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Multi-objective optimization of integrated energy systems in natural gas industrial zones: Enhancing performance through variable hydrogen blending ratios

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APPLIED THERMAL ENGINEERING 2025年 268卷

作者： Mao, Yudong Li, Xinwei Liu, Jiying Yu, Mingzhi Kim, Moon Keun Yang, Kaimin Shandong Jianzhu Univ Sch Thermal Engn Jinan 250101 Peoples R China Oslo Metropolitan Univ Dept Built Environm N-0130 Oslo Norway

In order to solve the problem of new energy consumption and reduce the use of fossil fuels, a multi-objective optimization and evaluation system is constructed for Integrated energy system (IES). The system takes into account factors including economy, environmental conservation, and hydrogen blending ratio. It establishes a mixed-integer linear programming model for multi-objective optimal scheduling. The conversion of renewable energy sources into electricity, followed by its conversion into hydrogen. By changing the hydrogen blending ratio of natural gas pipeline network, each objective function can reach the best. The optimal operation of each equipment of the heat and cold networks is obtained by changing the proportion of hydrogen blending into the natural gas pipeline network. Based on the multi-objective comprehensive analysis under conditions of constant heat consumption, the optimal solution is a hydrogen blending ratio of 10.67 %. This solution results in a cost of $305,426.29, carbon emissions of 19,709,221.40 kg, and a total natural gas consumption of 19,128,759.35 m3. Compared with the conventional energy system, this scheme not only offers better economy but also has characteristics of energy saving and emission reduction, making it superior in comprehensive evaluation compared to other schemes. In conclusion, this system enhances energy utilization efficiency, maximizes the integration of renewable energy sources, and reduces carbon emissions.

关键词： Integrated energy system Multi-objective optimization algorithm mixed-integer linear programming Hydrogen blending of natural gas Renewable energy

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MILP models and effective heuristic for energy-aware parallel machine scheduling with shared manufacturing

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 271卷

作者： Cheng, Junheng Cheng, Jingya Lin, Yanhong Lu, Shu Wu, Peng Fujian Normal Univ Sch Econ Fuzhou 350117 Peoples R China Fuzhou Univ Sch Econ & Management Fuzhou 350116 Peoples R China

Driven by advancements in information technology and the modernization of the manufacturing industry, shared manufacturing has emerged as a novel industrial organization mode that enables manufacturing enterprises to mutually utilize external idle resources. Concurrently, to meet the requirements of sustainable development, manufacturing enterprises need to balance economic efficiency with energy consumption in their production practices. This study investigates a new energy-aware parallel machine scheduling problem shared manufacturing environments, where production time, cost, and energy consumption vary across different jobs and machines. The objective is to minimize the weighted sum of makespan, total energy consumption, and overall sharing costs. We first formulate the problem as a mixed-integer linear programming (MILP) model. Then the problem is further refined into an improved MILP model with fewer integer variables. Given the strong NP-hard nature of the problem, an effective tailored heuristic (ETH) is developed based on analyzed problem properties to solve large-scale instances. It includes three main phases: pre-processing, job assignment, and local search. Extensive experimental results demonstrate that the improved MILP model significantly outperforms the original one, and the ETH can solve large-sized instances with up to 400 machines and 20000 jobs within one second, with gaps of less than 1%. The proposed MILP models and ETH can assist manufacturing enterprises in making more informed decisions and scheduling resource allocation while considering economic and environmental factors.

关键词： Parallel machine scheduling Shared manufacturing Energy consumption mixed-integer linear programming Heuristic

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Planning maintenance tasks in airline operating companies: A mathematical programming model applied to a case study

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART O-JOURNAL OF RISK AND RELIABILITY 2025年

作者： Agostinho, Francisco Martinho, Martim Alves, Tiago Andrade, Antonio R. Univ Lisbon Inst Super Tecn IDMEC Ave Rovisco Pais P-1049001 Lisbon Portugal

This paper explores a mixed-integer linear programming (MILP) approach to plan the maintenance tasks in an airline. The MILP model finds feasible maintenance plans, by minimizing the costs associated with maintenance activities and costs associated with unavailability. A constructive matheuristic approach is put forward, by solving the maintenance planning problem sequentially. The model and the matheuristic is then applied to a case study from the main Portuguese airline company. The results suggest that the proposed matheuristic approach for maintenance planning in practice, as explored in the case study, finds feasible solutions in a much lower computational time. Moreover, a sensitivity analysis is conducted to assess the impact of hangar's capacity and other input parameters in the maintenance plan. Our approach provides an effective way to support decision-making in maintenance planning for a fleet of aircraft, while complying with several operational, technical, and labour constraints.

关键词： Maintenance planning aircraft maintenance mixed-integer linear programming maintenance heuristic

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A Day-Ahead Optimal Battery Scheduling Considering the Grid Stability of Distribution Feeders

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ENERGIES 2025年第5期18卷

作者： Mumtahina, Umme Alahakoon, Sanath Wolfs, Peter Cent Queensland Univ Rockhampton Qld 4701 Australia Cent Queensland Univ Gladstone Qld 4680 Australia

This study presents a comprehensive framework for optimizing energy management systems by integrating advanced methodologies for weather forecasting, energy cost analysis, and grid stability using a mixed-integer linear programming (MILP) algorithm. A novel approach is proposed for day-ahead weather forecasting, leveraging real-time data extraction from reliable weather websites and applying clear sky modeling to estimate photovoltaic (PV) generation with high accuracy. By automating weather data acquisition, the methodology bridges the gap between weather predictions and practical energy management, providing utilities with a reliable tool for operating and integrating renewable energy. The optimization framework focuses on minimizing the utility bill by analyzing a distribution feeder representative of Australia's energy infrastructure, incorporating time-of-use (TOU) and flat tariff systems across eight Australian states to simulate realistic energy costs. Furthermore, voltage constraints are applied within the optimization framework to maintain system stability and improve voltage profiles, ensuring both technical reliability and economic efficiency. The proposed framework delivers actionable insights for utility industries, enhancing the scheduling of battery energy storage systems (BESS) and facilitating the integration of renewable energy into the grid.

关键词： optimal scheduling battery energy storage system energy management system mixed-integer linear programming weather forecasting

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Resilient pharmaceutical supply chains: Assessment of stochastic optimization strategies for process uncertainty integration in network design problems

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COMPUTERS & CHEMICAL ENGINEERING 2025年 195卷

作者： Sarkis, Miriam Shah, Nilay Papathanasiou, Maria M. Imperial Coll London Sargent Ctr Proc Syst Engn London SW72BB England Imperial Coll London Dept Surg & Canc London SW72BB England

In recent years, the market boom of next-generation therapies and vaccines has pressured the pharmaceutical industry to rapidly scaleup capacity to meet societal needs. Manufacturers catering for these markets reported shortages due to unforeseen demand trends and a crucial uncertainty in capabilities of platforms still under development. In this work, we present an optimization-simulation framework for the design of resilient supply chains to manufacturing uncertainty. Given previously quantified probability distributions of process parameters, we formulate stochastic optimization problems integrating process uncertainty via a sampling-based methodology. Stochastic programming results in networks of higher optimal costs compared to deterministic approaches. Furthermore, stochastic designs ensure product supply meets target demands under simulated uncertainty and result in a larger probability of achieving lower costs per dose. The optimization- simulation framework is used to test solution stability for a varying number of optimization scenarios, highlighting that the minimum number of samples to guarantee stability is problem-specific, thus motivating the investigation of scenario reduction techniques to ensure stability of scenario sets a priori. Overall, the cost-supply benefits of integrating manufacturing uncertainty are quantified, demonstrating the scope for its consideration in strategic planning problems in the sector.

关键词： Supply chain optimization mixed-integer linear programming Stochastic programming Manufacturing uncertainty Monte Carlo Sampling

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A hybrid local search algorithm for the continuous energy-constrained scheduling problem

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JOURNAL OF SCHEDULING 2025年第1期28卷 65-84页

作者： Brouwer, Roel van den Akker, Marjan Hoogeveen, Han Univ Utrecht Dept Informat & Comp Sci Utrecht Netherlands

We consider the continuous energy-constrained scheduling problem (CECSP). A set of jobs has to be processed on a continuous, shared resource. A schedule for a job consists of a start time, completion time, and a resource consumption profile. The goal is to find a schedule such that each job does not start before its release time, is completed before its deadline, satisfies its full resource requirement, and respects its lower and upper bounds on resource consumption during processing. The objective is to minimize the total weighted completion time. We present a hybrid local search approach, using simulated annealing and linear programming, and compare it to a mixed-integer linear programming (MILP) formulation. We show that the hybrid local search approach matches the MILP formulation in solution quality for small instances and is able to find a feasible solution for larger instances in reasonable time.

关键词： Continuous scheduling Resource-constrained scheduling mixed-integer linear programming Simulated annealing

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Generalized Optimal Scheduling of Cluster Tools With Reentrance and Residency Time Constraints

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2025年第1期55卷 307-318页

作者： Li, Xin Educ Univ Hong Kong Dept Math & Informat Technol Hong Kong Peoples R China

Cluster tools have been widely used in the semiconductor industry. In previous work, singe-arm and dual-arm cluster tools are dealt with separately. When considering residency time constraints, algorithms for scheduling robot moving sequences are mainly developed based on backward and swap strategies, respectively. How about the performance of other robot moving sequences is still an open problem. The present work addresses general conditions by considering all feasible robot moving sequences-beyond backward- and swap-based strategies. In addition, both dual-arm and single-arm cluster tools with reentrance are handled simultaneously. To solve this, the problem is first divided into two parts-the basic one without reentrance and reentrant operations. Based on detailed analysis of all available robot operations in a cluster tool, mixed-integer programming formulations are developed for both parts. Subsequently, both illustrative examples and randomly generated instances are tested to validate the efficiency of the proposed approach. Results of one example show improvement comparing to previous work. Another two examples-not scheduled previously-are scheduled well by the proposed approach. Meanwhile, the flexibility of operations obtained by the proposed model is demonstrated by an example with six processing modules and 12 stages in a dual-arm cluster tool. Additionally, more randomly generated instances are tested more to validate the proposed approach and analyze computation time.

关键词： Generalized formulation mixed-integer linear programming reentrance residency time single-arm and dual-arm cluster tools

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Container Pre-marshalling Problem Minimizing CV@R under Uncertainty of Ship Arrival Times

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Journal of Japan Industrial Management Association 2025年第4 E期75卷 172-182页

作者： Ikuma, Daiki Ikeda, Shunnosuke Sukegawa, Noriyoshi Takano, Yuichi Graduate School of Science and Technology University of Tsukuba Japan Department of Advanced Sciences Hosei University Japan Institute of Systems and Information Engineering University of Tsukuba Japan

This paper is concerned with the container pre-marshalling problem, which involves relocating containers in the storage area so that they can be efficiently loaded onto ships without reshuffles. In reality, however, ship arrival times are affected by various external factors, which can cause the order of container retrieval to be different from the initial plan. To represent such uncertainty, we generate multiple scenarios from a multivariate probability distribution of ship arrival times. We derive a mixed-integer linear optimization model to find an optimal container layout such that the conditional value-at-risk is minimized for the number of misplaced containers responsible for reshuffles. Moreover, we devise an exact algorithm based on the cutting-plane method to handle large-scale problems. Numerical experiments using synthetic datasets demonstrate that our method can produce high-quality container layouts compared with the conventional robust optimization model. Additionally, our algorithm can speed up computations for solving large-scale problems. © 2025 Japan Industrial Management Association. All rights reserved.

关键词： mixed-integer linear programming

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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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Asteroid mining: ACT&Friends’ results for the GTOC12 problem

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Astrodynamics 2025年第1期9卷 19-40页

作者： Dario Izzo Marcus Märtens Laurent Beauregard Max Bannach Giacomo Acciarini Emmanuel Blazquez Alexander Hadjiivanov Jai Grover Gernot Heißel Yuri Shimane Chit Hong Yam European Space Technology and Research Center Noordwijk2201 AZthe Netherlands European Space Operations Centre Darmstadt64293Germany Surrey Space Center University of SurreyGuildfordGU27XHUK School of Aerospace Engineering Georgia Institute of TechnologyAtlantaGeorgia30332USA Mission Design and Operations Group ispace inc.Sumitomo Fudosan Hamacho Building 3F3-42-3Nihonbashi HamachoChuo-kuTokyo103-0007Japan

In 2023, the 12th edition of Global Trajectory Competition was organized around the problem referred to as “Sustainable Asteroid Mining”. This paper reports the developments that led to the solution proposed by ESA’s Advanced Concepts Team. Beyond the fact that the proposed approach failed to rank higher than fourth in the final competition leader-board, several innovative fundamental methodologies were developed which have a broader application. In particular, new methods based on machine learning as well as on manipulating the fundamental laws of astrodynamics were developed and able to fill with remarkable accuracy the gap between full low-thrust trajectories and their representation as impulsive Lambert transfers. A novel technique was devised to formulate the challenge of optimal subset selection from a repository of pre-existing optimal mining trajectories as an integer linear programming problem. Finally, the fundamental problem of searching for single optimal mining trajectories (mining and collecting all resources), albeit ignoring the possibility of having intra-ship collaboration and thus sub-optimal in the case of the GTOC12 problem, was efficiently solved by means of a novel search based on a look-ahead score and thus making sure to select asteroids that had chances to be re-visited later on.

关键词： GTOC low thrust asteroid mining machine learning mixed-integer linear programming nonlinear programming

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