A Multi-Attribute Decision Support System for Allocation of Humanitarian Cluster Resources Based on Decision Makers’ Perspective

Abstract

The rush of the humanitarian suppliers into the disaster area proved to be counter-productive. To reduce this proliferation problem, the present research is designed to provide a technique for supplier ranking/selection in disaster response using the principles of utility theory. A resource allocation problem is solved using optimisation based on decision maker’s preferences. Due to the lack of real-time data in the first 72 h after the disaster strike, a Decision Support System (DSS) framework called EDIS is introduced to employ secondary historical data from disaster response in four humanitarian clusters (WASH: Water, Sanitation and Hygiene, Nutrition, Health, and Shelter) to estimate the demand of the affected population. A methodology based on multi-attribute decision-making (MADM), Analytical Hierarchy processing (AHP) and Multi-attribute utility theory (MAUT) provides the following results. First a need estimation technique is put forward to estimate minimum standard requirements for disaster response. Second, a method for optimization of the humanitarian partners selection is provided based on the resources they have available during the response phase. Third, an estimate of resource allocation is provided based on the preferences of the decision makers. This method does not require real-time data from the aftermath of the disasters and provides the need estimation, partner selection and resource allocation based on historical data before the MIRA report is released.

Serious Games as a Validation Tool for PREDIS: A Decision Support System for Disaster Management

“…The experts gave the highest value for WASH (9.7%) shelter (7%), health (5%), and nutrition (35%) whilst non-experts gave the highest value for WASH (8.6%) health (5%) nutrition (36%) and shelter (2%). These preferences in combination with the resources available to the suppliers can be used to calculate the utility of each supplier using PREDIS [ 1 , 2 , 3 , 4 , 5 , 6 ] as articulated in Appendix B , Figure A1 . This shows that in order to optimise the decision, the following resources need to be selected.…”

“…The human development index (HDI) drew from and disaster risk index or DRI [ 50 ]. Pre-test also utilises the secondary data of suppliers’ list drawn from EDIS [ 5 ]. An example of this dataset is provided in Table 2 .…”

“…However, the decision about resource allocation and life-saving activities needs to be taken before the MIRA report [ 1 ]. In the absence of the real time data, a model called PREdictive model for DISaster response supplier selection (PREDIS) was introduced by authors previously [ 2 , 3 , 4 , 5 , 6 ]. This paper aims to validate this model using an experimental technique called the simulation game.…”

“…It also assists in selecting the humanitarian-response suppliers. This Decision Support System (DSS) is a combination of a PREDictive component (PRED) for predicting the disaster human impact [ 6 ] and an estimation component to Estimate the DISaster (EDIS) needs [ 5 ] to optimise supplier-based resource allocation.…”

Selection of Digital Investment Instruments Applying the Multi-Objective Optimization by Ratio Analysis Method

“…A decision support system is a computer-based system that can be used to assist the decision-making process in solving semi-structured or unstructured problems by utilizing certain data and methods [7][8][9]. Decision support systems have been widely used by previous researchers to help solve decision-making problems [10][11][12][13][14].…”