peoples R China Shanghai Univ Sch Management Shanghai 200444 Peoples R China

Renewable energy generation has attracted increasing attention in port energy systems due to the urgent need for sustainable development. This study focuses on an integrated energy system that involves wind energy, photovoltaic energy, hydrogen energy and energy storage in the sustainable port. The multiple energy sources are used to generate electricity to support container loading and unloading in vessels. The realistic container loads are unknown to the port because of the uncertain arrival information, which affect the specific integrated energy scheduling. A twostage stochastic programming model is proposed to incorporate uncertain demand, multi-energy supply, electricity storage and sales. The vessel delay costs and the related energy costs that are generated from electricity consumption, storage and sales are minimized when allocating the integrated energy to serve berthing vessels. A metaheuristic algorithm based on the adaptive large neighborhood search (ALNS) framework is proposed for solving the model. The proposed metaheuristic algorithm fixes the decision variable values of the first-stage problem and allows transfers to solve sub-problems under all uncertain scenarios. The effectiveness of the proposed algorithm is demonstrated through small-scale, medium-scale, and large-scale numerical experiments in terms of solution quality and computation time. Some experiments are further conducted to analyze the impact of renewable energy generation, renewable energy sources, berthing vessel types, and vessel delay tolerances. Managerial insights can be obtained for optimizing the integrated energy scheduling schemes in sustainable ports. The findings can also provide implications for ports with different scales when optimizing the configurations of renewable energy supply.

关键词： Renewable energy Uncertain container loads stochastic programming Metaheuristic algorithm Sustainable development

来源：评论

学校读者我要写书评

暂无评论

INEXACT CUTS IN stochastic DUAL DYNAMIC programming

引用

SIAM JOURNAL ON OPTIMIZATION 2020年第1期30卷 407-438页

作者： Guigues, Vincent FGV Appl Math BR-22250900 Rio De Janeiro Brazil

We introduce an extension of stochastic dual dynamic programming (SDDP) to solve stochastic convex dynamic programming equations. This extension applies when some or all primal and dual subproblems to be solved along the forward and backward passes of the method are solved with bounded errors (inexactly). This inexact variant of SDDP is described for both linear problems (the corresponding variant being denoted by ISDDP-LP) and nonlinear problems (the corresponding variant being denoted by ISDDP-NLP). We prove convergence theorems for ISDDP-LP and ISDDP-NLP for both bounded and asymptotically vanishing errors. Finally, we present the results of numerical experiments comparing SDDP and ISDDP-LP on a portfolio problem with direct transaction costs modeled as a multistage stochastic linear optimization problem. In these experiments, ISDDP-LP allows us to strike a different balance between policy quality and computing time, trading off the former for the latter.

关键词： stochastic programming inexact cuts for value functions bounding epsilon-optimal dual solutions SDDP inexact SDDP

来源：评论

学校读者我要写书评

暂无评论

Efficient Cloud Server Deployment Under Demand Uncertainty

引用

M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT 2025年第2期27卷 339-678, C2页

作者： Liu, Rui Peng Mellou, Konstantina Gong, Evelyn Xiao-Yue Li, Beibin Coffee, Thomas Pathuri, Jeevan Simchi-Levi, David Menache, Ishai Microsoft Res Machine Learning & Optimizat Redmond WA 98052 USA Carnegie Mellon Univ Tepper Sch Business Pittsburgh PA 15213 USA Microsoft Cloud Supply Chain Redmond WA 98052 USA MIT Dept Civil & Environm Engn Cambridge MA 02139 USA MIT Inst Data Syst & Soc Cambridge MA 02139 USA

Problem definition: Cloud computing is a multibillion-dollar business that draws substantial capital investments from large companies such as Amazon, Microsoft, and Google. Large cloud providers need to accommodate the growing demand for computing resources while avoiding unnecessary overprovisioning of hardware and operational costs. The underlying decision processes are challenging, as they involve long-term hardware and infrastructure investments under future demand uncertainty. In this paper, we introduce the cloud server deployment problem. One important aspect of the problem is that the infrastructure preparation work has to be planned for before server deployments can take place. Furthermore, a combination of temporal constraints has to be considered together with a variety of physical constraints. Methodology/results: We formulate the underlying optimization problem as a two-stage stochastic program. After carefully examining the demand data and on-the-ground deployment operations, we distill two structural properties on deployment throughput constraints and provide tightness results on a convex relaxation of the second stage. Based on that, we develop efficient cutting-plane methods that exploit the special structure of the problem and can accommodate different risk measures. We test our algorithms with real production traces from Microsoft Azure and demonstrate sizeable cost reductions. We show empirically that the algorithms remain optimal even when the two properties are not fully satisfied. Managerial implications: Cloud supply chain operations were largely executed manually due to their complexity and dynamic nature. In this paper, we show that the key decision processes can be systematically optimized. In particular, we demonstrate that accounting for the stochastic nature of demands results in substantial cost reductions in cloud server deployments. Another benefit of our stochastic optimization approach is the ability to seamlessly integrate configu

关键词： cloud supply chain optimization stochastic programming benders decomposition capacity expansion inventory management

来源：评论

学校读者我要写书评

暂无评论

CONSISTENCY OF SAMPLE-BASED STATIONARY POINTS FOR INFINITE-DIMENSIONAL stochastic OPTIMIZATION

引用

SIAM JOURNAL ON OPTIMIZATION 2025年第1期35卷 42-61页

作者： Milz, Johannes Georgia Inst Technol H Milton Stewart Sch Ind & Syst Engn Atlanta GA 30332 USA

We consider stochastic optimization problems with possibly nonsmooth integrands posed in infinite-dimensional decision spaces and approximate these stochastic programs via a sample- based approaches. We establish the asymptotic consistency of approximate Clarke stationary points of the sample-based approximations. Our framework is applied to risk-averse semilinear PDEconstrained optimization using the average value-at-risk and risk-neutral bilinear PDE-constrained optimization.

关键词： PDE-constrained optimization uncertainty quantification bilinear optimal control stochastic programming sample average approximation optimization under uncertainty

来源：评论

学校读者我要写书评

暂无评论

Renewable Energy-Aware Machine Scheduling Under Intermittent Energy Supply

引用

IEEE ACCESS 2024年 12卷 23613-23625页

作者： Ertem, Mehmet Eskisehir Osmangazi Univ Ind Engn Dept TR-26040 Eskisehir Turkiye

The integration of renewable energy sources into the manufacturing sector is a recent development, which is prompting companies to explore innovative approaches to enhance their production systems through efficient renewable energy utilization. Within this context, renewable energy-aware machine scheduling has emerged as a pivotal area requiring novel strategies for sustainability. This study aims to evaluate the effect of intermittent renewable energy supply on sustainable machine scheduling by incorporating the option of machine speed for the first time in the literature. A two-stage stochastic program is developed to model the problem and examine the value of the stochastic solution. Additionally, an efficient genetic algorithm is proposed for approximate problem solving. Extensive test study is conducted to explore and generalize the sustainable operational policies derived from the proposed method. The results reveal key scheduling strategies that promote sustainability in production by reducing non-renewable dependence. Notably, the study underlines the importance of considering uncertainties in renewable supply in scheduling, especially under conditions of substantial job and machine scales, high-capacity but variable renewable supply, and flexible job deadlines. As an illustration, in the context of test cases involving a significant number of jobs and machines, the stochastic solutions demonstrate a substantial impact on reducing energy expenditure. Failing to account for the stochastic nature of renewable energy supply would lead to considerable additional energy consumption from the grid. These findings offer valuable insights for corporate management and scheduling operations, particularly as they navigate the transition towards green manufacturing practices.

关键词： Energy-aware scheduling genetic algorithm machine scheduling stochastic programming sustainable manufacturing

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：