检索结果-内蒙古大学图书馆

Multi-stage scenario-based stochastic programming for managing lot sizing and workforce scheduling at Vestel

ANNALS OF OPERATIONS RESEARCH 2025年第2-3期344卷 911-936页

作者： Seyfi, Seyed Amin Yanikoglu, Ihsan Yilmaz, Goerkem Ozyegin Univ Ind Engn Dept TR-34794 Istanbul Turkiye Izmir Univ Econ Ind Engn Dept TR-35330 Izmir Turkiye Vestel Home Appliances R&D TR-45030 Manisa Turkiye

This study proposes a multi-stage stochastic production planning approach for a joint lot sizing and workforce scheduling problem under demand uncertainty. Scenario trees are used to model uncertainty in demand, and a multi-stage scenario-based stochastic linear program is developed. This model allows for both here-and-now and wait-and-see decisions providing flexibility for decision-makers to adjust production quantities according to the realized portion of demand and improve the overall effectiveness of production planning by better managing the number of active lines, workforce, and inventory levels. A matheuristic is developed for large-sized instances, which yields near-optimal solutions in practicable computation times. The proposed methods are demonstrated over a real data set taken from a Turkish home and professional appliances company, Vestel. The results show significant improvements in cost and CPU time performances for benchmark approaches, verifying the effectiveness of the proposed method.

关键词： Production planning Workforce scheduling Multi-stage decision making stochastic programming

来源：评论

学校读者我要写书评

暂无评论

Optimization design method for biofuel resilient supply chain considering node disruption impacts in a two-stage stochastic programming framework

引用

Frontiers of Chemical Science and Engineering 2025年第6期19卷 1-15页

作者： Wei, Ronghui Zhang, Wenhui Luo, Yiqing Yu, Yang Yuan, Xigang Chemical Engineering Research Center School of Chemical Engineering and Technology Tianjin University Tianjin300354 China State Key Laboratory of Chemical Engineering and Low-Carbon Technology Tianjin University Tianjin300354 China

As economic globalization accelerates, biofuel supply chain systems are becoming increasingly complex and large-scale, with businesses facing rising uncertainties and an increased risk of disruptions. Designing resilient biofuel supply chains that can withstand these risks while maintaining security and competitiveness has become a major concern and an urgent issue for enterprises. However, due to the lack of effective methods for quantifying and evaluating supply chain disruption risks, existing supply chain design approaches fail to adequately address the problem of mitigating such risks. To address this issue, this paper proposes an improved Node Disruption Impact Index with adjustable parameters, based on cost changes in the supply chain caused by disruptions at different nodes. This index enables the identification of nodes with varying risk levels and provides a means for evaluating disruption impact. The adjustable parameters can be tailored to meet the needs of supply chain enterprises, facilitating a trade-off between economic benefits and supply chain resilience. Furthermore, the paper applies the index to the fluctuation range of node uncertainties and develops a two-stage stochastic programming supply chain optimization model. This model incorporates a mechanism for addressing potential high disruption risks. By applying the model to a biofuel supply chain case in Guangdong Province, the results demonstrate that, when high-risk nodes are interrupted, the proposed model outperforms traditional models in terms of cost and market delivery rate. This confirms the effectiveness of the method in the optimization design of resilient supply chain. © Higher Education Press 2025.

关键词： stochastic programming

来源：评论

学校读者我要写书评

暂无评论

Urban transit decarbonization via stochastic programming and statistical inference under uncertainty

引用

TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT 2025年 142卷

作者： Wang, Jing Shirkoohi, Majid Gholami Akter, Rubina Merida, Walter Univ British Columbia Clean Energy Res Ctr 2360 East Mall Vancouver BC V6T 1Z3 Canada

Urban transit decarbonization is integral to achieving a net-zero public transportation systems. This work proposes an optimization model for bus fleet transition planning, involving purchases and allocation to routes, fueling and charging infrastructure, and financing. The model adopts stochastic programming to address decision-making under uncertainty and is formulated as a mixed-integer linear program. A confidence interval estimation method is derived to accommodate diverse decision values and non-uniform scenario probabilities, alongside an efficient scenario construction approach. A case study of the Metro Vancouver regional bus network is conducted to explore transition pathways for adopting battery electric and hydrogen fuel cell buses. Results indicate that shifting to a battery electric fleet is more cost-effective overall, while the hydrogen pathway demands smaller infrastructure investments. The competitiveness of hydrogen could significantly improve if the substantial potential for cost reductions is realized. A mixed fleet can integrate the advantages of both pathways.

关键词： Transit decarbonization Optimization under uncertainty stochastic programming Statistical inference Battery electric bus Hydrogen fuel cell bus

来源：评论

学校读者我要写书评

暂无评论

Two-Stage Hybrid Interval stochastic programming for Sustainable Utility Systems with Energy Storage

引用

INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH 2025年第18期64卷 9326-9337页

作者： Yang, Kangyuan Mei, Hua Zhao, Liang East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai 200237 Peoples R China East China Univ Sci & Technol Engn Res Ctr Proc Syst Engn Minist Educ Shanghai 200237 Peoples R China

The utility system is a crucial power source for chemical production processes, and the sustainable utility system is one of the key research topics in the field of energy and chemical engineering. To reduce the carbon emissions of the system, this study integrates the utility system with renewable energy and energy storage devices, transforming it into a sustainable utility system. To address the impact of multiscale uncertainties in renewable energy supply and steam demand on system decision optimization, this study develops a two-stage hybrid interval-stochastic programming method using stochastic intervals to model multiscale uncertainties to enhance modeling flexibility and reduce computational time. In the first stage, the capacities of renewable energy and storage systems are planned. The second stage involves solving an optimization problem under the uncertainties of renewable energy. The stochastic behavior of wind speed, solar irradiance, and steam demand is captured using scenario trees in the stochastic programming framework. In constructing the scenario tree, uncertainties are modeled by combining stochastic intervals. A risk coefficient is defined for the approximate representation of stochastic intervals to address the challenge of solving interval uncertainties while ensuring the flexibility of steam and power generation among the utility system, renewable energy system, and storage devices. Finally, a case study of a utility system in an actual ethylene chemical process validated the economic and environmental benefits of sustainable retrofitting, as well as the effectiveness of the proposed method in handling uncertainties. The optimization results indicate that the proposed model reduces carbon emissions by 5.2%, and the proposed method decreases computational time by 91% compared to stochastic programming.

关键词： stochastic programming

来源：评论

学校读者我要写书评

暂无评论

CHANCE CONSTRAINED stochastic programming IN DESIGN OF A FRAME STRUCTURE

引用

Civil Engineering Journal 2025年第1期34卷 1-11页

作者： Mrázková, Eva Brno University of Technology Faculty of Mechanical Engineering Institute of Mathematics Technická 2 Brno616 69 Czech Republic

A civil engineering problem concerning the optimal design of a loaded frame structure with a random Young's modulus is discussed. The developed multi-criteria optimization model involves ODE-type constraints and also one chance constraint related to the structure’s reliability. A computational scheme for this type of problem is proposed using the finite difference method for the approximation of the ODE constraint and the scenario-based approach for random variable approximation. The chance constraint is handled by two approaches – the analytical approach and penalty reformulation. A posteriori check of satisfying the chance constraint is made, and the upper bounds of the obtainable reliability are computed. © 2025 Czech Technical University in Prague. 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

Machine Learning-Enabled Evolutionary Two-Stage stochastic programming

引用

IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2025年

作者： Pan, Jeng-Shyang Song, Pei-Cheng Chu, Shu-Chuan Snasel, Vaclav Watada, Junzo Nanjing Univ Informat Sci & Technol Sch Artificial Intelligence Nanjing Peoples R China Chaoyang Univ Technol Dept Informat Management Taichung Taiwan Hong Kong Polytech Univ Dept Ind & Syst Engn Hong Kong Peoples R China Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao Peoples R China VSB Tech Univ Ostrava Dept Comp Sci Ostrava Czech Republic Waseda Univ Grad Sch Informat Prod & Syst Kitakyushu Japan

Two-stage stochastic programming (2SP) is an effective framework for decision-making and modeling under uncertainty. Some 2SP problems are challenging due to their high dimensionality and nonlinearity. Machine learning can assist in solving 2SP problems by providing data-driven insights and approximations. Evolutionary algorithms are more general and effective methods for handling various 2SP problems by exploiting their structures and features. However, there is still a research gap in combining machine learning and evolutionary algorithms for solving 2SP problems. Therefore, this paper proposes for the first time a Machine Learning-enabled Evolutionary 2SP framework (MLE2SP), which uses machine learning to construct surrogate-assisted evolutionary optimization frameworks for 2SP. It constructs a novel multi-output 2SP surrogate model that considers scenarios and decision variables of both stages for the first time and proposes a data conversion method to handle the high-dimensional decision variables and scenarios. It also proposes a Machine Learning-enabled Differential Evolution Sampling (MLDES) method to update candidate solutions, which extracts knowledge from dominant candidate solutions to guide the evolutionary direction. Moreover, this work provides open sources of linear and nonlinear two-stage stochastic mixed-integer programming problem instances as benchmark test functions. The effectiveness and generality of the proposed algorithm and framework are verified by the test results on the benchmark test functions and a disaster relief logistics problem, which provide a new research direction for designing general and effective two-stage stochastic programming solving frameworks.

关键词： stochastic processes programming Machine learning Evolutionary computation Machine learning algorithms Linear programming Random variables Metaheuristics Heuristic algorithms Decision making stochastic programming machine learning evolutionary computation surrogate model uncertainty

来源：评论

学校读者我要写书评

暂无评论

A stochastic integer programming approach to reserve staff scheduling with preferences

引用

INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2025年第1期32卷 289-313页

作者： Perreault-Lafleur, Carl Carvalho, Margarida Desaulniers, Guy Univ Montreal CIRRELT Montreal PQ H3T 1J4 Canada Univ Montreal Dept Informat & Rech Operat Montreal PQ H3T 1J4 Canada Polytech Montreal GERAD Montreal PQ H3T 1J4 Canada Polytech Montreal Dept Math & Genie Ind Montreal PQ H3T 1J4 Canada

Nowadays, reaching a high level of employee satisfaction in efficient schedules is an important and difficult task faced by companies. We tackle a new variant of the personnel scheduling problem under unknown demand by considering employee satisfaction via endogenous uncertainty depending on the combination of their preferred and received schedules. We address this problem in the context of reserve staff scheduling, an unstudied operational problem from the transit industry. To handle the challenges brought by the two uncertainty sources, regular employee and reserve employee absences, we formulate this problem as a two-stage stochastic integer program with mixed-integer recourse. The first-stage decisions consist in finding the days off of the reserve employees. After the unknown regular employee absences are revealed, the second-stage decisions are to schedule the reserve staff duties. We incorporate reserve employees' days-off preferences into the model to examine how employee satisfaction may affect their own absence rates.

关键词： personnel scheduling stochastic programming integer programming employee preferences transit industry endogenous uncertainty

来源：评论

学校读者我要写书评

暂无评论

Improved Konno Yamazaki model: Portfolio optimization based on stochastic and fuzzy programming

JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GA...

引用

JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY 2025年第2期40卷 995-1010页

作者： Kelce, Mehves Guliz Atalay, Kumru Didem Derya, Tusan Baskent Univ Fac Engn Dept Ind Engn TR-06790 Ankara Turkiye

Return is a term that refers to the financial results of an investment or financial asset, usually over a given period. Returns play an important role in investors' financial decision-making. Investors who want to maximize their returns should also consider the risk of the investment, the maturity and other factors. Financial life is full of uncertainties and it is difficult to predict what the future holds for investors. Since earnings are in most cases not certain and involve many uncertainties, the concept of expected return emerges, which provides an estimated return. In cases where the expected return cannot be determined exactly, rational investors choose investments with the highest expected return at a certain risk level;at a certain level of expected return, they prefer the investments with the lowest risk. Moreover, in order to improve the quality of investment, it is very important to provide investors with alternative portfolio options for the future. In this study, the fuzzy and stochastic Konno-Yamazaki model is considered to determine the investment amounts, risk and expected return values made in the stock by taking the end-of-day closing prices of the stocks in Borsa Istanbul 50 (BIST 50). Fuzzy linear programming and chance constrained programming approaches are used to solve the model under the assumptions that the expected returns are fuzzy and stochastic.

关键词： Portfolio optimization Chance constrained programming stochastic programming Fuzzy mathematical programming

来源：评论

学校读者我要写书评

暂无评论

stochastic programming based multi-arm bandit offloading strategy for internet of things

引用

Digital Communications and Networks 2023年第5期9卷 1200-1211页

作者： Bin Cao Tingyong Wu Xiang Bai State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876China National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of ChinaChengdu611731China China Electrics Technology Cyber Security Co..Ldt Chengdu611731China

In order to solve the high latency of traditional cloud computing and the processing capacity limitation of Internet of Things(IoT)users,Multi-access Edge Computing(MEC)migrates computing and storage capabilities from the remote data center to the edge of network,providing users with computation services quickly and *** this paper,we investigate the impact of the randomness caused by the movement of the IoT user on decision-making for offloading,where the connection between the IoT user and the MEC servers is *** uncertainty would be the main obstacle to assign the task ***,if the assigned task cannot match well with the real connection time,a migration(connection time is not enough to process)would be *** order to address the impact of this uncertainty,we formulate the offloading decision as an optimization problem considering the transmission,computation and *** the help of stochastic programming(SP),we use the posteriori recourse to compensate for inaccurate ***,in heterogeneous networks,considering multiple candidate MEC servers could be selected simultaneously due to overlapping,we also introduce the Multi-Arm Bandit(MAB)theory for MEC *** extensive simulations validate the improvement and effectiveness of the proposed SP-based Multi-arm bandit Method(SMM)for offloading in terms of reward,cost,energy consumption and *** results showthat SMMcan achieve about 20%improvement compared with the traditional offloading method that does not consider the randomness,and it also outperforms the existing SP/MAB based method for offloading.

关键词： Multi-access computing Internet of things Offloading stochastic programming Multi-arm bandit

来源：评论

学校读者我要写书评

暂无评论

stochastic programming for decentralized newsvendor with transshipment

引用

INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2012年第2期137卷 292-303页

作者： Summerfield, Nichalin S. Dror, Moshe Univ Arizona MIS Dept Tucson AZ 85721 USA

This paper discusses a family of two-stage decentralized inventory problems using a unifying framework (taxonomy) depicted as a multilevel graph. This framework allows us to model and link different problems of competing retailers who independently procure inventory in response to uncertain demand and anticipated inventory decisions of other retailers. In this family of problems, in the ex-post stage, the retailers exercise recourse actions in response to the realized demand and competitors' chosen procurement levels. For example, retailers could coordinate inventory transshipment to satisfy shortage with overage based on profit sharing agreements. Our framework provides a unifying parsimonious view using a single methodological prism for a variety of problems. Equally importantly, as recourse options are laid out, our framework clarifies and contributes a modeling connection between problems in a clear taxonomy of models. This unifying perspective explicitly links work that appeared in isolation and offers future research directions. (C) 2012 Elsevier B.V. All rights reserved.

关键词： stochastic programming Inventory management Cooperative and non-cooperative game

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：