To ensure the competitiveness of manufacturing companies in the market, batching and batch scheduling are among the most important tasks. This paper presents a simulation-optimization approach that combines the discrete event simulation (DES) and the genetic algorithm (GA) to solve the batching and batch scheduling problem in a hybrid flow shop (HFS). HFS is widely used for the production of medium and large quantities of different technologically complex products. Based on a real-world manufacturing company, the HFS simulation model was developed using the Tecnomatix Plant Simulation software package. By analysing the influencing factors that represent production costs, a new formulation of the total cost of production was proposed. The purpose of this case study was to ensure timely delivery and minimize production costs by integrating simulation and optimization tools. This research considers sequence-dependent setup times, and availability of manufacturing and transportation equipment. The results of this research showed that the proposed simulation-optimization approach can be applied to solve the problem in many industrial case studies.

Batch sizing and scheduling problems are usually tough to solve because they seek solutions in a vast combinatorial space of possible solutions. This research aimed to test and further develop a scheduling method based on a modified steady-state genetic algorithm and test its performance, in both the speed (low computational time) and quality of the final results as low makespan values. This paper explores the problem of determining the order and size of the product batches in a hybrid flow shop with a limited buffer according to the problem that is faced in real-life. Another goal of this research was to develop a new reliable software/computer program tool in c# that can also be used in production, and as result, obtain a flexible software solution for further research. In all of the optimizations, the initial population of the genetic algorithm was randomly generated. The quality of the obtained results, and the short computation time, together with the flexibility of the genetic paradigm prove the effectiveness of the proposed algorithm and method to solve this problem.

Quality control of welded joint is an indispensable part of the welding production process. As part of spot resistance welding group, cross-wire welding process showed great application for welding of products for everyday usage. The non-contact quality control checking is fit for purpose due to specific characteristics of welded products that consist of two cross welded wires or a combination of wires and strips. This work proposes a new method for detecting and measuring of required dimensional parameters, but also founds its applicability for other products if required. A crucial parameter of this research is the height of welded joint, which is necessary for calculating the penetration of the wire into the wire. The proposed measuring method with a reconfigurable measuring system is explained in this paper. The main component of this system is using a machine vision system, which has become an indispensable part of industrial metrology and is considered one of the industry 4.0 concepts. The calibration process for such systems could be very complicated. This work shows an elaborated calibration procedure for this kind of measuring system with referenced standards made for this purpose. Measurement results are compared with ones obtained by conventional method. The focus of vision system is a substantial part as it dictates the quality of the system. This research is done within the project in collaboration with the industrial sector and all samples are from real processes. The results of measured penetration on one product group are showing the applicability of a reconfigurable measuring system in the welding sector, and demonstrate that measurement of welding penetration based on machine vision is feasible and can ensure accuracy.

Planning the order and size of batches is an extremely complex task especially if these tasks are related to production companies in a real environment. This research deals with the problem of determining the entry order and size of...

Production plants have always been confronted with the problem of scheduling, as this has a direct impact on production time and therefore on production costs. This is especially true in today's world, where it is necessary to produce a quality product at a low price and in a short time, while at the same time responding flexibly to customer demands. Due to the complexity and for economic reasons, testing different variants in a real production environment is insufficient and ineffective. For this reason, this paper proposes a new method that combines discrete event simulation and factorial design experiment to find the optimal schedule in hybrid flow shop. It was tested with the goal of achieving the minimum makespan. The results show that this method makes it possible to find improved solution very quickly. Compared to the original production schedule, the makespan could be reduced by 3 hours and 31 minutes which is reduction of 16.3%. The proposed methodology can be used in many discrete and process production plants.