OPTIMIZING HUMANITARIAN RELOCATION OF CONTAGIOUS AND NON-CONTAGIOUS POPULATIONS DURING THE RECOVERY PHASE: A MODEL FOR MINIMIZING COST AND TIME UNDER UNCERTAINTY
Optimizing Post-Disaster Relocation : A Cost & Time Model Under Uncertainty
DOI:
https://doi.org/10.23055/ijietap.2023.30.6.9699Keywords:
Post-disaster relocation; multi-objective transportation problem; single-valued hexagonal neutrosophic number; goal programming; fuzzy goal programming; global criterion method; neutrosophic compromise approach; LINGO.Abstract
In recent years, there has been a growing significance of research on humanitarian logistics for both researchers and practitioners. This research is crucial for aiding relief operations. While there has been extensive study of mathematical models for disaster operations management in the preparedness and response phases, the recovery phase models still need more attention. One of the significant challenges during the recovery phase is the spread of contagious diseases in the affected area, which necessitates the timely and cost-effective transportation of both contagious and non-contagious populations while preventing further casualties and disease spread. The paper proposes a multi-objective solid transportation model with different conveyance types for the relocation process to address these challenges. The proposed multi-objective model seeks to minimize two essential objectives: the cost and time required for relocation, and includes factors such as transportation, penalties, accommodations, medical expenses, halts, refueling, and maintenance. To account for the unpredictability and vagueness of input data in post-disaster scenarios, the proposed model incorporates fuzzy inputs and introduces a novel defuzzification technique that is validated by comparing it with an existing methodology. The research employs optimization techniques using the LINGO optimizing solver and presents a case study and particular cases that provide valuable management insights for improving decision support systems. Among the optimization techniques, namely the Neutrosophic compromise approach, Goal programming, Fuzzy goal programming, and Global criterion method, the optimal solution is obtained using the Neutrosophic compromise approach. The cost and time objective values obtained using the Neutrosophic compromise approach are 2034725 and 3923, respectively.
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