检索结果-内蒙古大学图书馆

Dual dominance: how Harry Markowitz and William Ziemba impacted portfolio management

ANNALS OF OPERATIONS RESEARCH 2024年第1期346卷 181-216页

作者： Lleo, Sebastien Maclean, Leonard C. NEOMA Business Sch Finance Dept 59 Rue Pierre Taittinger F-51100 Reims France Dalhousie Univ Fac Management 6100 Univ Ave Halifax NS B3H 4R2 Canada

Models for determining a portfolio of investment decisions in risky assets have been at the forefront of financial research for almost a century. Among the celebrated researchers are Harry Markowitz and William Ziemba. These titans devoted their working years to developing quantitative models and adapting the models to changes in financial markets and investor attitudes. This paper presents a general lens through which the Markowitz mean-variance model and the Ziemba capital growth model can be viewed. This lens is risk-sensitive stochastic control. The optimal control approach places the expected utility, mean-variance, and capital growth models in a common setting to elucidate their connection. In particular, benchmarking and risk factors, two standard refinements to control risk, are seamlessly incorporated into the stochastic control model. The solution to the risk-sensitive control problem isolates the effect of benchmarks and factors to provide insights into model-based portfolios.

关键词： Portfolio selection Mean-variance optimization Harry Markowitz Kelly criterion stochastic programming William Ziemba Risk-sensitive control

来源：评论

学校读者我要写书评

暂无评论

Multi-period emergency facility location-routing problems under uncertainty and risk aversion

引用

SOCIO-ECONOMIC PLANNING SCIENCES 2024年 96卷

作者： Hu, Qing-Mi Hu, Yan Li, Xiaoping Jiangsu Univ Sci & Technol Sch Econ & Management Zhenjiang 212100 Peoples R China

This paper addresses a multi-period emergency facility location-routing problem, in which the uncertainties in material demands and transportation time, as well as dynamic inventory replenishment and carryover are incorporated in the design of multi-level emergency logistics networks. To measure the risks stemming from uncertain transportation time, mean-CVaR method is used. Then, a risk-averse stochastic programming model for the presented problem is formulated to minimize the total rescue time of the network. Moreover, a genetic algorithm is developed to solve the proposed model. Extensive numerical experiments including the randomly generated instances and a case study on the Wenchuan earthquake in China are conducted to verify the effectiveness of the presented model and algorithm. Experimental results show that the genetic algorithm significantly performs better than the Gurobi solver in terms of both solution quality and solution time.

关键词： Emergency facility location-routing Uncertainty Dynamic inventory replenishment and carryover Risk-averse stochastic programming Genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

Green retailer: A stochastic bi-level approach to support investment decisions in sustainable energy systems

引用

OPERATIONS RESEARCH PERSPECTIVES 2024年 12卷

作者： Beraldi, Patrizia Lab Financial Engn & Risk Management Dept Mech Energy & Management Engn Via P Bucci I-87036 Arcavacata Di Rende Cosenza Italy

This paper presents a bi-level approach to support retailers in making investment decisions in renewable -based systems to provide clean electricity. The proposed model captures the strategic nature of the problem and combines capacity sizing decisions for installed technologies with pricing decisions regarding the electricity tariffs to offer to a reference end -user, representative of a class of residential prosumers. The interaction between retailer and end -user is modeled using the Stackelberg game framework, with the former acting as a leader and the latter as follower. The reaction of the follower to the electricity tariff affects the retailer's profit, which is calculated as the difference between the revenue generated from selling electricity and the total investment, operation and management costs. To account for uncertainty in wholesale electricity prices, renewable resource availability and electricity request, the upper -level problem is formulated as a two -stage stochastic programming model. First -stage decisions refer to the sizing of installed technologies and electricity tariffs, whereas second -stage decisions refer to the operation and management of the designed system. The model also incorporates a safety measure to control the average profit that can be achieved in a given percentage of worst -case situations, thus providing a contingency against unforeseen changes. At the lower level, the follower reacts to the offered tariffs by defining the procurement plan in terms of energy to purchase from the retailer or potential competitors, with the final aim of minimizing the expected value of the electricity bill. A tailored approach that exploits the specific problem structure is designed to solve the proposed formulation and extensively tested on a realistic case study. The numerical results demonstrate the efficiency of the proposed approach and validate the significance of explicitly dealing with the uncertainty and the importance of incorporat

关键词： Green electricity retailer Sustainable energy system Bi-level optimization stochastic programming

来源：评论

学校读者我要写书评

暂无评论

Optimising rainwater harvesting systems under uncertainty: A multi-objective stochastic approach with risk considerations

引用

RESOURCES CONSERVATION & RECYCLING ADVANCES 2025年 26卷

作者： Shefaei, Alireza Maleki, Arash van der Hoek, Jan Peter van de Giesen, Nick Abraham, Edo Delft Univ Technol Fac Civil Engn & Geosci Dept Water Management Stevinweg 1 NL-2628 CN Delft Netherlands Waternet Res & Innovat Korte Ouderkerkerdijk 7 NL-1096 AC Amsterdam Netherlands

Optimising rainwater harvesting (RWH) systems' design involves sizing the storage and catchment areas enhance cost-effectiveness, self-sufficiency, and water quality indicators. This paper considers the design RWH systems under long-term uncertainty in precipitation and demands. In this work, we formulate and solve a multi-objective stochastic optimisation problem that allows explicit trade-offs under uncertainty, maximising system efficiency and minimising deployment cost. We use the yield after spillage (YAS) approach to incorporate the physical and operational constraints and the big-M method to reformulate the nonlinear min\max rules of this approach as a mixed-integer linear programming (MILP) problem. By posing a risk averseness measure on efficiency as a conditional value at risk (CVaR) formulation, we guarantee the designer against the highest demand and driest weather conditions. We then exploit the lexicographic method effectively solve the multi-objective stochastic problem as a sequence of equivalent single-objective problems. detailed case study of a botanical garden in Amsterdam demonstrates the framework's practical application;we show significant improvements in system efficiency of up to 15.5% and 28.9% in the driest scenarios under risk neutral and risk-averse conditions, respectively, compared to deterministic approaches. The findings highlight the importance of taking into account multiple objectives and uncertainties when designing RWH systems, allowing designers to optimise efficiency and costs based on their specific requirements without extensive parameterisation.

关键词： Rainwater harvesting system stochastic programming Multi-objective optimisation Risk assessment

来源：评论

学校读者我要写书评

暂无评论

Research on Multiple Products Aggregate Production Planning under Random Environment

引用

IAENG International Journal of Applied Mathematics 2024年第3期54卷 562-575页

作者： Liu, Lanfen Yang, Xinfeng Yang, Ke School of Traffic and Transportation Lanzhou Jiaotong University Lanzhou730070 China School of Traffic and Transportation Lanzhou Jiaotong University Lanzhou730070 China Engineer of Beijing Rail Transit Design & Research Institute Beijing100037 China

Uncertain factors can significantly reduce the applicability of production plans and the production stability of enterprises. Therefore, considering the uncertain factors caused by external demands and internal capabilities, the impact of production in advance or backorder is analyzed, and the cost of backorder and loss of sales is determined. Moreover, a multi-objective stochastic programming model for aggregate production planning (APP) problem that minimizes the expected chance cost and employee instability is constructed considering external demand and internal capabilities as random variables. At a certain level of confidence, the opportunity constraints are transformed. In addition, combined with a local search strategy, two stochastic hybrid genetic algorithm-particle swarm optimization algorithms based on stage and multiple population strategies are designed to solve this model. Sensitivity analysis is performed on different confidence levels by using numerical examples, and numerical experiments show that the two hybrid algorithms proposed by this paper can effectively improve the solving efficiency. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： stochastic programming

来源：评论

学校读者我要写书评

暂无评论

A risk-averse stochastic optimization model for community resilience planning

引用

SOCIO-ECONOMIC PLANNING SCIENCES 2024年 92卷

作者： Ibn Faiz, Tasnim Harrison, Kenneth W. Univ Maryland 8051 Regents Dr College Pk MD 20742 USA Natl Inst Stand & Technol NIST Profess Res Experience Program PREP Postdocto Engn Lab 100 Bur DrMail Stop 8611 Gaithersburg MD 20899 USA Natl Inst Stand & Technol Engn Lab 100 Bur DrMail Stop 8611 Gaithersburg MD 20899 USA

Community resilience planning is challenging as it involves several large-scale systems with interdependency, populations with diverse socio-economic characteristics, and numerous stakeholders. This study introduces a new optimization model to decrease a community's burden in developing viable alternative sets of decisions while considering costs and risks associated with uncertain hazard events. The model captures the essential features of a community, and its scope extends beyond infrastructure and buildings to include social goals. Structural engineering and social science approaches are adapted and incorporated into the model formulation to facilitate the identification of engineering decisions meeting the social goals of minimizing population dislocation and time for recovery. A risk-averse approach frames the optimization problem as a two-stage mean-risk stochastic programming model, which enables effective planning for low-probability, high-consequence hazard events. A case study simulating flood hazards in Lumberton, North Carolina, is developed, and the model is run with the generated data set to showcase the model's capability in developing risk-informed mitigation and recovery plans to achieve resilience goals. The insights drawn from the numerical experiments show the effect of changing risk preference on community resilience metrics.

关键词： Community resilience Disaster resilience Risk -averse stochastic programming Hazards Optimization

来源：评论

学校读者我要写书评

暂无评论

Design of experiments for the stochastic unit commitment with economic dispatch models

引用

EURO JOURNAL ON COMPUTATIONAL OPTIMIZATION 2024年 12卷

作者： Sakhavand, Nahal Rosenberger, Jay Chen, Victoria C. P. Gangammanavar, Harsha Univ Texas Arlington Dept Ind Mfg & Syst Engn Arlington TX 76010 USA Southern Methodist Univ Dept Operat Res & Engn Management POB 750123 Dallas TX 75275 USA

We develop a Design and Analysis of the Computer Experiments (DACE) approach to the stochastic unit commitment problem for power systems with significant renewable integration. For this purpose, we use a two-stage stochastic programming formulation of the stochastic unit commitment-economic dispatch problem. Typically, a sample average approximation of the true problem is solved using a cutting plane method (such as the L-shaped method) or scenario decomposition (such as Progressive Hedging) algorithms. However, when the number of scenarios increases, these solution methods become computationally prohibitive. To address this challenge, we develop a novel DACE approach that exploits the structure of the first-stage unit commitment decision space in a design of experiments, uses features based upon solar generation, and trains a multivariate adaptive regression splines model to approximate the second stage of the stochastic unit commitment-economic dispatch problem. We conduct experiments on two modified IEEE-57 and IEEE-118 test systems and assess the quality of the solutions obtained from both the DACE and the L-shaped methods in a replicated procedure. The results obtained from this approach attest to the significant improvement in the computational performance of the DACE approach over the traditional L-shaped method.

关键词： Unit commitment Economic dispatch stochastic programming Design and analysis of computer experiments Multivariate adaptive regression splines

来源：评论

学校读者我要写书评

暂无评论

Integration of text-mining and telemedicine appointment optimization

引用

ANNALS OF OPERATIONS RESEARCH 2024年第1期341卷 621-645页

作者： Ji, Menglei Mosaffa, Mohammad Ardestani-Jaafari, Amir Li, Jinlin Peng, Chun Beijing Inst Technol Sch Management & Econ Beijing 100081 Peoples R China Univ British Columbia Fac Management 1137 Alumni Ave Kelowna BC V1V 1V7 Canada Beijing Jiaotong Univ Sch Econ & Management Beijing 100044 Peoples R China

Nowadays, many countries view profitable telemedicine as a viable strategy for meeting healthcare needs, especially during the pandemic. Existing appointment models are based on patients' structured data. We study the value of incorporating textual patient data into telemedicine appointment optimization. Our research contributes to the healthcare operations management literature by developing a new framework showing (1) the value of the text in the telemedicine appointment problem, (2) the value of incorporating the textual and structured data in the problem. In particular, in the first phase of the framework, a text-driven classification model is developed to classify patients into normal and prolonged service time classes. In the second phase, we integrate the classification model into two existing decision-making policies. We analyze the performance of our proposed policy in the presence of existing methods on a data set from the National Telemedicine Center of China (NTCC). We first show that our classifier can achieve 90.4% AUC in a binary task based on textual data. We next show that our method outperforms the stochastic model available in the literature. In particular, with a slight change of actual distribution from historical data to a normal distribution, we observe that our policy improves the average profit of the policy obtained from the stochastic model by 42% and obtains lower relative regret (18%) from full information than the stochastic model (148%). Furthermore, our policy provides a promising trade-off between the cancellation and postponement rates of patients, resulting in a higher profit and a better schedule strategy for the telemedicine center.

关键词： Telemedicine appointment scheduling Machine learning Text mining stochastic programming

来源：评论

学校读者我要写书评

暂无评论

Heuristic approach for optimising reliable supply chain network using drones in last-mile delivery under uncertainty

引用

INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE-OPERATIONS & LOGISTICS 2024年第1期11卷

作者： Tolooie, Ali Sinha, Ashesh Nouri, Aida Khosh Raftar Mercer Univ Stetson Hatcher Sch Business Macon GA 32107 USA Kansas State Univ Ind & Mfg Syst Engn Manhattan KS USA Mem Univ Newfoundland Fac Business Adm St John NF Canada

In addressing the challenges of last-mile logistics, the reliability of the supply chain network becomes paramount. These challenges are intensified due to drone performance limitations and various uncertainties in supply chain operations. While recent literature recognises the potential of drones for last-mile delivery, it falls short in effectively considering these uncertainties in drone-enabled supply chain models. Our research addresses this gap with two major contributions: first, a novel stochastic mixed-integer programming model is developed to construct a feasible delivery network, including warehouses and recharging stations, enhancing both coverage and reliability. Second, a modification in the genetic algorithm by considering each scenario independently improves computational efficiency, outperforming commercial software by an average of 40% and up to 55%. Empirical findings reveal that strategic investments in system hardening can yield substantial improvements in reliability. Despite the absence of real-world stochastic parameters as a limitation, this research pioneers the design of reliable networks under uncertainties and extends drone coverage through strategic charging stations. This work sets a significant milestone for future optimization in drone logistics, offering practical implications for supply chain managers considering drone adoption.

关键词： Supply chain network design unmanned aerial vehicle drone delivery stochastic programming reliability

来源：评论

学校读者我要写书评

暂无评论

A generic stochastic network-based formulation for production optimization of district heating systems

引用

ENERGY SYSTEMS-OPTIMIZATION MODELING SIMULATION AND ECONOMIC ASPECTS 2024年 1-38页

作者： Guericke, Daniela Schledorn, Amos Madsen, Henrik Univ Twente Dept High Tech Business & Entrepreneurship Drienerlolaan 5 NL-7522 NB Enschede Netherlands Tech Univ Denmark Dept Appl Math & Comp Sci Richard Petersens Plads DK-2800 Lyngby Denmark

District heating (DH) systems are an important component in the EU strategy to reach the emission goals, since they allow an efficient supply of heat while using the advantages of sector coupling between different energy carriers such as power, heat, gas and biomass. Most DH systems use several different types of units to produce heat for hundreds or thousands of households (e.g. natural gas-fired boilers, electric boilers, biomass-fired units, waste heat from industry, solar thermal units). Furthermore, combined heat and power units units are often included to use the synergy effects of excess heat from electricity production. To address the challenge of providing optimization tools for a vast variety of different system configurations, we propose a generic mixed-integer linear programming formulation for the operational production optimization in DH systems. The model is based on a network structure that can represent different system setups while the underlying model formulation stays the same. Therefore, the model can be used for most DH systems although they might use different combinations of technologies in different system layouts. The mathematical formulation is based on stochastic programming to account for the uncertainty of energy prices and production from non-dispatchable units such as waste heat and solar heat. Furthermore, the model is easily adaptable to different application cases in DH systems such as operational planning, bidding to electricity markets and long-term evaluation. We present results from three real cases in Denmark with different requirements.

关键词： District heating Production optimization Network formulation stochastic programming Bidding

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：