Improvement of Work Process Performance with Task Assignments and Mental Workload Balancing

The outcome of a work process depends heavily on which tasks assigned to which employees. However, sometimes-optimized assignments based on employees’ qualifications may result in an uneven and ineffective workload distribution among them. Likewise, an even workload distribution without considering the employee\u27s qualifications may cause unproductive employee-task matching that results in low performance of employees. This trade-off is even more noticeable for work processes during critical time junctions, such as in military command centers and emergency rooms that require being fast and effective without making errors. This study proposes that optimizing task-employee assignments according to their capabilities while also keeping them under a workload threshold, results in better performance for work processes, especially during critical time junctions. The goal is to select the employee-task assignments in order to minimize the average duration of a work process while keeping the employees under a workload threshold to prevent errors caused by overload. Due to uncertainties inherent in the problem related with the inter-arrival time of work orders, task durations and employees\u27 instantaneous workload, a utilized simulation-optimization approach solves this problem. More specifically, a discrete event human performance simulation model evaluates the objective function of the problem coupled with a genetic algorithm based meta-heuristic optimization approach to search the solution space. This approach proved to be useful in determining the right task-agent assignments by taking into consideration the employees\u27 qualifications and mental workload in order to minimize the average duration of a work process. Use of a sample work process shows the effectiveness of the developed simulation-optimization approach. Numerical tests indicate that the proposed approach finds better solutions than common practices and other simulation-optimization methods. Accordingly, by using this method, organizations can increase performance, manage excess-level workloads, and generate higher satisfactory environments for employees, without modifying the structure of the process itself

Employee-Task Assignments for Organization Modeling: A Review of Models and Applications

In this study, we present a review of task assignment problems in organizations, an area that has become more important as tasks become more complex and personnel skills become more specialized. The challenge is to design task assignments that meet all requirements and result in the best organizational performance. The consequences of poor design include failed tasks, reduced efficiency, and inability to meet deadlines. Moreover, inequity of workload between employees can cause lack of job satisfaction, loss of motivation and also boredom. In general, work processes in organizations consist of different tasks, which require different expertise. Personnel usually have various degrees of qualifications and their performance may vary for different tasks. The outcome of the work process depends heavily on which tasks are assigned to which personnel. Performance of an organization can be increased by assigning the tasks to the most qualified available personnel. However, it could result in overloading some personnel while the others remain under-loaded. So, the even distribution of the workload between them also needs to be taken into consideration. In addition, it helps to increase organization\u27s productivity by distributing personnel\u27s knowledge, time, and attention more extensively. We review task-employee assignment problems in organization modeling with the computational models that have been reported in the literature with their applications. Human factors engineering, in terms of workload in organizational modeling is included as the work process performance is heavily dependent on the human (personnel). Based on the initial findings, we propose to investigate an improved workload model that allows the use of optimization of select constraints, such as balancing workload among team members. While current workload models allow the evaluation of workload among team members interacting in a work process, it is up to the analyst to suggest improvements. By providing an optimized solution, we present the Engineering Manager, that can then be adjusted to meet practical criteria

A Minimum-Cost Flow Model for Workload Optimization on Cloud Infrastructure

Recent technology advancements in the areas of compute, storage and networking, along with the increased demand for organizations to cut costs while remaining responsive to increasing service demands have led to the growth in the adoption of cloud computing services. Cloud services provide the promise of improved agility, resiliency, scalability and a lowered Total Cost of Ownership (TCO). This research introduces a framework for minimizing cost and maximizing resource utilization by using an Integer Linear Programming (ILP) approach to optimize the assignment of workloads to servers on Amazon Web Services (AWS) cloud infrastructure. The model is based on the classical minimum-cost flow model, known as the assignment model.Comment: 2017 IEEE 10th International Conference on Cloud Computin

Pilot interaction with automated airborne decision making systems

An investigation was made of interaction between a human pilot and automated on-board decision making systems. Research was initiated on the topic of pilot problem solving in automated and semi-automated flight management systems and attempts were made to develop a model of human decision making in a multi-task situation. A study was made of allocation of responsibility between human and computer, and discussed were various pilot performance parameters with varying degrees of automation. Optimal allocation of responsibility between human and computer was considered and some theoretical results found in the literature were presented. The pilot as a problem solver was discussed. Finally the design of displays, controls, procedures, and computer aids for problem solving tasks in automated and semi-automated systems was considered

Adaptive Cognitive Interaction Systems

Adaptive kognitive Interaktionssysteme beobachten und modellieren den Zustand ihres Benutzers und passen das Systemverhalten entsprechend an. Ein solches System besteht aus drei Komponenten: Dem empirischen kognitiven Modell, dem komputationalen kognitiven Modell und dem adaptiven Interaktionsmanager. Die vorliegende Arbeit enthält zahlreiche Beiträge zur Entwicklung dieser Komponenten sowie zu deren Kombination. Die Ergebnisse werden in zahlreichen Benutzerstudien validiert

A Mixed-Integer Linear Programming Formulation for Human Multi-Robot Task Allocation

In this work, we address a task allocation problem for human multi-robot settings. Given a set of tasks to perform, we formulate a general Mixed-Integer Linear Programming (MILP) problem aiming at minimizing the overall execution time while optimizing the quality of the executed tasks as well as human and robotic workload. Different skills of the agents, both human and robotic, are taken into account and human operators are enabled to either directly execute tasks or play supervisory roles; moreover, multiple manipulators can tightly collaborate if required to carry out a task. Finally, as realistic in human contexts, human parameters are assumed to vary over time, e.g., due to increasing human level of fatigue. Therefore, online monitoring is required and re-allocation is performed if needed. Simulations in a realistic scenario with two manipulators and a human operator performing an assembly task validate the effectiveness of the approach.Comment: Accepted to 2021 IEEE International Conference on Robot and Human Interactive Communication (RO-MAN

Dynamic Resource Allocation For Coordination Of Inpatient Operations In Hospitals

Healthcare systems face difficult challenges such as increasing complexity of processes, inefficient utilization of resources, high pressure to enhance the quality of care and services, and the need to balance and coordinate the staff workload. Therefore, the need for effective and efficient processes of delivering healthcare services increases. Data-driven approaches, including operations research and predictive modeling, can help overcome these challenges and improve the performance of health systems in terms of quality, cost, patient health outcomes and satisfaction. Hospitals are a key component of healthcare systems with many scarce resources such as caregivers (nurses, physicians) and expensive facilities/equipment. Most hospital systems in the developed world have employed some form of an Electronic Health Record (EHR) system in recent years to improve information flow, health outcomes, and reduce costs. While EHR systems form a critical data backbone, there is a need for platforms that can allow coordinated orchestration of the relatively complex healthcare operations. Information available in EHR systems can play a significant role in providing better operational coordination between different departments/services in the hospital through optimized task/resource allocation. In this research, we propose a dynamic real-time coordination framework for resource and task assignment to improve patient flow and resource utilization across the emergency department (ED) and inpatient unit (IU) network within hospitals. The scope of patient flow coordination includes ED, IUs, environmental services responsible for room/bed cleaning/turnaround, and patient transport services. EDs across the U.S. routinely suffer from extended patient waiting times during admission from the ED to the hospital\u27s inpatient units, also known as ED patient `boarding\u27. This ED patient boarding not only compromises patient health outcomes but also blocks access to ED care for new patients from increased bed occupancy. There are also significant cost implications as well as increased stress and hazards to staff. We carry out this research with the goal of enabling two different modes of coordination implementation across the ED-to-IU network to reduce ED patient boarding: Reactive and Proactive. The proposed `reactive\u27 coordination approach is relatively easy to implement in the presence of modern EHR and hospital IT management systems for it relies only on real-time information readily available in most hospitals. This approach focuses on managing the flow of patients at the end of their ED care and being admitted to specific inpatient units. We developed a deterministic dynamic real-time coordination model for resource and task assignment across the ED-to-IU network using mixed-integer programming. The proposed \u27proactive\u27 coordination approach relies on the power of predictive analytics that anticipate ED patient admissions into the hospital as they are still undergoing ED care. The proactive approach potentially allows additional lead-time for coordinating downstream resources, however, it requires the ability to accurately predict ED patient admissions, target IU for admission, as well as the remaining length-of-stay (care) within the ED. Numerous other studies have demonstrated that modern EHR systems combined with advances in data mining and machine learning methods can indeed facilitate such predictions, with reasonable accuracy. The proposed proactive coordination optimization model extends the reactive deterministic MIP model to account for uncertainties associated with ED patient admission predictions, leading to an effective and efficient proactive stochastic MIP model. Both the reactive and proactive coordination methods have been developed to account for numerous real-world operational requirements (e.g., rolling planning horizon, event-based optimization and task assignments, schedule stability management, patient overflow management, gender matching requirements for IU rooms with double occupancy, patient isolation requirements, equity in staff utilization and equity in reducing ED patient waiting times) and computational efficiency (e.g., through model decomposition and efficient construction of scenarios for proactive coordination). We demonstrate the effectiveness of the proposed models using data from a leading healthcare facility in SE-Michigan, U.S. Results suggest that even the highly practical optimization enabled reactive coordination can lead to dramatic reduction in ED patient boarding times. Results also suggest that signification additional reductions in patient boarding are possible through the proposed proactive approach in the presence of reliable analytics models for prediction ED patient admissions and remaining ED length-of-stay. Future research can focus on further extending the scope of coordination to include admissions management (including any necessary approvals from insurance), coordination needs for admissions that stem from outside the ED (e.g., elective surgeries), as well as ambulance diversions to manage patient flows across the region and hospital networks

Operator Objective Function Guidance for a Real-time Unmanned Vehicle Scheduling Algorithm

Advances in autonomy have made it possible to invert the typical operator-to-unmanned-vehicle ratio so that asingle operator can now control multiple heterogeneous unmanned vehicles. Algorithms used in unmanned-vehicle path planning and task allocation typically have an objective function that only takes into account variables initially identified by designers with set weightings. This can make the algorithm seemingly opaque to an operator and brittle under changing mission priorities. To address these issues, it is proposed that allowing operators to dynamically modify objective function weightings of an automated planner during a mission can have performance benefits. A multiple-unmanned-vehicle simulation test bed was modified so that operators could either choose one variable or choose any combination of equally weighted variables for the automated planner to use in evaluating mission plans. Results from a human-participant experiment showed that operators rated their performance and confidence highest when using the dynamic objective function with multiple objectives. Allowing operators to adjust multiple objectives resulted in enhanced situational awareness, increased spare mental capacity, fewer interventions to modify the objective function, and no significant differences in mission performance. Adding this form of flexibility and transparency to automation in future unmanned vehicle systems could improve performance, engender operator trust, and reduce errors.Aurora Flight Sciences, U.S. Office of Naval Researc

Peachy Parallel Assignments (EduHPC 2018)

Peachy Parallel Assignments are a resource for instructors teaching parallel and distributed programming. These are high-quality assignments, previously tested in class, that are readily adoptable. This collection of assignments includes implementing a subset of OpenMP using pthreads, creating an animated fractal, image processing using histogram equalization, simulating a storm of high-energy particles, and solving the wave equation in a variety of settings. All of these come with sample assignment sheets and the necessary starter code.Departamento de Informática (Arquitectura y Tecnología de Computadores, Ciencias de la Computación e Inteligencia Artificial, Lenguajes y Sistemas Informáticos)Facilitar la inclusión de ejercicios prácticos de programación paralela en cursos de Computación Paralela o de alto rendimiento (HPC)Comunicación en congreso: Descripción de ejercicios prácticos con acceso a material ya desarrollado y probado