Decision making in humanitarian logistics - A multi-objective optimization model for relocating relief goods during disaster recovery operations

ABSTRACT Disaster recovery operations rarely proceed smoothly and disruptions often require the redistribution of relief items. Such a redistribution has to be carried out taking into account both the current disruption and the uncertainty regarding possible future incidents in the respective area. As decisions have to be made fast in humanitarian operations, extensive optimization runs cannot be conducted in such a situation. Nevertheless, sensible decisions should be made to ensure an efficient redistribution, considering not only satisfaction of needs but also operational costs, as the budget is usually scarce in the recovery phase of a disaster. In this work, different scenarios are generated and then solved with a multiobjective optimization model to explore possible developments. By evaluating the results of these scenarios, decision rules are identified which can support the decision maker in the actual disaster situation in making fast, but nevertheless well-founded, decisions

Making decisions for effective humanitarian actions: a conceptual framework for relief distribution

publishedVersio

Relief distribution networks : a systematic review

In the last 20 years, Emergency Management has received increasing attention from the scientific community. Meanwhile, the study of relief distribution networks has become one of the most popular topics within the Emergency Management field. In fact, the number and variety of contributions devoted to the design or the management of relief distribution networks has exploded in the recent years, motivating the need for a structured and systematic analysis of the works on this specific topic. To this end, this paper presents a systematic review of contributions on relief distribution networks in response to disasters. Through a systematic and scientific methodology, it gathers and consolidates the published research works in a transparent and objective way. It pursues three goals. First, to conduct an up-to-date survey of the research in relief distribution networks focusing on the logistics aspects of the problem, which despite the number of previous reviews has been overlooked in the past. Second, to highlight the trends and the most promising challenges in the modeling and resolution approaches and, finally, to identify future research perspectives that need to be explored

The Effects of a Disaster’s Onset on the Humanitarian Aid Supply Chain

Through the development of an illustrative model, this conceptual paper argues that the relative timeframe of the onset of a disaster – whether slow or sudden - is related to the form of the supply chain response of the humanitarian aid organization. Further, a proposed method for researching the interrelationship between the nature of a disaster and how it affects the formulation of the humanitarian aid supply chain is offered. Several contributing characteristics of humanitarian aid supply chains are identified and described within the context of the model. Finally, several potential avenues for future research are described including the efficiencies that may be realized from prepartnering among humanitarian aid organizations and suppliers. The ultimate goal of this research is to aid humanitarian aid organizations in fully realizing their goals through better understanding and administration of their supply chains

Integrated and periodic relief logistics planning for reaction phase in uncertainty condition and model solving by particles swarm optimization algorithm

Disaster relief logistics is considered to be one of the major activities in disaster management. This research studies response phase of the disaster management cycle. To do so, a multi-purpose integrated model for a three-level relief cycle logistics is provided under an uncertainty condition and on a periodic basis. In this model, inventory transfer, vehicle routing, distribution and sending relief goods are modeled on a periodic basis. In addition, in order to solve the proposed mathematical model, ultra-initiative particles swarm algorithm in combination with variable neighborhood search based on Pareto archive is proposed. To prove the efficiency of the proposed particles swarm algorithm, several sample problems are randomly selected considering the solved problems in the literature and are solved by particles swarm algorithm. These problems are also solved by genetic algorithm and the results obtained from these two algorithms are compared in terms of quality, dispersion and integrity indices. The results show that compared to genetic algorithm, particles swarm algorithm is more capable of producing more integrated, qualified and dispersed responses. Moreover, the results show that the solution time of genetic algorithm is less than that of the proposed algorithm

Integrated and periodic relief logistics planning for reaction phase in uncertainty condition and model solving by particles swarm optimization algorithm

Disaster relief logistics is considered to be one of the major activities in disaster management. This research studies response phase of the disaster management cycle. To do so, a multi-purpose integrated model for a three-level relief cycle logistics is provided under an uncertainty condition and on a periodic basis. In this model, inventory transfer, vehicle routing, distribution and sending relief goods are modeled on a periodic basis. In addition, in order to solve the proposed mathematical model, ultra-initiative particles swarm algorithm in combination with variable neighborhood search based on Pareto archive is proposed. To prove the efficiency of the proposed particles swarm algorithm, several sample problems are randomly selected considering the solved problems in the literature and are solved by particles swarm algorithm. These problems are also solved by genetic algorithm and the results obtained from these two algorithms are compared in terms of quality, dispersion and integrity indices. The results show that compared to genetic algorithm, particles swarm algorithm is more capable of producing more integrated, qualified and dispersed responses. Moreover, the results show that the solution time of genetic algorithm is less than that of the proposed algorithm

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

Logistics Orchestration Modeling and Evaluation for Humanitarian Relief

10.1109/SOLI.2012.6273499Proceedings of 2012 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 201225-3

Decision-making and operations in disasters: challenges and opportunities

Decision-making structures are commonly associated with the logistics challenges experienced during disaster operations. However, the alignment between the operational level and the decision-making structure is commonly overlooked. The purpose of this research is to provide an analysis of the fit of both levels and its impact on performance. The research is developed around a case study in Mexico. Through a review of the disaster management policy in the country, interviews, and secondary data, the article provides an analysis of the current decision-making structure, the logistics activities undertaken by authorities and the impact of the alignment between both components on logistics performance. The analysis suggests that several of the challenges commonly associated centralisation are actually rooted on its alignment with the operational level. Logistics performance is negatively affected by faulty assumptions, poorly planned procedures, inconsistent decision-making, and poorly designed structures. The case showed the need to align the operational level with a centralised perspective to increase responsiveness, flexibility and the interaction between different organisations. This article identifies the impact of the misalignment between the decision-making structure and the operational level on logistics performance, an area currently understudied. It moves from the current argument about the appropriate decision-making structure for disaster management to the identification of components to implement an efficient and effective disaster management system. Additionally, this paper provides recommendations for best practices in humanitarian logistics which are applicable to Mexico and other countries using a centralised decision-making approach