A Location Optimization Approach to Refugee Resettlement Decision-Making

Cilali, Buket; Barker, Kash; González, Andrés D.

doi:10.1016/j.scs.2021.103153

Cited by 10 publications

(7 citation statements)

References 21 publications

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“…Environmental migrants often overlap with policy imaginary, research, and public discourse for sustainable cities [1][2][3]. This work demonstrates that environmental (and all other) migrants ought to be included within governance frameworks as part of achieving sustainable cities.…”

Section: Introductionmentioning

confidence: 86%

An Urban Governance Framework for Including Environmental Migrants in Sustainable Cities

Kelman

Clark‐Ginsberg

2022

Climate

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This article proposes an urban governance framework for including environmental migrants in sustainable cities. It outlines the links among environmental migration, vulnerability, and sustainability, showing how vulnerability and sustainability are not about the environment or the human condition as snapshots in space and time, but rather are long-term, multi-scalar, ever-evolving processes. This theoretical baseline is followed by a description of some practical approaches already applied for including environmental migrants in sustainable cities. The wide variety and lack of cohesion justifies the need for a framework, leading to three principal characteristics of a governance framework suitable for addressing vulnerability and environmental migration for urban sustainability: horizontally and vertically networked, inclusive, and evidence-based. As the framework’s three dimensions represent principles or overarching structural solutions rather than presenting operational guidance, the concluding discussion covers the framework’s limitations and a research agenda.

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Section: Introductionmentioning

confidence: 86%

An Urban Governance Framework for Including Environmental Migrants in Sustainable Cities

Kelman

Clark‐Ginsberg

2022

Climate

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“…Bansak et al [7] used a combination of supervised machine learning and optimal matching to obtain a refugee matching that satisfies the constraints of refugees and localities. The dynamic version of the refugee resettlement problem [4,1,11] has also been considered in literature.…”

Section: Related Workmentioning

confidence: 99%

Fair Healthcare Rationing to Maximize Dynamic Utilities

Ganesh¹,

Nimbhorkar²,

Ghosal³

et al. 2023

Preprint

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Allocation of scarce healthcare resources under limited logistic and infrastructural facilities is a major issue in the modern society. We consider the problem of allocation of healthcare resources like vaccines to people or hospital beds to patients in an online manner. Our model takes into account the arrival of resources on a day-to-day basis, different categories of agents, the possible unavailability of agents on certain days, and the utility associated with each allotment as well as its variation over time. We propose a model where priorities for various categories are modelled in terms of utilities of agents. We give online and offline algorithms to compute an allocation that respects eligibility of agents into different categories, and incentivizes agents not to hide their eligibility for some category. The offline algorithm gives an optimal allocation while the online algorithm gives an approximation to the optimal allocation in terms of total utility. Our algorithms are efficient, and maintain fairness among different categories of agents. Our models have applications in other areas like refugee settlement and visa allocation. We evaluate the performance of our algorithms on real-life and synthetic datasets. The experimental results show that the online algorithm is fast and performs better than the given theoretical bound in terms of total utility. Moreover, the experimental results confirm that our utility-based model correctly captures the priorities of categories.

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“…The fuzzy values of the unmet demand (C 1 ) and cost (C 2 ) objectives for the 24 terminal outcomes of the decision tree. willingness to move (Cilali et al, 2023). We estimate relocation demand based on the countries and people that would be under severe adverse climate change but lack the required mitigation, response, and adaptation power, and as a result, would be in need of relocation assistance.…”

Section: Illustrative Examplementioning

confidence: 99%

Multi‐objective decision trees with fuzzy TOPSIS: Application to refugee resettlement planning

Cilali,

Rocco,

Barker

2023

Multi Criteria Decision Anal

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This study combines the use of multi‐objective decision trees (MODT) and a fuzzy TOPSIS approach to cope with sequential decision‐making under uncertainty with multiple conflicting objectives. To this aim, the MODT generates a Pareto‐optimal frontier of solutions for competing objectives, while fuzzy TOPSIS helps assess the noninferior solutions and assign weights to the objectives by considering possible uncertainties in outcomes as well as potential linguistic expression. The proposed approach is illustrated with a temporal decision problem concerning climate‐driven refugee resettlement, taking into consideration the challenges of global warming and system interdependencies in an uncertain context. The fuzzy TOPSIS approach was illustrated using two different procedures, and the procedure based on vector normalisation graphically depicts the fuzzy triangular representation of the final ranking.

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A Location Optimization Approach to Refugee Resettlement Decision-Making

Cited by 10 publications

References 21 publications

An Urban Governance Framework for Including Environmental Migrants in Sustainable Cities

An Urban Governance Framework for Including Environmental Migrants in Sustainable Cities

Fair Healthcare Rationing to Maximize Dynamic Utilities

Multi‐objective decision trees with fuzzy TOPSIS: Application to refugee resettlement planning

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