This paper considers a scheduling problem in industrial make-and-pack batch production process. This process equips with sequence-dependent changeover time, multipurpose storage units with limited capacity, storage ti...This paper considers a scheduling problem in industrial make-and-pack batch production process. This process equips with sequence-dependent changeover time, multipurpose storage units with limited capacity, storage time, batch splitting, partial equipment connectivity and transfer time. The objective is to make a production plan to satisfy all constraints while meeting demand requirement of packed products from various product families. This problem is NP-hard and the problem size is exponentially large for a realistic-sized problem. Therefore,we propose a genetic algorithm to handle this problem. Solutions to the problems are represented by chromosomes of product family sequences. These sequences are decoded to assign the resource for producing packed products according to forward assignment strategy and resource selection rules. These techniques greatly reduce unnecessary search space and improve search speed. In addition, design of experiment is carefully utilized to determine appropriate parameter settings. Ant colony optimization and Tabu search are also implemented for comparison. At the end of each heuristics, local search is applied for the packed product sequence to improve makespan. In an experimental analysis, all heuristics show the capability to solve large instances within reasonable computational time. In all problem instances, genetic algorithm averagely outperforms ant colony optimization and Tabu search with slightly longer computational time.展开更多
Natural products,as major resources for drug discovery historically,are gaining more attentions recently due to the advancement in genomic sequencing and other technologies,which makes them attractive and amenable to ...Natural products,as major resources for drug discovery historically,are gaining more attentions recently due to the advancement in genomic sequencing and other technologies,which makes them attractive and amenable to drug candidate screening.Collecting and mining the bioactivity information of natural products are extremely important for accelerating drug development process by reducing cost.Lately,a number of publicly accessible databases have been established to facilitate the access to the chemical biology data for small molecules including natural products.Thus,it is imperative for scientists in related fields to exploit these resources in order to expedite their researches on natural products as drug leads/candidates for disease treatment.PubChem,as a public database,contains large amounts of natural products associated with bioactivity data.In this review,we introduce the information system provided at PubChem,and systematically describe the applications for a set of PubChem web services for rapid data retrieval,analysis,and downloading of natural products.We hope this work can serve as a starting point for the researchers to perform data mining on natural products using PubChem.展开更多
基金Thailand Research Fund (Grant #MRG5480176)National Research University Project of Thailand Office of Higher Education Commission
文摘This paper considers a scheduling problem in industrial make-and-pack batch production process. This process equips with sequence-dependent changeover time, multipurpose storage units with limited capacity, storage time, batch splitting, partial equipment connectivity and transfer time. The objective is to make a production plan to satisfy all constraints while meeting demand requirement of packed products from various product families. This problem is NP-hard and the problem size is exponentially large for a realistic-sized problem. Therefore,we propose a genetic algorithm to handle this problem. Solutions to the problems are represented by chromosomes of product family sequences. These sequences are decoded to assign the resource for producing packed products according to forward assignment strategy and resource selection rules. These techniques greatly reduce unnecessary search space and improve search speed. In addition, design of experiment is carefully utilized to determine appropriate parameter settings. Ant colony optimization and Tabu search are also implemented for comparison. At the end of each heuristics, local search is applied for the packed product sequence to improve makespan. In an experimental analysis, all heuristics show the capability to solve large instances within reasonable computational time. In all problem instances, genetic algorithm averagely outperforms ant colony optimization and Tabu search with slightly longer computational time.
基金supported by the Intramural Research Program of the National Institutes of Health,National Library of Medicine
文摘Natural products,as major resources for drug discovery historically,are gaining more attentions recently due to the advancement in genomic sequencing and other technologies,which makes them attractive and amenable to drug candidate screening.Collecting and mining the bioactivity information of natural products are extremely important for accelerating drug development process by reducing cost.Lately,a number of publicly accessible databases have been established to facilitate the access to the chemical biology data for small molecules including natural products.Thus,it is imperative for scientists in related fields to exploit these resources in order to expedite their researches on natural products as drug leads/candidates for disease treatment.PubChem,as a public database,contains large amounts of natural products associated with bioactivity data.In this review,we introduce the information system provided at PubChem,and systematically describe the applications for a set of PubChem web services for rapid data retrieval,analysis,and downloading of natural products.We hope this work can serve as a starting point for the researchers to perform data mining on natural products using PubChem.
