检索结果-内蒙古大学图书馆

Data-driven robust optimization for pipeline scheduling under flow rate uncertainty

COMPUTERS & CHEMICAL ENGINEERING 2025年 193卷

作者： Baghban, Amir Castro, Pedro M. Oliveira, Fabricio Azarbaijan Shahid Madani Univ Dept Math Tabriz *** Iran Univ Lisbon CERENA Dept Chem Engn Inst Super Tecn P-1049001 Lisbon Portugal Aalto Univ Sch Sci Dept Math & Syst Anal POB 11100 Aalto 00076 Finland

Frequently, parameters in optimization models are subject to a high level of uncertainty coming from several sources and, as such, assuming them to be deterministic can lead to solutions that are infeasible in practice. Robust optimization is a computationally efficient approach that generates solutions that are feasible for realizations of uncertain parameters near the nominal value. This paper develops a data-driven robust optimization approach for the scheduling of a straight pipeline connecting a single refinery with multiple distribution centers, considering uncertainty in the injection rate. For that, we apply support vector clustering to learn an uncertainty set for the robust version of the deterministic model. We compare the performance of our proposed robust model against one utilizing a standard robust optimization approach and conclude that data-driven robust solutions are less conservative.

关键词： Straight liquid pipelines Continuous-time formulation mixed-integer linear programming Support vector clustering Robust optimization

来源：评论

学校读者我要写书评

暂无评论

A tight formulation for the dial-a-ride problem

引用

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2025年第2期321卷 363-382页

作者： Gaul, Daniela Klamroth, Kathrin Pfeiffer, Christian Stiglmayr, Michael Schulz, Arne Univ Wuppertal Sch Math & Nat Sci Gaussstr 20 D-42119 Wuppertal Germany Univ Hamburg Inst Operat Management Moorweidenstr 18 D-20148 Hamburg Germany Helmut Schmidt Univ Inst Quant Logist Holstenhofweg 85 D-22043 Hamburg Germany

Ridepooling services play an increasingly important role in modern transportation systems. With soaring demand and growing fleet sizes, the underlying route planning problems become increasingly challenging. In this context, we consider the dial-a-ride problem (DARP): Given a set of transportation requests with pickup and delivery locations, passenger numbers, time windows, and maximum ride times, an optimal routing for a fleet of vehicles, including an optimized passenger assignment, needs to be determined. We present tight mixed-integer linear programming (MILP) formulations for the DARP by combining two state-of-the-art models into novel location-augmented-event-based formulations. Strong valid inequalities and lower and upper bounding techniques are derived to further improve the formulations. We then demonstrate the theoretical and computational superiority of the new models: First, the linear programming relaxations of the new formulations are stronger than existing location-based approaches. Second, extensive numerical experiments on benchmark instances show that computational times are on average reduced by 53.9% compared to state-of-the-art event-based approaches.

关键词： Routing Transportation Dial-a-ride problem mixed-integer linear programming Valid inequalities

来源：评论

学校读者我要写书评

暂无评论

The single machine scheduling problem with bio-mathematical fatigue constraints

引用

INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2025年

作者： Wang, Zhaotong Deng, Tianhu Meng, Yize Guo, Han Tsinghua Univ Dept Ind Engn Beijing 100084 Peoples R China Qingzhi Optimizat Beijing Technol Co Ltd Beijing Peoples R China Univ Sci & Technol China Sch Management Hefei Anhui Peoples R China

Despite the adverse impacts of occupational fatigue such as accidents and injuries in the manufacturing industry, it has not been systematically examined in the literature on production scheduling. In this paper, we integrate the classic bio-mathematical fatigue prediction model from the brain science literature into the simple single machine scheduling problem with sequence-dependent setup times. Then, we formulate the problem as a mixed-integer linear programming model and propose an adaptive large neighborhood search heuristic. The effectiveness of the heuristic is numerically validated through real cases. Finally, we argue that considering bio-mathematical fatigue prediction can lead to safer production schedules, notably reducing the fatigue working hours in our real case.

关键词： Single machine scheduling bio-mathematical fatigue constraints adaptive large neighborhood search occupational safety and health mixed-integer linear programming

来源：评论

学校读者我要写书评

暂无评论

Novel mathematical formulations for parallel-batching processing machine scheduling problems

引用

COMPUTERS & OPERATIONS RESEARCH 2025年 173卷

作者： Zheng, Shaoxiang Xie, Naiming Wu, Qiao Liu, Caijie Kunming Univ Sci & Technol Fac Management & Econ Kunming 650500 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Econ & Management Nanjing 210016 Peoples R China Nanjing Univ Posts & Telecommun Sch Management Nanjing 210003 Peoples R China Yunnan Univ Finance & Econ Int Business Sch Kunming Peoples R China

We study mathematical formulations for batch-processing machine scheduling problems (BPMPs), which are the challenging issues in the machine scheduling literature where machines are capable of processing a batch of jobs simultaneously if jobs with non-identical sizes can be packed in a capacitated machine. In this paper, we tackle single- and parallel-machine BPMPs, and other interesting problem variants that aim at minimizing the makespan. We develop novel formulations along with valid inequalities and an algorithm framework that makes use of dual information and bounding techniques to achieve efficiency when instances are intractable. Extensive computational experiments on benchmark instances show that our approaches achieve state-of-the-art results and prove the optimality of intractable instances in the literature.

关键词： Scheduling Batch-processing machines Makespan Valid inequalities mixed-integer linear programming

来源：评论

学校读者我要写书评

暂无评论

Optimal investment planning for production networks with fixed production profiles

引用

COMPUTERS & OPERATIONS RESEARCH 2025年 176卷

作者： Liu, Donghao Leibowicz, Benjamin D. Bard, Jonathan F. Zhu, Yuzixuan Guo, Yuanyuan Shao, Yufen Univ Texas Austin Cockrell Sch Engn Operat Res & Ind Engn Austin TX 78712 USA ExxonMobil Technol & Engn Co 22777 Springwoods Village Pkwy Spring TX 77389 USA

In this paper, we consider an oilfield planning problem with decisions about where and when to invest in wells and facilities to maximize profit. The model, in the form of a mixed-integer linear program, includes an option to expand capacity for existing facilities, annual budget constraints, well closing decisions, and fixed production profiles once wells are opened. While fixed profiles area novel and important feature, they add another set of time-indexed binary variables that makes the problem difficult to solve. To find solutions, we develop a three-phase sequential algorithm that includes (1) ranking, (2) branching, and (3) refinement. Phases 1 and 2 determine which facilities and wells to open, along with well-facility assignments. Phase 3 ensures feasibility with respect to budget constraints and adjusts construction times and facility capacities to increase profit. We first demonstrate how our algorithm navigates the problem's complex features by applying it to a case study parameterized with realistic production profiles. Then, we perform computational experiments on small instances and show that our algorithm generally achieves the same objective function values as CPLEX but in much less time. Lastly, we solve larger instances using our three-phase algorithm and several variations to demonstrate its scalability and to highlight the roles of specific algorithmic components.

关键词： Oilfield planning Oil and gas mixed-integer linear programming Investment planning Project scheduling Network optimization

来源：评论

学校读者我要写书评

暂无评论

Planning maintenance tasks in airline operating companies: A mathematical programming model applied to a case study

引用

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART O-JOURNAL OF RISK AND RELIABILITY 2025年

作者： Agostinho, Francisco Martinho, Martim Alves, Tiago Andrade, Antonio R. Univ Lisbon Inst Super Tecn IDMEC Ave Rovisco Pais P-1049001 Lisbon Portugal

This paper explores a mixed-integer linear programming (MILP) approach to plan the maintenance tasks in an airline. The MILP model finds feasible maintenance plans, by minimizing the costs associated with maintenance activities and costs associated with unavailability. A constructive matheuristic approach is put forward, by solving the maintenance planning problem sequentially. The model and the matheuristic is then applied to a case study from the main Portuguese airline company. The results suggest that the proposed matheuristic approach for maintenance planning in practice, as explored in the case study, finds feasible solutions in a much lower computational time. Moreover, a sensitivity analysis is conducted to assess the impact of hangar's capacity and other input parameters in the maintenance plan. Our approach provides an effective way to support decision-making in maintenance planning for a fleet of aircraft, while complying with several operational, technical, and labour constraints.

关键词： Maintenance planning aircraft maintenance mixed-integer linear programming maintenance heuristic

来源：评论

学校读者我要写书评

暂无评论

Optimizing Multi-Row Layouts With the Through-Aisle Structure: A Hybrid Approach of Teaching-Learning-Based Optimization and linear programming

引用

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 4972-4987页

作者： Zhang, Zeqiang Zhang, Yu Chen, Feng Liu, Junqi Wang, Can Southwest Jiaotong Univ Sch Mech Engn Chengdu 610031 Peoples R China Technol & Equipment Rail Transit Operat & Maintena Chengdu 610031 Peoples R China Chengdu SIWI High Tech Ind Co Ltd Chengdu 610031 Peoples R China

The multi-row facility layout problem is a prevalent and significant planning challenge in manufacturing workshops. This problem requires distributing facilities with pairwise transport weights among several rows to attain a layout with minimal logistics costs. However, the significance of aisles in multi-row facility layout has frequently been overlooked. An efficient aisle structure can result in a smooth transportation path and reduced material-handling costs. This paper contributes to the existing literature by introducing a new multi-row facility layout problem that considers long-straight aisles. First, mathematical formulas for the actual transportation distance between facilities through aisles are defined, and a mixed-integer programming model is constructed. Second, a hybrid algorithm based on an intelligent algorithm and a mathematical model is proposed. This method utilizes an improved teaching-learning-based optimization algorithm as a framework for optimizing the discrete facility sequence, and two decoding methods based on linear programming are designed to obtain the facility locations and transportation paths. Experimental results demonstrate that the two decoding strategies have their own advantages in terms of solution quality, efficiency, and area utilization. Moreover, improvement strategies for teaching-learning-based optimization algorithms are observed to be effective. Finally, we present two actual workshop examples of multi-row layout designs. The comparison of different algorithms reveals that the proposed algorithm has significant advantages in terms of solution quality and stability.

关键词： Layout Transportation Logistics Costs Mathematical models Conferences Optimization Multi-row facility layout through-aisle structure mixed-integer linear programming teaching-learning-based optimization

来源：评论

学校读者我要写书评

暂无评论

Optimal Microgrid Protection Coordination for Directional Overcurrent Relays Through mixed-integer linear Optimization

引用

ENERGIES 2025年第8期18卷 2035-2035页

作者： Serna-Montoya, Leon F. Saldarriaga-Zuluaga, Sergio D. Lopez-Lezama, Jesus M. Munoz-Galeano, Nicolas Univ Antioquia UdeA Dept Elect Engn Res Grp Efficient Energy Management GIMEL Medellin 050010 Colombia Inst Univ Pascual Bravo Fac Ingn Dept Electr Calle 73 73A-226 Medellin 050036 Colombia

Microgrids (MGs) are revolutionizing modern power systems by enabling decentralized energy production, renewable energy integration, and enhanced grid resilience. However, the increasing complexity of MGs, particularly with the integration of Distributed Energy Resources (DERs), poses significant challenges for traditional protection schemes. This study addresses the coordination of Directional Overcurrent Relays (DOCRs) in MGs through a mixed-integer linear programming (MILP) model. The main contribution is a MILP model that optimizes relay settings, including Time Multiplier Settings (TMS) and standard characteristic curves, to minimize tripping times, while ensuring selectivity. Another key contribution of this work is the integration of both IEC and IEEE standard curves, which enhances coordination performance compared to using a single standard. The model was tested on the IEC benchmark microgrid, and the results demonstrated significant improvements in fault-clearing times across various operational modes. By leveraging advanced optimization techniques and diverse characteristic curves, this study contributes to the development of resilient and efficient protection systems for modern microgrids, ensuring reliable operation under varying fault conditions and DER penetration.

关键词： distributed generation microgrid power system protection overcurrent relay coordination directional overcurrent relay mixed-integer linear programming optimization

来源：评论

学校读者我要写书评

暂无评论

A rigorous optimization method for long-term multi-stage investment planning: Integration of hydrogen into a decentralized multi-energy system

引用

ENERGY REPORTS 2025年 13卷 117-139页

作者： Bornemann, Luka Lange, Jelto Kaltschmitt, Martin Hamburg Univ Technol Inst Environm Technol & Energy Econ Eissendorfer Str 40 D-21073 Hamburg Germany

Thoroughly assessing future energy systems requires examining both their end states and the paths leading to them. Employing dynamic investment or multi-stage optimization models is crucial for this analysis. However, solving these optimization problems becomes increasingly challenging due to their long time horizons - often spanning several decades - and their dynamic nature. While simplifications like aggregations are often used to expedite solving procedures, they introduce higher uncertainty into the results and might lead to suboptimal solutions compared to non-simplified models. Against this background, this paper presents a rigorous optimization method tailored for multi-stage optimization problems in long-term energy system planning. By dividing the solution algorithm into a design and operational optimization step, the proposed method efficiently finds feasible solutions for the non-simplified optimization problem with simultaneous quality proof. Applied to a real-life energy system of a waste treatment plant in Germany, the method significantly outperforms a benchmark solver by reducing the computational time to find the first feasible solution from more than two weeks to less than one hour. Furthermore, it exhibits greater robustness compared to a conventional long-term optimization approach and yields solutions closer to the optimum. Overall, this method offers decision-makers computationally efficient and reliable information for planning investment decisions in energy systems.

关键词： Green hydrogen Multi-energy system Multi-stage optimization Rigorous optimization Long-term planning mixed-integer linear programming

来源：评论

学校读者我要写书评

暂无评论

Balanced Team Formation Using Hybrid Graph Convolution Networks and MILP

引用

APPLIED SCIENCES-BASEL 2025年第4期15卷 2049-2049页

作者： Sharaf, Mohamed A. Alghamdi, Turki G. Jouf Univ Coll Informat Sci Dept Comp Engn & Networks Sakaka 72388 Saudi Arabia

In this paper, we propose a novel model that is based on a hybrid paradigm composed of a graph convolution network and an integer programming solver. The model utilizes the potential of graph neural networks, which have the ability to capture complex relationships and preferences among nodes. While the graph neural network forms node embeddings that are fed as input into the next layer of the model, the introduced MILP solver works to solve the team formation problem. Finally, our experimental work shows that the outcome of the model is balanced teams.

关键词： team formation problem graph neural network graph convolution network mixed-integer linear programming node embeddings

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：