Identifying the best design configuration for a first-response team is important for minimizing total operation time and reducing the human cost of natural and manmade disasters. This paper presents ongoing research that focuses on a disaster multiagent coordination simulation (DMCsim) system that is able to optimally design the first-response team and evaluate the team design configuration before initiation of a search and rescue operation. We developed an agent-based simulation system that uses machine learning techniques and design of experiments methods to test different configuration setups and determine the effects of various factors on operation completion time. The evaluation of a team design for a disaster-response operation revealed that some design factors have a significant effect on operation outcome. Removing the effects of uncontrollable factors, such as damage level and robot reliability, yielded a robust team design that could function in a particular disaster environment regardless of the effects of such factors. The DMCsim assists decision makers to evaluate an emergency-response operation, revise the current strategy based on resources on hand, redesign the available team, and visually track operation performance before launching the actual team in the disaster field. This research extends previous disaster response coordination systems by proposing a new simulation model and evaluating a first-response team design. K E Y W O R D S agent-based simulation, decision support system, design of experiments, first-response team design, machine learning, system optimization, system performance 322
Quality assurance during the operation of a system is critical for maintaining performance within desired specifications. Without proper monitoring and control, a system is capable of erratic behavior resulting in degraded performance, unplanned downtime for maintenance, or system failure. Statistical analysis has proven useful when appropriately applied for quality control activities, as is evidenced within the manufacturing industry. The collection of statistical techniques commonly applied to evaluate the performance of systems and/or processes is known as statistical process control. This paper presents an approach concerning the statistical process control technique of control charting, demonstrating its applicability to controlling and monitoring operational systems involving human processes with multiple quality characteristics. Typical control chart application assumes a normal distribution for analyzed data and sufficient data points necessary to collect an appropriately sized sample. The novelty of the proposed approach is in the intent of monitoring the overall operation of a process, focusing on initial inputs and final outputs, while utilizing 100% of process‐generated data from systems where the operational pace does not lend to the collection of large amounts of data points. In this paper, the applicability of the proposed approach is demonstrated on a corporate information technology help desk. An approach designed for systems within the scope of this paper would be beneficial to multiple industries and organizations for evaluation of systems consisting of human‐involved processes.
The operation and maintenance (O&M) activities of systems can account for 75% of total lifecycle cost. To effectively manage cost, optimize system "on" time, and mitigate defects/failures during the O&M phase of a system's lifecycle, the application of systems monitoring and control is encouraged. Statistical process control (SPC) in general, the control chart specifically, is the most common monitoring approach. The control chart provides alerts with respect to the behavior of systems and processes, as well as changes in process variability. Data applied to control charts is assumed to adhere to a normal distribution, a constraint often satisfied in manufacturing and similar industries where the natural variation in the process or system follows the Gaussian distribution. Systems involving people processes and business rhythms can compromise the normality assumption, reducing the reliability of SPC. Through the application of SPC, this paper proposes a novel approach to monitoring operational systems in the systems engineering O&M phase for the express purpose of reducing high costs by mitigating system discrepancies and uncovering inefficiencies. This paper focuses on processes that require 100% system data sampling due to the operational nature of the system.
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