An optimisation approach for the e-grocery order picking and delivery problem

Abstract

Traditional supermarket chains that are adopting an omni-channel approach must now carry out the order picking and delivery processes to serve online orders, previously done by the customer. The complexity of the logistics processes has increased, therefore modelling and optimising e-grocery operations becomes definitely important. Since there are few studies modelling order picking and delivery processes, we propose an approach that simultaneously optimises the decision variables of different functions which have traditionally been treated separately. In this study, we present a linear programming model for store-based e-fulfilment strategies with multiple picking locations. The proposed model optimises the allocation of online orders to stores, based on the e-fulfilment costs. As well as minimising the picking and delivery costs, the proposed approach consolidates workloads in order to avoid idle times and reduce the amount of resources required. A weighted sum method is applied to compute the solution, integrating parameters that represent different store features such as the product range, sales mode and physical store activities. The proposed model has been tested on one of the largest grocery sellers, showing that substantial savings can be achieved by reallocating orders to different stores, time windows and delivery vehicles. By focusing on optimising e-fulfilment resources, this approach serves as a guide for traditional grocery sellers to redesign their supply chains and to facilitate decision-making at a managerial level. KeywordsMILP • e-commerce • e-grocery • omni-channel retailing • order fulfilment • optimisation Mar Vazquez-Noguerol

Adoption and Usage of E-Grocery Shopping: A Context-Specific UTAUT2 Model

“…The essence is that for e-grocery services to be sustainable-both in terms of being profitable for e-grocers and in terms of their impact on the environment-they need to operate at scale. Otherwise, delivery vans drive around half-empty and/or have to navigate Sustainability 2021, 13, 4144 2 of 27 suboptimal routes [15,16]. COVID-19 has forced many people around the world to use an online grocery service for the first time or, for those who had already adopted it, rely on it more than before [17][18][19].…”

A mixed integer linear programming model to support e-fulfillment strategies in warehouse-based supermarket chains

“…In this way, some studies identified that operational costs are a key factor in the profitability of the new channel (Holzapfel et al 2016;Valle and Beasley 2020). Therefore, e-grocers should focus on improving delivery efficiency to reduce logistics costs and product quality loss (Darestani and Hemmati 2019;Vazquez-Noguerol et al 2020).…”

Enhancing in-store picking for e-grocery: an empirical-based model

“…On one hand, online grocery initiatives are proliferating, and both start-ups and big dotcoms are entering the market (MacCarthy et al, 2019). On the other hand, in the realm of the coronavirus disease 2019 (COVID-19) emergency, many previously sceptical customers have experienced online purchases and are now inclined to change their grocery shopping behaviour in favour of e-commerce (Vazquez-Noguerol et al, 2020). In this scenario, traditional grocery retailers have started to offer e-commerce services to stay competitive (Li et al, 2021).…”

“…As a result, they implement this solution only when their online volumes are significant enough and prefer, in the initial stages of the business, to manage operations in physical stores (MacCarthy et al, 2019). Based on the above, numerous grocery retailers are striving to find an efficient and effective way to keep up with the rapid evolution of e-commerce by leveraging their stores without investing a huge amount of resources (Vazquez-Noguerol et al, 2020).…”

“…Few scholars have proposed solutions to improve the in-store picking process. Vazquez-Noguerol et al (2020) recommended moving picking activities to the closing hours of the store. This shows how the absence of offline customers significantly decreases the picking time per order, improving overall picking performance.…”