Since the 1950s, the number of natural and man-made disasters has increased exponentially and the facility location problem has become the preferred approach for dealing with emergency humanitarian logistical problems. To deal with this challenge, an exact algorithm and a heuristic algorithm have been combined as the main approach to solving this problem. Owing to the importance that an exact algorithm holds with regard to enhancing emergency humanitarian logistical facility location problems, this paper aims to conduct a survey on the facility location problems that are related to emergency humanitarian logistics based on both data modeling types and problem types and to examine the pre- and post-disaster situations with respect to facility location, such as the location of distribution centers, warehouses, shelters, debris removal sites and medical centers. The survey will examine the four main problems highlighted in the literature review: deterministic facility location problems, dynamic facility location problems, stochastic facility location problems, and robust facility location problems. For each problem, facility location type, data modeling type, disaster type, decisions, objectives, constraints, and solution methods will be evaluated and real-world applications and case studies will then be presented. Finally, research gaps will be identified and be addressed in further research studies to develop more effective disaster relief operations.

Facility location optimization model for emergency humanitarian logistics

Drones for disaster response and relief operations: A continuous approximation model

Multicriteria optimization in humanitarian aid

We have reviewed disaster management research papers published in major operations management, management science, operations research, supply chain management and transportation/logistics journals. In reviewing these studies, our objective is to assess and present the macro level "architectural blue print" of disaster management research with the hope that it will attract new researchers and motivate established researchers to contribute to this important field. The secondary objective is to bring this disaster research to the attention of disaster administrators so that disasters are managed more efficiently and more effectively. We have mapped the disaster management research on the following five attributes of a disaster: (1) Disaster Management Function (decision-making process, prevention and mitigation, evacuation, humanitarian logistics, casualty management, and recovery and restoration), (2) Time of Disaster (before, during and after), (3) Type of Disaster (accidents, earthquakes, floods, hurricanes, landslides, terrorism and wildfires etc.), (4) Data Type (Field and Archival data, Real data and Hypothetical data), and (5) Data Analysis Technique (bidding models, decision analysis, expert systems, fuzzy system analysis, game theory, heuristics, mathematical programming, network flow models, queueing theory, simulation and statistical analysis). We have done cross tabulations of data among these five parameters to gain greater insights into disaster research. Recommendations for future research are provided.

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Disaster Management from a POM Perspective: Mapping a New Domain

Disaster preparedness in humanitarian logistics:A collaborative approach for resource management in floods

https://corpus.ulaval.ca/jspui/bitstream/20.500.11794/61/1/Multi_Obj_COR_Corpus.pdf

An exact solution approach for multi-objective location-transportation problem for disaster response

Currently, the energy consumption in the data centers is a major problem due to the increase in the number of servers and services. Physical machines represent huge energy consumers who work continuously to ensure quality in services through virtual instances. The placement of these instances as containers in hosts has an impact on the minimization of energy consumption. Precisely, the guarantee of an efficient placement without wasting material resources of the hosts, makes it possible to minimize the number of active hosts and thus reduce energy consumption. In this article, we will calculate the energy consumed by a data center using CloudSim before and after applying the placement of containers in the hosts by the First Fit Decreasing algorithm (FFD) of Bin Packing and Ant Colony Optimization (ACO). The results show the FFD's superiority over the ACO in minimizing the energy consumed by any type of Cloud system.

Forecasting the Energy Consumption of Cloud Data Centers Based on Container Placement with Ant Colony Optimization and Bin Packing

/pdf/meta-heuristic-and-heuristic-algorithms-for-forecasting-2xi1qd8b.pdf

Rachida Abounacer

Papers

An exact solution approach for multi-objective location-transportation problem for disaster response

Forecasting the Energy Consumption of Cloud Data Centers Based on Container Placement with Ant Colony Optimization and Bin Packing

Meta-heuristic and Heuristic Algorithms for Forecasting Workload Placement and Energy Consumption in Cloud Data Centers