No abstract

Supply chain management (SCM) has a dynamic structure involving the constant flow of information, product, and funds among different participants. SCM is a complex process and most often characterized by uncertainty. Many values are stochastic and cannot be precisely determined and described by classical mathematical methods. Therefore, in solving real and complex problems individual methods of artificial intelligence are increasingly used, or their combination in the form of hybrid methods. This paper has proposed the decision support system for determining economic order quantity and order implementation based on Adaptive neuro-fuzzy inference systems-ANFIS. A combination of two concepts of artificial intelligence in the form of hybrid neurofuzzy method has been applied into the decision support system in order to exploit the individual advantages of both methods. This method can deal with complexity and uncertainty in SCM better than classical methods because they it stems from experts' opinions. The proposed decision support system showed good results for determining the amount of economic order and it is presented as a successful tool for planning in SCM. Sensitivity analysis has been applied, which indicates that the decision support system gives valid results. The proposed system is flexible and can be applied to various types of goods in SCM.

ABSTRACT

The planning of road infrastructure undergoes major changes, especially in terms of sustainable development. Recycling of pavement structures involves the reuse of materials from existing pavement structures due to its timesaving and environmental benefits, as well as cost reduction. According to the recycling temperature, recycling can be hot and cold. This paper deals with cold in-place recycling and the determination of the optimum fluid content for by-product materials in mixtures compared with one containing natural zeolite. The content of bitumen emulsion and cement—which are the most used materials so far in cold recycling along with foam bitumen—was replaced with fly ash, slag or natural zeolite, and bakelite, respectively, while recycled asphalt pavement from Serbia (Žabalj) was used. Six different mixtures were made. The mixture with the addition of fly ash had the highest optimum fluid content (7.6%) compared with all test mixtures. Mixtures with slag, natural zeolite, and bakelite were in the range of a mixture containing 2% cement. Furthermore, the mixture with 3% cement had the lowest optimum fluid content (5.7%) in comparison to all the mixtures that were tested.

Identification of key indicators that cause safety challenges and vulnerable roads is crucial for improving traffic safety. This paper, therefore, entails to the development of a novel multiphase multicriteria decision-making (MCDM) model to evaluate the vulnerability of urban roads for traffic safety. This was conducted by using data from 17 important roads of a South African city and combining several methods such as CRiteria Importance through Intercriteria Correlation (CRITIC), data envelopment analysis (DEA), and measurement of alternatives and ranking according to compromise solution (MARCOS). Taking the elements of the DEA method, two new approaches for calculating the weights of criteria, the DEA-1 and DEA-2 models, were formed and integrated with the CRITIC method to obtain the final values of criteria weights. The MARCOS method was applied to evaluate 17 alternatives, for each direction separately. The aim of developing such a model is to use the advantages of obtaining objectivity of criteria weights through linear programming and correlation of values of the collected data. Also, the MARCOS method, as one of the newer and applicable methods, provides additional significance. Extensive sensitivity analyses were conducted to validate the model. The findings suggest that there are a certain number of roads that have a high level of safety for both directions, as well as a group of risky roads, which need traffic improvement measures. Thus, the results indicate that the model is sensitive to various approaches and can prioritize vulnerable roads comprehensively based on which safety measures can be taken.