Modelling and Optimization of a Non-Constrained Multi-objective Problem having Multiple Utility Functions using Bayesian Theory

One of the multi-objective optimization methods makes use of the utility function for the objective functions. Utility function creating the most satisfaction answers for decision makers (DMs) by considering the priorities of the DMs; in an available studies; there are only one utility function for each objective function. But due to practical situation in different decision making environments in an industry or trade lead each goal has multiple utility functions. This paper presents a model of multi- objective problem in which each of the objective function has multiple utility function applying Bayesian theory. This model allows DMs to calculate the probability of these utilities using conditional probability in conditions of uncertainty. In addition, examples are given to illustrate the usefulness of this model

Optimizing Employment and learning system using big data and knowledge management based on deduction graph

In recent years, big data has usefully been deployed by organizations with the aim of getting a better prediction for the future. Moreover, knowledge management systems are being used by organizations to identify and create knowledge. Here, the output from analysis of big data and a knowledge management system are used to develop a new model with the goal of minimizing the cost of implementing new recognized processes including staff training, transferring and employment costs. Strategies are proposed from big data analysis and new processes are defined accordingly. The company requires various skills to execute the proposed processes. Organization\u2019s current experts and their skills are known through a pre-established knowledge management system. After a gap analysis, managers can make decisions about the expert arrangement, training programs and employment to bridge the gap and accomplish their goals. Finally, deduction graph is used to analyze the model

A Public Bicycle Sharing System Considering Renting and Middle Stations

Recently, public bicycle sharing system (PBSS) has become one of the most favorite urban transportation systems that can help governments to decrease environmental problems such as pollution and traffic. This paper studies a sharing system that includes two types of stations. The first category contains stations that users can rent or return back bicycles and each bicycle can be rented by any new user who arrives to the stations. The second group is the stations which are near shopping centers, historical and other places that users and tourists can stop and visit them. These stations are used only for parking the rented bicycles for a period of time and after that, the users must ride their bicycles and turn them back to their destination stations. After discussing the network of the model under the closed Jackson network, the Mean Value Analysis (MVA) method will be used to calculate the mean queue of each station and analyzing the proposed model