An integrated location-inventory-routing humanitarian supply chain network with pre- and post-disaster management considerations. (December 2018)

• Record Type: Journal Article
• Authors: Tavana, Madjid; Abtahi, Amir-Reza; Di Caprio, Debora; Hashemi, Reza; Yousefi-Zenouz, Reza
• Is Part Of: Socio-economic planning sciences. Number 64(2018)
• Page Start: 21
• Page End: 37
• Publication Date: 2018-12
• Subjects: Humanitarian supply chain -- Facility location -- Vehicle routing -- Inventory management -- Multi-objective optimization; Planning -- Periodicals; Economic policy -- Periodicals; Social policy -- Periodicals; Planification -- Périodiques; Politique économique -- Périodiques; Politique sociale -- Périodiques; ECONOMIC PLANNING; SOCIAL PLANNING; DECISION-MAKING; 361
• DOI: 10.1016/j.seps.2017.12.004
• Languages: English
• ISSNs: 0038-0121

Abstract

Abstract: Efficiency is a key success factor in complex supply chain networks. It is imperative to ensure proper flow of goods and services in humanitarian supply chains in response to a disaster. To this end, we propose a multi-echelon humanitarian logistic network that considers the location of central warehouses, managing the inventory of perishable products in the pre-disaster phase, and routing the relief vehicles in the post-disaster phase. An epsilon-constraint method, a non-dominated sorting genetic algorithm (NSGA-II), and a modified NSGA-II called reference point based non-dominated sorting genetic algorithm-II (RPBNSGA-II) are proposed to solve this mixed integer linear programming (MILP) problem. The analysis of variance (ANOVA) is used to analyze the results showing that NSGA-II performs better than the other algorithms with small size problems while RPBNSGA-II outperforms the other algorithms with large size problems. Highlights: In humanitarian supply chains proper flows of relief items must be provided after a disaster. We propose a multi-echelon humanitarian logistic network for pre- and post-disaster phases. The former phase focuses on central warehouses location and perishable goods inventories. The latter phase deals with the multi-echelon multi-depot vehicle routing model proposed. The problem is solved using a novel reference point based variant of the NSGA-II algorithm.