This paper is a complete survey of flowshop-scheduling problems and contributions from early works of Johnson of 1954 Johnson, SM . 1954. Optimal two- and three-stage production schedules with set-up times included. Naval Res. Logist. Quart., 1: 61–68. to recent approaches of metaheuristics of 2004. It mainly considers a flowshop problem with a makespan criterion and it surveys some exact methods (for small size problems), constructive heuristics and developed improving metaheuristic and evolutionary approaches as well as some well-known properties and rules for this problem. Each part has a brief literature review of the contributions and a glimpse of that approach before discussing the implementation for a flowshop problem. Moreover, in the first section, a complete literature review of flowshop-related scheduling problems with different assumptions as well as contributions in solving these other aspects is considered. This paper can be seen as a reference to past contributions (particularly in n/m/p/c _max or equivalently F/prmu/c _max) for future research needs of improving and developing better approaches to flowshop-related scheduling problems.

In recent years, Operations Research/Management (OR/OM) has had a significant impact on improving the performance of hospital Emergency Departments (EDs). This includes improving a wide range of processes involving patient flow from the initial call to the ED through disposition, discharge home, or admission to the hospital. We review approximately 350 related papers to (i) demonstrate the influence of OR/OM in EDs, and (ii) assist both researchers and practitioners with the OR/OM techniques already available to optimize ED patient flow. In addition, we elaborate on some practical challenges yet to be addressed. By shedding light on some less studied aspects that can have significant impacts on ED operations, we also discuss important possibilities for future OR/OM researchers.

Crisis-level overcrowding conditions in emergency departments (EDs) have led hospitals to seek out new patient-flow designs to improve both responsiveness and safety. One approach that has attracted attention and experimentation in the emergency medicine community is a system in which ED beds and care teams are segregated and patients are “streamed” based on predictions of whether they will be discharged or admitted to the hospital. In this paper, we use a combination of analytic and simulation models to determine whether such a streaming policy can improve ED performance, where it is most likely to be effective, and how it should be implemented for maximum performance. Our results suggest that the concept of streaming can indeed improve patient flow, but only in some situations. First, ED resources must be shared across streams rather than physically separated. This leads us to propose a new “virtual-streaming” patient flow design for EDs. Second, this type of streaming is most effective in EDs with (1) a high percentage of admitted patients, (2) longer care times for admitted patients than discharged patients, (3) a high day-to-day variation in the percentage of admitted patients, (4) long patient boarding times (e.g., caused by hospital “bed-block”), and (5) high average physician utilization. Finally, to take full advantage of streaming, physicians assigned to admit patients should prioritize upstream (new) patients, whereas physicians assigned to discharge patients should prioritize downstream (old) patients.

Hospital emergency departments (EDs) typically use triage systems that classify and prioritize patients almost exclusively in terms of their need for timely care. Using a combination of analytic and simulation models, we demonstrate that adding an up-front estimate of patient complexity to conventional urgency-based classification can substantially improve both patient safety (by reducing the risk of adverse events) and operational efficiency (by shortening the average length of stay). Moreover, we find that EDs with high resource (physician and/or examination room) utilization, high heterogeneity in the treatment time between simple and complex patients, and a relatively equal number of simple and complex patients benefit most from complexity-augmented triage. Finally, we find that (1) although misclassification of a complex patient as simple is slightly more harmful than vice versa, complexity-augmented triage is relatively robust to misclassification error rates as high as 25%; (2) streaming patients based on complexity information and prioritizing them based on urgency is better than doing the reverse; and (3) separating simple and complex patients via streaming facilitates the application of lean methods that can further amplify the benefit of complexity-augmented triage.

This article develops a model and analysis to provide insight into two effective remedies to increase supply chain resilience: (i) contracting with a secondary flexible backup supplier; and (ii) monitoring primary suppliers to obtain disruption risk information. To investigate the true value of these strategies, an analysis is performed under imperfect information concerning the disruption risks and considering a two-stage setting with recourse. In this setting, the firm first monitors its suppliers and then utilizes a recourse option subject to the limited quantity of a capacity reserved a priori via a contract with a flexible backup supplier. The firm’s jointly optimal behavior is analytically characterized (utilizing only the information available to the firm) regarding two interconnected decisions: (i) the advance capacity investment/reservation level with a flexible backup supplier; and (ii) the inventory ordering policy of the underlying products from both primary and backup suppliers. The presented results quantify effective disruption risk mitigation strategies for firms and provide managerial insights into the value of (i) a flexible backup supplier; (ii) disruption risk information; (iii) a contracted recourse option; and (iv) flexibility in the backup system.