The multiobjective simulation optimization (MOSO) problem is a nonlinear multiobjective optimization problem in which multiple simultaneous and conflicting objective functions can only be observed with stochastic error. We provide an introduction to MOSO at the advanced tutorial level, aimed at researchers and practitioners who wish to begin working in this emerging area. Our focus is exclusively on MOSO methods that characterize the entire efficient or Pareto-optimal set as the solution to the MOSO problem; later, this set may be used as input to the broader multicriteria decision-making process. Our introduction to MOSO includes an overview of existing theory, methods, and provably convergent algorithms that explicitly control sampling error for (1) MOSO on finite sets, called multiobjective ranking and selection; (2) MOSO with integer-ordered decision variables; and (3) MOSO with continuous decision variables. In the context of integer-ordered and continuous decision variables, we focus on methods that provably converge to a local efficient set under the natural ordering. We also discuss key open questions that remain in this emerging field.

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The COVID-19 pandemic has impacted various aspects of society, and research has been published across different sectors beyond healthcare. This article presents the results of a literature review in Hospital Supply Chain management (HSCM) to identify current and future research areas. The literature review identified four main research themes associated with the scope of HSCM, the flow type, and the approaches employed. The results of this study suggest that COVID-19 is a catalyst for short-and long-term changes that require the attention of researchers, strategies to incorporate 4.0 technologies, and integration of different stakeholders to improve the resilience and sustainability of the Supply Chain (SC) in future pandemic scenarios. This paper provides relevant information to the implementation for future research regarding the management of the HSCM for the post-pandemic period.

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