Theses and Dissertations

ORCID

0000-0002-6178-3809

Advisor

Ma, Junfeng

Committee Member

Banicescu, Ioana

Committee Member

Marufuzzaman, Mohammad

Committee Member

Tajik, Nazanin

Committee Member

Wang, Jun

Date of Degree

8-12-2026

Original embargo terms

Visible MSU Only 1 year

Document Type

Dissertation - Campus Access Only

Major

Industrial & Systems Engineering

Degree Name

Doctor of Philosophy (Ph.D.)

College

James Worth Bagley College of Engineering

Department

Department of Industrial and Systems Engineering

Abstract

The integration of mobile additive manufacturing (MAM) and truck-drone delivery (TDD) systems offers a transformative approach for enhancing supply chain resilience (SCR), particularly in disaster-prone and uncertain environments. This dissertation develops integrated optimization frameworks that combine MAM and TDD technologies to address disrupted infrastructure, fluctuating demand, and time-sensitive delivery requirements. Several mixed-integer linear programming (MILP) models are proposed to jointly optimize production scheduling, truck routing, drone dispatching, and delivery coordination while minimizing operational costs and satisfying customer-specific service requirements. The research further incorporates robust and stochastic decision-making frameworks to capture disaster-related uncertainty, infrastructure degradation, and vibration-induced quality degradation in MAM processes. To efficiently solve the resulting large-scale optimization problems, multiple exact and heuristic solution approaches are developed, including Lagrangian relaxation, variable neighborhood descent (VND)-based heuristics, and a hybrid progressive hedging–variable neighborhood descent (PH–VND) algorithm. Extensive computational experiments and real-world case studies demonstrate the scalability, adaptability, and effectiveness of the proposed frameworks under varying operational and disaster conditions. Results indicate significant improvements in delivery responsiveness, operational efficiency, and service reliability while maintaining high-quality production performance in disrupted environments. This dissertation advances the state of the art in resilient supply chain (SC) and logistics system design by providing integrated, flexible, and uncertainty-aware production–delivery frameworks for disaster response and modern logistics operations. The findings offer valuable managerial insights for improving operational agility and emergency logistics planning under uncertainty.

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