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A model and algorithm for reactive multi-objective multi-skilled project scheduling under resource disruptions

COMPUTERS & INDUSTRIAL ENGINEERING 2025年 203卷

作者： Su, Yixuan Xu, Zhe Liu, Dongning Beihang Univ Sch Econ & Management 37 Xueyuan Rd Beijing 100191 Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China

The reactive multi-objective multi-skilled project scheduling under resource disruptions is more complex than the general scheduling problem. This complexity stems from the incorporation of overtime and proactive preemption as response strategies, as well as the consideration of the impact of a resource's skill level on setup time. We propose a reactive multi-objective scheduling model to address this problem. The objective is to generate a new schedule that minimizes deviations in activity start times and resource allocations from the baseline schedule, while also minimizing resource usage costs. To enhance the efficiency of commercial solvers in solving this model, some of the formulas are linearized. Furthermore, we propose a hybrid multi-objective evolutionary algorithm (HMOEA). To boost the performance of the HMOEA, several improvement strategies are introduced, including an adaptive procedure for fitness assignment and density estimation, a hybrid evolutionary strategy, and a local search strategy. Numerical experiments demonstrate the effectiveness of both the linearization measures and the improvement strategies. Five performance metrics and convergence comparisons are employed to assess the solution quality of the proposed algorithm in terms of convergence, diversity, and distribution. Computational results demonstrate that HMOEA leads to significant improvements in all metrics compared to five state-of-the-art algorithms.

关键词： multi-skilled Reactive project scheduling Resource disruption multi-objective evolutionary algorithm

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Balancing Operation Cost and Carbon Emissions in multi-Energy Microgrid Scheduling Using Joint Operator evolutionary algorithm 36

Balancing Operation Cost and Carbon Emissions in Multi-Energ...

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36th Chinese Control and Decision Conference (CCDC)

作者： Li, Bin Li, Qincheng Li, Jia Liu, Zhitao Su, Hongye Zhejiang Univ Inst Cyber Syst & Control State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China

ISBN: (纸本)9798350387780;9798350387797

A multi-energy microgrid (MEMG), incorporating a combined heat and power (CHP) system and electric vehicles (EVs), is an important infrastructure to meet multiple energy demands and reduce carbon emissions. However, operation cost minimization and carbon emissions minimization in the MEMG conflict with each other. Therefore, this paper proposes a multi-objective optimal scheduling model for the MEMG, aimed at achieving a balance between operation cost and carbon emissions. Moreover, a novel multi-objective evolutionary algorithm (MOEA) called joint operator evolutionary algorithm (JOEA) is developed to solve this model. Experimental results indicate that JOEA has excellent performance on the proposed multi-objective optimal scheduling problem. In addition, JOEA is compared with several well-known MOEAs on this engineering problem and 20 benchmark problems. The comparison results show that JOEA outperforms other MOEAs and has competitive performance.

关键词： multi-energy microgrid Operation cost Carbon emissions multi-objective evolutionary algorithm

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A novel membrane-inspired evolutionary framework for multi-objective multi-task optimization problems

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INFORMATION SCIENCES 2022年 596卷 236-263页

作者： Xu, Zhiwei Zhang, Kai He, Juanjuan Liu, Xiaoming Wuhan Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430065 Peoples R China Hubei Prov Key Lab Intelligent Informat Proc & Re Wuhan 430065 Peoples R China

In recent years, many different membrane-inspired evolutionary algorithms have been proposed to solve various complex optimization problems. Considering membrane systems' powerful computing performance and parallel capability, it has outstanding potential in solving multi-task optimization problems. However, there is no research to explore the performance of membrane-inspired evolutionary algorithms in solving multi-task optimization problems. In this paper, a novel membrane-inspired evolutionary framework with a hybrid dynamic membrane structure is proposed to solve the multi-objective multi-task optimization problems. First, a novel membrane-inspired two-stage evolution strategy algorithm is proposed as the algorithm in the membrane to improve the convergence of the algorithm and the diversity of multisets. Second, the information molecule concentration vector is proposed to reduce negative information transfer. The information molecule concentration vector is inspired by the binding process of information molecules and receptors and can control the information transfer probability adaptively. Finally, comprehensive experimental results show that the proposed algorithm performs better than most advanced multi-objective evolutionary multitasking algorithms. (c) 2022 Elsevier Inc. All rights reserved.

关键词： Membrane-inspired evolutionary algorithm Membrane computing evolutionary multitasking multi-objective multi-task optimization multi-objective evolutionary algorithm

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A copula-based chance-constrained programming framework for BMPs spatial configuration planning

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JOURNAL OF HYDROLOGY 2025年 659卷

作者： Ding, Wenlu Xia, Jun Huang, Sheng Sun, Lanxin Wuhan Univ State Key Lab Water Resources & Hydropower Engn Sc Wuhan 430072 Peoples R China Chinese Acad Sci Inst Geog Sci & Nat Resources Res Key Lab Cycle & Related Land Surface Proc Beijing 100101 Peoples R China

Optimizing spatial pattern of best management practices (BMPs) is crucial for managing non-point source pollution at the watershed scale. However, uncertainties of BMPs effects caused by variant hydro-meteorological conditions can pose challenges to achieving water quality management goals, emphasizing the need to incorporate these uncertainties into decision-making. This study established a credibility chance-constrained programming (CCP) framework that incorporates multiple techniques, including uncertainty modelling, stochastic simulation-optimization technique, clustering and fuzzy set theory. Correlated uncertainties of BMPs effects were described by vine copulas and propagated to the watershed outlet via a Markov-based surrogate model. Fast non-dominated sorting genetic algorithm (NSGA-II), was adopted to optimize cost and pollutant reduction goals while quantifying the reliability of each solution. The developed framework can provide the best compromising solutions (BCSs) among numerous solutions generated by NSGA-II. A case study was conducted to manage Total Nitrogen (TN) and Total Phosphorus (TP) pollution in the upper Boyang River watershed, China. The results show that the system cost increased by up to 3.4 times with the increase of reduction goal (30-60%). Specially, higher credibility levels allow for slight increases in pollution loads (1.48%-5.67%) without significantly raising costs. Overall, by incorporating uncertainty into pollutant reduction scenarios, the framework enables decision-makers to balance costs and environmental benefits while ensuring robust and reliable decisions. This approach is highly adaptable to BMP planning in complex environmental systems, enhancing its practicality for multi-objective watershed management.

关键词： Non-point source pollution BMPs-related decision-making Vine Copulas Uncertainty Chance-constrained programming multi-objective evolutionary algorithm

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Collaborative algorithm of workpiece scheduling and AGV operation in flexible workshop

ROBOTIC INTELLIGENCE AND AUTOMATION

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ROBOTIC INTELLIGENCE AND AUTOMATION 2024年第1期44卷 34-47页

作者： Cheng, Wenlong Meng, Wenjun Taiyuan Univ Sci & Technol Coll Mech Engn Taiyuan Peoples R China

PurposeThis study aims to solve the problem of job scheduling and multi automated guided vehicle (AGV) cooperation in intelligent manufacturing ***/methodology/approachIn this study, an algorithm for job scheduling and cooperative work of multiple AGVs is designed. In the first part, with the goal of minimizing the total processing time and the total power consumption, the niche multi-objective evolutionary algorithm is used to determine the processing task arrangement on different machines. In the second part, AGV is called to transport workpieces, and an improved ant colony algorithm is used to generate the initial path of AGV. In the third part, to avoid path conflicts between running AGVs, the authors propose a simple priority-based waiting strategy to avoid *** experiment shows that the solution can effectively deal with job scheduling and multiple AGV operation problems in the ***/valueIn this paper, a collaborative work algorithm is proposed, which combines the job scheduling and AGV running problem to make the research results adapt to the real job environment in the workshop.

关键词： multi-objective evolutionary algorithm Path optimization AGV collision avoidance multi-AGV system

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Surrogate model based multi-objective optimisation of supercritical CO2 ejectors

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JOURNAL OF SUPERCRITICAL FLUIDS 2025年 218卷

作者： Paul, Sanjoy Srikar, R. P. Rao, Srisha M., V Kumar, Pramod Indian Inst Sci Interdisciplinary Ctr Energy Res Bangalore 560012 India Indian Inst Sci Dept Aerosp Engn Bangalore 560012 India Indian Inst Sci Dept Mech Engn Bangalore 560012 India

A supercritical CO2 (sCO(2)) supersonic ejector improves the coefficient of performance (COP) in combined power and cooling systems by compressing a secondary stream through entrainment and mixing with a high-momentum primary stream. The performance of the ejector is crucial to system efficiency and is influenced by complex gas dynamic shock interactions and shear layers which is further complicated by the rapid variations in thermophysical of sCO(2). In this regard, aerodynamic duct shaping plays a pivotal role in optimizing ejector efficiency. The present paper seeks to optimize a sCO(2) ejector using a surrogate model derived from computational fluid dynamics data. The model relies on a comprehensive dataset generated using a simulation tool coupled with REFPROP database to account for variations in thermophysical properties of sCO(2). Subsequently, a genetic aggregation technique is used to train and improve the model via supervised machine learning. The influence of critical design parameters such as radius of mixing section, nozzle exit point, and mixing duct length on the performance of the ejector is enabled by a sensitivity analysis study facilitated by design space exploration. Finally, a multi-objective evolutionary algorithm is incorporated in the surrogate model to optimize the ejector performance by maximizing entrainment ratio and compression ratio, while minimizing entropy generation. It is found that stagnation temperature ratio is a key influencing parameter responsible for enhancing mixing layer growth to improve the ejector performance. The optimized ejector shows an enhanced efficiency of similar to 25 % compared to a non-optimized ejector.

关键词： Supercritical CO 2 ejector Surrogate model multi-objective evolutionary algorithm Stagnation temperature ratio CFD

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Hybrid Nearest-Neighbor Approximation for Message-Passing MIMO Detectors

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IEEE WIRELESS COMMUNICATIONS LETTERS 2024年第6期13卷 1591-1595页

作者： Ge, Yingmeng Zhang, Zaichen Wang, Haibo Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Southeast Univ Frontiers Sci Ctr Mobile Informat Commun & Secur Nanjing 210096 Peoples R China Purple Mt Labs Nanjing 211111 Peoples R China

Recently, message passing algorithms (MPAs) have been widely investigated in multiple-input multiple-output (MIMO) detection for achieving a good balance between performance and complexity. Some MPAs approximate the messages as Gaussian distribution using moment matching (MM), which involves hardware-unfriendly operations. In this letter, we demonstrate a general hybrid nearest-neighbor approximation (HNNA) optimized by multi-objective evolutionary algorithm (MOEA) to reduce the complexity of MM. The grouping strategy is then proposed to reduce the search space with improved optimization efficiency. Applied to approximate message passing (AMP) detector, numerical results indicate that with the proposed HNNA-based MM, AMP detector can achieve up to 17.6% complexity reduction with negligible performance loss. The proposed HNNA-based MM can also be applied to other MPAs, being a promising approach in simplifying the complexities.

关键词： MIMO detector message passing moment matching nearest-neighbor approximation multi-objective evolutionary algorithm

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multi-objective Optimization for multimodal multi-objective multi-Point Shortest Path Problem Considering Unforeseeable Road Eventualities

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年第6期26卷 8622-8640页

作者： Xu, Zhiwei Zhang, Kai Del Ser, Javier Li, Miqing Xu, Xin He, Juanjuan Wu, Ni Wuhan Univ Sci & Technol Sch Comp Sci & Technol Hubei Prov Key Lab Intelligent Informat Proc andRe Wuhan 430065 Hubei Peoples R China Tecnalia Basque Res & Technol Alliance BRTA Bizkaia 48160 Bilbao Spain Univ Basque Country Dept Commun Engn UPV EHU Bizkaia 48013 Bilbao Spain Univ Birmingham Sch Comp Sci Birmingham B15 2TT England

multi-objective multi-point shortest path planning problems are commonly encountered in real-world applications. Numerous path planning algorithms have been proposed to accommodate different model assumptions. However, most existing algorithms can only identify a subset of the Pareto optimal paths and overlook equivalent Pareto optimal paths. Relying solely on a subset of Pareto optimal solutions is insufficient to effectively respond to unforeseeable road eventualities in the real-world traffic environment. In this paper, multi-objective multi-point shortest path planning problem is modeled as a multimodal multi-objective optimization problem with necessary points constrains. A multimodal multi-objective evolutionary algorithm using constraint dominance principle-based path comparison strategy and path similarity-based multimodal solutions selection strategy is proposed to address this problem. The proposed constraint dominance principle-based path comparison strategy can effectively navigate through large infeasible regions by relaxing necessary point constraints, thereby obtaining a true constrained Pareto front. The proposed path similarity-based multimodal solutions selection strategy can effectively balance the distribution of solutions in the decision space, thereby preserving multiple equivalent optimal solutions. The proposed algorithm is compared with five state-of-the-art path planning algorithms from the benchmark test suite derived from the 2021 IEEE CEC path planning competition, where city maps are adapted from real transportation networks in Chinese cities, in our experiments. The exceptional performance is demonstrated through thirty independent runs, yielding experimental results that showcase the superiority of the proposed algorithm on the test problem set. This superior performance highlights the potential for designing more resilient path planners suitable for scenarios affected by unpredictable road eventualities.

关键词： Path planning Optimization Classification algorithms Shortest path problem Pareto optimization Search problems Heuristic algorithms Roads evolutionary computation Convergence multi-objective shortest path planning constrained multi-objective optimization multimodal multi-objective optimization multi-objective evolutionary algorithm

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A nondominated sorting simplified swarm optimization with local search mechanisms for multi-objective vehicle routing problems with time windows

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APPLIED SOFT COMPUTING 2025年 174卷

作者： Lai, Chyh-Ming Chiu, Chun-Chih Chen, Tzu-Li Natl Def Univ Grad Sch Resources Management & Decis Sci Taipei 112 Taiwan Natl Taipei Univ Technol Dept Business Management Taipei 106 Taiwan Natl Tsing Hua Univ Dept Ind Engn & Engn Management Hsinchu 30013 Taiwan

In addressing the complexities of modern logistics, this study introduces a novel multi-objective formulation for vehicle routing problems with time windows (MO-VRPTW), targeting minimizing travel distance, enhancing customer satisfaction, and equalizing driver workloads. We introduce an innovative hybrid multi-objective evolutionary algorithm (MOEA) leveraging nondominated sorting simplified swarm optimization to effectively merge the advantages of various optimization strategies. A key aspect of this advancement is the incorporation of the Lin- KernighanHelsgaun (LKH) heuristic, which delivers a superior initial solution, thereby markedly enhancing the speed of convergence. Additionally, we pioneered a local search method inspired by the A* algorithm designed to refine the search process's exploration and exploitation stages. Solomon's benchmark instances, a recognized standard in the VRPTW field, were used to validate our algorithm's effectiveness. Our algorithm demonstrated superior performance in addressing MO-VRPTW through meticulous statistical analysis, outperforming state-of-the-art algorithms, such as MOPSO, NSGA-II, MOEA/D, and SPEA2, regarding efficiency and solution diversity. This study not only advances algorithmic performance but also thoughtfully considers the interests of key supply chain stakeholders.

关键词： Vehicle routing problems with time windows multi-objective evolutionary algorithm Lin-Kernighan heuristic A* algorithm MOPSO NSGA-II MOEA/D SPEA2 Nondominated sorting simplified swarm optimization

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Manufacturing process optimization for real-time quality control in multi-regime conditions: Tire tread production use case

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JOURNAL OF MANUFACTURING SYSTEMS 2024年 76卷 293-313页

作者： Stankovic, Katarina Jelic, Dea Tomasevic, Nikola Krstic, Aleksandra Univ Belgrade Sch Elect Engn Bulevar Kralja Aleksandra 73 Belgrade 11000 Serbia Univ Belgrade Inst Mihajlo Pupin Volgina 15 Belgrade 11000 Serbia

The high-stake nature of most manufacturing processes empowers the importance of real-time quality control and assurance. In the event of a failure in production, a decision-making process can be time-consuming for the human and prevent timely actions. The agility can be boosted with a decision-support system based on artificial intelligence. Particularly, multi-objective process optimization can be employed to select the optimal control settings in real-time, and thus enhance relevant key performance indicators, concurrently. However, process optimization in manufacturing scenarios has never been an easy task, due to the complexity, non-convexity, and non-linearity of dependences among process parameters and physical constraints typical for strict production procedures. Precise and high-performative digital replicas of physical systems are required to simulate different scenarios. Physical models are computationally demanding for real-time applications and are usually hard to develop. In that light, this paper brings a novel solution based on multi-objective evolutionary optimization coupled with process surrogate data-driven models, in charge of predicting the relevant process responses. Based on process and quality parameters being streamed from the production plant in real-time, the optimizer can act in timely critical and quality-threatening situations and generate immediate corrective actions. The multi-regime operation of the plant and design space dimensionality can impact the convergence rate and add to execution time. Therefore, production regimes recognition and greedy search of suffix tree-based models of the process have been engaged, aiding in a better-focused and faster space search at an early phase of the algorithm run. Beyond simply reviewing the outputs, the user can leave feedback, which is utilized by the optimizer's reinforcement learning mechanisms. The process of tire tread production has served as the playground for methodology design and im

关键词： Process optimization Real-time quality control multi-objective evolutionary algorithm Surrogate model Clustering Suffix tree

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