Drone-Aided Blood Collection Routing Problem: A Column Generation Approach

TL;DR

This paper presents a novel mixed-integer linear programming model and a column generation algorithm for optimizing drone-assisted blood collection routes, significantly improving collection efficiency over traditional methods.

Contribution

It introduces a new integrated routing model for trucks and drones in blood collection, with a tailored column generation approach for efficient solution finding.

Findings

The proposed algorithm outperforms Gurobi and existing metaheuristics.

Drones significantly increase the number of viable blood units collected.

Operational benefits are demonstrated through comprehensive computational studies.

Abstract

Platelet extraction requires whole blood to be processed within six hours of donation. To meet this deadline, blood collection organizations must optimally route a fleet of vehicles to pick up blood units from donation sites and deliver them to a processing center. This paper introduces a drone-aided blood collection routing problem in which a fleet of trucks, each equipped with a drone, operates in a synchronized manner to collect blood units before their processing time limit expires. Each truck-drone tandem can perform multiple trips throughout the planning horizon, allowing donation sites to be visited repeatedly as new blood units become available over time. We formulate this problem as a mixed-integer linear program that jointly optimizes the routing of trucks and drones, pickup schedules, and timing decisions to maximize the total number of viable blood units collected. We also develop a column generation approach that decomposes the problem into a master problem to select the optimal set of truck-drone tours and a pricing subproblem, which is solved using a tailored memetic algorithm to generate promising new columns. Through a comprehensive computational study, we show the operational benefits of integrating drones into the blood collection system. In addition, we demonstrate the superior performance of the proposed algorithm over Gurobi and two metaheuristics from the literature, namely the hybrid genetic algorithm and the invasive weed optimization, in both the drone-aided and truck-only settings.