Redistribution for cost minimization in disaster management under uncertainty with trapezoidal neutrosophic number

Healthcare’s Sustainable Resource Planning Using Neutrosophic Goal Programming

We develop neutrosophic goal programming models for sustainable resource planning in a healthcare organization. The neutrosophic approach can help examine the imprecise aspiration levels of resources. For deneutrosophication, the neutrosophic value is transformed into three intervals based on the truth, falsity, and indeterminacy-membership functions. Then, a crisp value is derived. Moreover, multi-choice goal programming is also used to get a crisp value. The proposed models seek to draw a strategic plan and long-term vision for a healthcare organization. Accordingly, the specific aims of the proposed flexible models are meant to evaluate hospital service performance and to establish an optimal plan to meet the growing patient needs. As a result, sustainability's economic and social goals will be achieved so that the total cost would be optimized, patients' waiting time would be reduced, high-quality services would be offered, and appropriate medical drugs would be provided. The simplicity and feasibility of the proposed models are validated using real data collected from the Al-Amal Center for Oncology, Aden, Yemen. The results obtained indicate the robustness of the proposed models, which would be valuable for planners who could guide healthcare staff in providing the necessary resources for optimal annual planning.

Robust Design of Relief Distribution Networks Considering Uncertainty

In the post-disaster response phase, an efficient relief distribution strategy plays a vital role in alleviating suffering in disaster-stricken areas, which sometimes becomes challenging in humanitarian logistics. Most governments pre-located the relief goods at the pre-determined warehouses against possible disasters. Those goods must be shipped to the relief distribution centers (RDCs) to be further distributed to the victims in impacted areas upon the disasters. Secondary disasters can occur due to the first disaster and can occur relatively close in time and location, resulting in more suffering and making the relief distribution activities more challenging. The needs of additional RDCs must be determined as well in response to the secondary disasters. A robust optimization model is proposed to hedge against uncertainties in RDCs’ capacity and relief demand. Its objective is to minimize the sum of transportation cost, additional RDC cost, and shortage of commodities. The computational results are given to demonstrate the effectiveness of the proposed model. The sensitivity analysis gives an insight to the decision-makers.

A Systematic Literature Review on Mathematical Models of Humanitarian Logistics

Humanitarian logistics (HL) is considered one of the most significant issues of disaster operations and management. Thus, HL operation should be viable enough to function well under the uncertain and complex nature of the disaster. Many difficulties in pre-and post-disaster phases bring both human and economic losses. Therefore, it is essential to make sure that the HL operations are designed efficiently. In the last two decades, several publications have emphasized efficient HL operations and proposed several mathematical models and algorithms to increase the efficiency of HL operations and motivated the necessity of a systematic literature review. A systematic literature review is deemed pertinent due to its transparent and detailed article searching procedure. In this study, due to the importance of the mathematical optimization model, we reviewed more than one hundred articles published between 2000 and 2020 to investigate the optimization models in the field of HL. We classified the optimization models into three main problems: facility location problems, relief distribution, and mass evacuation where each of the classified areas includes both deterministic and non-deterministic models.

A decision support system for demand management in healthcare supply chains considering the epidemic outbreaks: A case study of coronavirus disease 2019 (COVID-19)

The disasters caused by epidemic outbreaks is different from other disasters due to two specific features: their long-term disruption and their increasing propagation. Not controlling such disasters brings about severe disruptions in the supply chains and communities and, thereby, irreparable losses will come into play. Coronavirus disease 2019 (COVID-19) is one of these disasters that has caused severe disruptions across the world and in many supply chains, particularly in the healthcare supply chain. Therefore, this paper, for the first time, develops a practical decision support system based on physicians' knowledge and fuzzy inference system (FIS) in order to help with the demand management in the healthcare supply chain, to reduce stress in the community, to break down the COVID-19 propagation chain, and, generally, to mitigate the epidemic outbreaks for healthcare supply chain disruptions. This approach first divides community residents into four groups based on the risk level of their immune system (namely, very sensitive, sensitive, slightly sensitive, and normal) and by two indicators of age and pre-existing diseases (such as diabetes, heart problems, or high blood pressure). Then, these individuals are classified and are required to observe the regulations of their class. Finally, the efficiency of the proposed approach was measured in the real world using the information from four users and the results showed the effectiveness and accuracy of the proposed approach.