In this paper, we provide a method to generate a three-dimensional detailed design of fishbone layouts. This method takes the
desired storage capacity and returns the location (x; y; z) of each opening of the warehouse in such a way that the total operational
cost - area cost and material handling cost- of the warehouse is minimal. We model the arrangement of the openings using
mathematical finite sequences and represent a fishbone layout in terms of four primary characteristics. Next, we develop an
algorithm that generates a detailed design of a fishbone layout given values of its four primary characteristics. Then, we present
an optimization model that finds the values for the four primary characteristics that minimize the total operational cost of the
warehouse. Finally, we solve the optimization model using a genetic algorithm. Our results suggest that in 91.74% of the cases, our
optimization procedure reaches a near optimum point - deviated only by 0.587% - in a reasonable computational time (maximum
4.5 minutes). This paper aims to diminish dependence upon experts and human decision making in the process of implementing
a fishbone layout on greenfield projects, and fulfills an identified need of warehouse practitioners by integrating the most recent
advances on non-traditional layouts and detailed warehouse design.
In this paper, we provide a method to generate a three-dimensional detailed design of fishbone layouts. This method takes the
desired storage capacity and returns the location (x; y; z) of each opening of the warehouse in such a way that the total operational
cost - area cost and material handling cost- of the warehouse is minimal. We model the arrangement of the openings using
mathematical finite sequences and represent a fishbone layout in terms of four primary characteristics. Next, we develop an
algorithm that generates a detailed design of a fishbone layout given values of its four primary characteristics. Then, we present
an optimization model that finds the values for the four primary characteristics that minimize the total operational cost of the
warehouse. Finally, we solve the optimization model using a genetic algorithm. Our results suggest that in 91.74% of the cases, our
optimization procedure reaches a near optimum point - deviated only by 0.587% - in a reasonable computational time (maximum
4.5 minutes). This paper aims to diminish dependence upon experts and human decision making in the process of implementing
a fishbone layout on greenfield projects, and fulfills an identified need of warehouse practitioners by integrating the most recent
advances on non-traditional layouts and detailed warehouse design.
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