Lean Manufacturing: Efficiency improvement application in multi-product area of the Aerospace Industry

This research addresses the problem of leveling workloads in a multi-product final assembly area. In which it was found that 27.4% of the time is used for set up and the current distribution presents areas of opportunity. The target was to implement improvement actions to make use of resources more efficient in the production process in the aforementioned area through Lean Manufacturing tools. The results obtained consist of eight products generated with the support of lean manufacturing support tools such as SMED, Workload Balancing and MUDA waste identification, achieving important results among which productivity in the area stands out from 109% to 125%, as well as a reduction in set-up time from 17 min to 4.4 min.

Fuzzy-Based Mobile Edge Orchestrators in Heterogeneous IoT Environments: An Online Workload Balancing Approach

Online workload balancing guarantees that the incoming workloads are processed to the appropriate servers in real time without any knowledge of future resource requests. Currently, by matching the characteristics of incoming Internet of Things (IoT) applications to the current state of computing and networking resources, a mobile edge orchestrator (MEO) provides high-quality service while temporally and spatially changing the incoming workload. Moreover, a fuzzy-based MEO is used to handle the multicriteria decision-making process by considering multiple parameters within the same framework in order to make an offloading decision for an incoming task of an IoT application. In a fuzzy-based MEO, the fuzzy-based offloading strategy leads to unbalanced loads among edge servers. Therefore, the fuzzy-based MEO needs to scale its capacity when it comes to a large number of devices in order to avoid task failures and to reduce service times. In this paper, we investigate and propose an online workload balancing algorithm, which we call the fuzzy-based (FuB) algorithm, for a fuzzy-based MEO. By considering user configuration requirements, server geographic locations, and available resource capacities for achieving an online algorithm, our proposal allocates the proximate server for each incoming task in real time at the MEO. A simulation was conducted in augmented reality, healthcare, compute-intensive, and infotainment applications. Compared to two benchmark schemes that use the fuzzy logic approach for an MEO in IoT environments, the simulation results (using EdgeCloudSim) show that our proposal outperforms the existing algorithms in terms of service time, the number of failed tasks, and processing times when the system is overloaded.

The emergency department physician rostering problem: obtaining equitable solutions via network optimization

In this study, we address workload balancing in Emergency Department Physician Rostering Problems. We propose a two-phase approach to deal with two common workload balancing issues: (1) the even distribution of worked weekends and weekend night shifts across physicians in the long term, and (2) the even distribution of morning and afternoon shifts in the medium term. To implement such an approach, we have developed two Integer Linear Programming (ILP) models, one for each phase. In the first phase, we determine the weekends that each physician will be on duty over the long term planning horizon (6-months) while evenly distributing the workload (worked weekends and weekend night shifts) across physicians. In the second phase, month by month, we iteratively determine the workday shifts of each physician while pursuing the even distribution of workload (morning and afternoon shifts) across physicians. The second phase relies on the solution of the first phase, i.e., the weekend shifts assigned to each physician in the first phase are considered preassigned shifts in the second phase. In both phases, we consider the constraints deriving from collective as well as individual contractual agreements (e.g. constraints limiting the maximum number of night shifts each physician can work every month, their maximum weekly and monthly workload, etc.) as well as individual physician’s preferences and desiderata. The problems addressed in the two phases differ in terms of the planning horizon, objective function, and constraints, yet they are both modeled as multicommodity ow problems and share the same network structure. Also, we define some families of simple yet effective, valid inequalities that are crucial to address the computational complexity of the first-phase problem. The proposed optimization models have been tested on real data from a leading European Hospital and on benchmark instances from the literature. The models’ effectiveness has been assessed through six key performance indicators purposely defined. Results demonstrate that the presented models allow considering the complex nature of physicians rostering problems and obtaining well-balanced and thus equitable work schedules.

A Clustered Approach for Load Balancing in Distributed Systems

Distributed systems are increasingly becoming the dominant and rapidly expanding computational paradigm of tomorrow. A cluster is really a form of parallel or distributed processing system that consists of a set of intertwined stand-alone machines that function together like truly coherent computing and storage resources with a single system image (SSI) which means that perhaps the clusters are viewed as a single platform by the consumers. Global resource management, on the other hand, poses several concerns due to the sheer complexity and range of tools, as well as the need for user accountability. The possible advantages of load balancing in addressing the occasional congestion faced by some nodes when everyone else is idle or congested are widely agreed on a level of performance. This is also widely acknowledged that neither specific load balancing algorithm can adequately address evolving device characteristics and complex capacity management in a distributed ecosystem. To have a systematic approach and also in distributed systems, a proposed approach is created for a holistic view of element load balancing and also the qualities features of load balancing algorithms. The nomenclature has been expanded. In order for adaptive algorithms to understand the problem and manner of prefixing resilience along with different components in distributed systems, they must first recognize the concerns. In addition, a proposed approach is specified. The much more effective load balancing techniques and the modeling hypotheses used in prior load balancing experiments are established through a study of related research. We consider the most appropriate load balancing algorithm and optimum metrics for parameter estimation of the algorithm as a consequence of and output of this assessment for a range of formulations of resulting goals, distributed system features, and workload balancing framework.

GPrimer: a fast GPU-based pipeline for primer design for qPCR experiments

Background Design of valid high-quality primers is essential for qPCR experiments. MRPrimer is a powerful pipeline based on MapReduce that combines both primer design for target sequences and homology tests on off-target sequences. It takes an entire sequence DB as input and returns all feasible and valid primer pairs existing in the DB. Due to the effectiveness of primers designed by MRPrimer in qPCR analysis, it has been widely used for developing many online design tools and building primer databases. However, the computational speed of MRPrimer is too slow to deal with the sizes of sequence DBs growing exponentially and thus must be improved. Results We develop a fast GPU-based pipeline for primer design (GPrimer) that takes the same input and returns the same output with MRPrimer. MRPrimer consists of a total of seven MapReduce steps, among which two steps are very time-consuming. GPrimer significantly improves the speed of those two steps by exploiting the computational power of GPUs. In particular, it designs data structures for coalesced memory access in GPU and workload balancing among GPU threads and copies the data structures between main memory and GPU memory in a streaming fashion. For human RefSeq DB, GPrimer achieves a speedup of 57 times for the entire steps and a speedup of 557 times for the most time-consuming step using a single machine of 4 GPUs, compared with MRPrimer running on a cluster of six machines. Conclusions We propose a GPU-based pipeline for primer design that takes an entire sequence DB as input and returns all feasible and valid primer pairs existing in the DB at once without an additional step using BLAST-like tools. The software is available at https://github.com/qhtjrmin/GPrimer.git.

Workload Balancing on Identical Parallel Machines: Theoretical and Computational Analysis

This paper considers the problem of assigning nonpreemptive jobs on identical parallel machines to optimize workload balancing criteria. Since workload balancing is an important practical issue for services and production systems to ensure an efficient use of resources, different measures of performance have been considered in the scheduling literature to characterize this problem: maximum completion time, difference between maximum and minimum completion times and the Normalized Sum of Square for Workload Deviations. In this study, we propose a theoretical and computational analysis of these criteria. First, we prove that these criteria are equivalent in the case of identical jobs and in some particular cases. Then, we study the general version of the problem using jobs requiring different processing times and establish the theoretical relationship between the aforementioned criteria. Based on these theoretical developments, we propose new mathematical formulations to provide optimal solutions to some unsolved instances in order to enhance the latest benchmark presented in the literature.