Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/182063
Authors: 
Schneeberger, Karl
Doerner, Karl F.
Schilde, Michael
Year of Publication: 
2018
Citation: 
[Journal:] Logistics Research [ISSN:] 1865-0368 [Volume:] 11 [Year:] 2018 [Issue:] 6 [Pages:] 1-18
Abstract: 
In this paper, we consider a steel coil storage and distribution problem for a steel producing company. The company has several warehouses, and relocations of steel coils to different warehouses before they are dispatched are inevitable. These relocations are driven by a lack of capacity or the necessity of a relocation (e.g., if a coil is dispatched by ship on a specific day, the coil must be relocated to the designated warehouse). These relocations are performed by specific truck and trailer and are time-consuming and costly. In an effort to bring greater efficiency to this problem, we developed a fast matheuristic solution method that has a simple design and performs well. We apply our technique to a set of large real world data (with up to 87 days). To evaluate the performance for realistic relocations, we consider many different sizes of coils (considering 225 different coil types, varying in dimensions). Therefore, the complexity of the problem to solve is high and many decisions concerning the best re ocations must be made. The results of the matheuristic approach show that it is possible to improve the current solution of the steel producing company by as much as 11% for larger test cases (considering a time frame of at least 30 days). In addition, we provide insights on the value of information (no information versus all information about the production and distribution). Our results show that more information leads to the better results (i.e., less relocations). But, also with no information on further production and distribution, we can improve the current real world solutions.
Subjects: 
steel coils
coil relocation
capacitated multilocation warehouses
Persistent Identifier of the first edition: 
Creative Commons License: 
https://creativecommons.org/licenses/by/4.0/
Document Type: 
Article

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