A Model for the Designing of Multimodal Transport Processes and the Concept of Its Integration with the EPLOS System

The paper proposes a new single criterion mathematical model for the designing of multimodal transport processes by taking into account the cargo’s susceptibility and the concept of its inclusion into the EPLOS system, which is done as part of the EUREKA initiative. This system will integrate the data from logistics sources and transport and logistics infrastructure from many sources. In the first phase of its implementation, it will cover the Czech Republic, Poland, and the Baltic States. Using the EPLOS system integrating data from various sources needed to solve this problem is a proposal to overcome the main barrier to the effective planning of multimodal transport processes – a lack of reliable information

The model of selecting multimodal technologies for the transport of perishable products

The main goal of this paper is to provide an original model of selecting multimodal technologies for the transport of perishable goods. The model in particular refers to the transportability of cargoes. The features of cargoes that have the most impact on transportability were specified. Formal representations of the key elements of the model were presented and characterized, including: perishable cargoes, form of transported goods (solid, liquid, etc.), means of handling (including loading devices and transport means), transport routes, categories of human labor, multimodal technologies and transportation tasks. A formal representation of decision variables, as well as constrains and a criterion function were provided. The model bases on two main solution assessment criteria: cost criterion and cargo safety criterion. A cargo safety criterion in the model is composed of 18 partial criterion functions. Each of these functions directly affects one safety aspect of the transported cargo. The exemplary partial criteria of cargo safety included in the model are: acceptable transport time, minimum or maximum temperature in the cargo’s direct surroundings, resistance to mechanical damage. In order to present a practical application of the presented mathematical model the paper shows also an example of selecting one of the multimodal technologies for the transport of perishable goods from the set of pre-defined types of multimodal transport technologies. The developed method uses different elements of the mathematical model provided in the paper, depending on the considered problem (including characteristics of cargo and their transport forms). For a significant group of perishable cargoes, it is not required to consider all defined criteria associated with cargo safety. The developed model allows for the accurate selection of transport technology for perishable cargoes for most transportation tasks. It should help to increase the efficiency of selection of multimodal transport technology for perishable products. The selected technology will then be characterized by the lowest transport cost and will ensure the safety of transported cargoes, as well as will meet other requirements determined by the transport task. As part of further work, it is possible to develop proposed method by considering additional characteristics of perishable cargoes

MODELING THE OPTIMAL MEASUREMENT TIME WITH A PROBE ON THE MACHINE TOOL USING MACHINE LEARNING METHODS

This paper explores the application of various machine learning techniques to model the optimal measurement time required after machining with a probe on CNC machine tools. Specifically, the research employs four different machine learning models: Elastic Net, Neural Networks, Decision Trees, and Support Vector Machines, each chosen for their unique strengths in addressing different aspects of predictive modeling in an industrial context. The study examines as input parameters such as material type, post-processing wall thickness, cutting depth, and rotational speed over measurement time. This approach ensures that the models account for the variables that significantly affect CNC machine operations. Regression value, mean square error, root mean square error, mean absolute percentage error, and mean absolute error were used to evaluate the quality of the obtained models. As a result of the analyses, the best modeling results were obtained using neural networks. Their ability to accurately predict measurement times can significantly increase operational efficiency by optimizing schedules and reducing downtime in machining processes