In this paper, we present a new algorithm for routing order pickers in manual warehouses and optimizing the total distance covered. The proposed approach is an adaptation of the recent water wave optimization (WWO) algorithm, a nature-inspired algorithm based on the shallow water wave theory. The algorithm is tested on a rectangular 1-block warehouse, with 12 aisles and 45 storage locations per aisle side (1080 storage locations overall). The depot is located at the bottom left corner of the warehouse. A Microsoft ExcelTM file was programmed to reproduce both the adapted WWO algorithm and to test its performance, in terms of the travel distance covered by pickers. A formal design of experiment (DOE) analysis is carried out on some of the algorithm parameters, to assess their effect on the performance observed (namely computational time and number of iterations required to reach convergence) and identify the optimal setting.
An adapted water wave optimization algorithm for routing order pickers in manual warehouses / Bottani, Eleonora; Rinaldi, Marta; Montanari, Roberto; Teresa, Murino; Piera, Centobelli. - 1:(2016), pp. 209-214. (Intervento presentato al convegno 21st Summer School Francesco Turco tenutosi a Naples, Italy nel 13-15 Settembre 2016).
An adapted water wave optimization algorithm for routing order pickers in manual warehouses
MONTANARI, Roberto;
2016-01-01
Abstract
In this paper, we present a new algorithm for routing order pickers in manual warehouses and optimizing the total distance covered. The proposed approach is an adaptation of the recent water wave optimization (WWO) algorithm, a nature-inspired algorithm based on the shallow water wave theory. The algorithm is tested on a rectangular 1-block warehouse, with 12 aisles and 45 storage locations per aisle side (1080 storage locations overall). The depot is located at the bottom left corner of the warehouse. A Microsoft ExcelTM file was programmed to reproduce both the adapted WWO algorithm and to test its performance, in terms of the travel distance covered by pickers. A formal design of experiment (DOE) analysis is carried out on some of the algorithm parameters, to assess their effect on the performance observed (namely computational time and number of iterations required to reach convergence) and identify the optimal setting.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
