This paper addresses the scheduling and inventory management of a straight pipeline system connecting a single refinery to multiple distribution centers.By increasing the number of batches and time periods,maintaining...This paper addresses the scheduling and inventory management of a straight pipeline system connecting a single refinery to multiple distribution centers.By increasing the number of batches and time periods,maintaining the model resolution by using linear programming-based methods and commercial solvers would be very time-consuming.In this paper,we make an attempt to utilize the problem structure and develop a decomposition-based algorithm capable of finding near-optimal solutions for large instances in a reasonable time.The algorithm starts with a relaxed version of the model and adds a family of cuts on the fly,so that a near-optimal solution is obtained within a few iterations.The idea behind the cut generation is based on the knowledge of the underlying problem structure.Computational experiments on a real-world data case and some randomly generated instances confirm the efficiency of the proposed algorithm in terms of the solution quality and time.展开更多
The purpose of this study is to describe an economical approach to an existing adaptive localization technique and its implementation in the proper orthogonal decomposition-based ensemble four-dimensional variational ...The purpose of this study is to describe an economical approach to an existing adaptive localization technique and its implementation in the proper orthogonal decomposition-based ensemble four-dimensional variational assimilation method(PODEn4DVar). Owing to the applications of the sparse processing and EOF decomposition techniques, the computational costs of this proposed sparse flow-adaptive moderation(SFAM) localization scheme are significantly reduced. The effectiveness of PODEn4 DVar with SFAM localization is demonstrated by using the Lorenz-96 model in comparison with the Smoothed ENsemble Correlations Raised to a Power(SENCORP) and static localization schemes, separately. The performance of PODEn4 DVar with SFAM localization shows a moderate improvement over the schemes with SENCORP and static localization, with low computational costs under the imperfect model.展开更多
Multi-Objective Evolutionary Algorithms(MOEAs)have significantly advanced the domain of MultiObjective Optimization(MOO),facilitating solutions for complex problems with multiple conflicting objectives.This review exp...Multi-Objective Evolutionary Algorithms(MOEAs)have significantly advanced the domain of MultiObjective Optimization(MOO),facilitating solutions for complex problems with multiple conflicting objectives.This review explores the historical development of MOEAs,beginning with foundational concepts in multi-objective optimization,basic types of MOEAs,and the evolution of Pareto-based selection and niching methods.Further advancements,including decom-position-based approaches and hybrid algorithms,are discussed.Applications are analyzed in established domains such as engineering and economics,as well as in emerging fields like advanced analytics and machine learning.The significance of MOEAs in addressing real-world problems is emphasized,highlighting their role in facilitating informed decision-making.Finally,the development trajectory of MOEAs is compared with evolutionary processes,offering insights into their progress and future potential.展开更多
In order to exploit the enhancement of the multi- objective evolutionary algorithm based on decomposition （MOEA/D）, we propose an improved algorithm with uniform de- sign （UD）, i.e. MOEA/D-UD. Three mechanisms in ...In order to exploit the enhancement of the multi- objective evolutionary algorithm based on decomposition （MOEA/D）, we propose an improved algorithm with uniform de- sign （UD）, i.e. MOEA/D-UD. Three mechanisms in MOEA/D-UD are modified by introducing an experimental design method called UD. To fully employ the information contained in the domain of the multi-objective problem, we apply UD to initialize a uniformly scattered population. Then, motivated by the analysis of the re- lationship between weight vectors and optimal solutions of scalar subproblems in the study of MOEND with adaptive weight ad- justment （MOEA/D-AWA）, a new weight vector design method based on UD is introduced. To distinguish real sparse regions from pseudo sparse regions, i.e. discontinuous regions, of the complex Pareto front, the weight vector adjustment strategy in MOEMD-UD adequately utilizes the information from neighbors of individuals. In the experimental study, we compare MOEA/D-UD with three outstanding algorithms, namely MOEA/D with the dif- ferential evolution operator （MOEA/D-DE）, MOEA/D-AWA and the nondominated sorting genetic algorithm II （NSGA-II） on nineteen test instances. The experimental results show that MOEA/D-UD is capable of obtaining a well-converged and well diversified set of solutions within an acceptable execution time.展开更多
文摘This paper addresses the scheduling and inventory management of a straight pipeline system connecting a single refinery to multiple distribution centers.By increasing the number of batches and time periods,maintaining the model resolution by using linear programming-based methods and commercial solvers would be very time-consuming.In this paper,we make an attempt to utilize the problem structure and develop a decomposition-based algorithm capable of finding near-optimal solutions for large instances in a reasonable time.The algorithm starts with a relaxed version of the model and adds a family of cuts on the fly,so that a near-optimal solution is obtained within a few iterations.The idea behind the cut generation is based on the knowledge of the underlying problem structure.Computational experiments on a real-world data case and some randomly generated instances confirm the efficiency of the proposed algorithm in terms of the solution quality and time.
基金partially supported by the National High Technology Research and Development Program of China (Grant No. 2013AA122002)the Knowledge Innovation Program of the Chinese Academy of Sciences (Grant No. KZCX2EW-QN207)the Special Fund for Meteorological Scientific Research in the Public Interest (Grant No. GYHY201306045)
文摘The purpose of this study is to describe an economical approach to an existing adaptive localization technique and its implementation in the proper orthogonal decomposition-based ensemble four-dimensional variational assimilation method(PODEn4DVar). Owing to the applications of the sparse processing and EOF decomposition techniques, the computational costs of this proposed sparse flow-adaptive moderation(SFAM) localization scheme are significantly reduced. The effectiveness of PODEn4 DVar with SFAM localization is demonstrated by using the Lorenz-96 model in comparison with the Smoothed ENsemble Correlations Raised to a Power(SENCORP) and static localization schemes, separately. The performance of PODEn4 DVar with SFAM localization shows a moderate improvement over the schemes with SENCORP and static localization, with low computational costs under the imperfect model.
文摘Multi-Objective Evolutionary Algorithms(MOEAs)have significantly advanced the domain of MultiObjective Optimization(MOO),facilitating solutions for complex problems with multiple conflicting objectives.This review explores the historical development of MOEAs,beginning with foundational concepts in multi-objective optimization,basic types of MOEAs,and the evolution of Pareto-based selection and niching methods.Further advancements,including decom-position-based approaches and hybrid algorithms,are discussed.Applications are analyzed in established domains such as engineering and economics,as well as in emerging fields like advanced analytics and machine learning.The significance of MOEAs in addressing real-world problems is emphasized,highlighting their role in facilitating informed decision-making.Finally,the development trajectory of MOEAs is compared with evolutionary processes,offering insights into their progress and future potential.
文摘In order to exploit the enhancement of the multi- objective evolutionary algorithm based on decomposition （MOEA/D）, we propose an improved algorithm with uniform de- sign （UD）, i.e. MOEA/D-UD. Three mechanisms in MOEA/D-UD are modified by introducing an experimental design method called UD. To fully employ the information contained in the domain of the multi-objective problem, we apply UD to initialize a uniformly scattered population. Then, motivated by the analysis of the re- lationship between weight vectors and optimal solutions of scalar subproblems in the study of MOEND with adaptive weight ad- justment （MOEA/D-AWA）, a new weight vector design method based on UD is introduced. To distinguish real sparse regions from pseudo sparse regions, i.e. discontinuous regions, of the complex Pareto front, the weight vector adjustment strategy in MOEMD-UD adequately utilizes the information from neighbors of individuals. In the experimental study, we compare MOEA/D-UD with three outstanding algorithms, namely MOEA/D with the dif- ferential evolution operator （MOEA/D-DE）, MOEA/D-AWA and the nondominated sorting genetic algorithm II （NSGA-II） on nineteen test instances. The experimental results show that MOEA/D-UD is capable of obtaining a well-converged and well diversified set of solutions within an acceptable execution time.
