INDUSTRIAL & SYSTEMS ENGINEER · RESEARCHER · FOUNDER
Associate Professor of Industrial and Manufacturing Engineering at Cal Poly Pomona · Founder of Optimal Flows
Ph.D. Industrial & Systems Engineering · ASQ CRE and CQE · Certificate in MBSE, Caltech
Operational decision systems for maintenance, fleets, and physical assets
I combine reliability engineering, optimization, MBSE, and AI to turn difficult operational problems into validated models, workflows, systems, and products.
My work focuses on environments where decisions must account for uncertainty, operational constraints, legacy systems, human judgment, and the consequences of failure.
I work on problems where analytical capability alone is insufficient. The model, data, software, operational workflow, and people responsible for the decision must function as one coherent system.
Decision systems that coordinate operational demand, asset availability, maintenance requirements, capacity, and uncertainty.
Models and workflows for replacement timing, lifecycle cost, degradation, reliability, risk, and capital allocation.
Architectures that connect analytical models, software, enterprise data, physical operations, and human decision-makers.
Turning validated research methods into usable pilots, decision services, software platforms, and commercial products.
I developed and computationally validated a mixed-integer method that plans aircraft utilization and preventive maintenance together for instances representing about 1,500 assets. Tested cases reached verified optimal solutions in under seven minutes; the work remains a research implementation, not an operational deployment.
Led end-to-end CMMS implementations across roughly ten manufacturing and petrochemical sites, translating maintenance requirements into asset structures and workflows, converting legacy text and spreadsheet records through SQL-based migration tooling, and supporting implementation through user training and site collaboration.
A solver-independent planning engine that integrates age-based maintenance with permutation flow-shop scheduling. Three formulations—deterministic, fuzzy bi-objective, and stochastic—balance delivery tardiness and maintenance cost while accounting for maintenance effects on processing, combined maintenance activities, and uncertainty in processing and maintenance times.
This paper is concerned with stochastic scheduling of production and maintenance activities in a permutation flow shop setting. We present a two-stage stochasti…
Construction projects usually get delayed for several time periods. When the planning horizon of a project is extended, projections for purchase and salvage of …
In this paper, we extend the formulation and solution algorithm of a Flight and Maintenance Planning (FMP) problem to cover a wider range of applications. We co…
A shadow can be physically accurate and still lead us to infer a creature that never existed. What does this tell us about probability distributions, optimization models, simulations, and the limits of the frameworks we use?
If you give AI a well-written operations research problem, it will produce the mathematical formulation instantly, which is impressive. But who decided what the problem actually is?
LLM access alone does not create organizational value. The current phase of AI adoption focuses on Specific AI Services (SAIS): tools built around defined workflows, human judgment, measurable outcomes, and transparent costs.
I collaborate with asset-intensive organizations, research teams, and technical founders on problems involving maintenance, fleets, reliability, asset lifecycle, operational modeling, and system architecture.
If your organization has data, analytical models, or technical capability but still lacks a usable decision system, I would be interested in understanding the problem. Schedule a 20-minute introductory conversation to discuss the problem, its current constraints, and whether there is a useful basis for collaboration.
