FSSP is a typical NP-Hard problem which is desired to be minimum makespan. This study consid- ers Migrating Birds Optimization (MBO) which is metaheuristic approach for the solution of Flow Shop Sequencing Problem (FS...FSSP is a typical NP-Hard problem which is desired to be minimum makespan. This study consid- ers Migrating Birds Optimization (MBO) which is metaheuristic approach for the solution of Flow Shop Sequencing Problem (FSSP). As the basic MBO algorithm is designed for discrete problems. The performance of basic MBO algorithm is tested via some FSSP data sets exist in literature. Obtained results are compared with optimal results of related data sets.展开更多
Shuffled frog leaping algorithm( SFLA) was used to solve multi-objective sequencing problem of mixed model assembly line( MMAL). Local convergence can be avoided and optimal solution can be obtained to a certain exten...Shuffled frog leaping algorithm( SFLA) was used to solve multi-objective sequencing problem of mixed model assembly line( MMAL). Local convergence can be avoided and optimal solution can be obtained to a certain extent. However,the multi-objective sequencing problem of MMAL is an non-deterministic polynomial hard( NP-hard) problem and the shortcomings are slow convergence rate and low precision. To solve the shortcomings for optimization objectives of minimizing total utility time and keeping average consumption rate of parts, a chaos differential evolution SFLA( CDESFLA) is proposed in this study. Because SFLA is easy to fall into local optimum,the evolution operator of differential evolution algorithms is introduced in SFLA as a local search strategy,and differential mutation operator is introduced in chaotic sequence to prevent premature convergence. The examples show that the proposed CDESFLA is better for convergence accuracy than SFLA,genetic algorithm( GA) and particle swarm optimization( PSO)展开更多
Multiple sclerosis（MS） is characterized by chronic inflammation in conjunction with neurodegeneration within the central nervous system. Most individuals with MS begin with a relapsing remitting course that later tr...Multiple sclerosis（MS） is characterized by chronic inflammation in conjunction with neurodegeneration within the central nervous system. Most individuals with MS begin with a relapsing remitting course that later transitions to secondary progressive MS. Currently available disease-modifying therapies（DMTs） for relapsing MS have been demonstrated to reduce disease activity, however most patients require a change in therapy over the course of their disease. Treatment goals include the prevention of relapses and disability accumulation and to achieve this objective requires careful planning. Sequencing of DMTs for individual patients should be designed in such a way to maximize disease control and minimize risk based on the mechanism of action, pharmacokinetic and pharmacodynamic properties of each therapy. This includes the DMT patients are being switched from to those they are being switched to. The reversibility of immune system effects should be a key consideration for DMT sequence selection. This feature varies across DMTs and should factor more prominently in decision making as newer treatments become available for the prevention of disability accumulation in patients with progressive MS. In this short review, we discuss the landscape of existing therapies with an eye to the future when planning for optimal DMT sequencing. While no cure exists for MS, efforts are being directed toward research in neuroregeneration with the hope for positive outcomes.展开更多
This research presents an approach to solving the limited re-sequencing problem for a JIT system when two objectives are considered for multiple processes. One objective is to minimize the number of setups;the other i...This research presents an approach to solving the limited re-sequencing problem for a JIT system when two objectives are considered for multiple processes. One objective is to minimize the number of setups;the other is to minimize the material usage rate [1]. For this research effort, each unique permutation of the problem’s demand structure is noted, and used as a mechanism for finding subsequent sequences. Two variants of this permutation approach are used: one employs a Monte-Carlo simulation, while the other employs a modification of Ant-Colony Optimization to find sequences satisfying the objectives of interest. Problem sets from the literature are used for assessment, and experimentation shows that the methodology presented here outperforms methodology from an earlier research effort [3].展开更多
基金supported by Scientific Research Project of Necmettin Erbakan University
文摘FSSP is a typical NP-Hard problem which is desired to be minimum makespan. This study consid- ers Migrating Birds Optimization (MBO) which is metaheuristic approach for the solution of Flow Shop Sequencing Problem (FSSP). As the basic MBO algorithm is designed for discrete problems. The performance of basic MBO algorithm is tested via some FSSP data sets exist in literature. Obtained results are compared with optimal results of related data sets.
基金National Natural Science Foundation of China(o.61370037)
文摘Shuffled frog leaping algorithm( SFLA) was used to solve multi-objective sequencing problem of mixed model assembly line( MMAL). Local convergence can be avoided and optimal solution can be obtained to a certain extent. However,the multi-objective sequencing problem of MMAL is an non-deterministic polynomial hard( NP-hard) problem and the shortcomings are slow convergence rate and low precision. To solve the shortcomings for optimization objectives of minimizing total utility time and keeping average consumption rate of parts, a chaos differential evolution SFLA( CDESFLA) is proposed in this study. Because SFLA is easy to fall into local optimum,the evolution operator of differential evolution algorithms is introduced in SFLA as a local search strategy,and differential mutation operator is introduced in chaotic sequence to prevent premature convergence. The examples show that the proposed CDESFLA is better for convergence accuracy than SFLA,genetic algorithm( GA) and particle swarm optimization( PSO)
文摘Multiple sclerosis（MS） is characterized by chronic inflammation in conjunction with neurodegeneration within the central nervous system. Most individuals with MS begin with a relapsing remitting course that later transitions to secondary progressive MS. Currently available disease-modifying therapies（DMTs） for relapsing MS have been demonstrated to reduce disease activity, however most patients require a change in therapy over the course of their disease. Treatment goals include the prevention of relapses and disability accumulation and to achieve this objective requires careful planning. Sequencing of DMTs for individual patients should be designed in such a way to maximize disease control and minimize risk based on the mechanism of action, pharmacokinetic and pharmacodynamic properties of each therapy. This includes the DMT patients are being switched from to those they are being switched to. The reversibility of immune system effects should be a key consideration for DMT sequence selection. This feature varies across DMTs and should factor more prominently in decision making as newer treatments become available for the prevention of disability accumulation in patients with progressive MS. In this short review, we discuss the landscape of existing therapies with an eye to the future when planning for optimal DMT sequencing. While no cure exists for MS, efforts are being directed toward research in neuroregeneration with the hope for positive outcomes.
文摘This research presents an approach to solving the limited re-sequencing problem for a JIT system when two objectives are considered for multiple processes. One objective is to minimize the number of setups;the other is to minimize the material usage rate [1]. For this research effort, each unique permutation of the problem’s demand structure is noted, and used as a mechanism for finding subsequent sequences. Two variants of this permutation approach are used: one employs a Monte-Carlo simulation, while the other employs a modification of Ant-Colony Optimization to find sequences satisfying the objectives of interest. Problem sets from the literature are used for assessment, and experimentation shows that the methodology presented here outperforms methodology from an earlier research effort [3].
