Comparison of MOEAs in an Optimization-Decision Methodology for a Joint Order Batching and Picking System

Author:

Miguel Fabio Maximiliano1ORCID,Frutos Mariano2ORCID,Méndez Máximo3ORCID,Tohmé Fernando4ORCID,González Begoña3ORCID

Affiliation:

1. Sede Alto Valle y Valle Medio, Universidad Nacional de Río Negro, CONICET, Villa Regina 8336, Argentina

2. Departamento de Ingeniería, Universidad Nacional del Sur, IIESS UNS-CONICET, Bahía Blanca 8000, Argentina

3. Instituto Universitario SIANI, Universidad de Las Palmas de Gran Canaria (ULPGC), 35017 Las Palmas de Gran Canaria, Spain

4. Departamento de Economía, Universidad Nacional del Sur, INMABB UNS-CONICET, Bahía Blanca 8000, Argentina

Abstract

This paper investigates the performance of a two-stage multi-criteria decision-making procedure for order scheduling problems. These problems are represented by a novel nonlinear mixed integer program. Hybridizations of three Multi-Objective Evolutionary Algorithms (MOEAs) based on dominance relations are studied and compared to solve small, medium, and large instances of the joint order batching and picking problem in storage systems with multiple blocks of two and three dimensions. The performance of these methods is compared using a set of well-known metrics and running an extensive battery of simulations based on a methodology widely used in the literature. The main contributions of this paper are (1) the hybridization of MOEAs to deal efficiently with the combination of orders in one or several picking tours, scheduling them for each picker, and (2) a multi-criteria approach to scheduling multiple picking teams for each wave of orders. Based on the experimental results obtained, it can be stated that, in environments with a large number of different items and orders with high variability in volume, the proposed approach can significantly reduce operating costs while allowing the decision-maker to anticipate the positioning of orders in the dispatch area.

Funder

“Consejería de Economía, Industria, Comercio y Conocimiento” of the Government of the Canary Islands

Campus of International Excellence CEI CANARIAS-ULPGC

Publisher

MDPI AG

Reference63 articles.

1. Durakbasa, N.M., and Gençyılmaz, M.G. (2021, January 7–9). Design of a Routing Algorithm for Efficient Order Picking in a Non-traditional Rectangular Warehouse Layout. Proceedings of the Digitizing Production Systems, Online.

2. Using a multiple-GA method to solve the batch picking problem: Considering travel distance and order due time;Tsai;Int. J. Prod. Res.,2008

3. A memetic algorithm for the integral OBP/OPP problem in a logistics distribution center;Miguel;Uncertain Supply Chain. Manag.,2019

4. Rossit, D.A., Tohmé, F., and Mejía Delgadillo, G. (2020, January 9–11). Solving Order Batching/Picking Problems with an Evolutionary Algorithm. Proceedings of the International Conference of Production Research—Americas, Bahía Blanca, Argentina. Communications in Computer and Information Science Series.

5. Increasing order picking efficiency by integrating storage, batching, zone picking, and routing policy decisions;Ramaekers;Int. J. Prod. Econ.,2018

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