The healthcare supply chain network design with traceability: A novel algorithm. (November 2021)

• Record Type: Journal Article
• Authors: Hajipour, Vahid; Niaki, Seyed Taghi Akhavan; Akhgar, Majid; Ansari, Mehdi
• Is Part Of: Computers & industrial engineering. Volume 161(2021)
• Publication Date: 2021-11
• Subjects: Healthcare systems -- Supply chain -- Facility location -- Inventory planning -- Traceability; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2021.107661
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: We presented an emergency relief supply chain network design problem. We formulated traceability concept to minimize the number of perishable items. We developed a multi-objective vibration damping-based optimization algorithm. We analyzed and compared the algorithm with two best-developed ones. We calibrated all optimization algorithms by Taguchi method. Abstract: Both governments and health-related organizations must immediately act after a natural disaster happened, i.e., providing essential equipment and medicines to injured people as soon as possible and adequately. To achieve this goal, planning the distribution and the inventory of crucial items during a scenario of disasters, a relief supply chain network with four echelons, namely suppliers, warehouses, disaster locations, and medical centers, is designed. In this work, a bi-objective nonlinear mathematical model that follows two main concerns is proposed. First, we wish to minimize the supply chain costs in terms of both the traveling time between echelons and the inventory costs. Second, we are to maximize the number of undamaged items that demand points receive by employing the RFID technology. The multi-objective Vibration Damping Optimization (MOVDO) meta -heuristic algorithm is applied to solve the proposed problem. This algorithm's performance is compared with two other algorithms: Non-dominated Sorting Genetic (NSGA-II) and Non-Dominated Ranking Genetic (NRGA) algorithms. The analysis of the results confirms that MOVDO outperforms the other two algorithms.