The transportation service for the cluster of small and medium enterprises (SMEs) is different with traditional vehicle routing problems. In the cluster of SMEs, parts of enterprises are pickup and delivery spots simultaneously, but some enterprises are partly pickup and delivery simultaneously. It is necessary to optimize this transportation service with an effective mathematics and algorithm to reduce transportation costs for manufacturers. However, traditional mathematics models and algorithms are not suitable to solve the vehicle routing problem partly simultaneous pickup and delivery (VRPPSD) because these items mainly focus on the vehicle routing problem with pickup and delivery simultaneously. In this paper, a mathematics operational model is proposed to analyze the transportation service of the cluster companies and to describe transportation processes. A hybrid algorithm which is composed by tabu search, genetic algorithm and local search is used to optimize the operational model. The crossover and mutation contained by genetic algorithm are used to generate neighborhood solutions for tabu search, and the local search is used to improve optimizing solutions. The data of a cluster of SMEs, investigating from Changzhou city, China, are employed to show the validity of our mathematical model and algorithm. The results indicate that our model and hybrid algorithm is effective to solve VRPPSD. In this paper, the satisfied solutions of VRPPSD are found by hybrid algorithm. At the same time, the results also show that carriers with optimal routs can service customers with more profits (increasing 5.6%). The potential saving of transport cost will increase profits of carriers in SMEs. Sensitivity analyses about adjusting service time and rate of new orders are lunched to analyze how these two factors influence the profits of the VRPPSPD in a dynamic case. A bottleneck that influences the profits is found, and there has a shorter service time which could increase gross profits, but not significantly.
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A method for air combat sensor resource management based on fuzzy Bayesian networks (FBN) is presented. Using the fuzzy value of target information gain, target threat level and pilot command, probabilistic reasoning among the networks is carried out. Simulation results indicate that FBN method performances better in the allocation of sensor resource compared to information gain (IG) method.
PL
W artykule przedstawiono zastosowanie sieci Bayes’a (ang. FBN) w zarządzaniu zasobem czujników w czasie walki powietrznej. Wykorzystując wartość rozmytą informacji o celu, poziom możliwego zagrożenia oraz komendy pilota, zbudowano sieć argumentowania probabilistycznego. Wyniki symulacyjne wykazują, że metoda FBN wykazuje lepsze właściwości alokacji zasobów niż metoda dywergencji Kullbacka-Leiblera (DKL, IG).
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Coastal wetlands are ecologically important all over the world, and they are relatively unstable with dramatic changes in aboveground vegetation. However, it is still unclear how the aboveground vegetation changes will influence the functioning of coastal wetland ecosystems, especially the decomposition processes. Here, we carried out a cotton strip experiment to examine the effects of Suaeda salsa community on the soil properties and the associated cellulose decomposition rates in the coastal wetlands of Liao River delta (NE China). Our results showed that S. salsa community significantly affected the contents of soil C, N, P, base cations, organic matter and the soil electrical conductivity (EC), and such effects might vary among different types or densities of aboveground vegetation. The soil cellulose decomposition rate (in terms of cotton strip tensile strength loss, CTSL) was slowed down when aboveground S. salsa communities are experiencing degradation or have been totally replaced by Phragmites australis communities. Moreover, there were positive partial correlations between soil N and CTSL, and between soil EC and CTSL, but a negative partial correlation between soil C and CTSL. Our results emphasized the importance of S. salsa community in determining the soil cellulose decomposition rate in this coastal region. The results suggest that vegetation degradation in coastal wetlands might lead to various changes in soil properties and hence affect other aspects of ecosystem functioning and services, especially nutrient cycling.
Coastal wetlands are ecologically important all over the world, and they are relatively unstable with dramatic changes in aboveground vegetation. However, it is still unclear how the aboveground vegetation changes will influence the functioning of coastal wetland ecosystems, especially the decomposition processes. Here, we carried out a cotton strip experiment to examine the effects of Suaeda salsa community on the soil properties and the associated cellulose decomposition rates in the coastal wetlands of Liao River delta (NE China). Our results showed that S. salsa community significantly affected the contents of soil C, N, P, base cations, organic matter and the soil electrical conductivity (EC), and such effects might vary among different types or densities of aboveground vegetation. The soil cellulose decomposition rate (in terms of cotton strip tensile strength loss, CTSL) was slowed down when aboveground S. salsa communities are experiencing degradation or have been totally replaced by Phragmites australis communities. Moreover, there were positive partial correlations between soil N and CTSL, and between soil EC and CTSL, but a negative partial correlation between soil C and CTSL. Our results emphasized the importance of S. salsa community in determining the soil cellulose decomposition rate in this coastal region. The results suggest that vegetation degradation in coastal wetlands might lead to various changes in soil properties and hence affect other aspects of ecosystem functioning and services, especially nutrient cycling.