In this paper we present a methodology to evaluate policies for automated storage and retrieval system (AS/RS) in warehouses. It is composed by four steps: (1) formal definition of the physical AS/RS and descriptive modeling on a simulation framework; (2) model validation and finding of potential bottlenecks by the statistical analysis of data logs; (3) definition of operational optimization policies to mitigate such bottlenecks; (4) evaluation of the policies using the simulation tool through key performance indicators (KPI). In particular, we take into consideration a unit-load AS/RS, we present a new simulation model combining discrete events and agent based paradigms. We consider an industrial test case, focusing on scheduling policies that exploit mathematical optimization, and we evaluate the effects of our approach on real world data. Experiments prove the effectiveness of our methodology.

Evaluating Automated Storage and Retrieval System Policies with Simulation and Optimization / M. Barbato, A. Ceselli, M. Premoli (AIRO SPRINGER SERIES). - In: Advances in Optimization and Decision Science for Society, Services and Enterprises / [a cura di] M. Paolucci, A. Sciomachen, P. Uberti. - [s.l] : Springer, 2019. - ISBN 9783030349592. - pp. 127-137 (( convegno ODS tenutosi a Genova nel 2019.

Evaluating Automated Storage and Retrieval System Policies with Simulation and Optimization

M. Barbato;A. Ceselli;M. Premoli
2019

Abstract

In this paper we present a methodology to evaluate policies for automated storage and retrieval system (AS/RS) in warehouses. It is composed by four steps: (1) formal definition of the physical AS/RS and descriptive modeling on a simulation framework; (2) model validation and finding of potential bottlenecks by the statistical analysis of data logs; (3) definition of operational optimization policies to mitigate such bottlenecks; (4) evaluation of the policies using the simulation tool through key performance indicators (KPI). In particular, we take into consideration a unit-load AS/RS, we present a new simulation model combining discrete events and agent based paradigms. We consider an industrial test case, focusing on scheduling policies that exploit mathematical optimization, and we evaluate the effects of our approach on real world data. Experiments prove the effectiveness of our methodology.
AS/RS policies; Simulation; Matching; Mathematical optimization
Settore MAT/09 - Ricerca Operativa
Settore INF/01 - Informatica
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/749890
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