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Integrated Planning Model for Optimizing Investments in Hydrogen Infrastructure Deployment: A Case Study of Yangtze River Delta Region, China

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ENERGY SCIENCE & ENGINEERING 2025年第3期13卷 1306-1322页

作者： Shen, Weijie Li, Hua Ding, Hongjian Zeng, Ming Xie, Chuansheng Zhang, Xiaochun North China Elect Power Univ Sch Econ & Management Beijing Peoples R China PowerChina Huadong Engn Corp Ltd Hangzhou Peoples R China

The hydrogen industry is of great significance for global energy system transition and decarbonization;thus, the holistic planning of hydrogen infrastructure from a supply chain level is necessary. To this end, first, the framework and methods in the hydrogen infrastructure deployment research were investigated systematically. Second, a hydrogen supply chain design (HSCD) model and a hydrogen refueling station location (HRSL) model were constructed. In view of the limitations of the two models, this paper reformulated the HRSL model through alternating the objective function and constraint, then proposed an integrated planning model using the & varepsilon;${\epsilon}$-constraint method, which relaxed the assumption condition and expanded the decision-making boundary of the original model, realizing the reasonable equilibrium of multiple segments. Finally, a case study was conducted using the Yangtze River Delta region as an example, and the main findings were as follows: (1) To meet the hydrogen demand of 25494t/d, the Yangtze River Delta region needs to build 94 coal gasification hydrogen plants, storage facilities with capacity of 12747t, transmission modes with capacity of 2333t/d, and 150 hydrogen refueling stations. (2) Railways have more advantages in large capacity and long-distance hydrogen transmission. (3) Road segments with higher traffic flow often have a higher capture proportion. (4) The investment cost accounts for the majority of hydrogen production plant construction, which is 43.80%.

关键词： hydrogen refueling station location hydrogen supply chain design mixed integer linear programming Yangtze River Delta region

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Cyclic scheduling of multi-type wafers concurrent processing in single-arm cluster tools with residency time constraints

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 269卷

作者： Li, Xin Yang, Wen Educ Univ Hong Kong Dept Math & Informat Technol Hong Kong 999077 Peoples R China Shenzhen Univ Coll Management Shenzhen 518055 Peoples R China

With the recent trend towards larger wafer sizes and smaller demand lots recently, there is a growing need for simultaneous production of multiple wafer types in a cluster tool. This presents a more complex scheduling challenge compared to identical types. Previous research primarily focuses on specific scenarios limited to the backward sequence, leaving a gap in the handling of more generalized scenarios without sequence restrictions. This paper proposes an approach towards the generalized cyclic scheduling of multiple wafer types in single- arm cluster tools, considering residency time constraints. A comprehensive operational scenario is described, using distinct symbols differentiate processing stages of wafers and their associated process modules. These elements are interconnected through shared process module sets. Based on detailed analysis of operations, a mixed integer linear programming model is proposed to handle the generalized scheduling problem. In the model, precedent relationships of robot operations are well defined, effectively preventing conflicts among them. Additionally, this model ensures the proper sequencing of processing stages in process modules - shared or not - to avoid overlap. Finally, the proposed approach is tested by both illustrative examples, especially including those previously considered unschedulable, and randomly generated instances. Testing results conduct the efficiency and validity of the proposed model.

关键词： Single-arm cluster tools Multiple wafer types Cyclic scheduling Residency time constraints mixed integer linear programming

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Scheduling distributed heterogeneous non-permutation flowshop to minimize the total weighted tardiness

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 272卷

作者： Xiong, Fuli Chen, Siyuan Xiong, Ningxin Jing, Lin Xian Univ Architecture & Technol Sch Informat & Control Engn Xian 710055 Peoples R China China Univ Petr Beijing Karamay Sch Petr Karamay 834000 Peoples R China

Flowshop scheduling is a critical problem in manufacturing and logistics, where jobs must be processed through a series of machines in a predefined order. In distributed heterogeneous flowshop scheduling, multiple factories with varying processing capacities and resources are involved, making the scheduling problem even more complex. Non-permutation flowshops (NPFS) further complicate this by allowing job sequences to differ across stages, thus significantly expanding the solution space compared to traditional permutation flowshops. Minimizing total weighted tardiness (TWT) is a key objective as it plays a crucial role in avoiding penalties for late deliveries. In this context, this paper addresses a distributed heterogeneous non-permutation flowshop scheduling problem with the objective of minimizing TWT (DHNPFSP_TWT). The problem involves multiple factories operating as NPFS, where job processing times differ across factories for the same production stage. Given the NP-hard nature of the problem, we first proposed a Manne-based mixed-integer linear programming model and a constraint programming (CP) model for small-scale instances. To solve medium- and largescale instances efficiently, we propose a three-phase adaptive evolutionary algorithm (TAE) that combines permutation and non-permutation search strategies, along with a job allocation adjustment phase. The TAE algorithm first finds a permutation solution using NEH3_en and random generation, followed by an adaptive local search and adaptive ruin and recreate algorithm for refinement and mutation. In the non-permutation phase, a greedy insertion strategy and local search techniques explore the solution space. The job allocation adjustment phase reallocates jobs based on the factory with the highest tardiness, and the second and third phases co-evolve to improve solution quality. Additionally, we propose a hybrid algorithm (AE_CP) integrating the strengths of adaptive evolutionary algorithms and CP to further enh

关键词： Distributed heterogeneous non-permutation flowshop Iterated greedy Constraint programming mixed integer linear programming Total weighted tardiness

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Disaster risk-informed optimization using buffered failure probability for regional-scale building retrofit strategy

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STRUCTURAL SAFETY 2025年 114卷

作者： Seok, Uichan Byun, Ji-Eun Song, Junho Seoul Natl Univ Dept Civil & Environm Engn Seoul South Korea Univ Glasgow James Watt Sch Engn Glasgow Scotland

Regional retrofit planning of buildings is critical to address the increasing threat of natural disasters exacerbated by urban growth and climate change. To identify an optimal plan, this paper introduces a novel optimization framework. By integrating performance-based engineering (PBE) and reliability-based optimization (RBO), we propose buffered optimization and reliability method based mixed integer linear programming (BORM-MILP). The proposed formulation can identify optimal solutions using general optimization solvers, while handling a large number of PBE samples and buildings. Furthermore, the formulation introduces a modified active-set strategy tailored to regional-scale building retrofit optimization problems, further reducing computational memory. The proposed optimization framework is validated by a benchmark example of Seaside, Oregon. The optimization results are presented along in a map, offering visual support for decision-making processes. The application results are further investigated to analyze computational efficiency of the proposed active-set strategy, study convergence to the normal distribution, and identify a dominant factor for the building retrofit selection.

关键词： Building retrofit Performance-based engineering Reliability-based optimization Buffered failure probability mixed integer linear programming Community resilience

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Hybrid quantum annealing decomposition framework for unit commitment

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ELECTRIC POWER SYSTEMS RESEARCH 2025年 238卷

作者： Ling, Jiajie Zhang, Quan Geng, Guangchao Jiang, Quanyuan Zhejiang Univ Coll Elect Engn Hangzhou 310027 Zhejiang Peoples R China

Quantum computing is an emerging and promising technology that has overwhelming quantum advantages compared to its classical counterparts. Unit commitment (UC) is a critical issue in the power system, and it becomes more challenging with the integration of intermittent renewable energy. Therefore, this paper proposes an innovative decomposition and coordination optimization framework to accelerate the solution of UC, in which the interaction between an adiabatic quantum computer and a classical computer is designed to harness the immense computational power of quantum computers effectively. First, decomposition methods considering the requirements of quantum computers are introduced to decompose UC into smallscale models. Then, the paper presents a quadratic unconstrained binary optimization modeling method to transform UC problems into the form of quantum computing. Furthermore, due to the limitations of quantum computing resources, a reductive variable technique is proposed to reduce the number of slack variables in the optimization model and ensure that it remains feasible for quantum computers. Case studies conducted in test systems with a quantum annealing simulator and a real quantum annealing computer illustrate the feasibility and effectiveness of the method and demonstrate its potential in the era of quantum computing.

关键词： Decomposition method Quantum annealing mixed integer linear programming Unit commitment

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A Policy-Based Meta-Heuristic Algorithm for Energy-Aware Distributed No-Wait Flow-Shop Scheduling in Heterogeneous Factory Systems

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2025年第1期55卷 620-634页

作者： Zhao, Fuqing Song, Lisi Jiang, Tao Wang, Ling Dong, Chenxin Lanzhou Univ Technol Sch Comp & Commun Lanzhou 730050 Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Qingdao Hengxing Univ Sci & Technol Dept Automot Engn Qingdao 266100 Peoples R China

In the face of environmental deterioration and global climate change, the concept of carbon neutrality and carbon peaking has gained prominence as a means to balance development and environmental preservation worldwide. Energy-aware scheduling is becoming the key scenario for environment conservation in manufacturing. This study focuses on addressing the energy-aware distributed no-wait flow-shop scheduling problem in a heterogeneous factory system (EDNWFSP-HFS) to minimize total energy consumption (TEC) and total tardiness (TTDs). A mixed-integer linear programming (MILP) model is formulated and a policy-based meta-heuristic algorithm (MHA-PG) is specifically designed to solve EDNWFSP-HFS. First, the optimal allocation rules based on random sequence (OAR-RS) are designed to initialize the population. Second, a policy-based method is employed to guide the algorithm toward making a better decision. Third, the energy-saving strategy considering specific knowledge of EDNWFSP-HFS is summarized to further optimize the feasible solution. Extensive simulations are conducted, comparing the performance of MHA-PG against several state-of-the-art algorithms. The results demonstrate that the proposed algorithm outperforms the competing approaches in solving EDNWFSP-HFS, indicating its superior performance and effectiveness.

关键词： Climate change Manufacturing automation Metaheuristics Job shop scheduling Distributed management Energy management Heterogeneous networks Carbon neutral mixed integer linear programming Distributed flow-shop scheduling energy-aware scheduling heterogeneous factory system (HFS) policy-based meta-heuristic

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A Universal Cooperative Decision-Making Framework for Connected Autonomous Vehicles With Generic Road Topologies

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2025年第4期74卷 5414-5429页

作者： Huang, Zhenmin Liu, Wenru Shen, Shaojie Ma, Jun Hong Kong Univ Sci & Technol Guangzhou Robot & Autonomous Syst Thrust Guangzhou 511453 Peoples R China Hong Kong Univ Sci & Technol Div Emerging Interdisciplinary Areas Hong Kong Peoples R China Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China

Cooperative decision-making of Connected Autonomous Vehicles (CAVs) presents a longstanding challenge due to its inherent nonlinearity, non-convexity, and discrete characteristics, compounded by the diverse road topologies encountered in real-world traffic scenarios. The majority of current methodologies are only applicable to a single and specific scenario, predicated on scenario-specific assumptions. Consequently, their application in real-world environments is restricted by the innumerable nature of traffic scenarios. In this study, we propose a unified optimization approach that exhibits the potential to address cooperative decision-making problems related to traffic scenarios with generic road topologies. This development is grounded in the premise that the topologies of various traffic scenarios can be universally represented as Directed Acyclic Graphs (DAGs). Particularly, the reference paths and time profiles for all involved CAVs are determined in a fully cooperative manner, taking into account factors such as velocities, accelerations, conflict resolutions, and overall traffic efficiency. The cooperative decision-making of CAVs is approximated as a mixed-integer linear programming (MILP) problem building on the DAGs of road topologies. This favorably facilitates the use of standard numerical solvers and the global optimality can be attained through the optimization. Case studies corresponding to different multi-lane traffic scenarios featuring diverse topologies are scheduled as the test itineraries, and the efficacy of our proposed methodology is corroborated.

关键词： Decision making Topology Roads Optimization Games Merging Autonomous vehicles Trajectory planning Trajectory mixed integer linear programming Autonomous driving multi-agent systems connected autonomous vehicles (CAVs) cooperative decision-making non-convex optimization mixed-integer linear programming (MILP)

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Hydrogen energy storage train scheduling with renewable generation and demand response

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JOURNAL OF ENERGY STORAGE 2025年 115卷

作者： Gupta, Pranda Prasanta Kalkhambkar, Vaiju Sharma, Kailash Chand Bhui, Pratyasa GLA Univ Dept Elect Engn Mathura Uttar Pradesh India Rajarambapu Inst Technol Dept Elect Engn Sangli Maharashtra India Dr B R Ambedkar Natl Inst Technol Dept Elect & Commun Engn Jalandhar India Indian Inst Technol Dharwad Dept Elect Engn Dharwad India

Large-scale penetration of renewable and hydrogen energy sources represents promising trends toward carbon emission reductions in the power sector. The storage systems such as the hydrogen energy storage (HES) Train will be crucial in responding to extreme grid events due to their agility and flexibility. This manuscript proposes a stochastic network constrained unit commitment (NCUC) considering HES Train with solar PV generation and demand response program (DRP). The DRP is introduced as a flexible option for dealing with energy market prices, providing sustainable options, and modifying the load profile for peak load shaving. The proposed model is applied to manage an HES Train that provides hydrogen energy services with low electricity prices. Moreover, the vector autoregressive moving average (VARMA) model is used in the stochastic optimization strategy to handle uncertain solar PV power. The model is formulated as a mixed integer linear programming (MILP) problem along with a generalized bender decomposition technique (GBD) to obtain the global optimal solution. A sensitivity analysis is presented to analyze solar power's ability to handle the high demand of the system operation. The proposed NCUC problem is simulated using GAMS software on an IEEE 24-bus system. HES Train scheduling with DRP and uncertain solar PV reduces the overall cost by 8.71 % as compared to the thermal-HES Train system.

关键词： Demand response program Hydrogen energy service providers Hydrogen energy storage train Network-constrained unit commitment Solar power generation mixed integer linear programming

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Static and Dynamic Routing, Fiber, Modulation Format, and Spectrum Allocation in Hybrid ULL Fiber-SSMF Elastic Optical Networks

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IEEE ACCESS 2025年 13卷 21652-21664页

作者： Ouyang, Kangao Tang, Fengxian Yuan, Zhilin Li, Jun Li, Yongcheng Soochow Univ Sch Elect & Informat Engn Suzhou 215006 Jiangsu Peoples R China Shenzhen Polytech Univ Sch Integrated Circuits Shenzhen 518000 Guangdong Peoples R China

Traditional standard single-mode fibers (SSMFs) are unable to satisfy the future long-distance and high-speed optical channel transmission requirement due to their relatively large signal losses. To address this issue, the ultra-low loss and large effective area (ULL) fibers are successfully manufactured and expected to deployed in the existing optical networks. For such ULL fiber deployment, network operators prefer adding ULL fibers to each link rather than replace existing SSMFs, resulting in a scenario where both of SSMF and ULL fiber coexist on the same link. In this paper, we investigated the routing, fiber, modulation format, and spectrum allocation (RFMSA) problem in the context of an elastic optical network (EON) where ULL fiber and SSMF coexisting on each link under both the static and dynamic traffic demands. We formulated this RFMSA problem as a node-arc based mixed integer linear programming (MILP) model and developed Spectrum Window Plane (SWP)-based heuristic algorithms based on different fiber selection strategies, including spectrum usage based (SU), optical signal-to-noise ratio (OSNR) aware, ULL fiber first (UFF), and random strategies. Simulation results show that in the static traffic demand situation, the RFMSA algorithm based on the OSNR-aware (OA) strategy exhibits optimal performance, attaining a performance similar to that of the MILP model regarding the maximum number of frequency slots (FSs) used in the entire network. Moreover, in the dynamic traffic demand scenario, the SU strategy remarkably surpasses the other strategies in terms of the lightpath blocking probability.

关键词： Optical fiber networks Heuristic algorithms Routing Modulation Signal to noise ratio Resource management Optical noise Elastic optical networks Propagation losses Optical fiber losses ultra-low loss fiber mixed integer linear programming routing fiber modulation format spectrum allocation

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Optimal Allocation and Sizing of Battery Energy Storage System in Distribution Network Using Mountain Gazelle Optimization Algorithm

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ENERGIES 2025年第2期18卷 379-379页

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

This paper addresses the problem of finding the optimal position and sizing of battery energy storage (BES) devices using a two-stage optimization technique. The primary stage uses mixed integer linear programming (MILP) to find the optimal positions along with their sizes. In the secondary stage, a relatively new algorithm called mountain gazelle optimizer (MGO) is implemented to find the technical feasibility of the solution, such as voltage regulation, energy loss reduction, etc., provided by the primary stage. The main objective of the proposed bi-level optimization technique is to improve the voltage profile and minimize the power loss. During the daily operation of the distribution grid, the charging and discharging behaviour is controlled by minimizing the voltage at each bus. The energy storage dispatch curve along with the locations and sizes are given as inputs to MGO to improve the voltage profile and reduce the line loss. Simulations are carried out in the MATLAB programming environment using an Australian radial distribution feeder, with results showing a reduction in system losses by 8.473%, which outperforms Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), and Cuckoo Search Algorithm (CSA) by 1.059%, 1.144%, and 1.056%, respectively. During the peak solar generation period, MGO manages to contain the voltages within the upper boundary, effectively reducing reverse power flow and enhancing voltage regulation. The voltage profile is also improved, with MGO achieving a 0.348% improvement in voltage during peak load periods, compared to improvements of 0.221%, 0.105%, and 0.253% by GWO, WOA, and CSA, respectively. Furthermore, MGO's optimization achieves a reduction in the fitness value to 47.260 after 47 iterations, demonstrating faster and more consistent convergence compared to GWO (47.302 after 60 iterations), WOA (47.322 after 20 iterations), and CSA (47.352 after 79 iterations). This comparative analysis highlights the effectiveness of

关键词： battery energy storage system line loss reduction mixed integer linear programming mountain gazelle optimization algorithm radial distribution network voltage profile improvement

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