Optimized Procedure to Schedule Physicians in an Intensive Care Unit: A Case Study

Hospitals are facing an important financial pressure due to the increasing of the operating costs. Indeed, the growth for the hospitals’ services demand causes a rising in the number of required qualified personnel. Enlarging the personnel number increases dramatically the fixed total cost. Based on some studies, 50% of operating costs in US hospitals are allocated to healthcare personnel. Therefore, reducing these types of costs without damaging the service quality becomes a priority and an obligation. In this context, several studies focused on minimizing the total cost by producing optimal or near optimal schedules for nurses and physicians. In this paper, a real-life physicians scheduling problem with cost minimization is addressed. This problem is encountered in an Intensive Care Unit (ICU) where the current schedule is manually produced. The manual schedule is generating a highly unbalanced load within physicians in addition to a high cost overtime. The manual schedule preparation is a time consuming procedure. The main objective of this work is to propose a procedure that systematically produces an optimal schedule. This optimal schedule minimizes the total overtime within a short time and should satisfies the faced constraints. The studied problem is mathematically formulated as an integer linear program. The constraints are real, hard, and some of them are non-classical ones (compared to the existing literature). The obtained mathematical model is solved using a state-of-the-art software. Experimental tests on real data have shown the performance of the proposed procedure. Indeed, the new optimal schedules reduce the total overtime by up to 69%. In addition, a more balanced workload for physicians is obtained and several physician preferences are now satisfied

Comparative Analysis of Producer Mobility Management Approaches in Named Data Networking

Seamless management of producer mobility in named data networks (NDNs) has become an inherent requirement to satisfy the ever-increasing number of mobile user devices and the streaming of widespread real-time multimedia content. In this paper, we first classify the various producer mobility management (MM) schemes into four different approaches. Then, we select a representative scheme from each approach and conduct a comparative analysis between them to suggest the most suitable producer MM approach for a broad class of latency sensitive applications, such as video and audio streaming and broadcasting over NDNs. To assess and compare the efficiency and effectiveness of the representative schemes, we implemented them in the NDN defacto NdnSIM simulator and used the same network scenarios and mobility settings. The results show the superiority of the producer MM scheme that follows the data plane-based approach, which yielded lower data loss rates, lower data delivery delays and lower signaling overheads

Green Scheduling of Identical Parallel Machines with Release Date, Delivery Time and No-Idle Machine Constraints

Global warming and climate change are threatening life on earth. These changes are due to human activities resulting in the emission of greenhouse gases. This is caused by intensive industrial activities and excessive fuel energy consumption. Recently, the scheduling of production systems has been judged to be an effective way to reduce energy consumption. This is the field of green scheduling, which aims to allocate jobs to machines in order to minimize total costs, with a focus on the sustainable use of energy. Several studies have investigated parallel-machine shops, with a special focus on reducing and minimizing the total consumed energy. Few studies explicitly include the idle energy of parallel machines, which is the energy consumed when the machines are idle. In addition, very few studies have considered the elimination of idle machine times as an efficient way to reduce the total consumed energy. This is the no-idle machine constraint, which is the green aspect of the research. In this context, this paper addresses the green parallel-machine scheduling problem, including release dates, delivery times, and no-idle machines, with the objective of minimizing the maximum completion time. This problem is of practical interest since it is encountered in several industry processes, such as the steel and automobile industries. A mixed-integer linear programming (MILP) model is proposed for use in obtaining exact solutions for small-sized instances. Due to the NP-hardness of the studied problem, and encouraged by the successful adaptation of metaheuristics for green scheduling problems, three genetic algorithms (GAs) using three different crossover operators and a simulated annealing algorithm (SA) were developed for large-sized problems. A new family of lower bounds is proposed. This was intended for the evaluation of the performance of the proposed algorithms over the average percent of relative deviation (ARPD). In addition, the green aspect was evaluated over the percentage of saved energy, while eliminating the idle-machine times. An extensive experimental study was carried out on a benchmark of test problems with up to 200 jobs and eight machines. This experimental study showed that one GA variant dominated the other proposed procedures. Furthermore, the obtained numerical results provide strong evidence that the proposed GA variant outperformed the existing procedures from the literature. The experimental study also showed that the adoption of the no-idle machine time constraints made it possible to reduce the total consumed energy by 29.57%, while the makespan (cost) increased by only 0.12%

PF-ClusterCache: Popularity and Freshness-Aware Collaborative Cache Clustering for Named Data Networking of Things

Named Data Networking (NDN) has been recognized as the most promising information-centric networking architecture that fits the application model of IoT systems. In-network caching is one of NDN’s most fundamental features for improving data availability and diversity and reducing the content retrieval delay and network traffic load. Several caching decision algorithms have been proposed; however, retrieving and delivering data content with minimal resource usage, reduced communication overhead, and a short retrieval time remains a great challenge. In this article, we propose an efficient popularity and freshness caching approach named PF-ClusterCache that efficiently aggregates the storage of different nodes within a given cluster as global shareable storage so that zero redundancy be obtained in any cluster of nodes. This increases the storage capacity for caching with no additional storage resource. PF-ClusterCache ensures that only the newest, most frequent data content is cached, and caching is only performed at the edge of the network, resulting in a wide diversity of cached data content across the entire network and much better overall performance. In-depth simulations using the ndnSIM simulator are performed using a large transit stub topology and various networking scenarios. The results show the effectiveness of PF-ClusterCache in sharing and controlling the local global storage, and in accounting for the popularity and freshness of data content. PF-ClusterCache clearly outperforms the benchmark caching schemes considered, especially in terms of the significantly greater server access reduction and much lower content retrieval time, while efficiently conserving network resources

Minimizing the In-Cloud Bandwidth for On-Demand Reactive and Proactive Streaming Applications

Video streaming services are one of the most resource-consuming applications on the Internet. Thus, minimizing the consumed resources at runtime in general and the server/network bandwidth in particular are still challenging for researchers. Currently, most streaming techniques used on the Internet open one stream per client request, which makes the consumed bandwidth increases linearly. Hence, many broadcasting/streaming protocols have been proposed in the literature to minimize the streaming bandwidth. These protocols can be divided into two main categories, namely, reactive and proactive broadcasting protocols. While the first category is recommended for streaming unpopular videos, the second category is recommended for streaming popular videos. In this context, in this paper we propose an enhanced version of the reactive protocol Slotted Stream Tapping (SST) called Share All SST (SASST), which we prove to further reduce the streaming bandwidth with regard to SST. We also propose a new proactive protocol named the New Optimal Proactive Protocol (NOPP) based on an optimal scheduling of video segments on streaming-channel. SASST and NOPP are to be used in cloud and CDN (content delivery network) networks where the IP multicast or multicast HTTP on QUIC could be enabled, as their key principle is to allow the sharing of ongoing streams among clients requesting the same video content. Thus, clients and servers are often services running on virtual machines or in containers belonging to the same cloud or CDN infrastructure

Hybrid Live P2P Streaming Protocol

AbstractA considerable part of the Internet traffic is due to Peer-to-Peer (P2P) protocols. The scalability of the P2P networks encourages implementing many P2P applications such as the file sharing, the media on demand and the live streaming. While many P2P solutions have been proposed the media freshness and the smooth media playback are still a challenging issues in the P2P live streaming. In this paper we propose the Hybrid Live P2P Streaming Protocol (HLPSP) which a live P2P streaming system based on a hybrid overlay (tree and mesh topology). The simulation results show that HLPSP outperforms the enhanced version of the famous CoolStreaming P2P system called DenaCast in terms of the startup delay, the end-to-end delay, the play-back delay and the data loss