Decision support framework for location selection and disaster relief network design

Abstract

Purpose The purpose of this paper is to provide a decision support framework for locations identification to address network design in the domain of disaster relief supply chains. The solution approach is then applied to a real-life case about Indonesia. Design/methodology/approach An approach integrating geographic information system technology and fuzzy analytical hierarchy process has been used. Findings For the Indonesian case, distribution centers should be located in Pekanbaru, Surabaya, Banjarmasin, Ambon, Timika, and Manado. Research limitations/implications The main limitation of this work is that facilities being sited are incapacitated. Inclusion of constraints over capacity would elevate the framework to a further level of sophistication, enabling virtual pool of inventory that can be used to adsorb fluctuation in the demand due to disasters. Practical implications The use case provided in this paper shows a practical example of applicability for the proposed framework. This study is able to support worldwide decision makers facing challenges related with disaster relief chains resilience. In order to achieve efficiency and effectiveness in relief operations, strategic logistics planning in preparedness is key. Hence, initiatives in disaster preparedness should be enhanced. Originality/value It adds value to the previous literature on humanitarian logistics by providing a real-life case study as use case for the proposed methodology. It can guide decision makers in designing resilient humanitarian response, worldwide. Moreover, a combination of recommendations from humanitarian logistics practitioners with established models in facility location sciences provides an interdisciplinary solution to this complex exercise.

Using transportation problem in humanitarian supply chain to prepositioned facility locations: a case study in the Democratic Republic of the Congo

“…Nahleh et al ( 2013 ) findings also include an escalation of orders from local DC to regional, continental as far as the main warehouses, to replenish the initial relief items that were distributed to affected area. According to Timperio et al ( 2017 ), this model by Nahleh et al ( 2013 ) looks at facility location problem in emergency logistics more from a global perspective, rather than last mile (in country) logistics. In Nahleh et al ( 2013 ) model, the country affected by a disaster requests support from the nearest regional prepositioned DC which, while assisting beneficiaries, will place relief items restock orders to continent warehouse.…”

“…Degener et al ( 2013 ) uses of multi-criteria decision making in a small-scale to a real-life scenario in Bangladesh helped bring more transparency in results. Recently, MCDM was employed by Timperio et al ( 2017 ) to provide a decision support framework for facility locations identification with the purpose of addressing network design in Indonesia disaster relief supply chains. Among the researches listed and described above, none of them captures the technicalities of a real-life case as such covered by Timperio et al ( 2017 ), particularly from the perspective of multi-criteria decision making.…”

“…Recently, MCDM was employed by Timperio et al ( 2017 ) to provide a decision support framework for facility locations identification with the purpose of addressing network design in Indonesia disaster relief supply chains. Among the researches listed and described above, none of them captures the technicalities of a real-life case as such covered by Timperio et al ( 2017 ), particularly from the perspective of multi-criteria decision making. Most literature reviews that deal with facility location problem through optimization techniques with the support of quantitative data.…”

“…Most literature reviews that deal with facility location problem through optimization techniques with the support of quantitative data. Timperio et al ( 2017 ) approach, used in this research, is an interdisciplinary approach that integrated both quantitative and qualitative evidence, and providing a full-bodied and adjustable methodology when replicated in other contexts. The decision support used in this study integrates quantitative and qualitative data and applied in linear programming model.…”

Um Estudo Bibliométrico Sobre a Temática Gestão Estratégica Da Logística Humanitária: Um Panorama Da Produção Científica De 2005 a 2018 / Bibliometric Study on the Thematic Strategic Management of Humanitarian Logistics: A Panorama of Scientific Production From 2005 to 2018

“…É esperado um crescimento em número e escala nas crises humanitárias geradas, no futuro, por desastres naturais. Essa expectativa está baseada em fatores naturais, mas, também, pelo impacto do homem na terra provocando mudanças climáticas, rápida urbanização em zonas propensas a desastres e instabilidades políticas e sociais (TIMPERIO et al, 2017).…”