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2007 IEEE Symposium on Computational Intelligence in Scheduling最新文献

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Improving the Performance of Genetic Algorithm in Capacitated Vehicle Routing Problem using Self Imposed Constraints 利用自约束改进遗传算法在有能力车辆路径问题中的性能
2007 IEEE Symposium on Computational Intelligence in Scheduling Pub Date : 2007-04-01 DOI: 10.1109/SCIS.2007.367693
Ziauddin Ursani, R. Sarker, H. Abbass
{"title":"Improving the Performance of Genetic Algorithm in Capacitated Vehicle Routing Problem using Self Imposed Constraints","authors":"Ziauddin Ursani, R. Sarker, H. Abbass","doi":"10.1109/SCIS.2007.367693","DOIUrl":"https://doi.org/10.1109/SCIS.2007.367693","url":null,"abstract":"The capacitated vehicle routing problem (CVRP) is a well known member of the family of NP hard problems. In the past few decades, a number of heuristics was introduced to solve this problem but no heuristic can claim to work well in all possible scenarios. In the literature, genetic algorithm (GA) even lags behind the other heuristics. In this paper, we reveal some of the reasons for the inferior performance of GA, and propose a number of mechanisms to improve its performance. A number of test problems are solved to demonstrate the usefulness of the algorithm.","PeriodicalId":184726,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence in Scheduling","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132906190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 5
Global Estimations for Multiprocessor Job-Shop 多处理机作业车间的全局估计
2007 IEEE Symposium on Computational Intelligence in Scheduling Pub Date : 2007-04-01 DOI: 10.1109/SCIS.2007.367671
N. Vakhania
{"title":"Global Estimations for Multiprocessor Job-Shop","authors":"N. Vakhania","doi":"10.1109/SCIS.2007.367671","DOIUrl":"https://doi.org/10.1109/SCIS.2007.367671","url":null,"abstract":"Classical job-shop scheduling problem (JSP) is one of the heaviest (strongly) NP-hard scheduling problems, which is very difficult to solve in practice. No approximation algorithms with a guaranteed performance exist. We deal with a natural generalization of this problem allowing parallel processors instead of each single processor in JSP, and an arbitrary task graph (without cycles) instead of a serial-parallel task graph in JSP. Parallel processors might be identical, uniform or unrelated. The whole feasible solution space grows drastically compared to JSP. However, as it turned out, parallel processors can also be used to reduce the solution space to a subspace, which is essentially smaller than even the corresponding solution space for JSP. For large problem instances, this space still may remain too big. Here we propose different global estimations which allow us to reduce it further. By applying our bounds to the reduced solution space, a class of exact and approximation algorithms are obtained. We are in the process of the implementation of our reduction algorithm and the bounds. Then we aim to carry out the experimental study comparing the behavior and the efficiency of the proposed bounds in practice","PeriodicalId":184726,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence in Scheduling","volume":"568 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116172553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Hybrid GA-based Scheduling Algorithm for Heterogeneous Computing Environments 异构计算环境下基于混合遗传算法的调度算法
2007 IEEE Symposium on Computational Intelligence in Scheduling Pub Date : 2007-04-01 DOI: 10.1109/SCIS.2007.367674
Han Yu
{"title":"A Hybrid GA-based Scheduling Algorithm for Heterogeneous Computing Environments","authors":"Han Yu","doi":"10.1109/SCIS.2007.367674","DOIUrl":"https://doi.org/10.1109/SCIS.2007.367674","url":null,"abstract":"We design a hybrid algorithm to schedule the execution of a group of dependent tasks for heterogeneous computing environments. The algorithm consists of two elements: a genetic algorithm (GA) to map tasks to processors, and a heuristic-based approach to assign the execution order of tasks. This algorithm takes advantage of both the exploration power of GA and the heuristics embedded in the scheduling problem, so it can effectively reduce the search space while not sacrificing the search quality. The experiments show that this algorithm performs consistently better than heterogeneous earliest-finish-time (HEFT) without incurring much computational cost. Multiple runs of the algorithm can further improve the search result.","PeriodicalId":184726,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence in Scheduling","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132364017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 9
Construction of Initial Neighborhoods for a Course Scheduling Problem Using Tiling 用平铺法构造课程调度问题的初始邻域
2007 IEEE Symposium on Computational Intelligence in Scheduling Pub Date : 2007-04-01 DOI: 10.1109/SCIS.2007.367688
Douglas L. Moody, A. Bar-Noy, G. Kendall
{"title":"Construction of Initial Neighborhoods for a Course Scheduling Problem Using Tiling","authors":"Douglas L. Moody, A. Bar-Noy, G. Kendall","doi":"10.1109/SCIS.2007.367688","DOIUrl":"https://doi.org/10.1109/SCIS.2007.367688","url":null,"abstract":"A recent competition course scheduling competition saw many solution approaches which constructed an initial solution, and then improved that solution using local search. The initial solution appears to be crucial for the local search to be effective and in this work we propose a tiling technique which can quickly construct a solution which we hope can be used as a good starting point for a local search procedure","PeriodicalId":184726,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence in Scheduling","volume":"438 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132207636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
The application of Multilevel Refinement to the Vehicle Routing Problem 多级细化在车辆路径问题中的应用
2007 IEEE Symposium on Computational Intelligence in Scheduling Pub Date : 2007-04-01 DOI: 10.1109/SCIS.2007.367692
Demane Rodney, A. Soper, C. Walshaw
{"title":"The application of Multilevel Refinement to the Vehicle Routing Problem","authors":"Demane Rodney, A. Soper, C. Walshaw","doi":"10.1109/SCIS.2007.367692","DOIUrl":"https://doi.org/10.1109/SCIS.2007.367692","url":null,"abstract":"We discuss the application of the multilevel (ML) refinement technique to the vehicle routing problem (VRP), and compare it to its single-level (SL) counterpart. Multilevel refinement recursively coarsens to create a hierarchy of approximations to the problem and refines at each level. A SL heuristic, termed the combined node-exchange composite heuristic (CNCH), is developed first to solve instances of the VRP. A ML version (the ML-CNCH) is then created, using the construction and improvement heuristics of the CNCH at each level. Experimentation is used to find a suitable combination, which extends the global view of these heuristics. Results comparing both SL and ML are presented","PeriodicalId":184726,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence in Scheduling","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126749162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 10
Multi-Objective Semiconductor Manufacturing Scheduling: A Random Keys Implementation of NSGA-II 多目标半导体制造调度:NSGA-II的随机密钥实现
2007 IEEE Symposium on Computational Intelligence in Scheduling Pub Date : 2007-04-01 DOI: 10.1109/SCIS.2007.367684
S. Mason, M. E. Kurz, M. Pfund, J. Fowler, L. Pohl
{"title":"Multi-Objective Semiconductor Manufacturing Scheduling: A Random Keys Implementation of NSGA-II","authors":"S. Mason, M. E. Kurz, M. Pfund, J. Fowler, L. Pohl","doi":"10.1109/SCIS.2007.367684","DOIUrl":"https://doi.org/10.1109/SCIS.2007.367684","url":null,"abstract":"We examine a complex, multi-objective semiconductor manufacturing scheduling problem involving two batch processing steps linked by a timer constraint. This constraint requires that any job completing the first processing step must be started on the succeeding second machine within some allowable time window; otherwise, the job must repeat its processing on the first step. We present a random keys implementation of NSGA-II (nondominated sorting genetic algorithm) for our problem of interest and investigate the efficacy of different batching policies in terms of the number of approximate efficient solutions that are produced by NSGA-II over a wide range of experimental problem instances. Experimental results suggest a full batch policy can produce superior solutions as compared to greedy batching policies under the experimental conditions examined","PeriodicalId":184726,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence in Scheduling","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126768331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 12
A New Meta-heuristic Approach for Combinatorial Optimization and Scheduling Problems 组合优化与调度问题的一种新的元启发式方法
2007 IEEE Symposium on Computational Intelligence in Scheduling Pub Date : 2007-04-01 DOI: 10.1109/SCIS.2007.367663
N. Azizi, S. Zolfaghari, M. Liang
{"title":"A New Meta-heuristic Approach for Combinatorial Optimization and Scheduling Problems","authors":"N. Azizi, S. Zolfaghari, M. Liang","doi":"10.1109/SCIS.2007.367663","DOIUrl":"https://doi.org/10.1109/SCIS.2007.367663","url":null,"abstract":"This study presents a new metaheuristic approach that reasonably combines different features of several well-know heuristics. The core component of the proposed algorithm is a simulated annealing that utilizes three types of memories, two short-term memories and one long-term memory. The purpose of the two short-term memories is to guide the search toward good solutions. While the aim of the long term memory is to provide means for the search to escape local optima through increasing the diversification phase in a logical manner. The long-term memory is considered as a population list. In specific circumstances, members of the population might be employed to generate a new population from which a new initial solution for the simulated annealing component is generated. Job shop scheduling problem has been used to test the performance of the proposed method","PeriodicalId":184726,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence in Scheduling","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116017073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
A Discrete Differential Evolution Algorithm for the Total Earliness and Tardiness Penalties with a Common Due Date on a Single-Machine 单台机器上具有共同到期日的总早迟到罚的离散差分进化算法
2007 IEEE Symposium on Computational Intelligence in Scheduling Pub Date : 2007-04-01 DOI: 10.1109/SCIS.2007.367701
M. Tasgetiren, Q. Pan, Yun-Chia Liang, P. N. Suganthan
{"title":"A Discrete Differential Evolution Algorithm for the Total Earliness and Tardiness Penalties with a Common Due Date on a Single-Machine","authors":"M. Tasgetiren, Q. Pan, Yun-Chia Liang, P. N. Suganthan","doi":"10.1109/SCIS.2007.367701","DOIUrl":"https://doi.org/10.1109/SCIS.2007.367701","url":null,"abstract":"In this paper, a discrete differential evolution (DDE) algorithm is presented to solve the single machine total earliness and tardiness penalties with a common due date. A new binary swap mutation operator called Bswap is presented. In addition, the DDE algorithm is hybridized with a local search algorithm to further improve the performance of the DDE algorithm. The performance of the proposed DDE algorithm is tested on 280 benchmark instances ranging from 10 to 1000 jobs from the OR Library. The computational experiments showed that the proposed DDE algorithm has generated better results than those in the literature in terms of both solution quality and computational time","PeriodicalId":184726,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence in Scheduling","volume":"30 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115728612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 28
A Genetic Algorithm with Injecting Artificial Chromosomes for Single Machine Scheduling Problems 求解单机调度问题的人工染色体注入遗传算法
2007 IEEE Symposium on Computational Intelligence in Scheduling Pub Date : 2007-04-01 DOI: 10.1109/SCIS.2007.367662
P. Chang, Shih-Shin Chen, Q. Ko, C. Fan
{"title":"A Genetic Algorithm with Injecting Artificial Chromosomes for Single Machine Scheduling Problems","authors":"P. Chang, Shih-Shin Chen, Q. Ko, C. Fan","doi":"10.1109/SCIS.2007.367662","DOIUrl":"https://doi.org/10.1109/SCIS.2007.367662","url":null,"abstract":"A genetic algorithm with injecting artificial chromosomes was developed for solving the single machine scheduling problems with the objective to minimize the total deviation. Artificial chromosomes are generated according to a probability matrix which was transformed from the gene structure of an elite base. A roulette wheel selection method was applied to generate an artificial chromosome by assigning genes onto each position according to the probability matrix. The higher the probability is, the more possible that the gene will show up in that particular position. By injecting these artificial chromosomes, the genetic algorithm will speed up the convergence of the evolutionary processes. Intensive experimental results indicate that the proposed algorithm is very encouraging and it can improve the solution quality significantly","PeriodicalId":184726,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence in Scheduling","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122949270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Greedy Scheduling with Complex Obejectives 复杂目标贪心调度
2007 IEEE Symposium on Computational Intelligence in Scheduling Pub Date : 2007-04-01 DOI: 10.1109/SCIS.2007.367678
C. Franke, Joachim Lepping, U. Schwiegelshohn
{"title":"Greedy Scheduling with Complex Obejectives","authors":"C. Franke, Joachim Lepping, U. Schwiegelshohn","doi":"10.1109/SCIS.2007.367678","DOIUrl":"https://doi.org/10.1109/SCIS.2007.367678","url":null,"abstract":"We present a methodology for automatically generating an online scheduling process for an arbitrary objective with the help of evolution strategies. The scheduling problem comprises independent parallel jobs and multiple identical machines and occurs in many real massively parallel processing systems. The system owner defines the objective that may consider job waiting times and priorities of user groups. Our scheduling process is a variant of the simple and commonly used greedy scheduling algorithm in combination with a repeated sorting of the waiting queue. This sorting uses a criterion whose parameters are evolutionary optimized. We evaluate our new scheduling process with real workload data and compare it to the best offline solutions and to the online results of the standard EASY backfill algorithm. To this end, we partition the user of the workloads into groups and select an exemplary objective that prioritizes some of those groups over others","PeriodicalId":184726,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence in Scheduling","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132710613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 9
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