Automatic guided vehicle system (AGVs) plays a vital role in material handling operations for a flexible manufacturing system (FMS).Optimum AGVs fleet size selection is one of the most sig-nificant decisions in effective design and control of automated material handling system. The fleet size estimation and optimization of AGVs requires an in-depth understanding of the various factors that AGVs in the FMS relies on. In this paper, an investigation for fleet size optimization of AGVs in different layouts of FMS by application of the analytical method and grey wolf optimization al-gorithm (GWO) is carried out. Layout design is one of the significant factors for optimization of AGV’s fleet size in any FMS. Results yield from analytical and grey wolf optimization algorithm are compared and validated for the different sizes of FMS layouts by computational experiments.

Nowadays, with the expansion of economic businesses and also the dependency of economic activ-ists on the insurance industry to provide the capitals security, there is now a growing need to identi-fy and evaluate risks of the insurance industry. Therefore, in this study, a comprehensive model was developed to evaluate and manage business risk by reviewing the research literature, extensive-ly. For this purpose, an adaptive neuro-fuzzy inference system (ANFIS) was developed for every business risk after identifying the relevant dimensions and the criteria and collecting the necessary data from the central insurance databases. Finally, a general model was presented to evaluate and manage risks of the insurance industry. Four major problems were also considered: optimal and ef-ficient normalization, optimal training for testing ratio for every neural network, model validation, and the easiness of user communication with the system. The results show that the model can pro-vide an accurate estimation for risk evaluation and management. Thus, this system can be consid-ered as an appropriate tool for business risk evaluation and management of insurance companies. Furthermore, the effectiveness of this method in evaluating and managing the risk at insurance companies can be turned into a neural network and such a neural network can be used as an appro-priate decision-making support tool.

This paper uses the Nash bargaining model to examine the effects of the distribution system on firms purchasing multiple inputs. We show that network combination, that is, vertical integration between a manufacturer and a retail firm, does not always bring enterprise superiority to a non-integrated firm. Although classical economic theory articulates that vertically integrated firms typi-cally have an advantage over non-integrated firms in profits, as the former can eliminate double marginalization, this is not always the case if we consider a situation of multiple inputs trading.

Developed food production lines recently have built factories for novel products to find a position in the market faster, although innovation is still expensive and risky. This study focuses on con-tributors who persuade consumers to purchase new pasta products in Tehran, and tries to determine the correlation among the criteria. This is conducted from the market experts’ view points along with a blend of grounded theory and DEMATEL. The results indicate that quality, price, packag-ing, promotion, phantasm and place parameters enhance new pasta pictures on customers’ buying decision making. According to experts, quality and price are the most influential factors and promo-tion and phantasm are completely ineffective. In addition, cultural parameters, especially local reci-pes as well as customers’ palate are important in this process.

The mobility sector including all kinds of transportation systems are facing global challenges in re-spect of green environmental issues. There has been a paradigm shift in the concept of design and manufacturing of automotive vehicles keeping in mind the scarcity of fossil fuel and the impact of emission on environment due to burning of it. The addition of hybrid and electric vehicles in pas-senger car segment has got significant momentum to address the global challenges. This research investigates the performance of a group of hybrid vehicles from customers’ perspective. Among the different brands that are available in the hybrid vehicle market, smart customers have given pri-ority to vehicle cost, mileage, tail pipe emission, comfortness and high tank size volume for long drive. Considering these attributes, selection strategy for hybrid vehicles has been developed using entropy based multi-attributive border approximation area comparison (MABAC) method. This research highlights the best hybrid vehicle which reduces air pollution in cities with other significant environmental benefits, reduces dependence on foreign energy imports and minimizes the annual fuel cost.