Learning programming has become an important part of education.However,most students have extreme difficulty learning programming and complex algorithms.This is because programming has a hierarchical logic.Solving com...Learning programming has become an important part of education.However,most students have extreme difficulty learning programming and complex algorithms.This is because programming has a hierarchical logic.Solving complex problems requires students to develop skills in decomposing problems.To this end,this paper describes an effective method to develop an online platform for teaching complex algorithms.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
Remanufacturing contributes to achieving economical,environmental,and social sustainability,and one of its main steps is disassembly aiming to acquire a set of recyclable and reusable components from endof-life produc...Remanufacturing contributes to achieving economical,environmental,and social sustainability,and one of its main steps is disassembly aiming to acquire a set of recyclable and reusable components from endof-life products.This research considers a multi-objective multi-product disassembly sequence planning problem under uncertain circumstances to realize a trade-off among economic,environmental,and social sustainability.Firstly,a multi-objective chance-constrained programming model is formulized to achieve maximal disassembly profit and minimal noise pollution while satisfying energy consumption requirements and obeying various complex product structures.Secondly,a multi-objective group teaching optimization algorithm combining a stochastic simulation approach is particularly devised to handle the problem.In the designed approach,problem-specific encoding and decoding methods are employed to represent and produce feasible solutions.The stochastic simulation approach is utilized to assess the feasibility and performance of the obtained solutions under uncertain environments.Rank and crowding distance approaches are introduced to realize ability grouping,namely,dividing the population into two groups.Precedence preserving crossover and mutation operators are separately utilized on the two groups to achieve population evolution,and an adaptive local search method is developed to enhance exploitation.Thirdly,comparison experiments on some real-world test problems with different scales are carried out.Through dissecting the experimental results with three performance metrics,it can be observed that the devised approach outperforms its competitors by 9.39%-10.00%,11.37%-59.86%,and 2.36%-7.73%regarding performance,respectively.The experimental results demonstrate the efficiency and excellence of the devised approach in providing high-quality disassembly schemes for managers and engineers.展开更多
With the emergence of the artificial intelligence era,all kinds of robots are traditionally used in agricultural production.However,studies concerning the robot task assignment problem in the agriculture field,which i...With the emergence of the artificial intelligence era,all kinds of robots are traditionally used in agricultural production.However,studies concerning the robot task assignment problem in the agriculture field,which is closely related to the cost and efficiency of a smart farm,are limited.Therefore,a Multi-Weeding Robot Task Assignment(MWRTA)problem is addressed in this paper to minimize the maximum completion time and residual herbicide.A mathematical model is set up,and a Multi-Objective Teaching-Learning-Based Optimization(MOTLBO)algorithm is presented to solve the problem.In the MOTLBO algorithm,a heuristicbased initialization comprising an improved Nawaz Enscore,and Ham(NEH)heuristic and maximum loadbased heuristic is used to generate an initial population with a high level of quality and diversity.An effective teaching-learning-based optimization process is designed with a dynamic grouping mechanism and a redefined individual updating rule.A multi-neighborhood-based local search strategy is provided to balance the exploitation and exploration of the algorithm.Finally,a comprehensive experiment is conducted to compare the proposed algorithm with several state-of-the-art algorithms in the literature.Experimental results demonstrate the significant superiority of the proposed algorithm for solving the problem under consideration.展开更多
基金by the XJTLU Research Fund(Grant No.RDF-21-01-053,TDF21/22-R23-160)External Research Fund(Grant No.RDS10120220093,RDS10120220021).
文摘Learning programming has become an important part of education.However,most students have extreme difficulty learning programming and complex algorithms.This is because programming has a hierarchical logic.Solving complex problems requires students to develop skills in decomposing problems.To this end,this paper describes an effective method to develop an online platform for teaching complex algorithms.
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 in part by the National Natural Science Foundation of China(Nos.62173356 and 61703320)Shandong Province Outstanding Youth Innovation Team Project of Colleges and Universities(No.2020RWG011)+4 种基金Natural Science Foundation of Shandong Province(No.ZR202111110025)Science and Technology Development Fund(FDCT)Macao SAR(No.0019/2021/A)Innovation Centre for Digital Business and Capital Development of Beijing Technology and Business University(No.SZSK202208)Zhuhai Industry-University-Research Project with Hongkong and Macao(No.ZH22017002210014PWC).
文摘Remanufacturing contributes to achieving economical,environmental,and social sustainability,and one of its main steps is disassembly aiming to acquire a set of recyclable and reusable components from endof-life products.This research considers a multi-objective multi-product disassembly sequence planning problem under uncertain circumstances to realize a trade-off among economic,environmental,and social sustainability.Firstly,a multi-objective chance-constrained programming model is formulized to achieve maximal disassembly profit and minimal noise pollution while satisfying energy consumption requirements and obeying various complex product structures.Secondly,a multi-objective group teaching optimization algorithm combining a stochastic simulation approach is particularly devised to handle the problem.In the designed approach,problem-specific encoding and decoding methods are employed to represent and produce feasible solutions.The stochastic simulation approach is utilized to assess the feasibility and performance of the obtained solutions under uncertain environments.Rank and crowding distance approaches are introduced to realize ability grouping,namely,dividing the population into two groups.Precedence preserving crossover and mutation operators are separately utilized on the two groups to achieve population evolution,and an adaptive local search method is developed to enhance exploitation.Thirdly,comparison experiments on some real-world test problems with different scales are carried out.Through dissecting the experimental results with three performance metrics,it can be observed that the devised approach outperforms its competitors by 9.39%-10.00%,11.37%-59.86%,and 2.36%-7.73%regarding performance,respectively.The experimental results demonstrate the efficiency and excellence of the devised approach in providing high-quality disassembly schemes for managers and engineers.
基金supported by the National Natural Science Foundation of China(Nos.62273221 and 61973203)the Program of Shanghai Academic/Technology Research Leader(No.21XD1401000)the Shanghai Key Laboratory of Power Station Automation Technology.
文摘With the emergence of the artificial intelligence era,all kinds of robots are traditionally used in agricultural production.However,studies concerning the robot task assignment problem in the agriculture field,which is closely related to the cost and efficiency of a smart farm,are limited.Therefore,a Multi-Weeding Robot Task Assignment(MWRTA)problem is addressed in this paper to minimize the maximum completion time and residual herbicide.A mathematical model is set up,and a Multi-Objective Teaching-Learning-Based Optimization(MOTLBO)algorithm is presented to solve the problem.In the MOTLBO algorithm,a heuristicbased initialization comprising an improved Nawaz Enscore,and Ham(NEH)heuristic and maximum loadbased heuristic is used to generate an initial population with a high level of quality and diversity.An effective teaching-learning-based optimization process is designed with a dynamic grouping mechanism and a redefined individual updating rule.A multi-neighborhood-based local search strategy is provided to balance the exploitation and exploration of the algorithm.Finally,a comprehensive experiment is conducted to compare the proposed algorithm with several state-of-the-art algorithms in the literature.Experimental results demonstrate the significant superiority of the proposed algorithm for solving the problem under consideration.
