About

Michael Kay is the Director of IMSEI and an Associate Professor at NC State University. His research focus is in the field of industrial and systems engineering, contributing to areas related to operations research, system analytics, and optimization. As a key faculty member, he is involved in advancing the understanding and application of engineering principles to improve complex systems. His role encompasses leadership in research initiatives and academic programs within the department, supporting the development of innovative solutions in industrial engineering. His work is integral to the department's mission of fostering research and education that support the engineering community and industry partners.

Research topics

• Computer Science
• Engineering
• Computer Security
• Economics
• Operations research
• Business
• Marketing
• Systems engineering
• Microeconomics
• Mathematical optimization
• Artificial Intelligence
• Operations management
• Mathematics
• Manufacturing engineering
• Industrial engineering
• Reliability engineering
• Aerospace engineering
• Transport engineering
• Mechanical engineering
• Process management

Selected publications

• Air movement operations planning heuristic improvement

  Journal of Defense Analytics and Logistics · 2025-01-16

  articleOpen access

  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.

  PublisherOA PDFDOI

• I cast the drains down in Africa: AM-augmented casting as an enabler for the African manufacturing industry

  Proceedings of the Design Society · 2025-08-01

  articleOpen access

  ABSTRACT: Africa's manufacturing sector is pivotal for economic growth and technological advancement. However, challenges such as inadequate infrastructure, supply chain disruptions, geopolitical tensions, and high costs hinder its development. These issues impede domestic production and reduce global competitiveness. Addressing them is essential for economic resilience. While beneficial, traditional strategies often overlook fundamental production constraints, especially in manufacturing sectors reliant on repair, maintenance, specialized components, and tooling. Manufacturing methods like casting face limitations in flexibility, cost, precision, and lead times. This research proposes using additive manufacturing (AM)-assisted casting to address these challenges. We identify agriculture and automotive as sectors with high potential to implement AM-assisted casting.

  PublisherOA PDFDOI

• Synthetic Demand Flow Generation Using the Proximity Factor

  Forecasting · 2025-03-19

  articleOpen accessSenior author

  One of the biggest challenges in designing a logistics network is predicting the demand flows between all pairs of points in the network. Currently, the gravity model is mainly used for estimating the demand flow between points. However, the gravity model uses historical data to estimate values for its multiple parameters and distance between pairs to forecast the demand flow. Distance values close to zero and unprecedented changes in demand flow data create numerical instability for the gravity model’s output. Hence, the proximity factor, a single parameter model that uses the relative ordering of pairs instead of distance, was developed. In this paper, we systematically compare the proximity factor and the gravity model. It is shown that the proximity factor is a robust in terms of reliability and competitive alternative to the gravity model. According to our analysis, the proximity factor model can replace the gravity model in some applications when no historical data are available to adjust the parameters of the latter.

  PublisherOA PDFDOI

• Enhancing Military Load Planning: A Prioritized 2-D Orthogonal Packing Approach

  2025-12-20

  articleOpen access

  Military combat loading requires arranging equipment on maritime transport vessels to enable rapid, prioritized off-loading while maintaining unit cohesion and vessel stability. This paper extends a prioritized two-dimensional orthogonal packing framework to address the specific operational constraints of military logistics, incorporating global load balancing requirements alongside existing prioritization objectives. We introduce three solution techniques for this globally constrained problem: a monolithic mixed-integer linear programming (MILP) approach, a sliding-window matheuristic, and a sliding-window matheuristic with in-stride load balancing penalties. For any sliding-window solution that fails to achieve both feasible packing and load balancing in the initial stage, we develop a universal post-processing strategy that selectively relaxes and re-optimizes item positions to achieve balance with minimal disruption to the prioritized layout. Computational experiments demonstrate that the matheuristic approaches fundamentally outperform the monolithic MILP approach in load balance reliability, solution quality, and computational efficiency, providing practical guidance for integrating automated optimization into military load planning systems. The proposed methods generate high-quality, load-balanced solutions for single-vessel scenarios in approximately two minutes on average, enabling rapid evaluation of multiple loading configurations during time-critical deployment planning.

  PublisherOA PDFDOI

• Military maritime load planning instances for prioritized two-dimensional orthogonal packing

  Open MIND · 2025-12-04

  dataset

  This dataset supports the computational validation of prioritized two-dimensional orthogonal packing algorithms applied to military combat loading scenarios, as detailed in "Enhancing Military Load Planning: A Prioritized 2-D Orthogonal Packing Approach". The repository comprises 70 problem instances derived from authoritative U.S. Army equipment databases, specifically the Joint Equipment Characteristic Database (JECD) and the Modified Table of Organizational Equipment (MTOE) for a representative Armored Brigade Combat Team (ABCT). The data represents approximately two brigades of equipment filtered for roll-on/roll-off capability—including tracked combat vehicles, self-propelled artillery, and heavy wheeled vehicles—to simulate realistic amphibious embarkation requirements. Instances are provided in JSON format and categorize items by Unit Identification Code (UIC) and Paragraph Number (PARNO) to enforce hierarchical packing priorities that balance access-point proximity with unit cohesion. The dataset spans six representative vessel classes (Whidbey Island, Wasp, Harpers Ferry, Besson, America, and Runnymede) with target space utilization levels ranging from 65% to 85%. Supplementary Julia scripts utilizing the Gurobi optimizer are included to reproduce computational experiments across three solution methods: a monolithic Mixed-Integer Linear Program (MILP), a standard sliding-window matheuristic, and an in-stride balancing variant. These scripts evaluate algorithmic performance against strict center-of-gravity deviation tolerances (δ∈{0.01,0.05,0.10,0.15}), enabling the assessment of trade-offs between load balancing feasibility, solution quality, and computational efficiency.

  DOI

• Implementing Trades of the National Football League Draft on Blockchain Smart Contracts

  2025-02-24 · 1 citations

  preprintOpen access

  Purpose - Demonstrate proof-of-concept for conducting NFL Draft trades on a blockchain network using smart contracts. Design/Methodology/Approach - Using Ethereum smart contracts, we model several types of draft trades between teams. An example scenario is used to demonstrate contract interaction and draft results. Findings - We show the feasibility of conducting draft-day trades using smart contracts. The entire negotiation process, including side deals, can be conducted digitally. Originality/value - This research demonstrates the new application of smart contracts in the intersection of sports management and blockchain technology. 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.

  PublisherOA PDFDOI

• The 2-D Orthogonal Packing Problem with Multiple Levels of Prioritization: A Spatial Optimization Perspective

  2025-05-23

  preprintOpen access

  This paper addresses two-dimensional orthogonal packing within a confined space, integrating bin packing principles with facility layout concepts to address scenarios in which items must not only fit but also be arranged according to spatial priorities. We embed a prioritization matrix into the bin packing framework, enabling items to be clustered with one another or pulled toward certain bin access points based on assigned priority weights. Unlike traditional bin packing, which focuses on space utilization alone, our approach balances proximity to bin access points and adjacency among functionally related items, extending the utility of bin packing to applications requiring more nuanced layout preferences. We introduce a single mixed-integer linear programming (MILP) model and a complemen- tary sliding-window matheuristic that scales effectively to larger problem instances. Numerical experiments illustrate that this matheuristic approach consistently outperforms a direct MILP solve with a commercial solver in both runtime and solution quality, and also performs best among the adapted heuristic and metaheuristic alternatives considered in our study. This computational study underscores the flexibility and effectiveness of embedding multi-level priorities into orthogonal packing.

  PublisherOA PDFDOI

• Implementing A Letter Of Credit Style Business Process For Small-Scale Contracting Using Smart Contracts

  2024-08-19

  articleOpen access

  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.

  PublisherDOI

• Implementing a Letter of Credit Style Business Process for Small-Scale Contracting Using Smart Contracts

  SSRN Electronic Journal · 2024-01-01

  preprintOpen access
  PublisherDOI

• Shipper-Driven Consolidation Mechanisms for Freight Transport

  International Journal of Intelligent Transportation Systems Research · 2024-01-01 · 1 citations

  preprintOpen accessSenior author
  PublisherOA PDFDOI

Frequent coauthors

• Brandon M. McConnell

  North Carolina State University

  28 shared

• Russell E. King

  North Central State College

  27 shared

• Kristin Thoney-Barletta

  18 shared

• Thom J. Hodgson

  North Carolina State University

  16 shared

• Jeffrey A. Joines

  North Carolina State University

  8 shared

• Ryan D. Winz

  U.S. Air Force Institute of Technology

  8 shared

• Kenan Karagül

  Pamukkale University

  8 shared

• J.A. Janet

  7 shared