Operations research to the rescue

Ondertitel:increasing disaster preparedness by pre-positioning emergency supplies

Hundreds of millions of people suffer yearly as a result of natural and man-made disasters. No country is immune from the risk of disasters, but much human loss and suffering can be avoided by preparing to better deal with these emergencies. A common mechanism of increasing disaster preparedness is to pre-position the emergency supplies (such as water, food or medicine) at strategic locations, so that the aid can be distributed to affected areas immediately in the early post-disaster. Humanitarian logistics is a critical element of an effective and efficient disaster relief process: planning a pre-positioning strategy requires deciding on the number, location and size of storage facilities to be open, the quantities of various types of emergency supplies to be pre-positioned at each facility, and the distribution of the supplies to demand locations after a disaster, under uncertainty about demands, survival of pre-positioned aid and transportation network availability. Operations research has the potential to help relief agencies rescue lives and maximize the use of limited resources by optimizing emergency strategies, but also the potential to help the field of humanitarian logistics to mature further. One of the most vexing issues in the research on pre-positioning and other humanitarian logistics problems is the lack of data. With the aim of making the first step in assembling a set of benchmark instances for the pre-positioning (and related) problem(s), we generated and shared 30 diverse case studies, and a random instance generator that can be used to construct arbitrarily many various instances of any size. Another important issue that we have identified is the common mathematical model that includes intangible and controversial costs for unmet demand. We provide both theoretical and numerical evidence that shows that there exists an alternative mathematical formulation which obtains the same quality of emergency strategy without any performance loss. Since the pre-positioning problem is NP-hard, we developed a matheuristic optimization algorithm which is able to solve large instances within limited computation time. An extensive computational study that employs the developed framework (problem library, mathematical formulation and solution algorithm) helps us to gain insights about the influence of a number of disaster properties and their interactions on the pre-positioning planning decisions. We use this knowledge about the problem to also bridge theory and practice by deriving trustworthy guidelines which humanitarian workers can use on the ground to mitigate the devastating impacts of new potential disasters.

ISBN:978-90-8994-186-2

Jaar van publicatie:2019

Trefwoorden:Doctoral thesis

Toegankelijkheid:Open