# A Hybrid Brain Storm Optimization Algorithm to Solve the Emergency Relief Routing Model
## 2 Related Work  
### The Emergency Relief Problem  
There are various performance metrics defined for the distribution of relief goods. Efficiency measures the extent to which the objective of rapid and adequate distribution is achieved, while equity measures the degree to which all recipients are served in a comparable manner. Examining how efficiency, efficacy, and equity influence the structure of vehicle routes and resource allocation is an important question [1]. After a disaster, some routes may become partially or entirely inaccessible, leading to a need for a new bi-objective, multi-modal, and multi-commodity model. This model considers reliability as one of its parameters to aid managers in making more informed decisions [2]. In emergency logistics, quick and well-informed decisions regarding warehouse locations and vehicle routes are essential for post-disaster relief efforts. [PERSON] et al. designed a distribution system for a homogeneous fleet of rescue vehicles that considered a set of candidate warehouse locations to deliver relief supplies to disaster areas after an incident [3]. The assessment routing problem involves sending teams to different communities to assess damage and relief needs after a disaster. To address time sensitivity, a continuous approximation method is proposed [4]. As part of the response operation, post-disaster assessments are critical. Selecting roadway segments and population points for assessment activities should be based on their value to the continuous response operations [35]. Forecasting methods for estimating rescue needs are currently inadequate. This issue can be addressed by dispatching assessment vehicles to the affected region during a disaster. A Markov decision process is proposed as a multi-intelligence assessment and response system to enhance emergency response operations and reduce fatalities [5]. Social media can improve situational awareness during disaster relief by quickly identifying needs. However, needs identified through social media must be validated initially, as some may be inaccurate. This validation challenge can hinder their use during disaster response decisions [6]. In the aftermath of a disaster, the lack of basic necessities, such as clean water, food, shelter, and medical care, often leads to an increase in the number of casualties. A critical issue that affects the distribution of relief supplies is the condition of the road network following the disaster. The dispatch and routing of repairers to optimize accessibility to towns and villages in need of humanitarian relief is a critical problem, commonly referred to as the network repairer dispatch and routing problem [7]. To reduce the negative effects of disasters on human health, it is essential to prioritize which roads to clean to transport relief items. Debris removal has been studied mostly during the recovery or reconstruction phase of a disaster, but [PERSON] et al. aimed to provide a solution to the debris removal problem during the response phase [36]. Mobile ad-hoc networks (MANETs) are well-suited for disaster relief scenarios that often lack network infrastructure, and a proactive routing protocol called MQ-Routing has been proposed to maximize the minimum node lifetime and quickly adapt to network topology changes [8]. Effective communication between local authorities, first responders, and the population in disaster-affected areas is critical to the success of relief operations. However, the typical telecommunication network infrastructure may be damaged or not working properly, resulting in a lack of communication. To address this issue, a device-to-device based framework is proposed to organize users in the disaster area into user clusters and select a gateway for each cluster to maximize energy efficiency [37]. The intelligent collaborative evolution customized industrial Internet of Things (IoT) has also demonstrated superiority and stability in this regard [38].