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31st International Static Analysis Symposium

作者： Mour, Julian Drachsler-Cohen, Dana Technion Haifa Israel

ISBN: (纸本)9783031747755;9783031747762

Multi-label neural networks are important in various tasks, including safety-critical tasks. Several works show that these networks are susceptible to adversarial attacks, which can remove a target label from the predicted label list or add a target label to this list. To date, no deterministic verifier determines the list of labels for which a multilabel neural network is locally robust. The main challenge is that the complexity of the analysis increases by a factor exponential in the multiplication of the number of labels and the number of predicted labels. We propose MuLLoC, a sound and complete robustness verifier for multi-label image classifiers that determines the robust labels in a given neighborhood of inputs. To scale the analysis, MuLLoC relies on fast optimistic queries to the network or to a constraint solver. Its queries include sampling and pair-wise relation analysis via numerical optimization and mixed-integer linear programming (MILP). For the remaining unclassified labels, MuLLoC performs an exact analysis by a novel mixed-integer programming (MIP) encoding for multi-label classifiers. We evaluate MuLLoC on convolutional networks for three multi-label image datasets. Our results show that MuLLoC classifies all labels as robust or not within 23.22 min on average and that our fast optimistic queries classify 96.84% of the labels.

关键词： mixed-integer linear programming

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Exact algorithm for autonomous dump truck routing in open-pit mines considering coal production

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FRONTIERS OF ENGINEERING MANAGEMENT 2025年 1-15页

作者： Yang, Linying Zhen, Lu Shanghai Univ Sch Management Shanghai 200444 Peoples R China

This study investigates a truck scheduling problem in open-pit mines, which focuses on optimizing truck transportation and commercial coal production. Autonomous dump trucks are essential transportation tools in the mines;they transport the raw coals and rocks excavated by electric shovels to the unloading stations. Raw coals with different calorific values are processed to produce commercial coals for sale. This process requires maintaining a calorific balance between the excavated raw coals and the blended commercial coals. We formulate a mixed-integer linear programming model for the truck scheduling problem in open-pit mines. The objective of this decision model is to minimize the total working time of all trucks. To solve the proposed model efficiently in large-scale instances, a branch-and-price based exact algorithm is devised. Based on real data of an open-pit mine in Holingol, Inner Mongolia, China, numerical experiments are performed to validate the efficiency of the proposed algorithm. The experiment results show that the optimality gap of the proposed algorithm by comparing with CPLEX is zero;and the solution time of CPLEX is 2.46 times that of the proposed algorithm. Moreover, sensitivity analyses are conducted to derive some managerial insights. For example, open-pit mine managers should carefully consider the truck fleet deployment, including the number of trucks and the capacity of trucks. Additionally, the spatial distribution of unloading stations and electric shovels is crucial for enhancing transportation efficiency in open-pit mines.

关键词： open-pit mines truck transportation mixed-integer linear programming branch-and-price

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Design of sustainable perishable food supply chain network under uncertainty

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OPSEARCH 2025年第1期62卷 406-422页

作者： Souri, Fateme Ghomi, S. M. T. Fatemi Amirkabir Univ Technol Dept Ind Engn 424 Hafez Ave Tehran 159634311 Iran

Managing a perishable food supply chain is a challenging task because of the short lifetime of the product and the uncertainty of demand. Perishable products require special plans with environmental, social and economic effects. Therefore, the sustainability of food supply chains plays a key role in supply chain efficiency. This paper designs a sustainable perishable food supply chain network under uncertainty. A multi-objective mixed-integer linear programming model is proposed that optimizes total costs, emissions, and shipping time to meet environmental demand. Since the demand parameter is assumed to be uncertain, the two-stage planning optimization approach under the scenario has been used. Also, the method of sum weights has been applied to make the model single-objective, and some tests are performed to select the best coefficients. In the end, deterministic and stochastic objective values compare with the Expected value, Wait and see, and Here and now methods.

关键词： Supply chain Two-stage Perishable food Uncertainly mixed-integer linear programming

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The effect of planning horizon length and green manure on net income in Crop Rotation Problem

The effect of planning horizon length and green manure on ne...

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2024 Workshop on Data Science for Agriculture in Africa, DAAfrica 2024

作者： Gbedevi, Akouyo Yvette Atchonouglo, Kossi Lamrous, Sid Ahmed Manier, Marie-Ange Lomé Togo Université de Technologie Belfort-Montbéliard FEMTO-ST Belfort France

The world population is increasing rapidly, and recent awareness of the limits of natural resources and the pollution of soil, air and water, is pushing towards a new form of agriculture, sustainable agriculture. Sustainable agriculture can be defined as an agriculture that combines environmental, social and economic objectives for the well-being of farmers and the soil. Crop rotation took hold during this decade by emphasizing the management of soil resources, while improving economic, environmental and social factors. The goal of the crop rotation planning problem discussed in this paper is to maximize the total net return, with a particular emphasis on incorporating a plant that contributes green manure to the soil alongside nutrient amendments by choosing the best horizon for plannification. The rotations generated are of fixed duration for all the plots and the objective is to maximize the income of the farmers. The results showed that the determined algorithm was feasible. © 2025 Copyright for this paper by its authors.

关键词： mixed-integer linear programming

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Enhancing the Sustainability of Frozen Food Processing: A Production Environment Perspective and Impacts on Quality and Operating Technology 11th

Enhancing the Sustainability of Frozen Food Processing: A Pr...

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11th International Conference on Industrial Engineering and Applications, ICIEA 2024

作者： Chen, Wan Chiao Rau, Hsin Department of Industrial and Systems Engineering Chung Yuan Christian University Taoyuan City32023 Taiwan

ISBN: (纸本)9789819764914

The frozen food sector accounts for a significant share of the food market, a trend that is being fueled by shifting socioeconomic and technical advancements. However, the production process consumes a significant quantity of natural resources and has a particularly high energy demand. Resource depletion and ecological issues are occurring as a result. In response to these challenges, the relationship between the production process and its environmental impact becomes an essential consideration but lacks thorough discussion in the existing research. As a result, the objective of this study is to enhance eco-efficiency in frozen food processing with a focus on the ambient changes trend. Our research first takes the uncertainty of processing surroundings as a process variation and explores its association with quality and operating technology, which are vital factors for first-pass yield. An optimal model then is presented by using mixed-integer linear programming, further enhancing the sustainability of frozen food processing. To verify the effectiveness of the proposed approach, a case study with frozen seafood is conducted and the paper discusses the practical implications of its findings. In this research, the challenge of complex and quantified difficulties with environmental impacts is surmounted, and the study findings present a significant opportunity for the frozen food industry to transition toward sustainability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： mixed-integer linear programming

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Blockchain-backed resilient strategies in a stochastic supply chain sourcing and distribution environment under disruption: implications for B2B sector

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JOURNAL OF BUSINESS & INDUSTRIAL MARKETING 2025年第1期40卷 223-249页

作者： Manzoor, Rizwan Sahay, B. S. Gumte, Kapil Singh, Sujeet Kumar Inst Management Technol Ghaziabad Dept Operat & Supply Chain Ghaziabad India Indian Inst Management Jammu Dept Operat & Supply Chain Jammu India Indian Stat Inst Hyderabad SQC & OR Hyderabad India

PurposeWith the changing landscape of the globalised business world, business-to-business supply chains face a turbulent ocean of disruptions. Such is the effect that supply chains are disrupted to the point of failure, supply is halted and its adverse effect is seen on the consumer. While previous literature has extensively studied risk and resilience through mathematical modelling, this study aims to envision a novel supply chain model that integrates blockchain to support visibility and recovery resilience ***/methodology/approachThe stochastic bi-objective (cost and shortage utility) optimisation-based mixed-integer linear programming model integrates blockchain through a binary variable, which activates at a particular threshold risk-averse level of the ***, visibility is improved, as identified by the average reduction of penalties by 36% over the different scenarios. Secondly, the average sum of shortages over different scenarios is consequently reduced by 36% as the recovery of primary suppliers improves. Thirdly, the feeling of shortage unfairness between distributors is significantly reduced by applying blockchain. Fourthly, unreliable direct suppliers resume their supply due to the availability of timely information through blockchain. Lastly, reliance on backup suppliers is reduced as direct suppliers recover *** limitations/implicationsThe findings indicate that blockchain can enhance visibility and recovery even under high-impact disruption conditions. Furthermore, the study introduces a unique metric for measuring visibility, i.e. penalty costs (lower penalty costs indicate higher visibility and vice versa). The study also improves upon shortages and recoveries reported in prior literature by 6%. Finally, blockchain application caters to the literature on shortage unfairness by significantly reducing the feeling of shortage unfairness among *** implicationsThis study establi

关键词： Supply chain risk B2B supply chain Supply chain disruption Supply chain resilience Risk aversion Blockchain technology mixed-integer linear programming

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Sustainable Development Index Using MILP to Assign Relative Weight To Different UNSDG Parameters 18th

Sustainable Development Index Using MILP to Assign Relative...

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18th International Conference on Learning and Intelligent Optimization, LION 2024

作者： Shah, Keyaan Dhirubhai Ambani International School Mumbai400098 India

ISBN: (纸本)9783031756221

This report presents a novel approach to quantifying and comparing national efforts towards the United Nations Sustainable Development Goals (UNSDGs) by developing a Sustainable Development Index (SDI) using mixed-integer linear programming (MILP). The methodology uniquely integrates MILP to optimize the weighting of various sustainability indicators, offering a robust and mathematical foundation for the SDI. This approach not only captures the discrete and linear aspects of sustainability indicators but also aligns the resulting index with existing measures such as the Human Development Index (HDI) and Environmental Performance Index (EPI). Through meticulous data collection, normalization, and MILP-based analysis, the report examines 17 unique indicators, each representing a specific UNSDG. The study employs advanced optimization tools, including Gurobi Optimizer and Julia programming language, to compute the SDI, ensuring an objective and reliable comparison of countries’ sustainability performances. The report reveals insightful findings, with the SDI offering a clear and quantifiable measure of progress towards the UNSDGs. The outcome of this research has significant implications for policymakers, researchers, and the public, providing a nuanced understanding of national sustainability efforts. It underscores the utility of MILP in addressing complex global challenges and sets a precedent for future research in sustainable development assessment. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： mixed-integer linear programming

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Research on Two-Stage Distributed Robust Optimization Scheduling of Electric Thermal Hydrogen Integrated Energy System Considering Stepped Carbon Trading 11th

Research on Two-Stage Distributed Robust Optimization Schedu...

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11th Frontier Academic Forum of Electrical Engineering, FAFEE 2024

作者： Zhou, Junsong Dang, Cunlu School of Electrical Engineering and Information Engineering Lanzhou University of Technology Lanzhou730050 China

ISBN: (纸本)9789819787791

To effectively address the uncertain risks posed by the inherent intermittency and volatility of wind and solar power output to the Electricity-Heat-Hydrogen Integrated Energy System (EHH-IES) and to reduce carbon emissions, a two-stage distributionally robust optimization scheduling model considering a stepwise carbon trading mechanism is proposed. Firstly, the accurate characterization of the probability distribution of wind and solar power output fuzzy sets is achieved by combining the Wasserstein distance, solving the uncertainty issue of wind and solar power output on the EHH-IES. Secondly, a stepwise carbon trading mechanism is introduced to reduce the carbon emissions of the EHH-IES through policy guidance and economic incentives. Thirdly, for the EHH-IES, a day-ahead and intra-day two-stage distributionally robust optimization scheduling model based on the probability distribution fuzzy set is constructed, and it is transformed into a mixed-integer linear programming model for solving using linear decision rules and strong duality theory. Finally, simulation validation is conducted based on actual wind and solar power data. The results prove that the stepwise carbon trading can significantly reduce the carbon emissions of the EHH-IES, and the proposed model can further ensure robustness under the uncertainty risk of wind and solar power while considering the economy of the EHH-IES. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： mixed-integer linear programming

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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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