A Crowdsourcing-based vehicle routing problem for combined prescription pickup and medicine delivery

The sudden uprising of e-commerce has significantly accelerated the global growth of last-mile delivery services. Notably, it is evident in the online pharmaceutical delivery system, which aims to reduce human contact. Companies became more competitive, which automatically necessitated novel approaches to increasing operational efficiency. These technological developments have indeed made it possible to explore new methods, such as crowdsourcing, to create cost-effective, flexible, and sustainable systems. This research incorporates crowdsourcing for the collection and delivery of goods. A bi-objective MINLP model is developed for collecting prescriptions and delivering medications to customers by crowdsourced or pharmacy couriers. The model assumes multiple distribution centers (pharmacies) and a high number of end customers (patients). The model was subsequently linearized. It is assumed that couriers act in accordance with their past behavior when deciding whether to accept prescription collection or delivery tasks. The problem is modeled such that if a customer's prescription is to be collected by a crowdsourced courier, the medication delivery is also performed, either by the same courier or other available crowdsourced couriers, based on an optimized routing plan. However, if a patient is scheduled to be visited by the pharmacy’s courier for medication delivery, the prescription collection is also carried out by the same courier during the same trip. A Grasshopper Optimization Algorithm (GOA) was employed to manage the computational challenges at larger scales. The problem was solved numerically and analyzed across various scales. The sensitivity analysis yielded valuable managerial insights, providing practical guidance for implementation.