The transport is an important part of logistic systems. Improper management of transport operations may contribute to the low level of the usage of vehicles and to high transport costs, as well as to the formation of unnecessary high inventory at each location of storage, as well as prolonged time of order realization and not full use of company capacity. It is therefore important the appropriate dimensioning, planning of the transport system and performed transport operations so as to allow the supply of certain goods at the right time and the amount to the appropriate points of the system. The article presents the methods of transport operations modelling, taking into account different criteria based on discrete event simulation. In the article the case study of modelling transport operations in the small cross-docking centre is also presented
The return on long-term investments depends on the adaptability of logistic systems. To meet the market requirements logistics processes must be planned in a flexible and versatile method with the use of mutual synergy. The article was based on own research focused on the application of synergistic planning in connection with the digitalization of planning processes in a factory. Furthermore, the design of manufacturing and logistics processes must be associated with planning objects. Synergistic planning synchronizes the phases of production planning with the stages of object planning and describes the factory life cycle from preparation to planning to the exploitation of the facility.
Logistic networks are complex systems, composed of many elements connected through nonlinear relations. This property makes it difficult to study these systems with traditional analytical methods. Therefore, computer simulation is a valuable tool in the practical applications of modeling the structure of the logistics network, the relationship between its components and the rules controling its functioning. The simulation model can be run in order to imitate the operation of the actual system in a given time interval and analyze its behavior under different scenarios. In the paper resilience measures for logistic networks are proposed and a multi-method approach of computer simulation for analysing their resistance is presented. The proposed concept allows determining and checking the possibilities ways of building long-term robustness of logistics networks for serious disturbances. The application of the model is illustrated by an example from the steel industry. AnyLogic 8.2 software was used for the implementation.
This paper presents the problem of public transport planning in terms of the optimal use of the available fleet of vehicles and reductions in operational costs and environmental impact. The research takes into account the large fleet of vehicles of various types that are typically found in large cities, including the increasingly widely used electric buses, many depots, and numerous limitations of urban public transport. The mathematical multi-criteria mathematical model formulated in this work considers many important criteria, including technical, economic, and environmental criteria. The preliminary results of the Mixed Integer Linear Programming solver for the proposed model on both theoretical data and real data from urban public transport show the possibility of the practical application of this solver to the transport problems of medium-sized cities with up to two depots, a heterogeneous fleet of vehicles, and up to about 1500 daily timetable trips. Further research directions have been formulated with regard to larger transport systems and new dedicated heuristic algorithms.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.