The impact of dwell point policy in an Automated Storage/Retrieval System

Abstract

Automated storage and retrieval systems (AS/RS) are devices that allow intensive storage of materials. They can improve the supply chain performance, assuring more available volume for storage, lower labour costs and higher handling throughput of warehousing. Furthermore, the automated control allows the probability of errors in storage and retrieval to be minimised, along with the probability of product damage during movements. The purpose of the paper is to find the best solution in order to find the optimal dwell point policy, among different rules, able to minimise the travel time and distance travelled by stacker cranes, and consequently warehousing costs. An original and innovative model was developed in order to investigate the dwell point position for random allocation of unit loads. A software platform was developed to validate the proposed model by computer simulations. The performance of the system was analysed in a parametric/continuous way, varying at the same time the number of spans and levels, the height of the input/output point and the interval between requested missions. The results show that the developed model allowed convenience areas to be identified among the policies in which the travel time, distance travelled, and consequently warehousing costs are minimised, by varying different parameters.

Unit-load storage assignment strategy for warehouses in seismic areas

“…In the latter, the cranes start from the P/D point to store an item, then retrieve a different item from a different location before returning to the P/D point (Pohl, Meller, & Gue, 2009). Research and industrial practice show that AS/RS and manual warehouses adopt both single-command and dual-command policies (Eynan, Rosenblatt, & Chapman, 1994;Hur, 2004;Hwang, Moon, & Gen, 2002;Regattieri, Santarelli, Manzini, & Pareschi, 2013;Rouwenhorst et al, 2000).…”

Stochastic bounds for order flow times in parts-to-picker warehouses with remotely located order-picking workstations

“…The first three terms in (12) correspond to the parts of the cycle time that always occur: N service periods, a switching and an intervisit period. The last term represents the mean idle time during a visit: the average time 1/λ that an idle server has to wait for a customer to arrive multiplied by the average number of times the server becomes idle during a visit.…”

“…Recently, dimensioning of racks has also been studied from an energy-efficiency point of view [9]. Storage assignment strategies have also been investigated [10,11], as well as dwell-point strategies, i.e., strategies for positioning the crane in the aisle when temporarily no totes have to be picked in that aisle [12,13]. Although batching strategies can also reduce the travel times of cranes, these strategies have mainly been studied in case of picker-to-parts order-picking systems, where this is more challenging [14,15].…”

Development of Optimization Model and Algorithm for Storage and Retrieval in Automated Stereo Warehouses

“…A popular way of goods location allocation is to allocate the relevant goods close to each other and process the historical picking data [4][5][6]. One of the main strategies for picking route optimization lies in setting up a planning and optimization model for stacker picking routes that minimizes the outbound time [7][8][9][10]. The two methods are integrated in many studies.…”