Multiple depots vehicle routing based on the ant colony with the genetic algorithm

Purpose: the distribution routing plans of multi-depots vehicle scheduling problem will increase exponentially along with the adding of customers. So, it becomes an important studying trend to solve the vehicle scheduling problem with heuristic algorithm. On the basis of building the model of multi-depots vehicle scheduling problem, in order to improve the efficiency of the multiple depots vehicle routing, the paper puts forward a fusion algorithm on multiple depots vehicle routing based on the ant colony algorithm with genetic algorithm. Design/methodology/approach: to achieve this objective, the genetic algorithm optimizes the parameters of the ant colony algorithm. The fusion algorithm on multiple depots vehicle based on the ant colony algorithm with genetic algorithm is proposed. Findings: simulation experiment indicates that the result of the fusion algorithm is more excellent than the other algorithm, and the improved algorithm has better convergence effective and global ability. Research limitations/implications: in this research, there are some assumption that might affect the accuracy of the model such as the pheromone volatile factor, heuristic factor in each period, and the selected multiple depots. These assumptions can be relaxed in future work. Originality/value: In this research, a new method for the multiple depots vehicle routing is proposed. The fusion algorithm eliminate the influence of the selected parameter by optimizing the heuristic factor, evaporation factor, initial pheromone distribute, and have the strong global searching ability. The Ant Colony algorithm imports cross operator and mutation operator for operating the first best solution and the second best solution in every iteration, and reserves the best solution. The cross and mutation operator extend the solution space and improve the convergence effective and the global ability. This research shows that considering both the ant colony and genetic algorithm together can improve the efficiency multiple depots vehicle routing.Peer Reviewe

The composition and origination of particles from surface water in the Chukchi Sea, Arctic Ocean

Suspended particle samples were collected at 11 stations on the shelf and slope regions of the Chukchi Sea and the central Arctic Ocean during the fifth Chinese National Arctic Research Expedition (summer 2012). The particle concentration, total organic carbon (TOC), total nitrogen (TN) and the isotopic composition of the samples were analyzed. The suspended particle concentration varied between 0.56 and 4.01 mg.L-1; the samples collected from the sea ice margin have higher concentrations. The organic matter content is higher in the shelf area (TOC: 9.78%–20.24%; TN: 0.91%–2.31%), and exhibits heavier isotopic compositions (δ13C: –23.29‰ to –26.33‰ PDB; δ15N: 6.14‰–7.78‰), indicating that the organic matter is mostly marine in origin with some terrigenous input. In the slope and the central Arctic Ocean, the organic matter content is lower (TOC: 8.06% – 8.96%; TN: 0.46%–0.72%), except for one sample (SR15), and has lighter isotopic compositions (δ13C: –26.93‰ to – 27.78‰ PDB; δ15N: 4.13‰–4.84‰). This indicates that the organic matter is mostly terrestrially-derived in these regions. The extremely high amount of terrigenous organic matter (TOC: 27.94%; TN: 1.16%; δ13C: –27.43‰ PDB; δ15N: 3.81‰) implies that it was carried by transpolar currents from the East Siberian Sea. Material, including sea ice algae, carried by sea ice are the primary source for particles in the sea ice margins. Sea ice melting released a substantial amount of biomass into the shelf, but a large amount of detrital and clay minerals in the slope and the central Arctic Ocean

Multiple depots vehicle routing based on the ant colony with the genetic algorithm

Purpose: the distribution routing plans of multi-depots vehicle scheduling problem will increase exponentially along with the adding of customers. So, it becomes an important studying trend to solve the vehicle scheduling problem with heuristic algorithm. On the basis of building the model of multi-depots vehicle scheduling problem, in order to improve the efficiency of the multiple depots vehicle routing, the paper puts forward a fusion algorithm on multiple depots vehicle routing based on the ant colony algorithm with genetic algorithm. Design/methodology/approach: to achieve this objective, the genetic algorithm optimizes the parameters of the ant colony algorithm. The fusion algorithm on multiple depots vehicle based on the ant colony algorithm with genetic algorithm is proposed. Findings: simulation experiment indicates that the result of the fusion algorithm is more excellent than the other algorithm, and the improved algorithm has better convergence effective and global ability. Research limitations/implications: in this research, there are some assumption that might affect the accuracy of the model such as the pheromone volatile factor, heuristic factor in each period, and the selected multiple depots. These assumptions can be relaxed in future work. Originality/value: In this research, a new method for the multiple depots vehicle routing is proposed. The fusion algorithm eliminate the influence of the selected parameter by optimizing the heuristic factor, evaporation factor, initial pheromone distribute, and have the strong global searching ability. The Ant Colony algorithm imports cross operator and mutation operator for operating the first best solution and the second best solution in every iteration, and reserves the best solution. The cross and mutation operator extend the solution space and improve the convergence effective and the global ability. This research shows that considering both the ant colony and genetic algorithm together can improve the efficiency multiple depots vehicle routing

Nanoporous Activated Carbon Derived from Rice Husk for High Performance Supercapacitor

Nanoporous activated carbon material was produced from the waste rice husks (RHs) by precarbonizing RHs and activating with KOH. The morphology, structure, and specific surface area were investigated. The nanoporous carbon has the average pore size of 2.2 nm and high specific area of 2523.4 m2 g−1. The specific capacitance of the nanoporous carbon is calculated to be 250 F g−1 at the current density of 1 A g−1 and remains 80% for 198 F g−1 at the current density of 20 A g−1. The nanoporous carbon electrode exhibits long-term cycle life and could keep stable capacitance till 10,000 cycles. The consistently high specific capacitance, rate capacity, and long-term cycle life ability makes it a potential candidate as electrode material for supercapacitor

Replication study confirms link between TSPAN18 mutation and schizophrenia in Han Chinese.

Schizophrenia (SCZ) is a severe psychiatric disorder associated with many different risk factors, both genetic and environmental. A recent genome-wide association study (GWAS) of Han Chinese identified three single-nucleotide polymorphisms (SNPs rs11038167, rs11038172, and rs835784) in the tetraspanins gene TSPAN18 as possible susceptibility loci for schizophrenia. Hoping to validate these findings, we conducted a case-control study of Han Chinese with 1093 schizophrenia cases and 1022 healthy controls. Using the LDR-PCR method to genotype polymorphisms in TSPAN18, we found no significant differences (P>0.05) between patients and controls in either the allele or genotype frequency of the SNPs rs11038167 and rs11038172. We did find, however, that the frequency of the 'A' allele of SNP rs835784 is significantly higher in patients than in controls. We further observed a significant association (OR= 1.197, 95%CI= 1.047-1.369) between risk for SCZ and this 'A' allele. These results confirm the significant association, in Han Chinese populations, of increased SCZ risk and the variant of the TSPAN18 gene containing the 'A' allele of SNP rs835784

<b> </b>Demographic characteristics of study subjects.

<p><b> </b>Demographic characteristics of study subjects.</p

Allele frequency distribution among different ethnic groups for the three SNPs in <i>TSPAN18.</i>

<p>Allele frequency distribution among different ethnic groups for the three SNPs in <i>TSPAN18.</i></p

llele frequency distribution by sex and age group for three SNPs in <i>TSPAN18.</i>

†<p>A1/A2, indicates minor allele/major allele.</p>§<p>The minor allele frequency.</p>*<p>Cochran-Armitage trend test p-values for minor allele.</p