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Multi-Agent Task Allocation with Interagent Distance Constraints

JOURNAL OF AEROSPACE INFORMATION SYSTEMS 2024年第2期21卷 168-177页

作者： Choi, Euihyeon Chang, Woohyuk Korea Adv Inst Sci & Technol Dept Aerosp Engn 291 Daehak Ro Daejeon 34141 South Korea Agcy Def Dev Aerosp Technol Res Inst Yuseong POB 35 Daejeon 34186 South Korea

In this paper, we consider the task allocation problem for autonomous agents where the distances between any two agents should always be less than a predetermined value. To easily tackle the problem, we introduce a novel concept referred to as a virtual task that helps to decouple the task allocation from the path planning in the joint task allocation and path planning problem. An overall task allocation procedure with two steps is proposed. After the virtual tasks are generated with preliminary clustering in the first step, tasks are clustered into groups and allocated to agents in the second step. To reduce the computational complexity of optimal clustering and task allocation, we propose a suboptimal method called the cluster-level traveling salesman problem (TSP). Numerical results show that the cluster-level TSP suffers only little performance degradation compared to the optimal method, even though its computational costs are dramatically reduced.

关键词： mixed integer linear programming Autonomous Technology Robots Task Allocation Path Planning Traveling Salesman Problem Inter-Agent Distance Constraints Multi-Agent System

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Interference aware path planning for mobile robots under joint communication and sensing in mmWave networks

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COMPUTER COMMUNICATIONS 2024年 225卷 1-9页

作者： Ren, Yijing Friderikos, Vasilis Kings Coll London Ctr Telecommun Res Dept Engn London WC2R 2LS England

The beyond fifth-generation (B5G) and emerging sixth-generation (6G) wireless networks are considered as key enablers in supporting a diversified set of applications for industrial mobile robots (MRs). In this paper, we consider mobile robots that autonomously roam on an industrial floor and perform a variety of tasks at different locations, whilst utilizing high directivity beamformers in millimeter wave (mmWave) small cells for joint communication and sensing. In such scenarios, the potential close proximity of MRs connected to different base stations, may cause excessive levels of interference having as a net result a decrease in the overall achievable data rate and network service degradation. To mitigate this effect an interference aware path planning algorithm is proposed by explicitly taking into account both achievable communication and sensing performance. More specifically, the proposed heuristic scheme aims to find paths with minimal interfering time whilst improving the overall joint communication and sensing quality of service. A wide set of numerical investigations reveal that the proposed heuristic path planning scheme for the mmWave networked mobile robots can improve the overall achievable communication throughput and the sensing mutual information by up to 35.6% and 23.6% respectively compared with an interference oblivious scheme. Most importantly, those gains are attained without penalizing noticeably the total travel time of the MRs.

关键词： Beyond 5G Beamforming Industry 4.0 Joint communication and sensing Mobile robots mixed integer linear programming Path planning

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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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Model Predictive Control of a Stand-Alone Hybrid Battery-Hydrogen Energy System: A Case Study of the PHOEBUS Energy System

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ENERGIES 2024年第18期17卷 4720-4720页

作者： Holtwerth, Alexander Xhonneux, Andre Mueller, Dirk Forschungszentrum Julich GMBH Inst Climate & Energy Syst Energy Syst Engn ICE 1 D-52425 Julich Germany RWTH Aachen Univ Germany Inst Energy Efficient Bldg & Indoor Climate EON Energy Res Ctr D-52056 Aachen Germany

Model predictive control is a promising approach to robustly control complex energy systems, such as hybrid battery-hydrogen energy storage systems that enable seasonal storage of renewable energies. However, deriving a mathematical model of the energy system suitable for model predictive control is difficult due to the unique characteristics of each energy system component. This work introduces mixed integer linear programming models to describe the nonlinear multidimensional operational behavior of components using piecewise linear functions. Furthermore, this paper develops a new approach for deriving a strategy for seasonal storage of renewable energies using cost factors in the objective function of the optimization problem while considering degradation effects. An experimentally validated simulation model of the PHOEBUS Energy System is utilized to compare the performance of two model predictive controllers with a hysteresis band controller such as utilized for the real-world system. Furthermore, the sensitivity of the model predictive controller to the prediction horizon length and the temporal resolution is investigated. The prediction horizon was found to have the highest impact on the performance of the model predictive controller. The best-performing model predictive controller with a 14-day prediction horizon and perfect foresight increased the total energy stored at the end of the year by 18.9% while decreasing the degradation of the electrolyzer and the fuel cell.

关键词： model predictive control optimization mixed integer linear programming hybrid battery-hydrogen energy storage system

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Research on Distributed Heterogeneous Factory Task Assignment Problem With Loading Efficiency Constraints: A Case Study

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IEEE ACCESS 2024年 12卷 164427-164443页

作者： Zhong, Meisong Zeng, Zhiqiang Hong, Zhiyong Chen, Chuangquan Wuyi Univ Sch Elect & Informat Engn Jiangmen 529020 Peoples R China Dongguan Univ Technol Sch Mech Engn Dongguan 523808 Peoples R China

In a distributed heterogeneous factory (DHF), an order can be assigned to multiple factories for production. Although this method reduces production costs, it can cause reduced loading efficiency and increase transportation costs. Balancing loading efficiency and total cost is a key issue that must be resolved in the task assignment problem (TAP) for DHF. This study investigates the DHFTAP with total cost objective and loading efficiency constraints. To solve this problem, three mixed integer linear programming (MILP) models are constructed based on different production strategies: Model I: split orders based on the combination of factory and warehouse;Model II: merge orders based on product types;Model III: split orders based on tasks. In the experiment, three MILP models and three evolutionary algorithms (EAs) are compared with the rule-based traditional method, and the sensitivity of minimum loading efficiency and the impact of factory load rate are analyzed. The experimental results reveal that MILP and EA can effectively solve the researched problem. Finally, valuable suggestions are provided for enterprises.

关键词： Production facilities Costs Loading Load modeling Manufacturing systems Transportation Heuristic algorithms Metaheuristics Job shop scheduling Biological system modeling Task analysis mixed integer linear programming Evolutionary computation Distributed heterogeneous factory task assignment problem mixed integer linear programming evolutionary algorithm

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Medium- and long-term interval optimal scheduling of cascade hydropower-photovoltaic complementary systems considering multiple uncertainties

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APPLIED ENERGY 2024年 353卷

作者： Lu, Na Wang, Guangyan Su, Chengguo Ren, Zaimin Peng, Xiaoyue Sui, Quan Zhengzhou Univ Yellow River Lab Zhengzhou 450001 Peoples R China Zhengzhou Univ Coll Water Conservancy & Transportat Zhengzhou 450001 Peoples R China Power China Huadong Engn Corp Ltd Hangzhou 311122 Peoples R China Zhengzhou Univ Sch Elect & Informat Engn Zhengzhou 450001 Peoples R China

An effective way to promote the consumption of renewable energy resource generation is by forming a joint power supply system between photovoltaic (PV) power plants widely distributed along a river basin and largescale cascade hydropower plants, using shared transmission lines to transmit power to the power grid system. However, renewable energy resource generation has significant prediction uncertainty and can flood into the power grid, which is detrimental to its safe and stable operation. To solve the above issues, this paper proposes an improved interval optimization method to model the uncertainties of the inflow runoff of a cascade hydropower plant and the photovoltaic power output. On this basis, a medium- and long-term interval optimization scheduling model is established for a cascade hydro-PV complementary system based on extreme scenarios. This model does not need to obtain an accurate probability density distribution for renewable energy, and can realize the overall optimization of runoff-PV uncertainty scenarios within a certain range, taking into account the economy and robustness of system operation. In order to ensure the solution efficiency, the original model is transformed into a mixed integer linear programming (MILP) model by a variety of deterministic transformation methods and linearization techniques. The case study shows that: 1) Under the premise that the system power output is relatively stable, the power curtailment of the interval optimized scheduling scheme proposed in this paper is reduced by 47.5% compared with the robust scheduling scheme. The ability to cope with extreme scenarios is also significantly improved compared with the deterministic scheduling scheme. 2) There is a game relationship between the robustness and economy of the obtained scheduling scheme. When the fluctuation interval width of random variables increases from 20% to 30% and 40%, the corresponding total power generation decreases by 2.4% and 4.7%, respectively.

关键词： Cascade hydro-PV complementary system Uncertainties of inflow runoff and PV power output Interval optimization mixed integer linear programming Extreme scenarios

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A new mathematical formulation and a hybrid evolutionary algorithm for re-entrant flow-shop problem with release date

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SCIENTIA IRANICA 2024年第6期31卷 500-517页

作者： Behmanesh, R. Kianfar, K. Naghshejahan Higher Educ Inst Dept Ind Engn Esfahan *** Iran Univ Isfahan Fac Engn Esfahan *** Iran

Tn this paper, we address the weighted multi-objective re-entrant flowshop scheduling problem considering release dates in order to minimize makespan, total completion time, total tardiness, maximum idle time, and number of tardy jobs. Each job is taken into account with deterministic processing times, and release dates. The flow-shop comprised of two workshops in whose jobs are entered to the main workshop and after the first part of the processing, they are transferred to the second workshop and after this stage, the jobs are returned to the main workshop for the last part of the processing. We model the problem by a new mixed integer programming based on formulating sum of idle time as a new concept. Moreover, a hybrid evolutionary algorithm is proposed based on some dispatching rules, ant colony optimization, and genetic algorithm. The performance of the proposed algorithm on some test instances is compared to the mixed integer linear programming model as well as the state-of-the-art algorithms called genetic algorithm, tabu search, bio-geography based optimization, and artificial bee colony. The computational experiments show that our proposed approach outperforms other algorithms and the results indicate efficiency and capability of the proposed algorithm in comparison with the traditional algorithms. (c) 2024 Sharif University of Technology. All rights reserved.

关键词： Re-entrant flow-shop scheduling Tdle time Release date Makespan Tardiness Hybrid evolutionary algorithm mixed integer linear programming

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Maintenance scheduling at high-speed train depots: An optimization approach

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2024年 243卷

作者： Wang, Jiaxi Zhejiang Lab Res Ctr Intelligent Transportat Hangzhou Zhejiang Peoples R China

Under the preventive maintenance policy, high-speed trains need to conduct periodical inspection/overhaul at dedicated depots, where maintenance facilities and maintenance crews are available. Due to the complicated structure of trains, the maintenance work is rich in a variety of types, characterized by mechanical components repaired, maintenance levels and etc. In practice, these types of maintenance work are integrated into maintenance packets, and different packets may require the specific types of crews. In this paper, a mixed integer linear programming model is proposed for the depot maintenance packet assignment and crew scheduling problem. The objective of the problem is to minimize the overall crew worktime, while the main constraints include the compatibility between maintenance packets and crew types, maintenance packet duration time and execution order, maintenance time window, and crew worktime limit. Besides, two families of valid inequalities are proposed to improve the baseline model. Computational experiments on a set of randomly generated instances show the effectiveness and efficiency of the improved model compared to the baseline one. Finally, a real-world case study from Shanghai South Depot is carried out to further validate the proposed approach. Improvements on both solution time and quality are achieved in contrast with the manual schedule.

关键词： High-speed train depot Maintenance packet Crew scheduling mixed integer linear programming Valid inequality

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A Fast Predictive Control Method for Vehicle Path Tracking Based on a Recurrent Neural Network

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IEEE ACCESS 2024年 12卷 141104-141115页

作者： Wu, Xialai Lin, Ling Chen, Junghui Du, Shuxin Huzhou Univ Huzhou Key Lab Intelligent Sensing & Optimal Contr Huzhou 313000 Peoples R China Chung Yuan Christian Univ Dept Chem Engn Taoyuan 32023 Taiwan

The paper introduces a Fast Model Predictive Control (FMPC) approach for vehicle path tracking, addressing the challenge of real-time performance in highly nonlinear systems. Utilizing a recurrent neural network with symmetric saturating linear transfer functions (SSL-RNN), our method efficiently constructs an SSL-RNN model for the vehicle. By transforming the MPC optimal control problem into a mixed integer linear programming problem, a swift online solution is achieved. Through simulations on a CarSim/Simulink platform, our FMPC outperforms RNN-based nonlinear MPC and long-short-term memory network-based MPC, demonstrating superior accuracy in vehicle path tracking and enhanced controller solution efficiency.

关键词： Vehicle dynamics Adaptation models Accuracy Heuristic algorithms Data models Computational modeling mixed integer linear programming Predictive control Recurrent neural networks Path planning model predictive control recurrent neural network symmetric saturating linear transfer functions vehicle path tracking

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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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