Evaluation of routing policies using an interval-valued TOPSIS approach for the allocation rules

Abstract The success of warehouse management in a supply chain widely depends on an efficient and effective retrieve of customer orders, which is known as the picking process. This paper investigates various routing policies of pickers under two different allocation methods of items in a warehouse of fixed layout, and evaluates their performance in terms of the resulting travel distance by means of a simulation approach. The allocation strategies taken into account are the random storage and a multi-criteria approach, called Interval-Value TOPSIS (IV-T), which is expressively proposed in this paper as a new way to solve the storage allocation problem of items in a warehouse. Because of the newness of the approach proposed, an ad hoc performance measure is also introduced to evaluate the effectiveness of the IV-T allocation. Results show that the usage of the IV-T approach involves interesting savings in the travel distance compared to the random allocation.

[1]  An Caris,et al.  Designing efficient order picking systems by combining planning problems: State-of-the-art classification and review , 2017, Eur. J. Oper. Res..

[2]  Mauro Gamberi,et al.  Modeling class-based storage assignment over life cycle picking patterns , 2015 .

[3]  W. H. M. Zijm,et al.  Warehouse design and control: Framework and literature review , 2000, Eur. J. Oper. Res..

[4]  Marcele Elisa Fontana,et al.  Multi-criteria approach for products classification and their storage location assignment , 2017 .

[5]  Hing Kai Chan,et al.  Improving the productivity of order picking of a manual-pick and multi-level rack distribution warehouse through the implementation of class-based storage , 2011, Expert Syst. Appl..

[6]  Kees Jan Roodbergen,et al.  Design and control of warehouse order picking: A literature review , 2006, Eur. J. Oper. Res..

[7]  Charles G. Petersen.  An evaluation of order picking routeing policies , 1997 .

[8]  András Kovács,et al.  Optimizing the storage assignment in a warehouse served by milkrun logistics , 2011 .

[9]  Roger W. Schmenner,et al.  An Evaluation of Routing and Volume‐based Storage Policies in an Order Picking Operation , 1999 .

[10]  Yong Xie,et al.  An ant colony optimization routing algorithm for two order pickers with congestion consideration , 2013, Comput. Ind. Eng..

[11]  Eleonora Bottani,et al.  Optimisation of storage allocation in order picking operations through a genetic algorithm , 2012 .

[12]  Maurizio Faccio,et al.  New methodological framework to improve productivity and ergonomics in assembly system design , 2011 .

[13]  Mauro Gamberi,et al.  Design of a class based storage picker to product order picking system , 2007 .

[14]  Natália Veloso Caldas de Vasconcelos,et al.  Multicriteria Decision Model to Support the Assignment of Storage Location of Products in a Warehouse , 2015 .

[15]  Marco Bortolini,et al.  A hierarchical procedure for storage allocation and assignment within an order-picking system. A case study , 2012 .

[16]  Rajan Batta,et al.  Optimal placement of warehouse cross-aisles in a picker-to-part warehouse with class-based storage , 2012 .

[17]  Cristiano Alexandre Virgínio Cavalcante,et al.  Use of Promethee method to determine the best alternative for warehouse storage location assignment , 2014 .

[18]  Gino Marchet,et al.  Routing policies and COI-based storage policies in picker-to-part systems , 1998 .

[19]  Osman Kulak,et al.  Joint order batching and picker routing in single and multiple-cross-aisle warehouses using cluster-based tabu search algorithms , 2012 .

[20]  F. Lotfi,et al.  Ranking DMUs by l1-norm with fuzzy data in DEA , 2009 .

[21]  Ediz Atmaca,et al.  Defining order picking policy: A storage assignment model and a simulated annealing solution in AS/RS systems , 2013 .

[22]  Ewa Roszkowska,et al.  Multi-criteria Decision Making Models by Applying the Topsis Method to Crisp and Interval Data , 2011 .

[23]  Birger Raa,et al.  Using a TSP heuristic for routing order pickers in warehouses , 2010, Eur. J. Oper. Res..

[24]  Fumio Kojima,et al.  Design method of material handling systems for lean automation-Integrating equipment for reducing wasted waiting time , 2017 .

[25]  Tho Le-Duc,et al.  Determining the number of zones in a pick-and-sort order picking system , 2012 .

[26]  Charles G. Petersen.  The impact of routing and storage policies on warehouse efficiency , 1999 .

[27]  Eleonora Bottani,et al.  Design and optimization of order picking systems: An integrated procedure and two case studies , 2019, Comput. Ind. Eng..

[28]  Charles G. Petersen,et al.  A comparison of picking, storage, and routing policies in manual order picking , 2004 .

[29]  Marc Goetschalckx,et al.  Research on warehouse operation: A comprehensive review , 2007, Eur. J. Oper. Res..

[30]  G. La Scalia,et al.  A combined interval-valued ELECTRE TRI and TOPSIS approach for solving the storage location assignment problem , 2019, Comput. Ind. Eng..

[31]  Mohammad Izadikhah,et al.  An algorithmic method to extend TOPSIS for decision-making problems with interval data , 2006, Appl. Math. Comput..

[32]  Kathleen M. Carley,et al.  Simulation modeling in organizational and management research , 2007 .

[33]  Pavel V. Sevastjanov,et al.  A direct interval extension of TOPSIS method , 2013, Expert Syst. Appl..

[34]  Tho Le-Duc,et al.  Travel time estimation and order batching in a 2-block warehouse , 2007, Eur. J. Oper. Res..

[35]  Timothy S. Vaughan,et al.  The effect of warehouse cross aisles on order picking efficiency , 1999 .

[36]  Iris F. A. Vis,et al.  A model for warehouse layout , 2006 .