ABSTRACT
Influenced by the Covid-19 pandemic, fresh food e-commerce market in China developed quickly. Efficient solution for Order Batch Problem (OBP) could achieve efficient batching operation and then reduce costs and control risks. However, the OBP model proposed by the previous researches did not consider the characteristics of fresh food products such as the less demand of orders, the large variety of products, perishability of products and etc. Therefore, this paper proposed a model of OBP with freshness constraint of perishable food products, and proposed a two-stage heuristic algorithm to solve the target problem of the model. Our solution could improve the efficiency of the sorting process while ensuring the freshness of food products. © 2022 The authors and IOS Press.
ABSTRACT
This article unveils stories from a "lived experience"by frontline medical-surgical nurses caring for SARS-CoV-2 (COVID-19) using a compilation of written and verbal examples. The article intends to enrich the readers' perspective regarding nurses' thoughts, emotions, and actions as they care for patients diagnosed and hospitalized with COVID-19. A culmination of lessons learned via the "lived"knowledge exchange completes the article. © 2022 American Society on Aging.
ABSTRACT
The COVID-19 pandemic has caused critical challenges for e-commerce warehouses that strive to fulfill surging customer demand while facing a high virus infection risk. Current literature on picking optimization overlooks warehouse safety under pandemic conditions. Meanwhile, scattered storage and zone-wave-batch picking have been used in parallel by many large e-commerce warehouses, these two operational policies have not been considered together in picking optimization studies. This paper fills these gaps by solving an order batching problem considering scattered storage, zone-wave-batch picking, and pickers' proximity simultaneously. We formulate and solve the mathematical model of the discussed problem and propose the Aisle-Based Constructive Batching Algorithm (ABCBA) to help warehouses pick more efficiently and safely. Experiments with extensive datasets from a major third-party logistics (3PL) company show that, compared to the current picking strategy, ABCBA can reduce the total picking time and the virus infection risk due to pickers' proximity by 46% and 72%, respectively. Compared to other heuristics like tabu + nLSA3 (Yang, Zhao, and Guo 2020), ABCBA gets better results using less computation time.
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.