Maryam Abdirad

Maryam Abdirad, Jamal Shahrabi

One of the main topics in human resources management is the subject of informal organizations in the organization such that recognizing and managing such informal organizations play an important role in the organizations. Some managers are trying to recognize the relations between informal organizations and being a member of them by which they could assist the formal organization development. Methods of recognizing informal organizations are complicated and occasionally even impossible. This study aims to provide a method for recognizing such organizations using data mining techniques. This study classifies indices of human resources influencing the creation of informal organizations, including individual, social, and work characteristics of an organizations employees. Then, a questionnaire was designed and distributed among employees. A database was created from obtained data. Applied data mining techniques in this study are factor analysis, clustering by K-means, classification by decision trees, and finally association rule mining by GRI algorithm. At the end, a model is presented that is applicable for recognizing the similar characteristics between people for optimal recognition of informal organizations and usage of this information.

Maryam Abdirad, Krishna Krishnan, Deepak Gupta

Companies are eager to have a smart supply chain especially when they have a dynamic system. Industry 4.0 is a concept which concentrates on mobility and real-time integration. Thus, it can be considered as a necessary component that has to be implemented for a Dynamic Vehicle Routing Problem. The aim of this research is to solve large-scale DVRP (LSDVRP) in which the delivery vehicles must serve customer demands from a common depot to minimize transit cost while not exceeding the capacity constraint of each vehicle. In LSDVRP, it is difficult to get an exact solution and the computational time complexity grows exponentially. To find near optimal answers for this problem, a hierarchical approach consisting of three stages callled cluster first, route construction second, route improvement third is proposed. The major contribution of this paper is dealing with large-size real-world problems to decrease the computational time complexity. The results confirmed that the proposed methodology is applicable.

Maryam Abdirad, Krishna Krishnan, Deepak Gupta

Industry 4.0 is a concept that assists companies in developing a modern supply chain (MSC) system when they are faced with a dynamic process. Because Industry 4.0 focuses on mobility and real-time integration, it is a good framework for a dynamic vehicle routing problem (DVRP). This research works on DVRP. The aim of this research is to minimize transportation cost without exceeding the capacity constraint of each vehicle while serving customer demands from a common depot. Meanwhile, new orders arrive at a specific time into the system while the vehicles are executing the delivery of existing orders. This paper presents a two-stage hybrid algorithm for solving the DVRP. In the first stage, construction algorithms are applied to develop the initial route. In the second stage, improvement algorithms are applied. Experimental results were designed for different sizes of problems. Analysis results show the effectiveness of the proposed algorithm.