Efficient heuristics to minimize the total tardiness of chemotherapy drugs production and delivery

Alexis Robbes (1) , Yannick Kergosien (1) , Virginie André (2) , Jean-Charles Billaut (1)

1 LIFAT - Laboratoire d'Informatique Fondamentale et Appliquée de Tours
2 CHRU Tours - Centre Hospitalier Régional Universitaire de Tours

This study considers the production of chemotherapy drugs for cancer treatment. An important factor determining the quality of service of chemotherapy treatment is the time the patient must wait to receive his or her injection of the chemotherapy drug. Chemotherapy production and delivery are modeled as a production scheduling problem combined with a vehicle routing problem. The scheduling problem is a three-stage hybrid flow shop scheduling problem, and the routing problem is a variant of the multi-trip vehicle routing problem with due dates. The objective function is the minimization of the total time delay for chemotherapy treatment. To solve this problem, we propose several heuristic algorithms to provide quality solutions within reasonable computation times. Computational experiments are used to compare the performance of the heuristics applied to real data-based random instances.

Mots clés: Healthcare management; Production and distribution scheduling; Greedy randomized adaptive search procedure; Hybrid fow shop scheduling problem; Multi-trip vehicle routing problem; Chemotherapy production; Hybrid flow shop scheduling problem

Domaines: Recherche opérationnelle [math.OC]

Alexis Robbes, Yannick Kergosien, Virginie André, Jean-Charles Billaut. Efficient heuristics to minimize the total tardiness of chemotherapy drugs production and delivery. Flexible Services and Manufacturing Journal, 2022, ⟨10.1007/s10696-021-09431-w⟩. ⟨hal-03345079⟩