In the last decade, simulation software as a tool for managing and controlling business processes has received a lot of attention. Many of the new software features allow businesses to achieve better quality results using optimisation, such as genetic algorithms. This article describes the use of modelling and simulation in shipment and sorting processes that are optimised by a genetic algorithm’s involvement. The designed algorithm and simulation model focuses on optimising the duration of shipment processing times and numbers of workers. The commercially available software Tecnomatix Plant Simulation, paired with a genetic algorithm, was used for optimisation, decreasing time durations, and thus selecting the most suitable solution for defined inputs. This method has produced better results in comparison to the classical heuristic methods and, furthermore, is not as time consuming. This article, at its core, describes the algorithm used to determine the optimal number of workers in sorting warehouses with the results of its application. The final part of this article contains an evaluation of this proposal compared to the original methods, and highlights what benefits result from such changes. The major purpose of this research is to determine the number of workers needed to speed up the departure of shipments and optimise the workload of workers.
As the sale of agricultural machinery and spare parts is a highly seasonal business, manufacturing companies need to respond to the changing market demand by adjusting their production level in a cost-effective and flexible manner (to reduce storage costs). In view of this, designers of manufacturing systems must embrace the challenge of designing modular systems, which structure can be quickly adapted to the changing product range and production volume. One approach that addresses this challenge is the concept of reconfigurable manufacturing systems (RMS), which was developed at the end of the 20th century. The key characteristics of RMS are modularity, integrability, customized flexibility, convertibility, scalability and diagnosability, all of which are consistent with the assumptions of the philosophy of Industry 4.0. These featuresalongside the adjusted system's capacity and flexibility to current manufacturing needsallow to extend the life cycle of a designed system. The aim of this paper was to enable (using computer simulation method) the selection of an RMS structure what will correspond to the expected characteristics determining the throughput of the system under design and to select the most appropriate cycle time that allows to reduce the necessary capacity of buffers between the next stages of the designed system. In particular, eight RMS structures using Tecnomatix Plant Simulation software were modelled and the system's throughput for each of those structures was analysed. As a part of presented conclusions, general guidelines how to choose the best structure during the process of reconfigurable manufacturing system's design have been pointed out.
This paper describes an algorithm of dynamic inventory control system for large numbers of material items with continuous non-stationary demand. It uses principles of pull inventory control systems, statistical inventory analysis and joint replenishment inventory systems. This algorithm was processed in software module (VBA for Excel) and applied in practice. The target of described algorithm is to keep an optimum inventory level and optimum customer service level in terms of inventory control of items with non-stationary demand.
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