@ARTICLE{Izdebski_Mariusz_The,
 author={Izdebski, Mariusz and Jacyna-Gołda, Ilona and Gołębiowski, Piotr and Plandor, Jaroslav},
 volume={Vol. 66},
 number={No 3},
 journal={Archives of Civil Engineering},
 pages={505-524},
 howpublished={online},
 publisher={WARSAW UNIVERSITY OF TECHNOLOGY FACULTY OF CIVIL ENGINEERING and COMMITTEE FOR CIVIL ENGINEERING POLISH ACADEMY OF SCIENCES},
 abstract={The article presents a new optimization tool supporting supply chain management in the multi-criteria aspect. This tool was implemented in the EPLOS system (European Logistics Services Portal system). The EPLOS system is an integrated IT system supporting the process of creating a supply and distribution network in supply chains. This system consists of many modules e.g. optimization module which are responsible for data processing, generating results. The main objective of the research was to develop a system to determine the parameters of the supply chain, which affect its efficiency in the process of managing the goods flow between individual links in the chain. These parameters were taken into account in the mathematical model as decision variables in order to determine them in the optimization process. The assessment of supply chain management effectiveness was carried out on the basis of the global function of the criterion consisting of partial functions of the criteria described in the mathematical model. The starting point for the study was the assumption that the effectiveness of chain management is determined by two important decision-making problems that are important for managers in the supply chain management process, i.e. the problem of assigning vehicles to tasks and the problem of locating logistics facilities in the supply chain. In order to solve the problem, an innovative approach to the genetic algorithm was proposed, which was adapted to the developed mathematical model. The correctness of the genetic algorithm has been confirmed in the process of its verification.},
 type={Article},
 title={The Optimization Tool Supporting Supply Chain Management in the Multi-Criteria Approach},
 URL={http://ochroma.man.poznan.pl/Content/117477/PDF/28.ACE00042%20do%20druku_B5.pdf},
 doi={10.24425/ace.2020.134410},
 keywords={multi-criteria optimization, genetic algorithm, transport infrastructure, supply chain management},
}