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Optimal capacitated multi-product robust cement supply chain Network design considering carbonemission policies

CLEAN TECHNOLOGIES AND ENVIRONMENTAL POLICY 2025年 1-43页

作者： Golpira, Heris Javanmardan, Ahvan Islamic Azad Univ Dept Ind Engn Sanandaj Branch Sanandaj Iran Islamic Azad Univ Dept Ind Engn North Tehran Branch Tehran Iran

This study presents a novel risk-based robust mixed-integer linear programming model for configuring a complex cement supply chain network under carbon emission policies (CEPs). In addition to the non-deterministic model solved with the robust approach, its corresponding deterministic model is also solved and the results are compared. In the models, critical dimensions of the cement industry, including inventory and shortages, are mathematically examined. Other important features of the industry, including the large number of suppliers of different materials, the production of diverse products with different processing flows, and diverse transportation methods appropriate to different types of cement and market climates, are also considered in the models. A scenario-based conditional value-at-risk approach is used to deal with the inherent uncertainty of cement demand in the uncertain counterpart of the model. The results demonstrate the effectiveness of the robust approach for the optimal configuration of the integrated cement inventory-production system and its inbound-outbound logistics. Analyzing the results establishes widespread trade-offs between the parameters. In particular, the model suggests the highest level of risk-taking for a profit-sensitive DM to maximize her/his profit. It may push the robustness-sensitive DM toward more conservatism by accepting lower profits. Besides, an emission-sensitive DM is recommended to implement both the carbon-cap-and-trade and carbon tax under carbon cap-and-trade regulation policies.

关键词： Supply chain network design Cement supply chain mixed-integer linear programming Robust optimization Conditional value-at-risk Carbon emission policy

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Optimal reactive operation of general topology supply chain and manufacturing networks under disruptions

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AICHE JOURNAL 2025年

作者： Ovalle, Daniel Pulsipher, Joshua L. Ye, Yixin Harshbarger, Kyle Bury, Scott Laird, Carl D. Grossmann, Ignacio E. Carnegie Mellon Univ Dept Chem Engn Pittsburgh PA 15213 USA Univ Waterloo Dept Chem Engn Waterloo ON Canada Dow Chem Co USA Core R&D Lake Jackson TX USA Dow Chem Co USA Supply Chain Innovat Midland MI USA Dow Chem Co USA Core R&D Midland MI USA

Supply and manufacturing networks in the chemical industry involve diverse processing steps across different locations, rendering their operation vulnerable to disruptions from unplanned events. Optimal responses should consider factors such as product allocation, delayed shipments, and price renegotiation, among other factors. In such context, we propose a multiperiod mixed-integer linear programming model that integrates production, scheduling, shipping, and order management to minimize the financial impact of such disruptions. The model accommodates arbitrary supply chain topologies and incorporates various disruption scenarios, offering adaptability to real-world complexities. A case study from the chemical industry demonstrates the scalability of the model under finer time discretization and explores the influence of disruption types and order management costs on optimal schedules. This approach provides a tractable, adaptable framework for developing responsive operational plans in supply chain and manufacturing networks under uncertainty.

关键词： supply chain optimization disruptions mixed-integer linear programming operation scheduling

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On a fair and risk-averse urban air mobility resource allocation problem under demand and capacity uncertainties

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NAVAL RESEARCH LOGISTICS 2025年第1期72卷 111-132页

作者： Sun, Luying Deng, Haoyun Wei, Peng Xie, Weijun Virginia Tech Dept Ind & Syst Engn Blacksburg VA USA Georgia Inst Technol H Milton Stewart Sch Ind & Syst Engn Atlanta GA 30332 USA George Washington Univ Dept Mech & Aerosp Engn Washington DC USA

Urban air mobility (UAM) is an emerging air transportation mode to alleviate the ground traffic burden and achieve zero direct aviation emissions. Due to the potential economic scaling effects, the UAM traffic flow is expected to increase dramatically once implemented, and its market can be substantially large. To be prepared for the era of UAM, we study the fair and risk-averse urban air mobility resource allocation model (FairUAM) under passenger demand and airspace capacity uncertainties for fair, safe, and efficient aircraft operations. FairUAM is a two-stage model, where the first stage is the aircraft resource allocation, and the second stage is to fairly and efficiently assign the ground and airspace delays to each aircraft provided the realization of random airspace capacities and passenger demand. We show that FairUAM is NP-hard even when there is no delay assignment decision or no aircraft allocation decision. Thus, we recast FairUAM as a mixed-integer linear program (MILP) and explore model properties and strengthen the model formulation by developing multiple families of valid inequalities. The stronger formulation allows us to develop a customized exact decomposition algorithm with both benders and L-shaped cuts, which significantly outperforms the off-the-shelf solvers. Finally, we numerically demonstrate the effectiveness of the proposed method and draw managerial insights when applying FairUAM to a real-world network.

关键词： fairness mixed-integer linear programming resource allocation risk-averse urban air mobility

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Overcoming modeling and computational complexity challenges in food-energy-water nexus optimization

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COMPUTERS & CHEMICAL ENGINEERING 2025年 192卷

作者： Di Martino, Marcello Linke, Patrick Pistikopoulos, Efstratios N. Texas A&M Univ Artie McFerrin Dept Chem Engn 3122 TAMU College Stn TX 77843 USA Texas A&M Univ Texas A&M Energy Inst 1617 Res Pkwy College Stn TX 77845 USA Texas A&M Univ Qatar Dept Chem Engn Texas A&M Engn BldgEduc CityPOB 23874 Doha Qatar

The food-energy-water nexus (FEWN) postulates that sustainable decision-making regarding the interconnected resources food, energy and water must consider all involved resources holistically. Due to its multi-scale complexity, modeling challenges and computational intractability regarding the interconnected FEWN optimization remain. To overcome these challenges, this work proposes employing surrogate models based on data-driven and model optimization techniques, while quantifying the introduced errors due to both the selected approximation and optimization methods. In turn, we derive a mixed-integer linear FEWN planning and scheduling optimization model based on a greenhouse farming, a renewable energy and a reverse osmosis desalination water supply system, which is initially computationally intractable. This computational complexity is first discussed and overcome for the energy-water nexus supply system, before solving the complete FEWN supply system by utilizing strategies such as relaxation, modularization and convex hull reformulation.

关键词： Food-energy-water nexus Planning and scheduling Modularization mixed-integer linear programming Multi-objective optimization Sustainability

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Learning-Based Branching Acceleration for Unit Commitment with Few Training Samples

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APPLIED SCIENCES-BASEL 2025年第6期15卷

作者： Zhang, Chi Qin, Zhijun Sun, Yan Guangxi Univ Guangxi Key Lab Power Syst Optimizat & Energy Tech Nanning 530004 Peoples R China Guangxi Univ Sch Elect Engn Nanning 530004 Peoples R China Guangxi Power Grid Co Ltd Elect Power Dispatch Control Ctr Nanning 530023 Peoples R China

Unit commitment (UC) is a critical challenge in power system optimization, typically formulated as a high-dimensional mixed-integer linear programming (MILP) problem with non-deterministic polynomial-time hard (NP-hard) complexity. While the branch-and-bound (B&B) algorithm can determine optimal solutions, its computational cost increases exponentially with the number of units, which limits the practical application of UC. Machine learning (ML) has recently emerged as a promising tool for addressing UC, but its effectiveness relies on substantial training samples. Moreover, ML models suffer significant performance degradation when the number of units changes, a phenomenon known as the task mismatch problem. This paper introduces a novel method for Branching Acceleration for UC, aiming to reduce the computational complexity of the B&B algorithm while achieving near-optimal solutions. The method leverages invariant branching tree-related features and UC domain-specific features, employing imitation learning to develop an enhanced pruning policy for more precise node pruning. Numerical studies on both standard and practical testing systems demonstrate that the method significantly accelerates computation with few training samples and negligible accuracy loss. Furthermore, it exhibits robust generalization capability for handling task mismatches and can be seamlessly integrated with other B&B acceleration techniques, providing a practical and efficient solution for UC problems.

关键词： machine learning unit commitment mixed-integer linear programming branch-and-bound algorithm imitation learning

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Integrated market scheduling with flexibility options

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RENEWABLE & SUSTAINABLE ENERGY REVIEWS 2025年 208卷

作者： Koltsaklis, Nikolaos E. Knapek, Jaroslav Czech Tech Univ Fac Elect Engn Tech 2 Prague 16627 Czech Republic

This work presents a generic optimization framework using advanced mixed-integer programming techniques to integrate day-ahead and balancing markets with distributed energy resources such as storage, electric vehicles, demand response, and Transmission and Distribution System Operators' coordination schemes. The day-ahead model determines optimal initial energy scheduling, while the balancing model optimizes energy and reserve products for three market designs with varying Transmission and Distribution System Operators' coordination. The framework is applied to the interconnected Greek-Bulgarian-Romanian power system, considering 2030 installed capacities. The model outputs illustrate market coupling scenarios among Romania, Bulgaria, and Greece, highlighting clear price signals and distributed energy resources flexibility. Results reveal the significance of a diverse energy mix for energy security and show that Transmission and Distribution System Operators' coordination significantly influences ancillary service prices, with reductions of up to 80 % in certain scenarios. Net demand values determine electricity flow direction. Flexibility providers like storage can cover up to 100 % of the upward congestion management requirements and 11 % of upward balancing energy, helping smooth energy allocation from intermittent renewables. The impact of electric vehicle penetration on generation scheduling is minimal. The proposed model offers valuable insights for system operators, market participants, and policymakers, enabling them to provide accurate price signals and optimize resource allocation. The integrated day-ahead and balancing market models support efficient renewable energy integration, emissions reduction, enhanced grid stability, and investment in low-carbon technologies. This aligns with the United Nations Framework Convention on Climate Change goals, contributing to the development of sustainable and resilient energy systems.

关键词： Balancing markets Day-ahead markets Demand response Electric vehicles Energy storage EUPHEMIA mixed-integer linear programming

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Algorithms for minimization of total flow time to achieve resilience in a flow shop environment

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FLEXIBLE SERVICES AND MANUFACTURING JOURNAL 2025年 1-49页

作者： Thiagarajan, Vaidyanathan Jessin, Thamarassery Abduljaleel Rajendran, Chandrasekharan Indian Inst Technol Madras Dept Management Studies Chennai 600036 Tamil Nadu India TKM Coll Engn Kollam 691005 Kerala India

Resilient inventory systems in every stage of a supply chain are becoming increasingly crucial to ensure its smooth functioning. In the context of production and manufacturing systems, flow time minimization plays a pivotal role in enhancing the resilience of inventories. By minimizing flow times, firms decrease the amount of Work-In-Progress (WIP) inventory at any given moment, thereby reducing vulnerability to disruptions caused by the bullwhip effect, supply chain delays or market fluctuations. Optimizing flow time to control WIP is extremely important in flow shop environments, where the high production volume places a heavy premium on avoidable delays. In this work, the aim is to develop models and approaches for flow time minimization in Permutation Flow Shops that cleverly exploit problem characteristics to quickly provide optimal solutions or strong lower bounds. To this end, we first propose two polynomial-time algorithms-position-based lower bound algorithm (PBLBA) and Improved Position-Based Lower Bound Algorithm (IPBLBA)-to rapidly determine lower bounds on Total Flow Time. Then, we develop a novel, parameter-free Self-Tuning Lower Bound Algorithm (STLBA) that iteratively solves a mixed-integer linear programming model to strengthen the lower bounds identified by the PBLBA and the IPBLBA and provides upper bounds. This STLBA is then integrated with a Local Search procedure to further improve the upper bounds and get an implementable schedule. Finally, we test the developed approaches on 600 well-known benchmark instances to ascertain their strengths and gather valuable theoretical and practical insights.

关键词： Inventory Supply chain resilience mixed-integer linear programming Scheduling Permutation flow shops Lower bounds

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Including mechanical requirements in a bi-objective nesting and scheduling model for additive manufacturing

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European Journal of Operational Research 2025年第3期325卷 416-432页

作者： Kucukkoc, Ibrahim Finco, Serena Peron, Mirco Aydin Keskin, Gulsen Department of Industrial Engineering Balikesir University Cagis Campus Balikesir10145 Turkey Department of Management and Engineering University of Padova Vicenza36100 Italy Department of Information Systems Supply Chain Management & Decision Support NEOMA Business School Mont-Saint-Aignan76130 France

Following the increasing relevance of Additive Manufacturing (AM) as Manufacturing-as-a-service (Maas), the AM scheduling (and related nesting) problem has been increasingly investigated. Due to their business nature, Maas companies are interested in minimizing both the makespan and the total tardiness;however, most of the literature focuses only on one of them. This work fills this gap proposing a mixed-integer linear programming (MILP) model that minimizes both makespan and total tardiness. In doing so, for the first time in the literature, considerations on parts’ strength are included. During nesting procedures, indeed, parts can be oriented in different ways, with this choice affecting not only the total processing time (as considered by the literature) but also the strength achievable: if this is lower than what planned, parts might fail unexpectedly with detrimental consequences. Thus, this work ensures that parts are produced with the required strength. In doing so, we focus on a parallel unrelated AM batch scheduling problem for metallic parts. Considering the multi-objective and NP-hard nature of the problem, an Ε-constraint algorithm and a non-dominated sorting genetic algorithm-II (NSGA-II) are developed to solve the problem. Four different problem-specific decoding mechanisms are integrated into the NSGA-II to improve its search capability and solution-building performance. Their performances are evaluated through computational experiments, showing that the integrated mechanisms improve the performance of the NSGA-II. Finally, through numerical instances and analysis of the super Pareto front, we derive managerial insights on the impact of strength requirements and machines’ number and features on the objectives. © 2025 Elsevier B.V.

关键词： mixed-integer linear programming

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integer programming for multi-mode resource-constrained project scheduling

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ANNALS OF OPERATIONS RESEARCH 2025年 1-28页

作者： Klein, Nicklas Univ Bern Dept Business Adm Engehaldenstr 4 CH-3012 Bern Switzerland

Project scheduling is a vital management task in many organizations across various industries. A project typically consists of activities that require time and scarce resources for execution. In many projects, there are trade-offs between the resource requirements and the duration of activities. These trade-offs can be represented via multiple execution modes of activities, which are considered in the multi-mode resource-constrained project scheduling problem (MRCPSP). The MRCPSP comprises determining the activities' start times and execution modes to minimize the project completion time while respecting precedence relations and availabilities of renewable and nonrenewable resources. In this paper, we propose several discrete-time (DT) and one continuous-time (CT) mixed-integer linear programming (MILP) model for the MRCPSP based on models for the single-mode resource-constrained project scheduling problem (RCPSP). We first analyze the models' LP relaxations and show an equivalence between DT models. Then, we compare the computational performance of the models on benchmark instances. Our results indicate that the proposed CT model outperforms the state-of-the-art models on large problem instances and problem instances with relatively long activity durations, while it is competitive on smaller problem instances. A possible explanation for this outperformance is that the LP relaxations of the proposed CT model can be solved considerably faster than the LP relaxations of the state-of-the-art models.

关键词： Project scheduling mixed-integer linear programming Multi-mode resource-constrained project scheduling Experimental comparison

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Proactive Frequency Stability Scheme Based on Bayesian Filters and Spectral Clustering

IEEE OPEN ACCESS JOURNAL OF POWER AND ENERGY

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IEEE OPEN ACCESS JOURNAL OF POWER AND ENERGY 2025年 12卷 100-110页

作者： Paramo, Gian Baquedano-Aguilar, Mario D. Bretas, Arturo Meyn, Sean Univ Florida Dept Elect & Comp Engn Gainesville FL 32611 USA Sandia Natl Labs Elect Grid Secur Albuquerque NM 87185 USA Univ Grenoble Alpes Grenoble INP CNRS G2Elab F-38000 Grenoble France

This work presents a proactive distributed model for power system frequency stability. High-level penetration of renewable energy sources into the grid have introduced unforeseen and unmodeled system dynamics. Underfrequency load shedding state-of-the-art solutions are reactive in design, with efficiency constrained by the modeling error. Being able to detect unstable conditions early makes it possible to generate optimized corrective actions. In this work, phasor measurement units are used to predict frequency values. When a disturbance is detected, the state of frequency is predicted a few seconds into the future via a particle filter algorithm. Corrective actions are modeled through a mixed integer linear programming algorithm within system areas established through spectral clustering. The solution is implemented on Matlab, considering IEEE test systems. The proactive design of the method combined with its multiple layers of optimization deliver results that outperform state-of-the-art solutions. Easy-to-implement model, without hard-to-derive parameters, highlights potential aspects towards real-life implementation.

关键词： Mathematical models Power system dynamics Particle measurements Filters Clustering algorithms Atmospheric measurements Power system stability Phasor measurement units Weight measurement Time-frequency analysis spectral clustering dynamic state estimation mixed-integer linear programming particle filter frequency stability

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