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57th Hawaii International Conference on System Sciences (HICSS)

作者： Kulturel-Konak, Sadan Konak, Abdullah Gavirneni, Nagesh Penn State Berks Reading PA 19610 USA Cornell Univ Ithaca NY 14853 USA

ISBN: (纸本)9780998133171

The percentage of the US population who live in assisted living facilities is increasing steadily. In many cases, seniors solely rely on meals served in these facilities to meet their nutritional needs. Research shows that most assisted-living elderly residents have deficiencies of several key macro- and micronutrients and an excessive intake of sodium, fat, cholesterol, and saturated fat, considering their age group. This paper focuses on designing healthy menus for assisted living facilities under multiple objectives and complex nutritional constraints. The proposed system considers various factors in the construction of menus, including the USDA healthy eating guidelines, chefs' choices and experiences, diversity of menu items, cost, and residents' preferences with different dietary requirements or diet patterns. The paper's main contribution to earlier work is to include people's behaviors while selecting menu items so that resulting menus can better reflect what might happen when implemented and to increase the feeling of autonomy.

关键词： Menu optimization assisted living facilities mixed-integer linear programming goal programming healthy eating

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mixed-integer programming Model for Transmission Network Expansion Planning with Battery Energy Storage Systems (BESS)

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ENERGIES 2020年第17期13卷

作者： Andres Mora, Camilo Danilo Montoya, Oscar Rivas Trujillo, Edwin Univ Dist Francisco Jose de Caldas Fac Ingn Bogota 11021 DC Colombia Univ Tecnolog Bolivar Lab Inteligente Energia Cartagena 131001 Colombia

This article assesses the costs and benefits of incorporating battery energy storage systems (BESS) in transmission network expansion planning (TEP) over multiple time periods. We propose a mixed-integer programming model (MIP) for joint planning of the installation of battery energy storage systems (BESS) and construction of new transmission lines in multiple periods of time. The mathematical formulation of the presented model is based on the strategies of the agents of a transmission network to maximize their benefit, and on the operational restrictions of the power flows in transmission networks. This analysis is performed for the Garver 6 node test system takes into account the power losses in the lines and the restrictions for the energy stored in BESS. The power flows obtained with the MIP model are compared with AC power flows generated with specialized software for flows in power systems. This allows us to demonstrate the potential of models based on DC power flows to achieve approximate results applicable to the behavior and characteristics of real transmission networks. The results show that the BESS increase the net profit in the transmission networks and reduce their power losses.

关键词： mixed-integer linear programming transmission expansion planning battery energy storage systems

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Multi-agent Scheduling for Shared Manufacturing Systems 6

Multi-agent Scheduling for Shared Manufacturing Systems

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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Duran, Ege Ozturk, Cemalettin O'Sullivan, Barry SFI Centre for Research Training in AI School of Computer Science IT University College Cork Ireland Department of Process Energy & Transport Engineering Munster Technological University Cork Ireland Insight SFI Research Centre for Data Analytics School of Computer Science & IT University College Cork Ireland

Advances in digitization and resource-sharing business models have created new opportunities for manufacturing companies, enhancing competitiveness and resilience. However, these benefits bring computational challenges in efficiently planning and scheduling shared resources. Therefore, there is a need for scalable and quick solutions for practical applications. Shared manufacturing systems share characteristics with parallel machine scheduling, allowing for the application of advancements from this domain. This research focuses on Multi-Agent Parallel Machine Scheduling (MAPMS) in shared manufacturing, specifically addressing scenarios involving two parallel machines and distinct agents managing exclusive, set of non-overlapping orders. The study introduces a novel multi-objective mixed integer programming (MIP) model for order scheduling across multiple facilities, accounting for sequence-dependent setup times between orders. It also proposes a new heuristic method designed for industrial use. Benchmark instances demonstrate the practicality of both the MIP model and heuristic, contributing valuable insights into MAPMS challenges in shared manufacturing environments. © 2025 The Authors.

关键词： mixed-integer linear programming

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An Efficient Device Placement Method for Distributed Training of Multi-branch Neural Network-Based Remote Sensing Interpretation 25th

An Efficient Device Placement Method for Distributed Traini...

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25th International Conference on Web Information Systems Engineering, WISE 2024

作者： Long, Ao Wang, Yuewei Huang, Xiaohui Han, Wei Fan, Runyu Chen, Yunliang Li, Jianxin School of Computer Science China University of Geosciences Wuhan China School of Information Technology Deakin University Melbourne Australia

ISBN: (纸本)9789819605699

Multi-branch neural networks are widely used in remote sensing intelligent interpretation because they can fuse multi-modal remote sensing data to improve interpretation accuracy. Meanwhile, in the current "big data era" of remote sensing, the application of distributed deep learning has been steadily growing to improve the training efficiency of remote sensing interpretation models. However, prevailing distributed training systems necessitate users to manually devise a parallelization plan or automatically generate one from a restricted space of model parallelism configurations. At the same time, these techniques are not suitable for extending the application of multi-branch neural network-based remote sensing interpretation on computing devices. In this paper, considering both the data and the model parallelism, we design and implement a fast and near-optimal device placement method for automatically placing branches in multi-branch neural networks across multiple devices. The core idea is to consider the trade-off between the computation and the inter-device communication to minimize the total training time. Through careful formulation of the problem as an integer program, our method can offer optimal placement and scheduling of the branches. We conduct experiments to deploy and evaluate the proposed method on two multi-branch networks. The results indicate that the method finds non-trivial device placements outperforming baselines. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： mixed-integer linear programming

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Robust Portfolio Optimization for Recommender Systems Considering Uncertainty of Estimated Statistics 21st

Robust Portfolio Optimization for Recommender Systems Consi...

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Yanagi, Tomoya Ikeda, Shunnosuke Takano, Yuichi Graduate School of Science and Technology University of Tsukuba 1–1–1 Tennodai Ibaraki Tsukuba-shi305–8573 Japan Institute of Systems and Information Engineering University of Tsukuba 1–1–1 Tennodai Ibaraki Tsukuba-shi305–8573 Japan

ISBN: (纸本)9789819601240

This paper is concerned with portfolio optimization models for creating high-quality lists of recommended items to balance the accuracy and diversity of recommendations. However, the statistics (i.e., expectation and covariance of ratings) required for mean–variance portfolio optimization are subject to inevitable estimation errors. To remedy this situation, we focus on robust optimization techniques that derive reliable solutions to uncertain optimization problems. Specifically, we propose a robust portfolio optimization model that copes with the uncertainty of estimated statistics based on the cardinality-based uncertainty sets. This robust portfolio optimization model can be reduced to a mixed-integer linear optimization problem, which can be solved exactly using mathematical optimization solvers. Experimental results using two publicly available rating datasets demonstrate that our method can improve not only the recommendation accuracy but also the diversity of recommendations compared with conventional mean–variance portfolio optimization models. Notably, our method has the potential to improve the recommendation quality of various rating prediction algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： mixed-integer linear programming

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High-Dimensional State Estimation Using a Combinatorial Approach: Application to Finance 10

High-Dimensional State Estimation Using a Combinatorial Appr...

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10th International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Bellina, Jeremy Georges, Didier Girerd-Potin, Isabelle Univ Grenoble Alpes CERAG Grenoble INP F-38000 Grenoble France Univ Grenoble Alpes GIPSA Lab Grenoble INP CNRS F-38000 Grenoble France

ISBN: (纸本)9798350373981;9798350373974

This paper focuses on the state estimation of a specific class of state-intermittent dynamical systems evolving in a high-dimensional state-space. As the dimension is large, a classical estimator such as a Kalman filter would require a large observation horizon to converge. To overcome this limitation, a mixed-integer formulation is proposed and solved by using a Genetic Algorithm. We also propose a reduced-order state estimation as a variant of this approach under some mild assumptions, which reduces the observation window size even more. In particular, we show, through a case study in finance, that the reduced model and its associated genetic algorithm can reduce the observation window size needed by almost 75% and that this method is faster than the non-reduced mixed integer optimization problem. We also show that the reduced-order state estimation approach can be used to discover a mutual fund portfolio composition at each date of a given period.

关键词： mixed-integer linear programming

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Integrated Reinforcement Learning and Optimization for Railway Timetable Rescheduling 17

Integrated Reinforcement Learning and Optimization for Railw...

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17th IFAC Symposium on Control of Transportation Systems (CTS)

作者： Zhang, Hengkai Liu, Xiaoyu Sun, Dingshan Dabiri, Azita De Schutter, Bart Delft Univ Technol Delft Ctr Syst & Control NL-2628CD Delft Netherlands

The railway timetable rescheduling problem is regarded as an efficient way to handle disturbances. Typically, it is tackled using a mixed integer linear programming (MILP) formulation. In this paper, an algorithm that combines both reinforcement learning and optimization is proposed to solve the railway timetable rescheduling problem. Specifically, a value-based reinforcement learning algorithm is implemented to determine the independent integer variables of the MILP problem. Then, the values of all the integer variables can be derived from these independent integer variables. With the solution for the integer variables, the MILP problem can be transformed into a linear programming problem, which can be solved efficiently. The simulation results show that the proposed method can reduce passenger delays compared with the baseline, while also reducing the solution time. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Railway timetable rescheduling mixed-integer linear programming reinforcement learning

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Advancing Multi-Energy Hub Design: an Integrated Approach for Optimizing Residential Clusters in High RES Penetration Scenarios

Advancing Multi-Energy Hub Design: an Integrated Approach fo...

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International Conference on Smart Energy Systems and Technologies (SEST) - Driving the Advances for Future Electrification

作者： Di Somma, Marialaura Jin, Lingkang Papadimitriou, Christina Bianco, Nicola Univ Naples Federico II Dept Ind Engn Naples Italy Eindhoven Univ Technol Elect Engn Dept Eindhoven Netherlands

ISBN: (纸本)9798350386509;9798350386493

With the energy transition underway, the wider penetration of multi-energy hubs (MEHs) is inevitable as they allow for the integration of multiple energy carriers at the local level and especially at the users' side by enhancing flexibility of energy supply and renewables penetration. Thus, addressing their design properly is imperative. While numerous energy modeling software and tools are extensively discussed in the literature, they primarily focus on a single energy carrier, mainly dedicated to the electrical network. In this study, an optimization planning tool capable of analyzing the synergies of various energy carriers is presented. The tool aims to determine the optimal configuration of MEHs with sizes of the chosen technologies based on the modeler's preferences. The tool offers flexibility in modelling a wide range of technologies as potential options for the optimal configuration, and in considering different types of objectives as economic and environmental ones that can be assessed through a multi-objective approach. It is formulated through mixed-integer linear programming in a modular manner, facilitating the easy implementation of new emerging technologies, by enhancing scalability and applicability in real context. To prove the effectiveness of the optimization tool, it is applied for the design of a MEH for a residential building cluster located in Torino (Italy). Different scenarios are analyzed to determine the impact of high levels of renewables penetration on the design of the MEH while guaranteeing the economic sustainability of the solution.

关键词： Optimal design Multi-energy hubs Energy planning Residential clusters mixed-integer linear programming

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Assembly line balancing with fractional task allocations

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2022年第5期60卷 1569-1586页

作者： Lopes, Thiago Cantos Brauner, Nadia Magatao, Leandro Fed Univ Technol Parana UTFPR Grad Program Elect & Comp Engn CPGEI Curitiba Parana Brazil Univ Grenoble Alpes CNRS Grenoble INP G SCOP Grenoble France

Assembly line balancing usually presupposes binary task-station assignments. Some authors have previously described efficiency increases due to fractional task allocations or work-sharing. However, the internal storage requirements for such efficiency increases have not been analytically described. This paper defines the Fractional Allocation Assembly Line Balancing Problem and presents mixed-integer linear programming models to bridge that gap. The main opportunity afforded by the studied flexibility is increased throughput, which is associated to higher internal storage costs. Worst-case analyses define mathematical expressions for these costs both for paced (line length) and unpaced lines (buffers). A screening on a 1050-instance dataset is conducted. Results suggest that fractional allocations can often allow better resource utilisation with relatively low costs: the higher space requirement costs are often one-time investments, while lower cycle time represents fundamentally continuous gains. Lastly, the proposed formulation was adapted and applied to industrial data. This mixed-model assembly line case study suggests that fractional allocations can also lead to more robust balancing regarding demand uncertainty.

关键词： Assembly line balancing cyclical scheduling fractional task allocation mixed-integer linear programming space cost minimisation

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Novel MILP and CP models for distributed hybrid flowshop scheduling problem with sequence-dependent setup times

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SWARM AND EVOLUTIONARY COMPUTATION 2022年第0期71卷

作者： Meng, Leilei Gao, Kaizhou Ren, Yaping Zhang, Biao Sang, Hongyan Chaoyong, Zhang Liaocheng Univ Sch Comp Sci Liaocheng 252000 Peoples R China Macau Univ Sci & Technol Macau Inst Syst Engn Macau 999078 Peoples R China Jinan Univ Sch Intelligent Syst Sci & Engn Dept Ind Engn Zhuhai 519070 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Manufacturing Equipment & Te Wuhan 430074 Peoples R China

As regards distributed hybrid flow shop scheduling with sequence-dependent setup times (DHFSP-SDST), three novel mixed-integer linear programming (MILP) models and a constraint programming (CP) model are formulated for the same-factory and different-factory environments. The three novel MILP models are based on two different modeling ideas. The existing MILP model and the three proposed MILP models are compared in detail from several aspects, such as binary decision variables, continuous decision variables, constraints, solution performance and solution time. By solving the benchmarks in existing studies, the effectiveness and superiority of the proposed MILP and CP models are proved. Experimental results show that the MILP model of sequence-based modeling idea performs best, the MILP model of adjacent sequence-based modeling idea takes the second place and the existing MILP model of position-based modeling idea performs worst. The CP model is more efficient and effective than MILP models. In addition, compared with the existing meta-heuristic algorithms (e.g., DABC and IABC), the proposed MILP models prove the optimal solutions of 37 instances and improve 17 current best solutions. The CP model solves all the 45 instances to optimality and improves 19 current best solutions for benchmarks in the existing studies

关键词： Distributed hybrid flow shop scheduling Setup time mixed-integer linear programming Constraint programming Modeling idea

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