This study examines a two-stage three-machine flow-shop assembly scheduling model in which job processing time is considered as a mixed function of a controlled truncation parameter with a sum-of-processing-times-base...
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This study examines a two-stage three-machine flow-shop assembly scheduling model in which job processing time is considered as a mixed function of a controlled truncation parameter with a sum-of-processing-times-based learning effect. However, the truncation function is very limited in the two-stage flow-shop assembly scheduling settings. To overcome this limitation, this study investigates a two-stage three-machine flow-shop assembly problem with a truncation learning function where the makespan criterion (completion of the last job) is minimized. Given that the proposed model is NP hard, dominance rules, lemmas and a lower bound are derived and applied to the branch-and-bound method. A dynamic differential evolution algorithm, a hybrid greedy iterated algorithm and a genetic algorithm are also proposed for searching approximate solutions. Results obtained from test experiments validate the performance of all the proposed algorithms.
The existing radio access network system is static and rigid which could not satisfy the communication requirements of the modern society-flexibility, mobility and intelligence. This paper studies the problems of virt...
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ISBN:
(纸本)9781509022182
The existing radio access network system is static and rigid which could not satisfy the communication requirements of the modern society-flexibility, mobility and intelligence. This paper studies the problems of virtualized resource allocation with multi-objective mapping in heterogeneous radio access network. First, a mathematical model of the multi-objective mapping for virtualized resources is established for heterogeneous radio access network. Next, the dynamicdifferentialevolutionary algorithm is used to solve the multi-objective model. During the process, the weight values of objective function are adjusted by machines learning algorithm, so as to realize the result that the unilaterally value of multi-objective optimization is close to the value of solo-objective optimization. Finally, simulated experiment shows that the proposed algorithm has good fast convergence in different network load scenarios.
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