Showing papers by "Xueping Li published in 2016"
TL;DR: This paper studies the scheduling problem of uniform parallel batch processing machines with arbitrary job sizes and proposes an effective differential evolution-based hybrid algorithm for solving large-scale problems.
59 citations
TL;DR: It was discovered that fathers used various social media to discuss their concerns and, in turn, obtained informational, companionship, and emotional supports and insights into a father-centered technology intervention design are provided.
Abstract: Although parents of premature infants experience many challenges when transitioning home from the neonatal intensive care unit, healthcare providers and social support systems tend to focus on mothers and infants rather than fathers. Unfortunately, very little is known about paternal concerns and needs as compared with maternal ones. The lack of understanding about paternal needs may lead to inadequate designs of neonatal intensive care unit family support programs with less involved fathers, all of which contribute to increased burdens on mothers and poor health outcomes for their infants. Although information technology (IT) might have the potential to increase support for the fathers of preterm infants, only a few studies have examined systematically how IT applications can be beneficial. This study aims to advance the understanding of needs and concerns of fathers with preterm infants and how fathers use the IT applications (eg, social networking Web sites) to support themselves. We observed qualitatively various social networking Web sites (ie, 29 Web sites) where fathers share their experiences about preterm infants. We discovered that fathers used various social media to discuss their concerns and, in turn, obtained informational, companionship, and emotional supports. On the basis of our analysis, we provide insights into a father-centered technology intervention design.
31 citations
TL;DR: A population-based evolutionary method named estimation of distribution algorithm (EDA) is proposed, where the individuals in the population are coded into job sequences and the best-fit heuristic is used to group jobs into batches.
Abstract: This paper studies the problem of minimising makespan in a no-wait flowshop with two batch processing machines (comprised of a parallel batch processing machine and a serial batch processing machine), non-identical job sizes and unequal ready times. We propose a population-based evolutionary method named estimation of distribution algorithm (EDA). Firstly, the individuals in the population are coded into job sequences. Then, a probabilistic model is built to generate new population and an incremental learning method is developed to update the probabilistic model. Thirdly, the best-fit heuristic is used to group jobs into batches and a least idle/waiting time approach is proposed to sequence the batches on batch processing machines. In addition, some problem-dependent local search heuristics are incorporated into the EDA to further improve the searching quality. Computational simulation and comparisons with some existing algorithms demonstrate the effectiveness and robustness of the proposed algorithm. Fur...
23 citations