End-of-life vehicle (ELV) recycling is a process that spends energy and could be an energy source as well. This part of energy recovering depends on many different factors related to the broad and local aspects of ELV recycling. The ELV recycling process is consuming energy from different energy sources (electrical, fossil), however, this consumption is lower in relation to energy consumption during the production of new vehicle parts from the very beginning. This article attempts to promote an integrated approach in the analysis of the problem of energy recovery through ELV recycling. Authors aim to analyze the ELV recycling process as an energy generator and to present possibilities for its energy recovery. The research analyses are based on the empirical investigation of ELV recycling in the Republic of Serbia, as a developing country, and on defined statistical model presenting the impact of ELV recycling on energy generation, spending, and conservation during one-year intervals. Research results showed that the higher ELV generation rates may led to a higher energy recovery, and environmental and socio-economic sustainability.

In order to adapt to ever-changing customer needs and satisfy them, good Business Process Management (BPM) in Small and Medium-sized Enterprises (SMEs) is crucial. The target group of this research is production SMEs whose BPM can be monitored respecting the values of key performance indicators (KPIs). This paper shows how improving the performance of the observed business processes can improve the level of customer satisfaction. This improvement should lead to the sustainability of SMEs in the market. In this paper, evaluation of business processes performance is defined as a multi-criteria decision problem. The relative importance of considered KPIs and their imprecise values are described by linguistic expressions, which are then modeled by triangular intuitionistic fuzzy numbers (TIFNs). Calculation of KPI weights is done by using the fuzzy analytic hierarchy process (FAHP). Evaluation of BPM success is conducted respecting the obtained KPI weights and KPI values. An optimal solution for BPM success improvement, respecting customer satisfaction indicators, is calculated using the Artificial Neural Network (ANN) and Genetic Algorithm (GA) approaches. By applying the proposed model, managers of production SMEs can determine the management initiatives that will improve their business and the sustainability of their companies.

The difference between the production cost and selling price of the products may be viewed as a criterion that determines an organization’s competitiveness and market success. In such circumstances, it is necessary to impact these criteria in order to maximize this difference. The selling products’ price, in modern market conditions, is a category which may not be significantly affected. So organizations have one option, which is the production cost reduction. This is the motive for business organizations and the imperative of each organization. The key parameters that influence the costs of production and therefore influence the competitiveness of organizations are the parameters of production machines and processes used to create products. To define optimal parameter values for production machines and processes that will reduce production costs and increase competitiveness of production organizations, the authors have developed a new mathematical model. The model is based on application of the ABC classification method to classify production line processes based on their costs and an application of a genetic algorithm to find the optimal values of production machine parameters used in these processes. It has been applied in three different modern production line processes; the costs obtained by the model application have been compared with the real production costs.

The current effects of rapid development, high population density in large residential areas and pressures on organizations to protect the environment, create a provocative framework for waste management in modern cities. The complexity of the process of garbage collection is large, and therefore a major concern for public authorities in terms of collection, transport and further processing of solid waste. In this paper, the authors have presented a two-step solution formed from a nearest neighbor search and genetic algorithm to optimize the path of trucks with a specified capacity for garbage collection. This method firstly performs a search for the optimal solution with a nearest neighbors' algorithm (NNA) over a set of possible solutions, and then in the second step gives that solution with other random solutions to a genetic algorithm (GA) for further improvement; the goal is to extract the solution with minimal trajectory and maximum capacity utilization of trucks that are available. Testing was done on a range of problems with a certain number of trucks, with a given capacity and the number and location of sites for waste collection.