Mitigating the risk of infection spread in manual order picking operations: A multi-objective approach

Abstract

In the aftermath of the COVID-19 pandemic, supply chains experienced an unprecedented challenge to fulfill consumers’ demand. As a vital operational component, manual order picking operations are highly prone to infection spread among the workers, and thus, susceptible to interruption. This study revisits the well-known order batching problem by considering a new overlap objective that measures the time pickers work in close vicinity of each other and acts as a proxy of infection spread risk. For this purpose, a multi-objective optimization model and three multi-objective metaheuristics with an effective seeding procedure are proposed and are tested on the data obtained from a major US-based logistics company. Through extensive numerical experiments and comparison with the company’s current practices, the results are discussed, and some managerial insights are offered. It is found that the picking capacity can have a determining impact on reducing the risk of infection spread through minimizing the picking overlap.

Highlights

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  Order batching problems are revisited considering infection spread in warehouses.

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  A tri-objective model for the order batching problem is proposed.

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  Three evolutionary algorithms are implemented on a real dataset.

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  The results are compared against the current practices of a logistics company.

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  Managerial insights are discussed.