Organizations can improve efficiency of process execution through a correct resource allocation, as well as increase income, improve client satisfaction, and so on. This work presents a novel approach for solving problems of resource allocation in business processes which combines process mining, statistical techniques, and metaheuristic algorithms for optimization. In order to get more reliable results of the simulation, in this paper, we use process mining analysis and statistical techniques for building a simulation model. For finding optimal human resource allocation in business processes, we use the improved differential evolution algorithm with population adaptation. Because of the use of a stochastic simulation model, noise appears in the output of the model. The differential evolution algorithm is modified in order to include uncertainty in the fitness function. In the end, validation of the model was done on three different data sets in order to demonstrate the generality of the approach, and the comparison with the standard approach from the literature was done. The results have shown that this novel approach gives solutions which are better than the existing model from literature.

Organizations use business process management to identify opportunities to reduce costs, increase service or product quality, etc. In this paper, a way to improve businesses processes using process mining techniques and standard methods of businesses process improvement is presented. Process mining has been used to fix the disadvantages of the existing standard methods. The presented approach is tested on a real process. After the process model is acquired using process mining analysis, business process improvement is proposed through a detailed analysis. Also, key performance indicators which are used to measure process performances, and a process model with a new resource allocation, which is improved from the aspect of the predefined key performance indications, is proposed. The experimental results have shown how the process can be improved with a better resource allocation.

Warehouse Management Systems (WMS) play a major role in optimizing warehouse logistic processes, archiving merchant trends of supply and demand, and also easen treating of goods which are close to expiring deadline, out of stock, broken or deposited by customers. Since warehouse management systems may be integrated with business intelligence, they may comprise algorithms for optimization of warehouse order picking and storing of goods, sales prediction etc. In this article case study of warehouse management system is presented, and afterwards problem of optimal warehouse order picking is exhibited. This problem is transformed into Travelling Salesman Problem (TSP), and solved using heuristic optimization methods.

Traffic is a complex system, in terms of functioning as well as organization. Traffic stop, i.e. failure to perform the required transport function from point A into point B, can be caused by variety of circumstances, and one of them is the occurrence of traffic accident. Therefore, it is of a great importance to pay attention to increasing the traffic safety level throughout the implementation of various measures. Traffic safety is one of the most important links of the traffic system. This paper analyzes in details the impact of the road and its environment, vehicles and drivers on traffic accidents. The model of classification of traffic accidents causes has been implemented on the basis of similarities of drivers, vehicles and road characteristics. k-means clustering algorithm has been used for this purpose. According to data available, time-series prediction model has been implemented in the second part of the work, for prediction of traffic accidents in analyzes regions. Results in both implemented cases are more than satisfactory.

This paper presents results of an analysis of the impact of the human resources changes in Scrum teams. Four Scrum teams were tracked (two developments and two quality assurance) along with their productivity and performance. Analysis showed that human resources changes have a significant impact on the entire team and its behavior. Their effort increased by adding overtime hours. In the same time, their performance and effective work decreased, which is reflected on the quantity of work that can be billed to the client. The analysis shows that it takes, in average, three sprints (each lasting fourteen days) for new team members to fully adjust to the team development process and acquire a business knowledge needed for maximum productivity. Teams whose members have been working together longer period and who have more senior members can adjust to team shifts more quickly. The analysis also showed a correlation between quality assurance and development team - when development team had extra utilization due to overtime, quality assurance team had an increase in overtime hours almost proportionately.

In today's global business environment, the importance of customer service, cost-competitiveness, and quality are key factors in determining an organization's success, or undesirable failure. Organizations try to optimize their processes to maximize their profits and make the very process faster. Users usually work with documents in the process. Working with documents makes the process more slowly, since the documents are important to be scanned and attached to the form. This work presents the optimization of such processes. It is achieved by automatic integration of Business Process Management and Electronic Document Management Systems. Improvements and results achieved by proposed integration are presented in this research. The model that is created enables monitoring of defined Key Performance Indicators in the identification process of the bottlenecks in the process. The process can be optimized by increasing the number of resources on the activities that are a bottleneck in the process. Such a solution has been tested in the process of opening a bank account.

Business process management is the process of modifying or adjusting an organization's business process in order to achieve higher productivity or lower costs. Each company or organization has a value creating process that usually involves people, machines and information. One of the main problems with such processes is that it is very difficult to predict how much of each resource is actually needed. In light of the above, the objective of this paper is to implement a methodology that is capable of optimizing the allocation of resources to tasks in a given business process. In this paper, the genetic algorithm was used for optimization. The idea is that once the units are properly presented, the optimal schedule of users should be determined using the genetic algorithm. The fitness function includes Key Performance Indicators of process: waiting time and cost of the resource. Since al the users are not qualified in performing all the tasks in the process, the algorithm has to consider minimal and the maximal available number of users for each activity. The usability of this approach is tested in the process of credit requirement. Finally, the results are compared to the current work process.

The aim of this work is to create a decision support system based on Adaptive Neuro-Fuzzy Inference System (ANFIS), which will be used for objective classification of employees in the employment process by analyzing available information about the candidates. Information about the candidates is extracted from the relevant documents on one company during the advertising for job in Information Technology (IT) sector. Filtration and normalization of the data were performed after they had been acquired, the decision support system being realized was implemented in the form of hybrid model, made of fuzzy logic synthesis and neural network, as well as presented by acronym ANFIS. The initial ANFIS system is divided into two subsystems as the result of complexity of the very problem, as well as of more attributes. By training and testing (checking) the obtained Fuzzy Inference System (FIS) structures, the one with the lowest error is chosen. The operation of the network has been demonstrated by the valuable simulation model. The successful validation of the resulting system has been made by validation data.