Michael G Kay’s research while affiliated with North Central State College and other places

Purpose The purpose of this paper is to improve the air movement operations planning heuristic in the literature to generate better solutions in a shorter time period. Design/methodology/approach Through a rigorous design of experiments (DOEs), we make significant heuristic improvements by evaluating alternative modular methodologies and tuning heuristic parameters for two scenarios. This includes a new approach to considering refueling operations. Findings We find the fine-tuned heuristic averages a 33% objective improvement and 70% reduction in computation time over the heuristic with original parameters for one of the scenarios. Additionally, we analyze the heuristic's quality of solution over time. Research limitations/implications Further analysis is required to generalize heuristic settings, which would require significant access to operational data or a portfolio of scenarios of interest. Practical implications Tuned heuristic parameters reduce the computation time from hours to minutes. This also makes it practically feasible to adjust parameters in the objective function to generate multiple courses of action (COAs) for a given instance. Originality/value This research provides novel vehicle assignment and routing heuristic improvement alternatives and demonstrates a DOEs-based heuristic tuning procedure.

Micro Nuclear Reactors (MNRs) are an emerging innovation in nuclear technology as small, portable, and self-sufficient reactor units in the size of a standard 40-foot shipping container. An MNR functions as a "nuclear battery," where each unit can power load capacities from 500 kilowatts (kW) to 5 megawatts (MW) over the lifetime of 1–10 years. This technology may deploy by the end of the 2020 decade, so private and government organizations have prepared for potential operational use for energy resilience. This research develops an emergency grid disruption timeline with MNR deployments to respond and recover grids after severe weather events. This response integrates a series of models for transportation networks, power distribution, and decision strategies that utilize MNR capabilities while using real-world disruption events within the past decade for scenarios. The study then seeks to analyze the performance of MNRs when using different deployment strategies for emergency grid disruption response. First, this research investigates the trade-offs between time and cost in the emergency grid disruption timeline when integrating MNRs. This research also explores the conditions of disruption scenarios that contribute to the best MNR performance in grid recovery.

Purpose - Demonstrate proof-of-concept for an expanded blockchain smart contract based small-scale contracting process that includes an internally managed arbitration service to manage disputes. Design/Methodology/Approach - Using Ethereum smart contracts, we model a small-scale general contracting scenario with disruptions. Execution is demonstrated with the Remix Integrated Development Environment (IDE). Findings - We show the feasibility of managing general contracting disputes with an internal arbitration service, completely encompassed within blockchain smart contracts. Originality/value - This research continues an original effort to model the small-scale general contracting scenario on a blockchain network. Research limitations/implications - Further work is required to expand the scope of dispute management and account for additional external factors. Also, full-scale decentralized application is not explored here. Practical implications - This process expands the scope of current practices and tools, such as Angi, in a decentralized manner with blockchain. Social Implications - Full-scale adoption at the small scale is likely difficult due to disbelief in technology, cost, and resistance to change.

Purpose - Demonstrate proof-of-concept for negotiating and executing a small-scale contracting job based on letter of credit within a blockchain network using smart contracts. Design/Methodology/Approach - Using Ethereum smart contracts, we model a small-scale general contracting scenario under perfect conditions. Execution is demonstrated with the Remix Integrated Development Environment (IDE). Findings - We show the feasibility of conducting a small-scale contracting job using smart contracts. The entire process, including job details, payment, and verification, can be conducted digitally. Originality/value - This research continues the efforts of previous research on letters of credit on blockchain but applies it to the general contracting scenario at small scale. Research limitations/implications - Further work is required to investigate variations in the assumptions, such as dishonesty and incompetence. Also, full-scale decentralized application is not explored here. Practical implications - This process expands the scope of current practices and tools, such as Angi, in a decentralized manner with blockchain. Social Implications - Full-scale adoption at the small scale is likely difficult due to disbelief in technology, cost, and resistance to change.

Purpose Demonstrate proof-of-concept for conducting NFL Draft trades on a blockchain network using smart contracts. Design/methodology/approach Using Ethereum smart contracts, the authors model several types of draft trades between teams. An example scenario is used to demonstrate contract interaction and draft results. Findings The authors show the feasibility of conducting draft-day trades using smart contracts. The entire negotiation process, including side deals, can be conducted digitally. Research limitations/implications Further work is required to incorporate the full-scale depth required to integrate the draft trading process into a decentralized user platform and experience. Practical implications Cutting time for the trade negotiation process buys decision time for team decision-makers. Gains are also made with accuracy and cost. Social implications Full-scale adoption may find resistance due to the level of fan involvement; the draft has evolved into an interactive experience for both fans and teams. Originality/value This research demonstrates the new application of smart contracts in the inter-section of sports management and blockchain technology.

US Army aviation units often organize into task forces to meet mission requirements. The manner in which they allocate assets affects their long-term capabilities to provide aviation support. We propose a model to allocate utility helicopters across geographically separated task forces to minimize the total time of flight and unsupported air movement air mission requests (AMRs) by priority level. We model the allocation problem with a two-stage stochastic program, with the first-stage problem allocating a fleet’s helicopter teams to task forces. The stochastic demand for each task force is then revealed. The second-stage US Army aviation air movement operations planning problem is modeled as a stochastic mixed integer linear program (MILP). A practical application uses the air movement operations planning heuristic to solve the second-stage problem at scale and generate an optimal stochastic solution task force allocation. This paper provides evidence for the practical use of the proposed two-stage stochastic programming model for US Army aviation asset allocation by military decision-makers. Furthermore, this research provides a novel first formulation of a stochastic programming dial-a-ride problem with multinode refuel and a sound framework for military aviation asset allocation decision-making.

Purpose - Improve the air movement operations planning heuristic in the literature to generate better solutions in a shorter time period. Design/Methodology/Approach - Through a rigorous design of experiments, we make significant heuristic improvements by evaluating alternative modular methodologies and tuning heuristic parameters for two scenarios. This includes a new approach to considering refueling operations. Findings - We find the fine-tuned heuristic averages a 33% objective improvement and 70% reduction in computation time over the heuristic with original parameters for one of the scenarios. Additionally, we analyze the heuristic's quality of solution over time. Research limitations/implications - Further analysis is required to generalize heuristic settings, which would require significant access to operational data or a portfolio of scenarios of interest. Originality/value - This research provides novel vehicle assignment and routing heuristic improvement alternatives and demonstrates a design of experiments-based heuristic tuning procedure. Practical implications - Tuned heuristic parameters reduce the computation time from hours to minutes. This also makes it practically feasible to adjust parameters in the objective function to generate multiple courses of action for a given instance.

Purpose: The purpose of this paper is to investigate US noncombatant evacuation operations (NEO) in South Korea and devise planning and management procedures that improve the efficiency of those missions. Design/methodology/approach: It formulates a time-staged network model of the South Korean noncombatant evacuation system as a mixed integer linear program to determine an optimal flow configuration that minimizes the time required to complete an evacuation. This solution considers the capacity and resource constraints of multiple transportation modes and effectively allocates the limited assets across a time-staged network to create a feasible evacuation plan. That solution is post-processed and a vehicle routing procedure then produces a high resolution schedule for each individual asset throughout the entire duration of the NEO. Findings: This work makes a clear improvement in the decision-making and resource allocation methodology currently used in a NEO on the Korea peninsula. It immediately provides previously unidentifiable information regarding the scope and requirements of a particular evacuation scenario and then produces an executable schedule for assets to facilitate mission accomplishment. Originality/value: The significance of this work is not relegated only to evacuation operations on the Korean peninsula; there are numerous other NEO and natural disaster related scenarios that can benefit from this approach.

Uncertainty is rampant in military expeditionary operations spanning high-intensity combat to humanitarian operations. These missions require rapid planning and decision-support tools to address the logistical challenges involved in providing support in often austere environments. The US Army’s adoption of an enterprise resource planning system provides an opportunity to develop automated decision-support tools and other analytical models designed to take advantage of newly available logistical data. This research presents a tool that runs in near-real time to assess risk while conducting capacity planning and performance analysis designed for inclusion in a suite of applications dubbed the Military Logistics Network Planning System, which previously only evaluated the mean sample path. Logistical data from combat operations during Operation Iraqi Freedom drive supply requisition forecasts for a contingency scenario in a similar geographic environment. A nonstationary queueing network model is linked with a heuristic logistics scheduling methodology to provide a stochastic framework to account for uncertainty and assess risk.