During the crisis, relief supply chain management (also known as humanitarian supply chain management) has received great attention these days. The core questions facing many humanitarian organizations are: where are their strengths/weaknesses? Are they positioned to be effective in their supply chain system? What challenges do you need to overcome? What do they need to do to take advantage of the technological opportunities offered nowadays? These questions have been addressed them extensively during the past two decades. This paper tries to review and classify some of the papers carried out in key areas of the humanitarian supply chain such as location, certainty and uncertainty, relief teams and injured (patient) classification, machine learning, queue theory, the employed research methods, solution methods, and the type of objective functions. The paper begins first to define what the “humanitarian” ecosystem may include, and which actors play important roles. After, certain critical views of the humanitarian relief supply chain are examined. The critical views of the humanitarian relief supply chain would help researchers to introduce further research orientations and areas to overcome crises in the real world. Manuscript profile

Great attention should be paid to planning and scheduling surgeries in the operating room which is the most sensitive ward in the health context in terms of cost and specific sensitivity due to its association with the life and death of individuals. In this case, reusable sterile equipment and devices are crucial issues because the hospital or nosocomial infections result from insufficient sterilization of these instruments. Therefore, sterilization of reusable medical devices is a necessity in the operating room to prevent possible infections. This study solves the integrated operating rooms and sterile section planning problem to minimize the total costs of sterilization, surgery postponement, and performance. This study also minimizes the completion time of surgery considering nondeterministic operating times and emergency-elective patients. In the real world, surgery time may be nondeterministic based on the conditions of the patient, surgeon, equipment, and instruments; hence, it is valuable to find a robust solution for planning under such circumstances. After presenting a bi-objective mathematical model for this problem, an improved epsilon constraint method was used to solve problems with small dimensions, and two metaheuristics NSGA-II and NRGA were developed for large dimensions regarding NP-hard problems. These two algorithms were analysed in terms of five indicators. The results indicated the superiority of the NSGA-II algorithm over NRGA to solve such problems. Manuscript profile