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

TL;DR

This work addresses time-sensitive blood collection by introducing the drone-aided blood collection routing problem (DABCRP), which synchronizes truck routes with drone sorties to maximize viable donations delivered to a processing center within the platelet extraction window. It advances the state of the art with a mixed-integer MILP formulation for the DAM and a column-generation framework, where a tailored memetic algorithm solves the pricing subproblem and a master problem selects tours. Computational results show that incorporating drones yields materially higher viable blood units and more robust performance than truck-only approaches and two existing metaheuristics, across a wide range of instance sizes. The study demonstrates the practical value of drone integration for blood logistics and provides a scalable solution methodology that can adapt to variability in donor arrivals and travel times.

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.

Figures (7)

• Figure 1: Truck-only and drone-aided solutions for a BCRP instance.
• Figure 2: A sample chromosome encoding.
• Figure 3: S/N ratio chart.
• Figure 4: Box plots of the objective values for instance 1 of class C2.
• Figure 5: Convergence curves for instance 1 of class C2.