Waiting time is a crucial performance metric in A&E departments. In this regard, longer waiting times are related to low patient satisfaction, high mortality rates and more severe physical health complications. To analyze patient flow in these departments, discrete-event simulation (DES) has been used; however, its applicatin has not been extended to evaluate the impact of improvement strategies. Therefore, this paper aims to design and pretest operational strategies for better ED care delivery using DES. First, input data analysis is carried out. Afterward, the DES model is developed and validated to establish whether it is statistically comparable with the real-world. Then, performance indicators of the current system are computed and analyzed. Finally, improvement strategies are proposed and evaluated by simulation modelling and statistical tests. A case study of an A&E department from a district general clinic is presented to validate the proposed framework. In particular, we will validate the effectiveness of introducing a triage system (Scenario 3), a strategy that is not currently adopted by the clinic. Results demonstrate that waiting times could be meaningfully diminished based on the proposed approaches within this paper.

Just-in-time delivery has become a key aspect of pharmaceutical industry when loyalizing customers and competing internationally. Additionally, prolonged lead times may lead to increased work-inprocess inventory, penalties for non-compliance and cost overrun. The problem is more complex upon considering a wide variety of products as often noted in pharmaceutical companies. It is then relevant to design strategies focusing on improving the delivery performance. Therefore, this paper proposes the use of Discrete-event simulation (DES) to identify inefficiencies and define solutions for the delivery problem. First, input data were gathered and analyzed. Then, a DES model was developed and validated. Finally, potential improvement scenarios were simulated and analyzed regarding productivity rate and proportion of tardy jobs. A case study in a pharmaceutical laboratory is presented to validate the proposed methodology. The results evidenced that, by implementing the best scenario, the productivity may be augmented by 44.83% which would generate zero tardy jobs.

Scheduling flexible job-shop systems (FJSS) has become a major challenge for different smart factories due to the high complexity involved in NP-hard problems and the constant need to satisfy customers in real time. A key aspect to be addressed in this particular aim is the adoption of a multi-criteria approach incorporating the current dynamics of smart FJSS. Thus, this paper proposes an integrated and enhanced method of a dispatching algorithm based on fuzzy AHP (FAHP) and TOPSIS. Initially, the two first steps of the dispatching algorithm (identification of eligible operations and machine selection) were implemented. The FAHP and TOPSIS methods were then integrated to underpin the multi-criteria operation selection process. In particular, FAHP was used to calculate the criteria weights under uncertainty, and TOPSIS was later applied to rank the eligible operations. As the fourth step of dispatching the algorithm, the operation with the highest priority was scheduled together with its initial and final time. A case study from the smart apparel industry was employed to validate the effectiveness of the proposed approach. The results evidenced that our approach outperformed the current company’s scheduling method by a median lateness of 3.86 days while prioritizing high-throughput products for earlier delivery.

Gynecobstetrics departments (GDs) oversee diagnosing, monitoring, and treating female reproductive diseases as well as assisting women during pregnancy. Their importance motivates the creation of suitable performance evaluation approaches for identifying weaknesses and designing focused interventions. Therefore, the aim of this paper is twofold: (a) provide an approach for GD performance evaluation and (b) propose interventions tackling the GDs' weaknesses. The fuzzy analytic hierarchy process (FAHP) was first applied to calculate the initial criteria and subcriteria weights under vagueness. Then, the decision-making trial and evaluation laboratory (DEMATEL) was implemented to evaluate interrelations. FAHP and DEMATEL were later combined to estimate the final criteria and subcriteria weights under vagueness and interdependency. Finally, the technique for order of preference by similarity to ideal solution (TOPSIS) was used to rank the GDs and detect improvement opportunities. A case study of a cluster including three GDs is presented to validate the proposed approach. The results evidenced that patient safety and service quality are the most critical aspects in GD performance evaluation. The results from this application can be used by healthcare managers for designing focused interventions targeting improved performance of GDs. This paper fully exploits the advantages of FAHP, DEMATEL, and TOPSIS methods for evaluating performances of GDs. Furthermore, this study presents a novel decision-making model representing the multifactorial context of the GD performance.

Appointment lead-time is a pivotal parameter in elderly outpatient clinics. In this regard, delayed medical care may represent complications in the elderly population and the development of more severe diseases. However, healthcare managers are not skilled in methods effectively reducing waiting times. Therefore, this paper presents the computer simulation modelling to tackle this problem. In this regard, the real-world system was initially simulated and then, three improvement scenarios were designed and validated operationally and financially. The results evidenced that Scenario 2 was the best choice since it provided a low investment per reduced day and a significant reduction (47.1%) regarding the probability of waiting for more than 8 days per appointment. With this proposal, the quality of medical care in elderly population can be meaningfully increased and decisionmaking process can be effectively supported.