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Integrating geometallurgical ball mill throughput predictions into short-term stochastic production Scheduling in mining complexes

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International Journal of Mining Science and Technology 2023年第2期33卷 185-199页

作者： Christian Both Roussos Dimitrakopoulos COSMO–Stochastic Mine Planning Laboratory Department of Mining and Materials EngineeringMcGill UniversityMontreal H3A 0E8Canada

This article presents a novel approach to integrate a throughput prediction model for the ball mill into short-term stochastic production Scheduling in mining complexes.The datasets for the throughput prediction model include penetration rates from blast hole drilling(measurement while drilling),geological domains,material types,rock density,and throughput rates of the operating mill,offering an accessible and cost-effective method compared to other geometallurgical programs.First,the comminution behavior of the orebody was geostatistically simulated by building additive hardness proportions from penetration rates.A regression model was constructed to predict throughput rates as a function of blended rock properties,which are informed by a material tracking approach in the mining complex.Finally,the throughput prediction model was integrated into a stochastic optimization model for short-term production Scheduling.This way,common shortfalls of existing geometallurgical throughput prediction models,that typically ignore the non-additive nature of hardness and are not designed to interact with mine production Scheduling,are overcome.A case study at the Tropicana Mining Complex shows that throughput can be predicted with an error less than 30 t/h and a correlation coefficient of up to 0.8.By integrating the prediction model and new stochastic components into optimization,the production schedule achieves weekly planned production reliably because scheduled materials match with the predicted performance of the mill.Comparisons to optimization using conventional mill tonnage constraints reveal that expected production shortfalls of up to 7%per period can be mitigated this way.

关键词： Geometallurgy Stochastic optimization Short-term open pit mine production Scheduling Measurement while drilling Non-additivity Hardness

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Aerial refueling Scheduling of multi-receiver and multi-tanker under spatial-temporal constraints for forest firefighting

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Chinese Journal of Aeronautics 2024年第5期37卷 71-91,I0001页

作者： Jing HUANG Jiaqi XING Jinrui REN Quan QUAN Youmin ZHANG Department of Information and Control Engineering Xi’an University of TechnologyXi’an 710048China Jiujiang Measuring Test Technology Research Institute Jiujiang 332000China School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China School of Automation Xi’an University of Posts&TelecommunicationsShaanxiXi’an 710121China Department of Mechanical Industrial and Aerospace EngineeringConcordia UniversityMontreal H3G 1M8Canada

Forest fires pose a significant threat to human life and property,so the utilization of unmanned aircraft systems provides new ways for forest firefighting.Given the constrained load capacities of these aircraft,aerial refueling becomes crucial to extend their operational time and range.In order to address the complexities of firefighting missions involving multi-receiver and multi-tanker deployed from various airports,first,a fuel consumption calculation model for aerial refueling Scheduling is established based on the receiver path.Then,two distinct methods,including an integrated one and a decomposed one,are designed to address the challenges of establishing refueling airspace and allocating tasks for tankers.Both methods aim to optimize total fuel consumption of the receivers and tankers within the aerial refueling Scheduling framework.The optimization problem is established as nonlinear optimization models along with restrictions.The integrated method seamlessly combines refueling rendezvous point Scheduling and tanker task allocation into unified process.It has a complete solution space and excels in optimizing total fuel consumption.The decomposed method,through the separation of rendezvous point Scheduling and task allocation,achieves a reduced computational complexity.However,this comes at the cost of sacrificing optimality by excluding specific feasible solutions.Finally,numerical simulations are carried out to verify the feasibility and effectiveness of the proposed methods.These simulations yield insights crucial for the practical engineering application of both the integrated and decomposed methods in real-world scenarios.This comprehensive approach aims to enhance the efficiency of forest firefighting operations,mitigating the risks posed by forest fires to human life and property.

关键词： Aerial refueling Scheduling Planning Spatial-temporal constraint Firefighting

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Distributed Source-Load-Storage Cooperative Low-Carbon Scheduling Strategy Considering Vehicle-to-Grid Aggregators

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JOURNAL OF MODERN POWER SYSTEMS AND CLEAN ENERGY 2024年第2期12卷 440-453页

作者： Xu, Xiao Qiu, Ziwen Zhang, Teng Gao, Hui Nanjing Univ Posts & Telecommun Sch Automat Nanjing 210023 Peoples R China

The vehicle-to-grid (V2G) technology enables the bidirectional power flow between electric vehicle (EV) batteries and the power grid, making EV-based mobile energy storage an appealing supplement to stationary energy storage systems. However, the stochastic and volatile charging behaviors pose a challenge for EV fleets to engage directly in multi-agent cooperation. To unlock the Scheduling potential of EVs, this paper proposes a source-load-storage cooperative low-carbon Scheduling strategy considering V2G aggregators. The uncertainty of EV charging patterns is managed through a rolling-horizon control framework, where the Scheduling and control horizons are adaptively adjusted according to the availability periods of EVs. Moreover, a Minkowski-sum based aggregation method is employed to evaluate the Scheduling potential of aggregated EV fleets within a given Scheduling horizon. This method effectively reduces the variable dimension while preserving the charging and discharging constraints of individual EVs. Subsequently, a Nash bargaining based cooperative Scheduling model involving a distribution system operator (DSO), an EV aggregator (EVA), and a load aggregator (LA) is established to maximize the social welfare and improve the low-carbon performance of the system. This model is solved by the alternating direction method of multipliers (ADMM) algorithm in a distributed manner, with privacy of participants fully preserved. The proposed strategy is proven to achieve the objective of low-carbon economic operation.

关键词： Scheduling Uncertainty Electric vehicle charging Computational modeling Vehicle-to-grid Carbon Behavioral sciences Electric vehicle (EV) low-carbon Scheduling mobile storage system Nash bargaining power flexibility alternating direction method of multipliers (ADMM) ENERGY SYSTEM

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Scheduling a Single-Arm Multi-Cluster Tool With a Condition-Based Cleaning Operation

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自动化学报:英文版 2023年第10期10卷 1965-1983页

作者： Qinghua Zhu Hongpeng Li Cong Wang Yan Hou School of Computer Science and Technology Guangdong University of TechnologyGuangzhou 510006China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102-1982 USA

As wafer circuit widths shrink less than 10 nm,stringent quality control is imposed on the wafer fabrication processes. Therefore, wafer residency time constraints and chamber cleaning operations are widely required in chemical vapor deposition, coating processes, etc. They increase Scheduling complexity in cluster tools. In this paper, we focus on Scheduling single-arm multi-cluster tools with chamber cleaning operations subject to wafer residency time constraints. When a chamber is being cleaned, it can be viewed as processing a virtual wafer. In this way, chamber cleaning operations can be performed while wafer residency time constraints for real wafers are not violated. Based on such a method, we present the necessary and sufficient conditions to analytically check whether a single-arm multi-cluster tool can be scheduled with a chamber cleaning operation and wafer residency time constraints. An algorithm is proposed to adjust the cycle time for a cleaning operation that lasts a long cleaning time.Meanwhile, algorithms for a feasible schedule are also derived.And an algorithm is presented for operating a multi-cluster tool back to a steady state after the cleaning. Illustrative examples are given to show the application and effectiveness of the proposed method.

关键词： Chamber cleaning multi-cluster tools Scheduling semiconductor manufacturing

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Optimal Scheduling of Residential Heating, Ventilation and Air Conditioning Based on Deep Reinforcement Learning

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Journal of Modern Power Systems and Clean Energy 2023年第5期11卷 1596-1605页

作者： Mingchao Xia Fangjian Chen Qifang Chen Siwei Liu Yuguang Song Te Wang School of Electrical Engineering Beijing Jiaotong UniversityBeijingChina State Grid State Power Economic Research Institute BeijingChina

Residential heating, ventilation and air conditioning(HVAC) provides important demand response resources for the new power system with high proportion of renewable energy. Residential HAVC Scheduling strategies that adapt to realtime electricity price signals formulated by demand response program and ambient temperature can significantly reduce electricity costs while ensuring occupants' comfort. However, since the pricing process and weather conditions are affected by many factors, conventional model-based method is difficult to meet the Scheduling requirements in complex environments. To solve this problem, we propose an adaptive optimal Scheduling strategy for residential HVAC based on deep reinforcement learning(DRL) method. The Scheduling problem can be regarded as a Markov decision process(MDP). The proposed method can adaptively learn the state transition probability to make economical decision under the tolerance violations. Specifically, the residential thermal parameters obtained by the leastsquares parameter estimation(LSPE) can provide a basis for the state transition probability of MDP. Daily simulations are verified under the electricity prices and temperature data sets, and numerous experimental results demonstrate the effectiveness of the proposed method.

关键词： Residential heating ventilation and air conditioning(HVAC) Scheduling deep reinforcement learning leastsquares parameter estimation(LSPE)

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Optimization of fixed aviation support resource station configuration for aircraft carrier based on aircraft dispatch mission Scheduling

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Chinese Journal of Aeronautics 2023年第2期36卷 127-138页

作者： Yujie LIU Wei HAN Xichao SU Rongwei CUI Aeronautical Foundation College Naval Aviation UniversityYantai 264001China Aeronautical Operations College Naval Aviation UniversityYantai 264001China

Before the dispatch of the carrier-based aircraft,a series of pre-flight preparation operations need to be completed on the flight deck.Flight deck fixed aviation support resource station configuration has an important impact on operation efficiency and sortie rate.However,the resource station configuration is determined during the aircraft carrier design phase and is rarely modified as required,which may not be suitable for some pre-flight preparation missions.In order to solve the above defects,the joint optimization of flight deck resource station configuration and aircraft carrier pre-flight preparation Scheduling is studied in this paper,which is formulated as a two-tier optimization decision-making framework.An improved variable neighborhood search algorithm with four original neighborhood structures is presented.Dispatch mission experiment and algorithm performance comparison experiment are carried out in the computational experiment section.The correlation between the pre-flight preparation time(makespan)and flight deck cabin occupancy percentage is given,and advantages of the proposed algorithm in solving the mathematical model are verified.

关键词： Carrier-based aircraft Multi-objective optimization Resource configuration Scheduling Variable neighborhood search

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Necessary and Sufficient Conditions for Feasible Neighbourhood Solutions in the Local Search of the Job-Shop Scheduling Problem

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Chinese Journal of Mechanical Engineering 2023年第4期36卷 139-154页

作者： Lin Gui Xinyu Li Liang Gao Cuiyu Wang State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and TechnologyWuhan 430074China

The meta-heuristic algorithm with local search is an excellent choice for the job-shop Scheduling problem(JSP).However,due to the unique nature of the JSP,local search may generate infeasible neighbourhood solutions.In the existing literature,although some domain knowledge of the JSP can be used to avoid infeasible solutions,the constraint conditions in this domain knowledge are sufficient but not necessary.It may lose many feasible solutions and make the local search inadequate.By analysing the causes of infeasible neighbourhood solutions,this paper further explores the domain knowledge contained in the JSP and proposes the sufficient and necessary constraint conditions to find all feasible neighbourhood solutions,allowing the local search to be carried out thoroughly.With the proposed conditions,a new neighbourhood structure is designed in this paper.Then,a fast calculation method for all feasible neighbourhood solutions is provided,significantly reducing the calculation time compared with ordinary methods.A set of standard benchmark instances is used to evaluate the performance of the proposed neighbourhood structure and calculation method.The experimental results show that the calculation method is effective,and the new neighbourhood structure has more reliability and superiority than the other famous and influential neighbourhood structures,where 90%of the results are the best compared with three other well-known neighbourhood structures.Finally,the result from a tabu search algorithm with the new neighbourhood structure is compared with the current best results,demonstrating the superiority of the proposed neighbourhood structure.

关键词： Scheduling Job-shop Scheduling Local search Neighbourhood structure Domain knowledge

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Scheduling Dual-Arm Multi-Cluster Tools With Regulation of Post-Processing Time

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IEEE/CAA Journal of Automatica Sinica 2023年第8期10卷 1730-1742页

作者： Qinghua Zhu Bin Li Yan Hou Hongpeng Li Naiqi Wu IEEE the School of Computer Science and Technology Guangdong University of TechnologyGuangzhou 510006China the Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and Systems Macao University of Science and TechnologyMacao 999078China

As wafer circuit width shrinks down to less than ten nanometers in recent years,stringent quality control in the wafer manufacturing process is increasingly important.Thanks to the coupling of neighboring cluster tools and coordination of multiple robots in a multi-cluster tool,wafer production Scheduling becomes rather complicated.After a wafer is processed,due to high-temperature chemical reactions in a chamber,the robot should be controlled to take it out of the processing chamber at the right time.In order to ensure the uniformity of integrated circuits on wafers,it is highly desirable to make the differences in wafer post-processing time among the individual tools in a multicluster tool as small as possible.To achieve this goal,for the first time,this work aims to find an optimal schedule for a dual-arm multi-cluster tool to regulate the wafer post-processing time.To do so,we propose polynomial-time algorithms to find an optimal schedule,which can achieve the highest throughput,and minimize the total post-processing time of the processing steps.We propose a linear program model and another algorithm to balance the differences in the post-processing time between any pair of adjacent cluster tools.Two industrial examples are given to illustrate the application and effectiveness of the proposed method.

关键词： Cluster tool optimization Scheduling

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Enterprise-wide optimization of integrated planning and Scheduling for refinery-petrochemical complex with heuristic algorithm

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Frontiers of Chemical Science and Engineering 2023年第10期17卷 1516-1532页

作者： Lifeng Zhang Haoyang Hu Zhiquan Wang Zhihong Yuan Bingzhen Chen State Key Laboratory of Chemical Engineering Department of Chemical EngineeringTsinghua UniversityBeijing 100084China Department of Chemical Engineering Tsinghua UniversityBeijing 100084China

This paper focuses on the integrated problem of long-term planning and short-term Scheduling in a largescale refinery-petrochemical complex,and considers the overall manufacturing process from the upstream refinery to the downstream petrochemical site.Different time scales are incorporated from the planning and Scheduling subproblems.At the end of each discrete time period,additional constraints are imposed to ensure material balance between different time scales.Discrete time representation is applied to the planning subproblem,while continuous time is applied to the Scheduling of ethylene cracking and polymerization processes in the petrochemical site.An enterprise-wide mathematical model is formulated through mixed integer nonlinear programming.To solve the problem efficiently,a heuristic algorithm combined with a convolutional neural network(CNN),is proposed.Binary variables are used as the CNN input,leading to the integration of a data-driven approach and classical optimization by which a heuristic algorithm is established.The results do not only illustrate the detailed operations in a refinery and petrochemical complex under planning and Scheduling,but also confirm the high efficiency of the proposed algorithm for solving large-scale problems.

关键词： planning Scheduling refinery-petrochemical convolutional neural network heuristic algorithm

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Seru Scheduling Problems with Multiple Due-Windows Assignment and Learning Effect

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Journal of Systems Science and Systems Engineering 2022年第4期31卷 480-511页

作者： Yujing Jiang Zhe Zhang Xiaoling Song Yong Yin School of Economics and Management Nanjing University of Science and TechnologyNanjing210094P.R.China Graduate School of Business Doshisha UniversityKarasuma-ImadegawaKamigyo-kuKyoto602-8580Japan

This paper deals with seru Scheduling problems with multiple due windows assignment and DeJong’s learning effect.Specific time intervals are assigned to jobs with multiple due windows and learning effect is introduced to characterize the decrease of processing times with the accumulation of the working experience.We assume that the set of jobs assigned to each due window is independent,and no inclusion exists between due windows.The objective is to determine the optimal due window positions and sizes,the set of jobs assigned to each due window,and the optimal schedule in each seru to minimize a multidimensional function,which consists of the earliness and tardiness punishment cost,as well as the due window related starting time and size cost.We find that when the number of jobs and the due windows assigned to each seru are pre-specified in advance,the problem can be solved in polynomial time.Meanwhile,the impacts of the due-window allocation strategy and learning effect on the total cost are respectively discussed based on numerical examples and special cases.The results show that if each seru is assigned with the same number of due windows,the total cost can be reduced with the increasing ratio of the due-window number to the to-be-processed job number.Furthermore,with an increasing learning effect,the total cost will be decreased.

关键词： Scheduling seru production system due windows learning effect

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