Robust Multi-Objective Sustainable Reverse Supply Chain Planning: An Application in the Steel Industry

In the design of the supply chain, the use of the returned products and their recycling in the production and consumption network is called reverse logistics. The proposed model aims to optimize the flow of materials in the supply chain network (SCN), and determine the amount and location of facilities and the planning of transportation in conditions of demand uncertainty. Thus, maximizing the total profit of operation, minimizing adverse environmental effects, and maximizing customer and supplier service levels have been considered as the main objectives. Accordingly, finding symmetry (balance) among the profit of operation, the environmental effects and customer and supplier service levels is considered in this research. To deal with the uncertainty of the model, scenario-based robust planning is employed alongside a meta-heuristic algorithm (NSGA-II) to solve the model with actual data from a case study of the steel industry in Iran. The results obtained from the model, solving and validating, compared with actual data indicated that the model could optimize the objectives seamlessly and determine the amount and location of the necessary facilities for the steel industry more appropriately.This article belongs to the Special Issue Uncertain Multi-Criteria Optimization Problem

Searching for superspreaders of information in real-world social media

A number of predictors have been suggested to detect the most influential spreaders of information in online social media across various domains such as Twitter or Facebook. In particular, degree, PageRank, k-core and other centralities have been adopted to rank the spreading capability of users in information dissemination media. So far, validation of the proposed predictors has been done by simulating the spreading dynamics rather than following real information flow in social networks. Consequently, only model-dependent contradictory results have been achieved so far for the best predictor. Here, we address this issue directly. We search for influential spreaders by following the real spreading dynamics in a wide range of networks. We find that the widely-used degree and PageRank fail in ranking users' influence. We find that the best spreaders are consistently located in the k-core across dissimilar social platforms such as Twitter, Facebook, Livejournal and scientific publishing in the American Physical Society. Furthermore, when the complete global network structure is unavailable, we find that the sum of the nearest neighbors' degree is a reliable local proxy for user's influence. Our analysis provides practical instructions for optimal design of strategies for "viral" information dissemination in relevant applications.Comment: 12 pages, 7 figure

Setting the Agenda: Different strategies of a Mass Media in a model of cultural dissemination

Day by day, people exchange opinions about a given new with relatives, friends, and coworkers. In most cases, they get informed about a given issue by reading newspapers, listening to the radio, or watching TV, i.e., through a Mass Media (MM). However, the importance of a given new can be stimulated by the Media by assigning newspaper's pages or time in TV programs. In this sense, we say that the Media has the power to "set the agenda", i.e., it decides which new is important and which is not. On the other hand, the Media can know people's concerns through, for instance, websites or blogs where they express their opinions, and then it can use this information in order to be more appealing to an increasing number of people. In this work, we study different scenarios in an agent-based model of cultural dissemination, in which a given Mass Media has a specific purpose: To set a particular topic of discussion and impose its point of view to as many social agents as it can. We model this by making the Media has a fixed feature, representing its point of view in the topic of discussion, while it tries to attract new consumers, by taking advantage of feedback mechanisms, represented by adaptive features. We explore different strategies that the Media can adopt in order to increase the affinity with potential consumers and then the probability to be successful in imposing this particular topic.Comment: 23 pages, 7 figure