2024
Authors
Amorim, P; Dehoratius, N; Eng Larsson, F; Martins, S;
Publication
MANAGEMENT SCIENCE
Abstract
Retailers face increasing competitive pressure to determine how best to deliver products purchased online to the end customer. Grocery retailers often require attended home delivery where the customer must be present to receive the delivery. For attended home delivery to function, the retailer and customer must agree on a delivery time slot that works for both parties. Using online data from a grocery retailer, we observe customer preferences for three delivery service attributes associated with each time slot: speed, precision, and timing. We define speed as the expected time between the placement of an order and its delivery, precision as the duration of the offered time slot, and timing as the availability of choices across times of the day and days of the week. We show that customers not only value speed as an attribute of delivery service but that precision and timing are also key drivers of the customer's time slot selection process. We also observe substantial customer heterogeneity in the willingness of customers to pay for time slots. Customers that differ in their loyalty to the retailer, basket value, basket size, and basket composition exhibit distinct differences in their willingness to pay. We show that retailers with the capability to tailor their time slot offerings to specific customer segments have the potential to generate approximately 9% more shipping revenue than those who cannot. Our findings inform practitioners seeking to design competitive fulfillment strategies and academics customer behavior in the attended home context.
2024
Authors
Pereira R.; Santos M.J.; Martins S.;
Publication
Communications in Computer and Information Science
Abstract
Food waste poses a significant challenge to the sustainability of traditional food production systems, prompting global efforts to combat waste throughout the supply chain. Sustainable food production emerges as a critical concept in response to increasing concerns about environmental degradation and the need for alternative protein sources driven by global population growth. In this context, insect production offers a promising solution by converting low-value organic waste into nutrient-rich products, thus reducing waste and environmental impact. This paper addresses the urgent need for sustainable and efficient food production systems by introducing a facility location problem within the network design of insect production. The objective is to develop methods to scale insect-derived product production by identifying optimal locations with the best conditions for establishing insect production facilities. Emphasis is placed on connecting suppliers with production, highlighting the critical role suppliers and their by-products play in promoting a sustainable industry. Instances were generated to assess model performance, including supplier and facility locations, by-product availability and selection. Varying by-product availability yielded different optimization outcomes. The experiments results offered insights into the model’s behavior under different conditions. The results shown that varying the composition of substrate had a major implication on the augment of costs compared to varying the by-product availability.
2024
Authors
Ferreira P.; Pardal A.; Martins S.;
Publication
Communications in Computer and Information Science
Abstract
Pickup and delivery problems are frequently encountered problems in transport companies. This paper presents a variant of the homogeneous vehicle, single-to-single Pickup and Delivery Problem with Time Windows, where several vehicles must fulfill transport requests from pickup nodes to delivery nodes, called missions, with associated service level agreements (SLA). A mathematical programming model is proposed to tackle this variant, focused on optimizing the allocation and sequencing of missions to be executed by autonomous vehicles. Numerical experiments are performed comparing instances with missions with long and short SLAs. The results show that the model takes longer to find the optimal solution when the missions have short SLAs and increased difficulty in meeting them if the number of vehicles is limited.
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