Designing a multistage supply chain in cross-stage reverse logistics environments: application of particle swarm optimization algorithms.

This study designed a cross-stage reverse logistics course for defective products so that damaged products generated in downstream partners can be directly returned to upstream partners throughout the stages of a supply chain for rework and maintenance. To solve this reverse supply chain design problem, an optimal cross-stage reverse logistics mathematical model was developed. In addition, we developed a genetic algorithm (GA) and three particle swarm optimization (PSO) algorithms: the inertia weight method (PSOA_IWM), V(Max) method (PSOA_VMM), and constriction factor method (PSOA_CFM), which we employed to find solutions to support this mathematical model. Finally, a real case and five simulative cases with different scopes were used to compare the execution times, convergence times, and objective function values of the four algorithms used to validate the model proposed in this study. Regarding system execution time, the GA consumed more time than the other three PSOs did. Regarding objective function value, the GA, PSOA_IWM, and PSOA_CFM could obtain a lower convergence value than PSOA_VMM could. Finally, PSOA_IWM demonstrated a faster convergence speed than PSOA_VMM, PSOA_CFM, and the GA did.

Hybrid algorithms for fuzzy reverse supply chain network design.

In consideration of capacity constraints, fuzzy defect ratio, and fuzzy transport loss ratio, this paper attempted to establish an optimized decision model for production planning and distribution of a multiphase, multiproduct reverse supply chain, which addresses defects returned to original manufacturers, and in addition, develops hybrid algorithms such as Particle Swarm Optimization-Genetic Algorithm (PSO-GA), Genetic Algorithm-Simulated Annealing (GA-SA), and Particle Swarm Optimization-Simulated Annealing (PSO-SA) for solving the optimized model. During a case study of a multi-phase, multi-product reverse supply chain network, this paper explained the suitability of the optimized decision model and the applicability of the algorithms. Finally, the hybrid algorithms showed excellent solving capability when compared with original GA and PSO methods.

Plasma resistin, associated with single nucleotide polymorphism -420, is correlated with C-reactive protein in Chinese Han patients with spontaneous basal ganglia hemorrhage.

We examined a possible relationship -420C>G SNP of the resistin gene with plasma resistin and C-reactive protein concentrations in intracerebral hemorrhage. Three hundred and forty-four Chinese Han patients with intracerebral hemorrhage and 344 age- and gender-matched healthy controls were included in our study. Plasma resistin and C-reactive concentrations were measured and SNP -420C>G was genotyped. The genotype frequencies in controls and patients were not significantly different (P = 0.672). Plasma resistin and C-reactive protein levels were significantly different between the SNP -420C>G genotypes, even after adjustment for age, gender and body mass index. The common homozygote (C-C) had the lowest resistin and C-reactive protein plasma concentrations; the plasma resistin and C-reactive protein concentrations in the heterozygote (C-G) and the rare allele homozygote (G-G) did not differ significantly. Plasma resistin levels were significantly associated with plasma C-reactive protein level. We conclude that SNP -420C>G of the resistin gene could be involved in the inflammatory component of intracerebral hemorrhage through enhanced production of resistin.