IMPROVING THE EFFICIENCY OF LOAD MANAGEMENT ON THE BASIS OF FUZZY LOGIC ALGORITHMS
In this study, a model of decision-making in a fuzzy environment was proposed according to several criteria. Estimating the unloading time of a truck that is part of a cross-docking terminal is seen as one of the most important decision-making processes for effectively planning a supply chain distribution center. Any delay or unsuitability in the input phase of maintenance will slow down further processes, namely internal processes and outbound operations. The last processes will not be able to be completed if all incoming loads are not yet completed. This description takes into account the importance of effective management of incoming operations. This article presents a model for calculating the expected time of unloading the truck, which allows in real time to assign and plan incoming operations at the docking station in real time. The purpose of the model was to minimize the total service time of trucks at the front door, thereby reducing the waiting time of the truck in the warehouse. The calculations performed in Excel for some cases served as a means of preliminary verification of the logic of the developed model. Compared to previous studies, for example, in which the unloading time was considered as a constant unit of time based on the number of products (one unit of time for one unit of product). Unloading time in this study is presented as an estimated value based on several input parameters using a fuzzy logic approach. The results of this method, relating to the range and values of unloading, were approved by experts from LLC "Microbor Ukraine". The estimated time of unloading the truck is valuable information, as it allows the decision maker to estimate the time of service of the truck (waiting time plus unloading time), in which the waiting time is calculated based on the estimated time of service of earlier trucks. Assignment of the truck to the door can be done systematically after registration, as the fastest of the available dock doors can be determined in advance.
Goliasa, M.M., S. Iveya, K. Jia and M. Lipinski, 2010. A bi-objective model to minimize service and storage time at a cross dock facility. Proceedings of the 51st Annual Transportation Research Forum, March 11-13, 2010, Arlington, VA., USA., pp: 1-13.
Заде Л. А. Понятие лингвистической переменной и ее применение к принятию приближенных решений // Математика сегодня. Москва : Знание, 1974.
Матвійчук А.В. Моделювання економічних процесів із застосуванням методів нечіткої логіки. Київ : КНЕУ, 2007. 264 с.
Boysen, N. and M. Fliedner, 2010. Cross dock scheduling: Classification, literature review and research agenda. Omega, 38: 413-422.
MATLAB® Creating Graphical User Interfaces. The MathWorks, Inc. 3 Apple Hill Drive Natick, MA 01760-2098, 502 c.
Goliasa, M.M., S. Iveya, K. Jia and M. Lipinski, 2010. A bi-objective model to minimize service and storage time at a cross dock facility. Proceedings of the 51st Annual Transportation Research Forum, March 11-13, 2010, Arlington, VA., USA., Pp. 1-13.
Zade L.A. The concept of a linguistic variable and its application to approximate decisions // Mathematics today. Moskva: Znanie, 1974. S. 5-49. (in Russian)
Matviychuk A.V. (2007). Modeling of economic processes using fuzzy logic methods. Kyiv: KNEU, 264 s. (in Ukranian)
Boysen, N. and M. Fliedner, (2010). Cross dock scheduling: Classification, literature review and research agenda. Omega, 38: 413-422.
MATLAB (2008) Creating Graphical User Interfaces. The MathWorks, Inc. 3 Apple Hill Drive Natick, MA 01760-2098.