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Artificial Intelligence Methods in Spare Parts Demand Forecasting

100%

Rosienkiewicz M.

Logistics and Transport

2013

tom 18

41-50

The paper discusses the problem of forecasting lumpy demand which is typical for spare parts. Several prediction methods are presented in the article – traditional techniques based on time series and advanced methods that use Artificial Intelligence tools. The research conducted in the paper focuses on comparison of eight forecasting methods, including classical, hybrid and based on artificial neural networks. The aim of the paper is to assess the efficiency of lumpy demand forecasting methods that apply AI tools. The assessment is conducted by a comparison with traditional methods and it is based on Root Mean Square Errors (RMSE) and relative forecast errors (ex post) values. The article presents also a new approach to the lumpy demand forecasting issue – a method which combines regression modelling, information criteria and artificial neural networks.

Artificial intelligence-based hybrid forecasting models for manufacturing systems

100%

Rosienkiewicz M.

Eksploatacja i Niezawodność

2021

tom Vol. 23, no. 2

263--277

The paper addresses the problem of forecasting in manufacturing systems. The main aim of the research is to propose new hybrid forecasting models combining artificial intelligencebased methods with traditional techniques based on time series – namely: Hybrid econometric model, Hybrid artificial neural network model, Hybrid support vector machine model and Hybrid extreme learning machine model. The study focuses on solving the problem of limited access to independent variables. Empirical verification of the proposed models is built upon real data from the three manufacturing system areas – production planning, maintenance and quality control. Moreover, in the paper, an algorithm for the forecasting accuracy assessment and optimal method selection for industrial companies is introduced. It can serve not only as an efficient and costless tool for advanced manufacturing companies willing to select the right forecasting method for their particular needs but also as an approach supporting the initial steps of transformation towards smart factory and Industry 4.0 implementation.

An Integrated Approach towards Building a Simulation Model Supporting the Management of the Passenger Transportation System. Part 2 – Case Study.

51%

Gąbka J. , Susz S. , Rosienkiewicz M.

Logistics and Transport

2016

tom 32

51-64

This article presents a simulation model designated as an advising and forecasting tool for designing, redesigning and managing ground-based transportation systems. It considers both public and private transport means. It enables visualisation of the results of changes in the transportation network such as a new transportation mode, schedule adjustment, technology improvements on shuttle speed and other modifications that can influence the effectiveness of the transportation network. The simulation tool enables predictions of future passenger flow size for different means of transport. The simulation tool was developed after thorough analysis of interdependencies between variables in the transportation network model built upon an econometric model, artificial neural network and mathematical model. The simulation model was tested on the real data and determined to be very effective, useful and flexible in use. Successive phases of the model development proved that development of a reliable advising and forecasting tool requires a combination of different methods.

An Integrated Approach towards Building a Simulation Model Supporting the Management of the Passenger Transportation System. Part 1 - Theoretical basis.

51%

Gąbka J. , Susz S. , Rosienkiewicz M.

Logistics and Transport

2016

tom 32

39-50

This article presents an integrated approach towards building a simulation model of the transportation network. The proposed method is based on the recommendations of the Blue Book for Sector of Public Transport in cities, agglomerations and regions issued by Jaspers (Joint Assistance to Support Projects in European Regions). It comprises six steps with such elements as econometric modelling, artificial neural networks and mathematical model. It is dedicated to developing simulation models for the purpose of picturing present situation and dependencies dominating in the transportation network, easily reflecting effects of potential modifications and providing forecasts for the future. The described hybrid method was verified in practice by building the simulation model which is presented in the next article in this series entitled: An Integrated Approach towards Building a Simulation Model Supporting the Management of the Passenger Transportation System. Part 2 – Case Study.