An Integrated Approach Based on MADM and MODM for Order Picking System Considering Human Factors

Fatemeh Nikkhoo (), Ali Husseinzadeh Kashan, Bakhtiar Ostadi () and Ehsan Nikbakhsh ()
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Fatemeh Nikkhoo: Faculty of Industrial and System Engineering, Tarbiat Modares University, Tehran, Iran
Ali Husseinzadeh Kashan: Faculty of Industrial and System Engineering, Tarbiat Modares University, Tehran, Iran
Bakhtiar Ostadi: Faculty of Industrial and System Engineering, Tarbiat Modares University, Tehran, Iran
Ehsan Nikbakhsh: Faculty of Industrial and System Engineering, Tarbiat Modares University, Tehran, Iran

International Journal of Information Technology & Decision Making (IJITDM), 2024, vol. 23, issue 05, 2017-2064

Abstract: Optimizing order-picking systems (OPSs) while considering human factors and integrating key decisions is a major challenge for warehouse managers. This study presents a two-stage framework based on multi-attribute decision-making (MADM) and multi-objective decision-making (MODM) models to integrate decisions on picker selection, order batching, batch assignment, picker routing, and scheduling. In the first stage, the human factors affecting picker selection are considered as the problem’s criteria and the available pickers are treated as alternatives. The fuzzy entropy method and fuzzy COmplex PRoportional ASsessment (COPRAS) are used to weight the factors and rank the pickers, respectively. In the second stage, a three-objective mathematical model is formulated to minimize makespan and the operating costs of picking while maximizing the total scores of the selected pickers. The improved augmented epsilon constraint method (AUGMECON2) and the non-dominated sorting genetic algorithm II (NSGA-II) are applied to solve the proposed model. The performance of the two methods is tested on well-known benchmark instances and a real-world case study. The NSGA-II algorithm can generate optimal results using only about 6.58% of the CPU time required by AUGMECON2 to solve the problem. Our computational experiments show that increasing the number of pickers from 2 to 8 and doubling their capacity reduces the makespan by 2.61% and 2.74%, respectively.

Keywords: Order picking system; human factors; multi-attribute decision-making; multi-objective optimization problems; non-dominated sorting genetic algorithm; improved augmented epsilon constraint method (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S0219622023500657

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