The Taguchi method is widely used in the field of manufacturing systems performance simulation and improvement. On the other hand, Arena/OptQuest is one of the most efficient contemporary simulation/optimization software tools. The objective of this paper is to evaluate and compare these two tools applied to a flexible manufacturing system performance optimization context, based on simulation. The principal purpose of this comparison is to determine their performances based on the quality of the obtained results and the gain in the simulation effort. The results of the comparison, applied to a flexible manufacturing system mean flow time optimization, show that the Arena/OptQuest optimization platform outperforms the Taguchi optimization method. Indeed, the Arena/OptQuest permits one, through the lowest experimental effort, to reliably minimize the mean flow time of the studied flexible manufacturing system more than the Taguchi method.

One of the main concepts for improving the sustainability of supply chains is the collaboration between stakeholders by increasing the efficiency of their shared resources. In the literature, there are many research papers related to vertical collaboration in the logistics industry. However, horizontal collaboration has not received the same degree of attention. In fact, horizontal collaboration such as shared freight carrier and freight consolidation can also be considered vital for low-carbon supply chain solutions. In this paper, the problem of the design of supply chain transportation pooling strategies (SCTPS) is studied, which considers both vertical and horizontal collaboration. The purpose of this paper is to study the impact of these SCTPSs to reduce CO₂ emissions using discrete-event simulation (DES)-based methodology. Using a numerical case study of two manufacturing companies and three customers, five SCTPS are studied including the following: (1) non-pooling strategy; (2) multi-pick strategy; (3) multi-drop strategy; (4) central hub strategy; and (5) combined hub and multi-drop strategy. The main result of the study is that all SCTPSs significantly reduce the CO₂ emissions compared to the non-pooled supply chain. In fact, the reduction in CO₂ emissions can reach 13% compared to the non-pooled strategy. Moreover, the best SCTPS that gives the minimum of CO₂ is the hub strategy, followed by the multi-pick strategy and the multi-drop strategy.

One of the basic components of Industry 4.0 is the design of a flexible manufacturing system (FMS), which involves the choice of parameters to optimize its performance. Discrete event simulation (DES) models allow the user to understand the operation of dynamic and stochastic system performance and to support FMS diagnostics and design. In combination with DES models, optimization methods are often used to search for the optimal designs, which, above all, involve more than one objective function to be optimized simultaneously. These methods are called the multi-objective simulation–optimization (MOSO) method. Numerous MOSO methods have been developed in the literature, which spawned many proposed MOSO methods classifications. However, the performance of these methods is not guaranteed because there is an absence of comparative studies. Moreover, previous classifications have been focused on general MOSO methods and rarely related to the specific area of manufacturing design. For this reason, a new conceptual classification of MOSO used in FMS design is proposed. After that, four MOSO methods are selected, according to this classification, and compared through a detailed case study related to the FMS design problem. All of these methods studied are based on Design of Experiments (DoE). Two of them are metamodel-based approaches that integrate Goal Programming (GP) and Desirability Function (DF), respectively. The other two methods are not metamodel-based approaches, which integrate Gray Relational Analysis (GRA) and the VIKOR method, respectively. The comparative results show that the GP and VIKOR methods can result in better optimization than DF and GRA methods. Thus, the use of the simulation metamodel cannot prove its superiority in all situations.

Different studies based on discrete-event simulation have been conducted to evaluate the operation of mass vaccination centers and assist healthcare planners in improving their performance. These studies explored different parameters and scenarios for the operation of mass vaccination centers and generated several results. This review aims to assess discrete-event simulation applications in mass vaccination center management. This critical analysis concentrated on every phase of the project method for discrete-event simulation. To ensure comprehensive coverage, scholarly works about the application of discrete-event simulation in mass vaccination centers were sourced from the World Health Organization’s COVID-19 research database and other databases, including Web of Science, Scopus, MDPI, Sage, PubMed, medRxiv, and the WHO COVID-19 research database. Only English-language studies detailing the application of discrete-event simulation in mass vaccination centers were taken into account. The vaccines, simulation software, strategies, or origins were all unrestricted. Hence, the search was expanded to include other vaccination campaigns besides COVID-19. Our investigation included vaccination campaigns against H1N1 and the influenza virus. Eleven studies were selected. The results show that most simulation studies of vaccination centers have several shortcomings that are visible at every phase of the discrete-event simulation project. Through an analysis of the current state of vaccination center simulation studies, our research identifies best practices for upcoming studies. Following these recommendations ought to raise the standard of upcoming studies at large vaccination centers.