Data analysis of ambient intelligence in a healthcare simulation system: a pilot study in high-end health screening process improvement

Background This study aimed to reduce the total waiting time for high-end health screening processes. Method The subjects of this study were recruited from a health screening center in a tertiary hospital in northern Taiwan from September 2016 to February 2017, where a total of 2342 high-end customers participated. Three policies were adopted for the simulation. Results The first policy presented a predetermined proportion of customer types, in which the total waiting time was increased from 72.29 to 83.04 mins. The second policy was based on increased bottleneck resources, which provided significant improvement, decreasing the total waiting time from 72.29 to 28.39 mins. However, this policy also dramatically increased the cost while lowering the utilization of this health screening center. The third policy was adjusting customer arrival times, which significantly reduced the waiting time—with the total waiting time reduced from 72.29 to 55.02 mins. Although the waiting time of this policy was slightly longer than that of the second policy, the additional cost was much lower. Conclusions Scheduled arrival intervals could help reduce customer waiting time in the health screening department based on the “first in, first out” rule. The simulation model of this study could be utilized, and the parameters could be modified to comply with different health screening centers to improve processes and service quality.

Bottleneck Resources, Market Relatedness, and the Dynamics of Organizational Growth

Entering a new product market requires assembling a bundle of resources. Because missing a single resource can foil the entire entry effort, we argue that bottleneck resources—those most difficult to obtain or sell externally—anchor the direction of firm growth. We characterize market resources as bottlenecks to product market entry, because they are (on average) more challenging to obtain and sell than technological resources, and we articulate why the importance of market resources varies with the strength of external markets for technology. Using cross-industry data linking firms’ product portfolios with patents, we find resource dynamics whereby market resources drive the strategic decision to enter, and firms fill technological gaps using both internal research and development and external acquisitions (joint ventures and alliances). Our study underscores the importance of resources for firm growth dynamics and specifically highlights market resources as the bottleneck that constrains and directs the direction of product market entry.

Theory of constraints case study in the make to order environment

Purpose: The theory of constraints (TOC) drum-buffer-rope methodology is appropriate when managing a production plant in complex environments, such as make-to-order (MTO) scenarios. However, some difficulties have been detected in implementing this methodology in such changing environments. This case study analyses a MTO company to identify the key factors that influence the execution of the third step of TOC. It also aims to evaluate in more depth the research started by Lizarralde et al. (2020) and compare the results with the existing literature.Design/methodology/approach: The case study approach is selected as a research methodology because of the need to investigate a current phenomenon in a real environment.Findings: In the case study analysed, the protective capacity of non-bottleneck resources is found to the key factor when subordinating the MTO system to a bottleneck (BN). Furthermore, it coincides with one of the two key factors defined by the literature, namely protective capacity and protective inventory.Originality/value: The three key contributions of this study focus on the MTO environment as follows. The first is about identifying the key factors in subordinating the system to the BN (step 3, TOC) according to the existing literature which have been identified through a systematic literature review. The second focuses on identifying the key factors in subordinating the system to the BN through a case study. Finally, the last contribution compares the results obtained in the case study with those obtained in the literature review.

Advanced Planning and Scheduling Based on the Constraint Theory and Improved Tabu Search Algorithm

Background: Planning and scheduling of manufacturing enterprises are required to be rapid and accurate, which make is easy for the traditional enterprise resource planning system to meet the needs of enterprises. Objective: This paper introduces the theory of constraints and explains the control methods for production planning and scheduling. Methods: The main characteristics of the advanced planning and scheduling system are analyzed. Then, the modeling method based on the theory of constraints is used to study the scheduling path. Results: An example is given to validate the scheduling model and realize the optimization of production tasks on the bottleneck resources. Conclusion: The feasibility of the advanced planning and scheduling methods based on the theory of constraints is proved.

On the Structure of Bottlenecks in Processes

Process capacity and the associated notions of bottleneck activities and bottleneck resources—which are responsible for limiting process capacity to its present value—are fundamental concepts in the operations management literature. However, for processes that involve collaboration and multitasking, there is little clarity in the literature on what bottlenecks are, what they look like, and how they can be identified. In this paper, we formulate and analyze graph-theoretic optimization problems that determine bottleneck structures of activities and the associated bottleneck sets of resources in deterministic, single-product processes with possibly multiple copies of one or more resources and possibly multiple sets of resources that can perform each activity. In the presence of both collaboration and multitasking, sets of activities that are interconnected in a specific manner via shared resources form bottleneck structures that are responsible for limiting capacity. We use the collaboration graph of the process to either characterize bottleneck structures completely or identify graphical structures that must necessarily be part of any bottleneck structure. Our analysis reveals a natural hierarchy in the algorithmic approach for identifying bottleneck structures as processes become increasingly sophisticated, ranging from the “easy” case where the simple bottleneck formula correctly identifies bottlenecks to more complex cases where one needs to solve progressively complicated mathematical programs. In turn, this understanding helps us obtain prescriptive answers to several questions of interest to managers, for example, the budget-constrained procurement of resources to maximize capacity improvement and the design of processes to increase capacity without procuring additional resources. This paper was accepted by Jayashankar Swaminathan, operations management.

Data Analysis of Ambient Intelligence in a Healthcare Simulation System: A Pilot Study in High-end Health Screening Process Improvement

This study aimed to reduce the total waiting time for high-end health screening processes. The subjects of this study were recruited from a health screening center in a tertiary hospital in northern Taiwan from September 2016 to February 2017 and a total of 2,342 high-end customers were collected. Arena software was used to simulate the examination process. We presented the simulation results of three different policies and compared those results to the current state. The first policy presented a predetermined proportion of customer types, in which the total waiting time was increased from 72.29 to 83.04 mins. The second policy was based on increased bottleneck resources and provided significant improvement, with the total waiting time was decreased from 72.29 to 28.39 mins. However, this policy also caused the cost to increase dramatically, while lowering the utilization of this exam station. The third policy was adjusting customer arrival times, which reduced the waiting time significantly, as the total waiting time was reduced from 72.29 to 55.02 mins. Although the wait time of this policy was slightly longer than that of the second policy, the additional cost was much lower than the second plan. Scheduled arrival intervals could help to reduce customer waiting time in the health screening department base on FIFO rule. The simulation model of this study could be utilized and the parameters could be modified to comply with different health examination centers to improve process and service quality.

Energy Efficient Resource Allocation using Improved BSP Technique in Cloud Environment

Cloud computing allows the user to access their resources from data center. The energy consumed by each resources will leads to low performance in the data center. The existing work deals with heuristic resource allocation which concentrates only on bottleneck resources. This research work proposes BSP algorithm to allocate the data center resources efficiently in terms of dynamic information and energy consumption. BSP is a placement technique which provides high quality placement for resources based on optimization process. The agent provide authentication to each user to ensure the privacy of the system. The overall resource allocation is achieved by BPS technique by performing continuous deployment and ongoing optimization. Continuously deployment is efficiently allocating the resources based on demand risk score. If any virtual machine in the cloud environment is overloaded, the ongoing optimization technique is applied to migrate the resources from overloaded VM to idle VM. The benefit of our improved BSP technique with the comparison of the results of the existing system is shown in performance analysis.

User Activity Based Application-Layer DoS/DDoS Attack Defense Algorithm

In application-layer DoS/DDoS attacks, malicious users attack the victim server by sending lots of legitimate requesting packages, which overwhelm the server bottleneck resources. Normal user’s request thus may not be satisfied. The traditional intrusion detection systems for network-layer cannot effectively identify this attack, and recent researches on this kind of attack are mainly for Web servers. This paper proposed a new defense algorithm based on user activity for topic-based Pub/Sub communication servers in mobile push notification systems. Users consuming system bottleneck resources the most can get high scores and thus are considered overactive. With some resource retaken strategy, overactive users’ connections will be dropped according to system performance level. Therefore, the system can get rid of latent threatens. Experiments indicated that this algorithm can identify normal and abnormal users well.