The aim of this paper is to propose a new particle swarm optimization algorithm to solve a hybrid flowshop scheduling with sequence-dependent setup times problem, which is of great importance in the industrial context. This algorithm is called diversified particle swarm optimization algorithm which is a generalization of particle swarm optimization algorithm and inspired by an anarchic society ...

Flow-shop scheduling problem (FSP) deals with the scheduling of a set of n jobs that visit a set of m machines in the same order. As the FSP is NP-hard, there is no efficient algorithm to reach the optimal solution of the problem. To minimize the holding, delay and setup costs of large permutation flow-shop scheduling problems with sequence-dependent setup times on each machine, this pap...

In this study, we consider single-machine scheduling problems with past-sequence-dependent (denoted by psd) setup times in which the of jobs are proportional to length already processed jobs. Under common (CON) and slack (SLK) due-date assignment methods, prove that weighted sum earliness, tardiness minimization remains polynomially solvable. We also give some extensions for psd times.

In this paper, we obtain the convergence rate for high-dimensional sample quantiles with φ-mixing dependent sequence. The resulting is shown to be faster than that obtained by Hoeffding-type inequalities. Moreover, of observation sequence taking discrete values also provided.

Material training is a new approach for endowing material with precise functionality based on principles of self-organization. We demonstrate examples nonlinear functions realized through training, that are difficult to attain by design.