A self-adaptive large neighborhood search method for scheduling n jobs on m non-identical parallel machines with mul- tiple time windows is presented. The problems＇ another feature lies in oversubscription, namely no...A self-adaptive large neighborhood search method for scheduling n jobs on m non-identical parallel machines with mul- tiple time windows is presented. The problems＇ another feature lies in oversubscription, namely not all jobs can be scheduled within specified scheduling horizons due to the limited machine capacity. The objective is thus to maximize the overall profits of processed jobs while respecting machine constraints. A first-in- first-out heuristic is applied to find an initial solution, and then a large neighborhood search procedure is employed to relax and re- optimize cumbersome solutions. A machine learning mechanism is also introduced to converge on the most efficient neighborhoods for the problem. Extensive computational results are presented based on data from an application involving the daily observation scheduling of a fleet of earth observing satellites. The method rapidly solves most problem instances to optimal or near optimal and shows a robust performance in sensitive analysis.展开更多
This paper considers the uniform parallel machine scheduling problem with unequal release dates and delivery times to minimize the maximum completion time.For this NP-hard problem,the largest sum of release date,proce...This paper considers the uniform parallel machine scheduling problem with unequal release dates and delivery times to minimize the maximum completion time.For this NP-hard problem,the largest sum of release date,processing time and delivery time first rule is designed to determine a certain machine for each job,and the largest difference between delivery time and release date first rule is designed to sequence the jobs scheduled on the same machine,and then a novel algorithm for the scheduling problem is built.To evaluate the performance of the proposed algorithm,a lower bound for the problem is proposed.The accuracy of the proposed algorithm is tested based on the data with problem size varying from 200 jobs to 600 jobs.The computational results indicate that the average relative error between the proposed algorithm and the lower bound is only 0.667%,therefore the solutions obtained by the proposed algorithm are very accurate.展开更多
A class of nonidentical parallel machine scheduling problems are considered in which the goal is to minimize the total weighted completion time. Models and relaxations are collected. Most of these problems are NP-hard...A class of nonidentical parallel machine scheduling problems are considered in which the goal is to minimize the total weighted completion time. Models and relaxations are collected. Most of these problems are NP-hard, in the strong sense, or open problems, therefore approximation algorithms are studied. The review reveals that there exist some potential areas worthy of further research.展开更多
The hybrid flow shop scheduling problem with unrelated parallel machine is a typical NP-hard combinatorial optimization problem, and it exists widely in chemical, manufacturing and pharmaceutical industry. In this wor...The hybrid flow shop scheduling problem with unrelated parallel machine is a typical NP-hard combinatorial optimization problem, and it exists widely in chemical, manufacturing and pharmaceutical industry. In this work, a novel mathematic model for the hybrid flow shop scheduling problem with unrelated parallel machine(HFSPUPM) was proposed. Additionally, an effective hybrid estimation of distribution algorithm was proposed to solve the HFSPUPM, taking advantage of the features in the mathematic model. In the optimization algorithm, a new individual representation method was adopted. The(EDA) structure was used for global search while the teaching learning based optimization(TLBO) strategy was used for local search. Based on the structure of the HFSPUPM, this work presents a series of discrete operations. Simulation results show the effectiveness of the proposed hybrid algorithm compared with other algorithms.展开更多
Due to the limited transmission resources for data relay in the tracking and data relay satellite system （TDRSS）, there are many job requirements in busy days which will be discarded in the conventional job scheduli...Due to the limited transmission resources for data relay in the tracking and data relay satellite system （TDRSS）, there are many job requirements in busy days which will be discarded in the conventional job scheduling model. Therefore, the improvement of scheduling efficiency in the TDRSS can not only help to increase the resource utilities, but also to reduce the scheduling failure ratio. A model of nonhomogeneous parallel machines scheduling problems with time window （NPM-TW） is firstly built up for the TDRSS, considering the distinct features of the variable preparation time and the nonhomogeneous transmission rates for different types of antennas on each tracking and data relay satellite （TDRS）. Then, an adaptive subsequence adjustment （ASA） framework with evolutionary asymmetric path-relinking （EvAPR） is proposed to solve this problem, in which an asymmetric progressive crossover operation is involved to overcome the local optima by the conventional job inserting methods. The numerical results show that, compared with the classical greedy randomized adaptive search procedure （GRASP） algorithm, the scheduling failure ratio of jobs can be reduced over 11% on average by the proposed ASA with EvAPR.展开更多
鉴于实际生产中工人的能力和加工效率存在差异,本文提出了考虑工人差异性和刀具限制的多资源约束并行机分批调度问题,以最小化最大完工时间、最小化交付时间偏差为优化目标,构建了工人熟练度、刀具资源约束的混合整数线性规划模型,研究...鉴于实际生产中工人的能力和加工效率存在差异,本文提出了考虑工人差异性和刀具限制的多资源约束并行机分批调度问题,以最小化最大完工时间、最小化交付时间偏差为优化目标,构建了工人熟练度、刀具资源约束的混合整数线性规划模型,研究子批数量和大小、机器分配、工人分配、子批加工顺序之间的耦合关系,设计了一种改进的多目标混合灰狼-鲸鱼群算法(manyobjective hybrid grey wolf optimizer and whale swarm algorithm,MO-HGWSA).根据模型特点,设计了一种两阶段编码和解码方案表示问题的可行解;利用多种引导策略提高算法的进化效率;设计了局部搜索策略,增强算法的局部搜索能力.最后引入案例验证算法的有效性,结果表明本文所提算法在收敛性、分布性和解集支配关系方面均优于对比算法.展开更多
基金supported by the National Natural Science Foundation of China (7060103570801062)
文摘A self-adaptive large neighborhood search method for scheduling n jobs on m non-identical parallel machines with mul- tiple time windows is presented. The problems＇ another feature lies in oversubscription, namely not all jobs can be scheduled within specified scheduling horizons due to the limited machine capacity. The objective is thus to maximize the overall profits of processed jobs while respecting machine constraints. A first-in- first-out heuristic is applied to find an initial solution, and then a large neighborhood search procedure is employed to relax and re- optimize cumbersome solutions. A machine learning mechanism is also introduced to converge on the most efficient neighborhoods for the problem. Extensive computational results are presented based on data from an application involving the daily observation scheduling of a fleet of earth observing satellites. The method rapidly solves most problem instances to optimal or near optimal and shows a robust performance in sensitive analysis.
基金supported by the National Natural Science Foundation of China (7087103290924021+2 种基金70971035)the National High Technology Research and Development Program of China (863 Program) (2008AA042901)Anhui Provincial Natural Science Foundation (11040606Q27)
文摘This paper considers the uniform parallel machine scheduling problem with unequal release dates and delivery times to minimize the maximum completion time.For this NP-hard problem,the largest sum of release date,processing time and delivery time first rule is designed to determine a certain machine for each job,and the largest difference between delivery time and release date first rule is designed to sequence the jobs scheduled on the same machine,and then a novel algorithm for the scheduling problem is built.To evaluate the performance of the proposed algorithm,a lower bound for the problem is proposed.The accuracy of the proposed algorithm is tested based on the data with problem size varying from 200 jobs to 600 jobs.The computational results indicate that the average relative error between the proposed algorithm and the lower bound is only 0.667%,therefore the solutions obtained by the proposed algorithm are very accurate.
基金the National Natural Science Foundation of China (70631003)the Hefei University of Technology Foundation (071102F).
文摘A class of nonidentical parallel machine scheduling problems are considered in which the goal is to minimize the total weighted completion time. Models and relaxations are collected. Most of these problems are NP-hard, in the strong sense, or open problems, therefore approximation algorithms are studied. The review reveals that there exist some potential areas worthy of further research.
基金Projects(61573144,61773165,61673175,61174040)supported by the National Natural Science Foundation of ChinaProject(222201717006)supported by the Fundamental Research Funds for the Central Universities,China
文摘The hybrid flow shop scheduling problem with unrelated parallel machine is a typical NP-hard combinatorial optimization problem, and it exists widely in chemical, manufacturing and pharmaceutical industry. In this work, a novel mathematic model for the hybrid flow shop scheduling problem with unrelated parallel machine(HFSPUPM) was proposed. Additionally, an effective hybrid estimation of distribution algorithm was proposed to solve the HFSPUPM, taking advantage of the features in the mathematic model. In the optimization algorithm, a new individual representation method was adopted. The(EDA) structure was used for global search while the teaching learning based optimization(TLBO) strategy was used for local search. Based on the structure of the HFSPUPM, this work presents a series of discrete operations. Simulation results show the effectiveness of the proposed hybrid algorithm compared with other algorithms.
基金supported by the National Natural Science Foundation of China(6113200291338101+3 种基金91338108)the National S&T Major Project(2011ZX03004-001-01)the Research Fund of Tsinghua University(2011Z05117)the Co-innovation Laboratory of Aerospace Broadband Network Technology
文摘Due to the limited transmission resources for data relay in the tracking and data relay satellite system （TDRSS）, there are many job requirements in busy days which will be discarded in the conventional job scheduling model. Therefore, the improvement of scheduling efficiency in the TDRSS can not only help to increase the resource utilities, but also to reduce the scheduling failure ratio. A model of nonhomogeneous parallel machines scheduling problems with time window （NPM-TW） is firstly built up for the TDRSS, considering the distinct features of the variable preparation time and the nonhomogeneous transmission rates for different types of antennas on each tracking and data relay satellite （TDRS）. Then, an adaptive subsequence adjustment （ASA） framework with evolutionary asymmetric path-relinking （EvAPR） is proposed to solve this problem, in which an asymmetric progressive crossover operation is involved to overcome the local optima by the conventional job inserting methods. The numerical results show that, compared with the classical greedy randomized adaptive search procedure （GRASP） algorithm, the scheduling failure ratio of jobs can be reduced over 11% on average by the proposed ASA with EvAPR.
文摘鉴于实际生产中工人的能力和加工效率存在差异,本文提出了考虑工人差异性和刀具限制的多资源约束并行机分批调度问题,以最小化最大完工时间、最小化交付时间偏差为优化目标,构建了工人熟练度、刀具资源约束的混合整数线性规划模型,研究子批数量和大小、机器分配、工人分配、子批加工顺序之间的耦合关系,设计了一种改进的多目标混合灰狼-鲸鱼群算法(manyobjective hybrid grey wolf optimizer and whale swarm algorithm,MO-HGWSA).根据模型特点,设计了一种两阶段编码和解码方案表示问题的可行解;利用多种引导策略提高算法的进化效率;设计了局部搜索策略,增强算法的局部搜索能力.最后引入案例验证算法的有效性,结果表明本文所提算法在收敛性、分布性和解集支配关系方面均优于对比算法.
