检索结果-内蒙古大学图书馆

DATA-DRIVEN scheduling FOR THE PHOTOLITHOGRAPHY PROCESS IN SEMICONDUCTOR MANUFACTURING

JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION 2025年第3期21卷 1946-1963页

作者： Huang, Cheng-Ting Hsieh, Tsung-Jung Lin, Bertrand M. T. Natl Yang Ming Chiao Tung Univ Inst Informat Management Hsinchu 300 Taiwan Synology Inc Taipei 220 Taiwan Natl Formosa Univ Ctr Gen Educ Huwei 632 Yunlin Taiwan Natl Yang Ming Chiao Tung Univ Inst Hosp & Hlth Care Adm Hsinchu 300 Taiwan

scheduling in semiconductor wafer manufacturing is sophisticated due to the diversity of client orders, the allocation of limited resources, and the bottleneck capacity of photolithography process. Under the manufacturing context of a Taiwan semiconductor company, this study aims to optimize three objective functions, including throughput, mean flow time, and mean weighted tardiness, simultaneously subject to the presence of sequence-dependent setup times and common deadline constraints. To solve the problem, a data-driven multi-objective optimization-based composite dispatching rule (CDR) is proposed. Further, a data envelopment analysis (DEA) accompanied by NSGA-II (abbreviated as NSGA-II-DEA) is designed for exploring near-optimal and efficient solutions. While multi-objective optimization faces a large number of Pareto frontier solutions, we deploy a DEA model to perform relative importance optimization by screening out less competitive frontier solutions. Therefore, this study employs the proposed CDR with the NSGA-II-DEA to determine the relative importance indicators of scheduled jobs. Experiments on test instances show that the proposed composite rule attains better overall performances than the elementary rules based on the global criteria approach (GCA) and rank frequency of the three objectives.

关键词： Semiconductor wafer fabs multi-objective scheduling composite dispatching rules NSGA-II data envelopment analysis

来源：评论

学校读者我要写书评

暂无评论

Integrated harvest and distribution scheduling of fresh agricultural products for multiple farms using a Q-learning-based artificial bee colony algorithm with problem knowledge

引用

SWARM AND EVOLUTIONARY COMPUTATION 2025年 95卷

作者： Ma, Xiaomeng Pu, Xujin Fu, Yaping Wang, Yuan Jiangnan Univ Sch Business Wuxi 214122 Peoples R China Qingdao Univ Sch Business Qingdao 266071 Peoples R China Singapore Univ Social Sci Sch Business Singapore 599494 Singapore

Nowadays, a great many of farmers sell fresh agricultural products through direct online sales. In this context, the farm-to-door supply mode has emerged, playing a crucial role in reducing transportation cost and quality deterioration. This work addresses an integrated harvest and farm-to-door distribution scheduling problem involving multiple farms. First, a mixed integer programming model is formulated to minimize total operation cost and maximize customer satisfaction regarding product quality. Second, a Q-learning-based artificial bee colony algorithm with problem knowledge (Q-ABC-K) is developed in particular. The algorithm is featured with the following strategies: (i) a hybrid initialization method with two rules to generate a high-quality population;(ii) a crossover operation to prompt a collaborative search between the population and external archive at the employed bee phase;(iii) a Q-learning method to favorably select premium neighborhood structures at the onlooker bee phase;and (iv) a knowledge-based local search method to refine the nondominated solutions. Finally, a large number of comparison experiments are conducted on a set of test instances. Through observing and analyzing the experimental results, three conclusions are acquired as follows: (i) The design of Q-learning and knowledge-based local search methods plays a significant role in enhancing the performance of Q-ABC-K;(ii) Q-ABC-K performs better than four state-of-the-art approaches in dealing with the considered problem;and (iii) Q-ABC-K has an advantage over an exact solver CPLEX in solving small-scale cases.

关键词： Integrated harvest and distribution multi-objective scheduling Artificial bee colony Q-learning Fresh agricultural product

来源：评论

学校读者我要写书评

暂无评论

A multi-attribute decision-making method of cascade reservoir joint scheduling schemes considering water-sediment, eco-environment, and socio-economy in the Yellow River Basin

引用

JOURNAL OF HYDROLOGY 2025年 660卷

作者： Li, Donglin Li, Jieyu Wang, Yuanjian Jiang, Enhui Zhao, Wanjie Liu, Gang Yang, Zhifeng Yellow River Water Conservancy Commiss Yellow River Inst Hydraul Res Zhengzhou 450003 Peoples R China Yellow River Lab Zhengzhou 450003 Peoples R China Minist Water Resources Key Lab Lower Yellow River Channel & Estuary Regul Zhengzhou 450003 Peoples R China Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing 210098 Peoples R China Zhengzhou Univ Sch Water Conservancy & Transportat Zhengzhou 450001 Peoples R China Guangdong Univ Technol Sch Ecol Environm & Resources Guangzhou 510006 Peoples R China Guangdong Univ Technol Sch Ecol Key Lab City Cluster Environm Safety & Green Dev Minist Educ Guangzhou 510006 Peoples R China

multi-attribute decision making for reservoir scheduling plays a crucial role in promoting the sustainable development of basins, requiring a well-structured evaluation index system and an efficient decision-making method to facilitate scientific scheduling. After obtaining Pareto solutions for the multi-objective scheduling model of cascade reservoirs, a multidimensional evaluation index system encompassing the water-sediment, ecoenvironment, and socio-economy from a basin system perspective was constructed. A multi-attribute decisionmaking method based on the modified cumulative prospect theory (MCPT) was then proposed, enabling the evaluation of both single and multiple reservoirs while also supporting scheduling program decisions for various scenarios. The MCPT was applied to the joint scheduling decision making of the Sanmenxia-Xiaolangdi cascade reservoir for multiple typical years in the Yellow River Basin. The results indicated a strong competition between sediment discharge and power generation targets, whereas ecological and power generation targets exhibited a weaker competition. Furthermore, the range and distribution characteristics of the modified whole prospect value (MWPV) varied among the three typical years with sediment discharge, power generation, and ecological water scarcity rate contributing the most significantly to MWPV. The optimal scheduling schemes for abundant, normal, and dry water years were identified as S136, S98, and S345, respectively, all aligned with actual scheduling practices. Compared with conventional decision-making methods, MCPT demonstrated superior sensitivity and suitability for scenarios that require the selection of numerous schemes. These findings offer valuable insights into the coordinated development of water-sediment, eco-environment, and socio-economy within the watershed.

关键词： multi-objective scheduling multi-attribute decision-making methods Evaluation index system Modified cumulative prospect theory Yellow River Basin

来源：评论

学校读者我要写书评

暂无评论

Application of new multi-objective optimization algorithm for EV scheduling in smart grid through the uncertainties

引用

JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2020年第5期11卷 2071-2103页

作者： Yin, WanJun Mavaluru, Dinesh Ahmed, Munir Abbas, Mazhar Darvishan, Aida Xidian Univ Sch Elect Sr Mech Engn Xian 710071 Peoples R China Sichuan Vocat Coll Informat Technol Guangyuan 628040 Sichuan Peoples R China Saudi Elect Univ Coll Comp & Informat Riyadh Saudi Arabia COMSATS Univ Islamabad Dept Management Sci Vehari Campus Islamabad Pakistan Univ Houston Dept Ind Engn Houston TX 77204 USA

Ecological and economics issues are caused to give careful consideration to electric vehicles (EV) and sustainable power source assets. One of the proposed answers for increment the impact of these assets, is to utilize the electric vehicles potential. The capability of electric vehicles require planning for Smart Distribution Systems (SDS). Request reaction programs, as a suitable device to utilize endorsers' potential in ideal administration of the system, gives dynamic nearness of supporters in control framework execution change and these projects, in basic conditions, can give the request prerequisites diminishment, in a brief timeframe. In this work, attempts to presents a multi-objective scheduling of EV based on the sustainable assets in smart grid, cover uncertainty caused by inexhaustible assets and EVs, by considering of the request reaction projects and EV battery stockpiling framework, limit the working expenses and the measure of intensity framework contamination, with enhancing procedures. Improved optimization algorithm is utilized for taking care of the advancing issue. Operating costs dropped much further utilizing monetary model of the demand response and vehicle charge/discharge and smart program in the hours when the load is lower. Effectiveness of proposed method is applied on 33 bus standard power system.

关键词： multi-objective scheduling EV Renewables source Demand response Optimization

来源：评论

学校读者我要写书评

暂无评论

multi-objective genetic-based algorithms for a cross-docking scheduling problem

引用

APPLIED SOFT COMPUTING 2011年第8期11卷 4954-4970页

作者： Arabani, A. Boloori Zandieh, M. Ghomi, S. M. T. Fatemi Shahid Beheshti Univ Management & Accounting Fac Dept Ind Management GC Tehran Iran Wayne State Univ Dept Ind & Syst Engn Detroit MI 48202 USA Amirkabir Univ Technol Dept Ind Engn Tehran Iran

The importance and applicability of cross-docking systems have grown rapidly in recent years. As these systems play a key role, particularly, in distribution networks, the launch of multi-objective approaches can contribute to solve the real-world cases and problems of such systems, in which many different and even conflicting objectives are considered. Hence, this paper addresses three famous multi-objective algorithms including non-dominated sorting genetic algorithm-II (NSGA-II), strength Pareto evolutionary algorithm-II (SPEA-II), and sub-population genetic algorithm-II (SPGA-II) to solve the cross-docking scheduling problem, in which product items are unloaded from inbound trailers in the receiving dock and then are categorized and loaded onto outbound trailers in the shipping dock. Since the time aspect of such activities is so determining and crucial, objective functions are considered as the total operational time (makespan) and the total lateness of all outbound trailers. Furthermore, In order to appraise the performance of these algorithms, four criteria are proposed and compared with each other to demonstrate the strengths of each applied algorithm. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Cross-docking multi-objective scheduling Makespan Lateness Inbound trailers Outbound trailers

来源：评论

学校读者我要写书评

暂无评论

A cross-docking scheduling problem with sub-population multi-objective algorithms

引用

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2012年第5-8期58卷 741-761页

作者： Arabani, A. Boloori Zandieh, M. Ghomi, S. M. T. Fatemi Shahid Beheshti Univ Dept Ind Management Management & Accounting Fac GC Tehran Iran Wayne State Univ Dept Ind & Syst Engn Detroit MI 48202 USA Amirkabir Univ Technol Dept Ind Engn Tehran Iran

This paper deals with a scheduling problem of inbound and outbound trucks shipping incoming and outgoing product items into/out of a cross-docking system. We consider an instance of cross-docking systems in which more than one objective are taken into account: minimization of the total operation time (makespan) and minimization of the total lateness of outbound trucks. In order to deal with this problem, three multi-objective algorithms are developed as follows (based on the sub-population concept of evolutionary algorithms): sub-population genetic algorithm-II (SPGA-II), sub-population particle swarm optimization-II (SPPSO-II), and sub-population differential evolution algorithm-II (SPDE-II). In addition, to evaluate the performance of these algorithms, four measures are presented and compared with each other whose results will demonstrate that the SPPSO-II has better characteristics in comparison with other two algorithms.

关键词： Cross-docking multi-objective scheduling Makespan Lateness

来源：评论

学校读者我要写书评

暂无评论

Hybrid multi-objective opposite-learning evolutionary algorithm for integrated production and maintenance scheduling with energy consideration

引用

NEURAL COMPUTING & APPLICATIONS 2021年第5期33卷 1587-1605页

作者： Zhou, Binghai Li, Xiujuan Liu, Wenlong Tongji Univ Sch Mech Engn Shanghai 201804 Peoples R China

While conventional scheduling researches take production efficiency, cost and quality as objectives, increasingly serious ecological problems and energy shortage have turned scholars' attention to energy-efficient scheduling. Meanwhile, maintenance activities are of great importance to equipment availability and production continuity. This paper addresses an energy-efficient multi-objective scheduling problem of a serial production line (SPSP) integrating production and maintenance, with the criteria of minimizing the makespan and the total energy consumption simultaneously. The impact of integrating preventive maintenance (PM) is analyzed, and the better interval mode is picked out. An energy-saving strategy combining shutdown windows of PM and energy-saving windows on idle machines is designed to cut down the total energy consumption. In order to tackle this NP-hard problem, a hybrid multi-objective opposite-based learning evolutionary algorithm (HMOLEA) is developed, in which the opposite-based learning and IGD indicator-based evolutionary mechanism are combined together and a special rank assignment is designed to improve the computational efficiency. Furthermore, a self-adaptive weighted mutation operator is fused into the framework of HMOLEA to enhance the exploration of the algorithm. Extensive computational experiments are carried out to verify the effectiveness and superiority of HMOLEA in solving the SPSP.

关键词： Energy efficient Serial production line multi-objective scheduling Preventive maintenance Production machines

来源：评论

学校读者我要写书评

暂无评论

multi-objective Patient Appointment scheduling Framework (MO-PASS): a data-table input simulation-optimization approach

引用

SIMULATION-TRANSACTIONS OF THE SOCIETY FOR MODELING AND SIMULATION INTERNATIONAL 2023年第4期99卷 363-383页

作者： Dehghanimohammadabadi, Mohammad Rezaeiahari, Mandana Seif, Javad Northeastern Univ Dept Mech & Ind Engn Boston MA USA Univ Arkansas Med Sci Fay W Boozman Coll Publ Hlth Little Rock AR USA Calif State Polytech Univ Pomona Dept Ind & Mfg Engn Pomona CA USA Northeastern Univ Dept Mech & Ind Engn 360 Huntington Ave Boston MA 02115 USA

Appointment scheduling is one of the critical factors for improving patient satisfaction with healthcare services. A practical and robust appointment scheduling solution allows clinics to efficiently utilize medical devices, equipment, and other resources. This study introduces a multi-objective Patient Appointment scheduling (MO-PASS) framework to enhance clinic operations and quality of care. The proposed framework integrates three modules: (1) Optimization (using MATLAB), (2) Data-Exchange (MS Excel), and (3) Simulation (Simio). To implement MO-PASS, the multi-objective Particle Swarm Optimization (MOPSO) algorithm is coded in MATLAB, and a Simio API is developed, which exchanges simulated scenarios with MOPSO via Excel. The efficiency of the proposed framework is evaluated in a breast cancer clinic with multiple physicians and patient types. Two objective functions are defined for evaluating the solutions of the AS problem: (1) minimizing the total service time and (2) maximizing the number of (admitted) patients with zero overtime. Finally, the performance of MO-PASS is tested against three heuristic approaches with respect to objective functions. The computational experiment results show that the proposed MO-PASS outperforms the existing heuristic benchmarks. Also, the framework is accompanied by all the necessary details to make it practical and easy to implement.

关键词： multi-appointment scheduling template scheduling multi-objective scheduling simulation-based optimization simheuristics multi-objective Particle Swarm Optimization (MOPSO)

来源：评论

学校读者我要写书评

暂无评论

multi-objective parallel machine scheduling problems by considering controllable processing times

引用

JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2016年第4期67卷 654-663页

作者： Liu, Cheng-Hsiang Tsai, Wan-Ni Natl Pingtung Univ Sci & Technol Neipu Taiwan

This study examines parallel machine scheduling problems with controllable processing times. The processing time of each job can be between lower and upper bounds, and a cost is associated with the processing of a job on a machine. The processing time of a job can be decreased, which may lower the cycle time, although doing so would incur additional costs. This study develops two multi-objective mathematical models, which consist of two and three inconsistent objective functions, respectively. The first model minimizes the total manufacturing cost (TMC) and the total weighted tardiness (TWT) simultaneously, while the second uses makespan (C-max) as an additional objective function. In contrast to conventional mathematical models, efficient solutions are attained using the lexicographic weighted Tchebycheff method (LWT). Experimental results indicate that the LWT yields better-spread solutions and obtains more non-dominated solutions than its alternative, that is the weighted-sum method, which is a widely used yet promising approach to achieve multi-objective optimization. Results of this study also demonstrate that in purchasing machines, the variation in the fixed costs associated with the processing of jobs on machines is critical to reducing TWT. Moreover, using Cmax as an additional objective function typically improves TWT and worsens TMC.

关键词： multi-objective scheduling controllable processing times lexicographic weighted Tchebycheff (LWT) pareto optimization

来源：评论

学校读者我要写书评

暂无评论

multi-objective home appliance scheduling with implicit and interactive user satisfaction modelling

引用

APPLIED ENERGY 2020年 267卷

作者： Pamulapati, Trinadh Mallipeddi, Rammohan Lee, Minho Kyungpook Natl Univ Sch Elect Engn Daegu 41566 South Korea

Residential consumers desire to minimize electricity bills while maximizing comfort by appropriate appliance scheduling. The conflicting nature of the objectives facilitates a multi-objective formulation that can provide a set of tradeoff schedules enabling better decision making. In literature, user preference or comfort regarding each device at each time instance is obtained explicitly. In addition, scheduling interval of 1-hour is considered because reducing scheduling interval to 1 or 5 min drastically increases - (1) the dimensionality of search space and complicates the search process, and (2) the number of time instances for which the user has to explicitly provide the preference resulting in human fatigue. However, it is essential to schedule the devices at lower scheduling intervals to precisely-estimate the electricity consumption due to the presence of high power devices such as microwave that operate for shorter intervals (< 5 min). In this work, we employ an efficient and scalable multi-objective evolutionary algorithm to solve the scheduling problem. In addition, the user preference is implicitly estimated from the past usage patterns obtained using energy disaggregation. And, if the estimated user preference deviates from the user expectation then the user can modify preference using weights referred to as priority weights. The novel implicit user satisfaction modeling and interactive customization through priority weights makes the proposed work a standalone approach suitable for any user. Experimental results and analysis for various user priorities and scheduling intervals (ranging from 1-minute to 1-hour) proves that the proposed framework is able to provide generalized schedules.

关键词： multi-objective scheduling Home appliance scheduling Electricity cost minimization Implicit user satisfaction estimation Interactive scheduling Energy disaggregation

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：