Metaheuristics in the Humanitarian Supply Chain

Abstract

Everyday there are more disasters that require Humanitarian Supply Chain (HSC) attention; generally these problems are difficult to solve in reasonable computational time and metaheuristics (MHs) are the indicated solution algorithms. To our knowledge, there has not been a review article on MHs applied to HSC. In this work, 78 articles were extracted from 2016 publications using systematic literature review methodology and were analyzed to answer two research questions: (1) How are the HSC problems that have been solved from Metaheuristics classified? (2) What is the gap found to accomplish future research in Metaheuristics in HSC? After classifying them into deterministic (52.56%) and non-deterministic (47.44%) problems; post-disaster (51.28%), pre-disaster (14.10%) and integrated (34.62%); facility location (41.03%), distribution (71.79%), inventory (11.54%) and mass evacuation (10.26%); single (46.15%) and multiple objective functions (53.85%), single (76.92%) and multiple (23.07%) period; and the type of Metaheuristic: Metaphor (71.79%) with genetic algorithms and particle swarm optimization as the most used; and non-metaphor based (28.20%), in which search algorithms are mostly used; it is concluded that, to consider the uncertainty of the real context, future research should be done in non-deterministic and multi-period problems that integrate pre- and post-disaster stages, that increasingly include problems such as inventory and mass evacuation and in which new multi-objective MHs are tested.

Scenario-Based Multi-Objective Location-Routing Model for Pre-Disaster Planning: A Philippine Case Study

“…According to constraint, each affected place can be reached by vehicle for evacuation (17). The types of decision variables are indicated by the constraints (18) and (19).…”

“…Numerous other metaheuristics exist, but some noteworthy to mention are ant colony optimization, tabu search, simulated annealing, variable neighborhood search, and genetic/evolutionary algorithms. The most popular metaheuristics, according to Santana Robles [19], are simulated annealing (SA), particle swarm optimization (PSO), and genetic algorithm (GA). The goal of this study is to determine how well MOPSO handles location-routing problems for planning humanitarian evacuations.…”

Quick-Response Model for Pre- and Post-Disaster Evacuation and Aid Distribution: The Case of the Tula River Flood Event

“…Despite the growing interest in the HSC, the pre-disaster phase has received relatively less attention. However, this phase is of great significance in operations and disaster management, particularly concerning the mass evacuation of people [6,13]. In the context of post-disaster decision-making, numerous proposals exist in the literature for optimizing the distribution of humanitarian aid using various modeling approaches.…”