Optimization of Aggregate Production Circuit through Modeling of Crusher Operation

The paper concerns investigation of the effect of impact crusher operation on selected qualitative characteristics of mineral aggregate products. Qualitative characteristics of crushing products in terms of size reduction ratio and fine particles contents were analyzed from the point of view of operational parameters of the impact crusher. An investigative program was carried out on a plant scale and two primary parameters of the impactor were analyzed: velocity of the crusher rotor and the width of the outlet gap. The models of the crushing device operation were built separately for each type of the tested material, as well as for general conditions.

Production Methods for Regular Aggregates and Innovative Developments in Poland

The aim of this article is to present possibilities of producing aggregates with an increased content of regular particles in some well-known examples, as well as to present previously unused methods of aggregate production. Traditional aggregate production systems require the use of three or four grinding stages (depending on the particle size of the feed), but fine fractions contain approximately 10% irregular grains on average. The conducted research has shown that in innovative patented technological systems, it is possible to obtain regular aggregates with a share of irregular grains below 3%. The separated irregular aggregates can be crushed again or used for other purposes. The advantages of such inventions include less grinding stages, reduced dust, and lower energy consumption. Regular aggregates also have benefits compared to typical ones. The analyzed aggregate products from various mines showed that regular aggregates have better properties, for example, higher resistance to grinding and abrasion. This article also describes the advantages of using such aggregates for concrete or other applications.

An Uncertain APP Model with Allowed Stockout and Service Level Constraint for Vegetables

Volatile markets and uncertain deterioration rate make it extremely difficult for manufacturers to make the quantity of saleable vegetables just meet the fluctuating demands, which will lead to inevitable out of stock or over production. Aggregate production planning (APP) is to find the optimal yield of vegetables, shortage and overstock symmetry, are not conducive to the final benefit.The essence of aggregate production planning is to deal with the symmetrical relation between shortage and overproduction. In order to reduce the adverse effects caused by shortage, we regard the service level as an important constraint to meet the customer demand and ensure the market share. So an uncertain aggregate production planning model for vegetables under condition of allowed stockout and considering service level constraint is constructed, whose objective is to find the optimal output while minimizing the expected total cost. Moreover, two methods are proposed in different cases to solve the model. A crisp equivalent form can be transformed when uncertain variables obey linear uncertain distributions and for general case, a hybrid intelligent algorithm integrating the 99-method and genetic algorithm is employed. Finally, two numerical examples are carried out to illustrate the effectiveness of the proposed model.

System of Parametric Modelling and Assessing the Production Staff Utilisation as a Basis for Aggregate Production Planning

The requirement to achieve effective solutions in the shortest possible time in the manufacturing environment is essential, and it can be solved only by effective production planning methods. The scientific problem is that traditional methods for creating and assessing the production plans are insufficient for the future and it is necessary to look for new alternatives. The planners in the framework of designing the production layouts and subsequent capacity planning of the employees are missing the information, methods and tools for making clear decisions. The production costs in general and especially the costs for the workforce create a large part of the operating costs in many manufacturing enterprises. The scientific goal of the article is to present a design of the system for parametric modelling and assessing the working utilisation of the production staff intended for reducing costs. The described solution is based on object-oriented analysis and contains a methodology of planning and controlling the production process in the industrial environment. The designed methodology was used for developing a planning module of project software and was shown through a case study in a company dealing with the production of automotive components. Effective modelling of the digital copy of the manufacturing system in the software environment is one of the most difficult and important steps for developing reliable information systems for planning and inspection in the industry. The methodology’s results in a company are that the solution can be used as a basis for the aggregate production planning that brings savings and efficiency increases. The research results can be used in any company with strictly defined working positions, working activities, and limiting conditions.

Multi-objective Aggregate Production Planning for Multiple Products: A Local Search-Based Genetic Algorithm Optimization Approach

The diversity of products and fierce competition make the stability and production cost of manufacturing industry more important. So, the purpose of this paper is to deal with the multi-product aggregate production planning (APP) problem considering stability in the workforce and total production costs, and propose an efficient algorithm. Taking into account the relationship of raw materials, inventory cost and product demand, a multi-objective programming model for multi-product APP problem is established to minimize total production costs and instability in the work force. To improve the efficiency of the algorithm, the feasible region of the planned production and the number of workers in each period are determined and a local search algorithm is used to improve the search ability. Based on the analysis of the feasible range, a genetic algorithm is designed to solve the model combined with the local search algorithm. For analyzing the effect of this algorithm, the information entropy strategy, NSGA-II strategy and multi-population strategy are compared and analyzed with examples, and the simulation results show that the model is feasible, and the NSGA-II algorithm based on the local search has a better performance in the multi-objective APP problem.