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An interregional optimization approach for time series aggregation in continent-scale electricity system models

ENERGY 2025年 324卷

作者： Brown, Patrick R. Cole, Wesley J. Mai, Trieu Natl Renewable Energy Lab Golden CO 80401 USA

Modeling electric power systems with high shares of weather-dependent resources requires tradeoffs between temporal, spatial, and operational resolution. Many studies perform time series aggregation using clustering algorithms to reduce the temporal dimension, but when modeling continent-scale electricity systems that are large enough to contain multiple independent weather systems, this approach requires large numbers of representative periods to minimize errors in regional wind and solar capacity factors. Here, a new optimization-based approach for representative period selection and weighting is introduced that minimizes regional errors in average renewable capacity factors and electricity demand. The method delivers higher regional fidelity with fewer representative periods than alternative clustering methods when applied to wind, solar, and demand profiles for the contiguous United States. When representative periods are selected from multiple weather years, the optimized method reproduces regional averages with lower error than a complete 365-day time series from any single weather year. The method identifies only representative (as opposed to outlying) periods but can be combined with an iterative "stress period" identification approach to guide efficient decision-making considering both average and high-risk weather conditions.

关键词： Time series aggregation mixed integer linear programming Temporal resolution Representative days Energy system modeling

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MILP Acceleration: A Survey from Perspectives of Simplex Initialization and Learning-Based Branch and Bound

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JOURNAL OF THE OPERATIONS RESEARCH SOCIETY OF CHINA 2025年第1期13卷 1-55页

作者： Huang, Meng-Yu Huang, Ling-Ying Zhong, Yu-Xing Yang, Hui-Wen Chen, Xiao-Meng Huo, Wei Wang, Jia-Zheng Zhang, Fan Bai, Bo Shi, Ling Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China Huawei Hong Kong Res Ctr Theory Lab Hong Kong Peoples R China

mixed integer linear programming (MILP) is an NP-hard problem, which can be solved by the branch and bound algorithm by dividing the original problem into several subproblems and forming a search tree. For each subproblem, linear programming (LP) relaxation can be solved to find the bound for making the following decisions. Recently, with the increasing dimension of MILPs in different applications, how to accelerate the solution process becomes a huge challenge. In this survey, we summarize techniques and trends to speed up MILP solving from two perspectives. First, we present different approaches in simplex initialization, which can help to accelerate the solution of LP relaxation for each subproblem. Second, we introduce the learning-based technologies in branch and bound algorithms to improve decision making in tree search. We also propose several potential directions and extensions to further enhance the efficiency of solving different MILP problems.

关键词： MILP acceleration Simplex initialization linear programming mixed integer linear programming Machine learning

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Generic model for capacity allocation on transportation terminals

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2025年 196卷

作者： Berktas, Nihal Zografos, Konstantinos G. Univ Lancaster Ctr Transport & Logist Cent Dept Management Sci Management Sch Lancaster LA1 4YX England Durham Business Sch Dept Mkt & Management Durham DH1 3LB England

Transportation terminals play an important role in the functioning of the transportation system. Therefore, the efficient use of the capacity of transportation terminals is considered a major determinant of the performance of transportation networks. An important decision related to the efficient functioning of congested terminals relates to the optimum allocation of the available capacity to different operators (users). The capacity allocation problem in transportation terminals, such as airports, railroad stations, ports, involves the optimum apportion of the available capacity to different users, such as airlines, rail, and shipping companies, while satisfying operational, and regulatory constraints and requirements. Motivated by the similarities across capacity allocation problems in terminals of different transportation modes and the lack of a unifying framework, this study introduces a generic mixed integer linear programming (MILP) formulation and demonstrates its applicability through a detailed application of the proposed model for rail networks. The generic mathematical model is a generalization of models highly utilized in airport slot allocation. We explicitly present how the model applies to the train timetabling problem and conduct computational experiments using publicly available data. Our computational experiments show that the model consistently achieves optimal solutions across almost all tested cases, including instances where published solutions are suboptimal. The analysis of the results for a specific instance indicates that incorporating station capacity constraints yields the same set of scheduled requests but alters the deviations from the desired arrival and departure times. In contrast, increase in the flexibility of the requested times significantly affect the solution, leading to increase in the number of scheduled trains, deviations, and the overall length of the journey.

关键词： Train timetabling Terminal capacity management mixed integer linear programming

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A Novel GA-CP Method for Fixed-Type Multi-Robot Collaborative Scheduling in Flexible Job Shop

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 13531-13543页

作者： Huang, Jin Li, Xinyu Gao, Liang Huazhong Univ Sci & Technol State Key Lab Intelligent Mfg Equipment & Technol Wuhan 430074 Peoples R China

With the rapid development of intelligent manufacturing, multi-robot collaborative systems are increasingly integrated into various production processes. In the flexible job shop environment of automotive stamping, achieving smooth operation and efficient manufacturing of production lines hinges on solving the critical issues of multi-robot task allocation and scheduling. However, for such fixed-type multi-robot collaboration problems, robots are constrained by specific areas or predetermined trajectories, and processing times can only be adjusted by varying the number of available robots. Therefore, the scheduling problem in multi-robot collaborative flexible job shop problems (MCFJSP) is divided into two sub-problems: FJSP with controllable processing times and multi-robot collaborative task balancing. To address these, we propose three distinct methods: mixed integer linear programming (MILP), constraint programming (CP), and a hybrid genetic algorithm-constraint programming (GA-CP). Finally, a set of 48 benchmark cases and two real-world cases are developed to test these methods. Comparative experiments demonstrate that the MILP model is superior in small-scale cases, while the GA-CP model exhibits the best overall performance in medium to large-scale cases. Furthermore, through comparisons with two advanced algorithms, the effectiveness and superiority of the GA-CP method in addressing real-world cases are confirmed.[8pt]Note to Practitioners-In modern manufacturing environments, particularly in industries like automotive manufacturing, multiple robots working together on complex tasks are increasingly common. This paper addresses the practical challenge of effectively scheduling these robots to maximize efficiency while reducing the number of robots assigned to each task. This study introduces and compares different methods, including MILP, CP, and GA-CP methods, that can help practitioners determine the best way to allocate tasks among robots and schedule the

关键词： Robots Robot kinematics Collaboration Job shop scheduling Manufacturing Service robots Resource management Optimal scheduling Assembly Process control Fixed-type multi-robot collaborative problem mixed integer linear programming constraint programming genetic algorithm-constraint programming

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Mathematical models and heuristics for double-load crane scheduling in slab yards

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2025年第3期324卷 773-786页

作者： Dong, Zixiong Che, Ada Feng, Jianguang Northwestern Polytech Univ Sch Management Xian 710072 Peoples R China

This paper studies a novel crane scheduling problem with unconstrained double-load operations (CSP-UDL) in slab yards. It aims to optimize the sequence of crane operations to minimize makespan. Unlike conventional crane operations, which handle one or two slabs per trip, the unconstrained double-load operation enables the transport of more than two slabs in a single trip, thereby improving logistic efficiency and reducing the makespan. To tackle this problem, we propose two mixed integer linear programming (MILP) models for solving small-and medium-sized instances. We develop two heuristics for large-sized instances: a hybrid heuristic and a matheuristic. The hybrid heuristic integrates tabu search within an adaptive large neighborhood search (ALNS) framework, while the matheuristic integrates this hybrid heuristic with an MILP model, leveraging the strengths of both exact and heuristic methods. Extensive computational experiments demonstrate that while the proposed MILP models can exactly solve instances with up to 50 tasks, the hybrid heuristic and the matheuristic demonstrate robust performance in solving large-sized instances.

关键词： Slab yard Crane scheduling Double-load operation mixed integer linear programming Heuristic

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minChemBio: Expanding Chemical Synthesis with Chemo-Enzymatic Pathways Using Minimal Transitions

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ACS SYNTHETIC BIOLOGY 2025年第3期14卷 756-770页

作者： Anand, Mohit Upadhyay, Vikas Maranas, Costas D. Penn State Univ State Coll PA 16802 USA

Chemo-enzymatic pathway design aims to combine the strengths of enzymatic with chemical synthesis to traverse biomolecular design space more efficiently. While chemical reactions often struggle with regioselectivity and stereoselectivity, enzymatic conversions often encounter limitations of low enzyme activity or availability. Optimally integrating both approaches provides an opportunity to identify efficient pathways beyond the capabilities of either modality. Recently, studies have shown the advantage of leveraging enzymatic steps into industrial-scale chemical processes, such as for the blood sugar regulator Sitagliptin (Merck) and the HIV protease inhibitor Darunavir (Prozomix). Designing optimal chemo-enzymatic pathways is a complex task. It requires navigating a high-dimensional search space of potential reactions that combine individual chemical and biochemical steps while at the same time minimizing transitions between chemical catalysis and bioreactions. Here, we introduce an algorithmic approach, minChemBio, that relies on solving a mixed-integer linear programming (MILP) problem by optimally searching through known chemical and enzymatic steps extracted from the United States Patent Office (USPTO) and MetaNetX databases, respectively. minChemBio allows for the minimization of transitions between chemical and biological reactions in the pathway, thus reducing the need for costly separation and purification steps required. minChemBio was benchmarked on three case studies involving the synthesis of 2-5-furandicarboxylic acid, terephthalate, and 3-hydroxybutyrate. Identified designs included both established literature pathways as well as unexplored ones which were compared against pathways identified by existing retrosynthetic tools. minChemBio fills a current gap in the space of pathway retrosynthesis tools by controlling and minimizing the transitions between chemical catalysis and biocatalytic steps. It is accessible to users through open-source code (htt

关键词： Computational Synthesis Planning Chemo-enzymatic Pathways Minimal Transitions mixed integer linear programming

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Integration of dynamic CHPP and gas boiler behaviour into the convex planning problem for the optimised operation of multimodal microgrids

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SMART ENERGY 2025年 17卷

作者： Roth, Maximilian Harmuth, Stephan Rinderknecht, Stephan Tech Univ Darmstadt Inst Mechatron Syst Mech Engn Otto Berndt Str 2 D-64287 Darmstadt Germany

Decentralized energy systems like microgrids can be a favourable alternative to the centralized organisation of the current energy supply system. An important problem regarding microgrids is the optimisation of their operational strategy, although most works assume time-independent behaviour of microgrid components in optimisation models. Therefore, the goal of this study is the integration of the dynamic behaviour of a Gas Stirling engine combined heat and power plant and an auxiliary gas boiler in the operational planning problem of a hybrid microgrid. A dynamic optimisation model is developed and compared to a static and approximated model based on the results of three different test scenarios. The simulation results show that the integration of dynamic component characteristics has a significant impact on the operational strategy. The resulting setpoints for the control of the components are able to consider the transient behaviour of these components and therefore their real behaviour is more accurately represented within the optimisation problem. However, the integration of the dynamic modelling approaches leads to more difficult optimisation problems which require more computational effort. The approximation to a convex quadratic problem represents a good compromise between computation time and setpoint accuracy.

关键词： Microgrid scheduling District heating mixed integer linear programming mixed integer quadratic programming mixed integer dynamic optimisation Smart energy systems

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A seminal contribution of Ailsa Land and Alison Doig Harcourt to the field of mathematical programming

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AUSTRALIAN & NEW ZEALAND JOURNAL OF STATISTICS 2025年第0期

作者： Laporte, Gilbert HEC Montreal Dept Decis Sci 3000 Chemin Cote Sainte Catherine Montreal PQ H3T 2A7 Canada GERAD 3000 Chemin Cote Sainte Catherine Montreal PQ H3T 2A7 Canada

In 1960, Ailsa Land and Alison Doig published the first linear programming-based branch-and-bound algorithm for the solution of mixed integer linear programs. This algorithm has had a major impact on the field of mathematical programming and remains to this day the only available solution methodology for generic mixed integer linear programs. This article recounts the genesis of this algorithm and describes some of the developments that have taken place since its publication.

关键词： branch-and-bound branch-and-cut Gomory cutting planes mixed integer linear programming simplex algorithm

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Optimization of garbage collection routes for evidence-based policy-making

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ARTIFICIAL LIFE AND ROBOTICS 2025年第1期30卷 156-164页

作者： Kaho, Tomoki Sakakibara, Kazutoshi Arimura, Mikiharu Watanabe, Shinya Muroran Inst Technol Muroran Hokkaido Japan Toyama Prefectural Univ Imizu Toyama Japan

This study models garbage collection in a local city in Hokkaido, Japan, driven by the increasing burden of collection costs despite a declining population. A unique problem in this city is the large number of garbage stations, which exacerbates the collection burden. We examine the impact of waste volume fluctuations and the number and layout of garbage stations on collection routes and costs to find solutions to this issue. This research aims to develop cost-effective and feasible garbage collection strategies to support evidence-based policymaking. We formulated a garbage collection challenge using mixed integer linear programming to minimize travel distances and operational burdens within vehicle capacity constraints. Numerical simulations reveal significant findings: (i) optimized routes reduce total travel distance by similar to\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sim$$\end{document}25% compared to existing routes, (ii) increased waste volumes lead to non-linear increases in route lengths, and (iii) the aggregation strength of garbage stations significantly impacts route efficiency and the number of required stations. Conclusively, this study provides empirical evidence to guide policymakers in optimizing garbage collection systems, ensuring effective resource utilization and maintaining service quality.

关键词： Route optimization Evidence-based policy-making mixed integer linear programming Branch-and-cut method Garbage collection Waste management

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Integrated Hybrid Renewable Energy System Optimization for Sustainable Agricultural Operations

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PROCESS INTEGRATION AND OPTIMIZATION FOR SUSTAINABILITY 2025年第1期9卷 367-386页

作者： Mohammed, Awsan King Fahd Univ Petr & Minerals Architectural Engn & Construct Management Dept Dhahran 31261 Saudi Arabia King Fahd Univ Petr & Minerals Interdisciplinary Res Ctr Smart Mobil & Logist Dhahran 31261 Saudi Arabia

The reliance on traditional fossil fuels in agriculture leads to rising energy costs, environmental degradation, and vulnerability to climate change. Transitioning to renewable energy systems can provide cost savings, enhance sustainability, and improve resilience against energy supply disruptions. Embracing these technologies is essential for promoting efficient agricultural practices. Consequently, this paper proposes a mixed-integer linear programming model (MILP) for optimizing the configuration of hybrid photovoltaic, wind, and hydrogen renewable energy systems, with the objective of reducing the overall life cycle costs of greenhouse farms in Saudi Arabia. The model calculates the capacities of individual components, including photovoltaic (PV) modules, wind turbines, hydrogen storage tanks, fuel cells, and electrolyzers. The proposed model is solved using the General Algebraic Modeling System (GAMS) software. The results show that for a farm consisting of 20 greenhouses, employee accommodations, and a water treatment facility, the optimal system includes 552 PV panels (with a capacity of 150 W), 7 wind turbines (with a capacity of 10 kW), 6 fuel cells, 9 electrolyzers, and 2144 hydrogen tanks for storage (with a nominal capacity of 0.3 kWh). The annual energy cost is estimated at $100,527.84, with a levelized cost of energy (LCOE) of $0.056/kWh. The system also yields a reduction in CO2 emissions of 903.631 tons per year, highlighting the potential of hydrogen as a feasible energy storage solution to mitigate fluctuations in renewable energy output. The findings demonstrate the viability of renewable energy in agricultural applications and emphasize the importance of hybrid configurations for energy efficiency. Future studies will explore the integration of other renewable resources and advanced optimization methods to further enhance cost savings and system performance.

关键词： Agricultural operations Hybrid renewable energy system mixed integer linear programming Optimization Hydrogen

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