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Timeliness-oriented rush repair optimization of workforce scheduling and routing for logically complex systems under uncertainty

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2025年 253卷

作者： Cui, Xinhao Li, Bo Wang, Shitao Zhang, Siyue Yang, Xue Ren, Yi Xiao, Yiyong Beihang Univ Sch Reliabil & Syst Engn Beijing 100191 Peoples R China

The optimization of workforce scheduling and routing problems (WSRPs) with a focus on timeliness-oriented rush repair in logically complex systems has received extensive attention. As there are many characteristics of complex system rush-repair, such as limited repair time and complex correlations, conventional approaches exhibit shortcomings in terms of timeliness (i.e., a comprehensive indicator that integrates both timely and mission reliability). To improve the overall emergency response efficiency under uncertain disaster scenarios, we develop a general decision-making framework for rush-repair WSRP. An optimization model is then formulated based on integer linear programming to maximize the overall timeliness while satisfying the skill-matching requirements and system-level recovery constraints. Furthermore, a novel matheuristic algorithm is proposed as a general solution approach for rush-repair decision-making. Finally, diverse benchmark groups, including different scales and sparsity levels, are simulated to conduct computational experiments. Compared to several representative algorithms, the results show that the proposed method has strong robustness and high efficiency.

关键词： Rush repair Scheduling and routing Timeliness mixed-integer linear programming Matheuristics

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Mathematical programming models for multistage rural electrification planning: Off-grid, grid and mini-grid options

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ENERGY 2025年 316卷

作者： Yazici, Gozde Karasan, Oya Ekin Kocaman, Ayse Selin Stoner, Rob Bilkent Univ Dept Ind Engn Ankara Turkiye MIT Energy Initiat Cambridge MA USA

Approximately 9% of the global population lacks access to electricity. The majority of this population resides in rural areas, highlighting the critical importance of rural electrification efforts. In this study, we introduce novel mathematical programming models aimed at addressing the technology choice and network design challenges in rural electrification. These models determine the optimal electrification technology among off- grid, grid and mini-grid options for each demand point while designing the cost effective grid and mini-grid networks. Furthermore, we present multistage versions of these mathematical models, demonstrating the cost advantage of multistage modeling. These formulations can serve as a comprehensive framework that incorporates investment requirements for system roll-out. Through numerical experiments utilizing both real- life and synthetic instances, we offer new insights into electrification in diverse environments. Our research is expected to contribute to the socio-economic development of developing countries and aid in achieving the targets outlined in Sustainable Development Goal 7.

关键词： Energy access Rural electrification mixed-integer linear programming Multistage programming SDG7

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Spectral Umixing Comparison with Sparse, Iterative and mixed integer programming Models

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arXiv 2025年

作者： Preston, Jade F. Basener, William F. School of Data Science University of Virginia CharlottesvilleVA United States

Hyperspectral unmixing is the analytical process of determining the pure materials and estimating the proportions of such materials composed within an observed mixed pixel spectrum. We can unmix mixed pixel spectra using linear and nonlinear mixture models. Ordinary least squares (OLS) regression serves as the foundation for many linear mixture models employed in Hyperspectral Image analysis. Though variations of OLS are implemented, studies rarely address the underlying assumptions that affect results. This paper provides an in depth discussion on the assumptions inherently endorsed by the application of OLS regression. We also examine variations of OLS models stemming from highly effective approaches in spectral unmixing – sparse regression, iterative feature search strategies and Mathematical programming. These variations are compared to a novel unmixing approach called HySUDeB. We evaluated each approach’s performance by computing the average error and precision of each model. Additionally, we provide a taxonomy of the molecular structure of each mineral to derive further understanding into the detection of the target materials. © 2025, CC BY.

关键词： mixed-integer linear programming

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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 3366-3366页

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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Logic-based Benders decomposition methods for the distributed permutation flow shop scheduling problem with production and transportation cost

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COMPUTERS & OPERATIONS RESEARCH 2025年 179卷

作者： Xiong, Fuli Shi, Jiangbo Jing, Lin Ping, An Xian Univ Architecture & Technol Sch Informat & Control Engn Xian 710055 Peoples R China

Distributed manufacturing mode can significantly enhance production flexibility and efficiency. Considering that factories and customers in distributed manufacturing environments may be geographically dispersed, address a distributed permutation flow shop scheduling problem (DPFSP) with direct transportation different cost of production and transportation while the goal is to minimize of weighted sum cost makespan (DPFSP-PTM). First, we formulate two mixed-integer linear programming (MILP) models and constraint programming (CP) model to optimize the objective simultaneously. Then, by decomposing DPFSPPTM into an order assignment master problem (AMP) and a series of scheduling subproblems (SSPs), develop two exact methods based on logic-based Benders decomposition (LBBD) and Branch-and-Check (BCH). To accelerate convergence, we propose three strong SSP relaxations based on the single-machine bottleneck enhance the MILP models and AMP. Additionally, we introduce an initial solution generated by the iterated greedy (IG) algorithm to warm-start the LBBD. Finally, we demonstrate the effectiveness of the proposed methods in achieving competitive average optimality gaps and lower bounds across both small-scale large-scale instances.

关键词： Distributed permutation flow shop scheduling problem Logic-based Benders decomposition Branch-and-Check mixed-integer linear programming Constraint programming Iterated greedy algorithm

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

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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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Flexible dynamic boundary microgrid operation considering network and load unbalances

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APPLIED ENERGY 2024年 371卷

作者： Su, Yu Li, Dingrui Wang, Fred Olama, Mohammed Ferrari, Maximiliano Ollis, Ben Liu, Yilu Univ Tennessee Dept Elect Engn & Comp Sci Knoxville TN 37996 USA ABB US Res Ctr Raleigh NC USA Oak Ridge Natl Lab Oak Ridge TN 37830 USA

Flexible microgrids with dynamic boundaries have recently been introduced in the literature. With the ability to reconfigure the topology of the microgrids dynamically through remotely controlled switches, flexible microgrids with dynamic boundaries can further improve the resiliency and energy efficiency of microgrids with distributed energy resources (DERs). This paper focuses on the optimal operation considering one of the predominant characteristics of microgrids and distribution systems - unbalanced networks and loads. In existing literature, balanced modeling of microgrids is more common due to its attractive simplicity. The three-phase power unbalance has not been considered as a constraint on the generation units in a microgrid. Negative sequence constraints have also been neglected. In this article, we propose a set of constraints that is specifically related to the capabilities of inverter interfaced resources to supply unbalanced current/power when the microgrid is islanded from the main distribution grid. We incorporate the new set of constraints into two optimization formulations leveraging two convex relaxations of the three-phase power flow equations: mixed-integer linear programming (MILP) and mixed-integer semidefinite programming (MISDP) that optimize the dispatch of controllable switches and DERs in the microgrid. The algorithms are then extended to networked microgrids with grid-forming sources. We test the algorithms on a realistic community microgrid model in Puerto Rico as well as standardized IEEE distribution test feeders. The testing results demonstrate the performance of the proposed algorithms. The MILP is fast and scalable, and the MISDP enforces the negative sequence voltage constraints.

关键词： Microgrids Dynamic boundaries Grid resilience mixed-integer linear programming mixed-integer semidefinite programming Inverter interfaced sources Unbalanced load

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Air traffic controller scheduling

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COMPUTERS & INDUSTRIAL ENGINEERING 2024年 191卷

作者： Guo, Jia Bard, Jonathan F. Univ Texas Austin Cockrell Sch Engn Operat Res & Ind Engn Austin TX 78712 USA

This paper investigates the air traffic controller scheduling problem in which each employee must be assigned up to 10 shifts over a two-week planning horizon. Additional assignments include a 30-minute lunch break and several 15-minute rest breaks within each shift. The goal is to select the minimum number of employees that balances a weighted combination of demand undercoverage, overcoverage and labor costs. Additional constraints include the minimum/maximum number of employees, minimum shift and demand coverage, minimum number of consecutive working hours, bounds on working hours, minimum days off, maximum consecutive days on, and minimum rest time between shifts. A 2-phase algorithm is developed to efficiently construct weekly schedules for the controllers. In Phase 1, employee-shift assignments are made with a randomization algorithm and a greedy post-processor. In Phase 2, 15-minute breaks are added to the selected shifts by solving a second optimization model. The proposed methodology is tested on both small instances defined by the Federal Aviation Administration in their 2022 challenge, and large instances with up to 52 employees. Compared to the actual schedule, the results verify that the 2-phase algorithm offers significantly better schedules with respect to all metrics. We conclude with a parametric analysis of the number of employees to determine how staffing levels effect solution quality.

关键词： Air traffic controller scheduling mixed-integer linear programming Greedy algorithm Randomization algorithm Lunch breaks Rest breaks Parametric analysis

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