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An analytic flow design algorithm for an automated distribution center with multiple shipping areas is presented. The main elements of the analytic flow design are the number of devices and the volumes of product flow between the devices. In the design of an automated distribution center, it is necessary to satisfy the demand throughput while minimizing construction costs. In the conventional design process, system engineers utilize experimental and intuitional approaches. However, conventional approaches are time-consuming and the design outcome is dependent on the skill of the designer. Therefore, a theoretical design algorithm is needed. We propose an analytic flow design algorithm using a dynamic network flow model and considering time-variable flow volumes according to shipment and storage schedules. To verify the feasibility of the proposed method, we perform analytic flow design using real data and confirm that the proposed method can yield a feasible analytic flow design in several minutes.

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In this paper, a design problem for an automated warehouse which deals with seasonal products is discussed. The warehouse-design problem is to determine the number of machines used in a warehouse and the flow among the equipment. This problem is usually solved with experimental and intuitional approaches. However, conventional approaches take large amounts of time, and the design depends on designers' experienced skill. In addition, some warehouse deals with seasonal products, and if the amount of shipment exceed the throughput of auto-warehouse system, extra seasonal labors are needed in order to compensate warehouse system throughput. Therefore, when designing a warehouse, not only initial investment but operational cost (payment for seasonal labors) should be considered. However, this kind of seasonal cost is not considered in the conventional design approaches. In order to shorten the design process and give a theoretical guideline to the design, we propose a new design method using an extended network flow model. We then evaluate the feasibility of the model using real data and confirm the effect of the proposed method. After that, we examine the possibility to apply the proposed method to the design of a warehouse which deals with seasonal products.