A hybrid decision support system for managing humanitarian relief chains

Decisions regarding location, allocation and distribution of relief items are among the main concerns of the Humanitarian Relief Chain (HRC) managers in response to no-notice large-scale disasters such as earthquakes. In this paper, a Hybrid Decision Support System (HDSS) consisting of a simulator, a rule-based inference engine, and a knowledge-based system (KBS) is developed to configure a three level HRC. Three main performance measures including the coverage, total cost, and response time are considered to make an explicit trade-off analysis between cost efficiency and responsiveness of the designed HRC. In the first step, the simulator calculates the performance measures of the different configurations of the HRC under generated number of disaster scenarios. Then, the rule-based inference engine attempts to build the best configuration of the HRC including facilities’ locations, relief items’ allocation and distribution plan of the scenario under investigation based on calculated performance measures. Finally, the best configuration for each scenario is stored in the KBS as the extracted knowledge from the above analyses. In this way, the HRC managers can retrieve the most appropriate HRC configuration in accordance with the realized post-disaster scenario in an effective and timely manner. The results of a real case study in Tehran demonstrate that the developed HDSS is an effective tool for fast configuration of HRCs using stochastic data

A Multi-Attribute decision support system for allocation of humanitarian cluster resources , based on decision makers’ perspective

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

A multi-attribute decision support system for allocation of humanitarian cluster resources based on decision makers’ perspective

The rush of the humanitarian suppliers into the disaster area proved to be counterproductive. 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

Application of a Blockchain Enabled Model in Disaster Aids Supply Network Resilience

The disaster area is a dynamic environment. The bottleneck in distributing the supplies may be from the damaged infrastructure or the unavailability of accurate information about the required amounts. The success of the disaster response network is based on collaboration, coordination, sovereignty, and equality in relief distribution. Therefore, a reliable dynamic communication system is required to facilitate the interactions, enhance the knowledge for the relief operation, prioritize, and coordinate the goods distribution. One of the promising innovative technologies is blockchain technology which enables transparent, secure, and real-time information exchange and automation through smart contracts. This study analyzes the application of blockchain technology on disaster management resilience. The influences of this most promising application on the disaster aid supply network resilience combined with the Internet of Things (IoT) and Dynamic Voltage Frequency Scaling (DVFS) algorithm are explored employing a network-based simulation. The theoretical analysis reveals an advancement in disaster-aids supply network strategies using smart contracts for collaborations. The simulation study indicates an enhance in resilience by improvement in collaboration and communication due to more time-efficient processing for disaster supply management. From the investigations, insights have been derived for researchers in the field and the managers interested in practical implementation

Humanitarian logistics optimization models: An investigation of decision-maker involvement and directions to promote implementation

Reports of successful implementation of humanitarian optimization models in the field are scarce. Incorporating real conditions and the perspective of decision-makers in the analysis is crucial to enhance the practical value and managerial implications. Although it is known that implementation can be hindered by the lack of practitioner input in the structure of the model, its priorities, and the practicality of solution times, the way these aspects have been introduced in humanitarian optimization models has not been investigated. This study looks at the way research has involved practitioners in different aspects of the design of optimization models to promote implementation. It investigates the aspects affecting the implementation of the models and opportunities to guide future optimization contributions. The article introduces a systematic literature review of 105 articles to answer the research questions. The results are contrasted with a multi-criteria decision analysis using responses from Mexican practitioners. The study found that only 10% of the articles involved practitioners for modelling decisions, which was confirmed by a major gap between the objectives used in the literature and the priorities of Mexican practitioners. In terms of swift decision-making, fewer than 22% of the articles surveyed introduced new solution methods to deliver results in a sensible time. The study also identified very limited inclusion of environmental concerns in the objective functions even though these are a priority in the global agenda. These findings are discussed to propose research directions and suggest best practices for future contributions to promote the implementation of humanitarian logistics models

Supporting Humanitarian Relief Distribution Decision-Making under Deep Uncertainty : A System Design Approach

With respect to copyright, all the papers were excluded from the dissertation.Disasters threaten society with widespread destruction of infrastructure and livelihood. For their survival, affected inhabitants depend on immediate humanitarian assistance from diverse organizations. During quick responses, humanitarian decision- makers (HDMs) act rapidly to distribute necessary relief goods, despite the deep, prevailing uncertainty that arises from scarce, conflicting, and uncertain information. To support HDMs in humanitarian relief distribution (HRD) decision-making, humanitarian logistics (HL) researchers have developed various mathematical models. These models are, however, specific to disaster scenarios, and most of them are detached from the realities of the field since end-users (mainly practitioners) have been absent in the development process. When tested, these decision-making models were found to be capable of producing good results, but they have not been implemented in practice because of operational inconsistency or complexity (i.e., lack of user-friendliness). Therefore, humanitarian responders are still in need of support systems to assist them in determining effective HRD. A computer-based decision support system (DSS) can fill this need by providing necessary recommendations and suggesting decision alternatives. Hence, developing such DSSs is always the priority in HL.publishedVersio

Resilience in Humanitarian Supply Chains: A Focus on the Procurement Decisions

This thesis looks into how the need for resilience in humanitarian aid supply chains influences procurement strategy decisions. Increasingly, the need for resilience in supply chains has become undoubted and management researchers have prescribed diverse ways of pursuing it; not only so that supply chains may be better prepared to avoid, respond and recover from disruptions, but to also provide them with competitive advantage. Considering that the procurement function has gone beyond a simple business function to include the strategic management of resources and suppliers when pursuing supply chain resilience (SCR), the role of procurement decisions cannot be understated, especially as suppliers could become significant sources of disruptions. This is even more pronounced in humanitarian supply chains where disruptions do not only result in the loss of limited resources but sometimes human lives as well. Due to this criticality for resilience in humanitarian supply chains and the limited research here particularly from a procurement perspective, this research collects qualitative data through semi-structured interviews and document analysis from 8 UK-based humanitarian organisations. The data is analysed to identify how these organisations pursue SCR formative elements from a procurement perspective and also how pre-contract procurement decisions relative to inter-organisational interactions are guided by the need for resilience. Findings show that cross-training, flexible contracting, and financial resilience are critical to attaining SCR in humanitarian supply chains as they influence many of the identified formative elements. Differences are identified in the relationships between decisions taken under procurement strategy towards resilience from those in commercial supply chains, with monetary value and donor requirements being major influencing factors. Donor influence on procurement decisions in humanitarian organisations is identified to positively influence multiple formative elements including risk avoidance, sustainability, decision making and culture. It however inhibits flexibility and agility. Contributions from this research include the presentation of a theoretical framework on procurement strategy decisions towards achieving SCR. This is then empirically tested in UK humanitarian supply chain context and a simple but useful framework to aid managerial decision making in the sector is provided