What Optimal Resource Allocation & Scheduling got wrong, from 59 theses

Tried and failed

greedy heuristics for resource allocation applied to dynamic model serving under SLOs. Outcome: worse than baseline. Reason: greedy optimization fails to achieve the optimal Pareto trade-off between accuracy and latency constraints

Efficient AI Stack: Deployment-Aware Neural Architecture Search and Serving of Deep Neural Networks · Georgia Tech · PhD

Tried and failed

graph centrality heuristics for DAG partitioning applied to distributed workflow scheduling. Outcome: worse than baseline. Reason: failed to account for downstream cumulative resource bottlenecks

Leveraging Feedback for Dynamic Execution Optimization · Penn · PhD

Tried and failed

simple job ordering heuristics applied to DAG task scheduling. Outcome: worse than baseline. Reason: heuristic scheduling produced non-exact schedulability analysis and sub-optimal schedules compared to linear programming formulations

Towards Efficient Autonomous Vehicle Systems: A Multi-Layer Approach · Virginia Tech · Doctorate

Tried and failed

greedy multi-resource scheduling applied to distributed deep learning training clusters. Outcome: worse than baseline. Reason: CPU and memory fragmentation caused severe GPU underutilization under high cluster load

Accelerating deep learning training : a storage perspective · UT Austin · PhD

Tried and failed

greedy heuristics for combinatorial scheduling applied to online matching and flow problems. Outcome: worse than baseline. Reason: natural greedy choices fail to achieve optimal competitive ratios, yielding poor worst-case approximation bounds

Designing Networks, Routing Fleets, and Trying to Find Parking · Cornell · PhD

Tried and failed

greedy task placement without bandwidth awareness applied to in-network computing resource scheduling. Outcome: worse than baseline. Reason: ignoring bandwidth distribution caused severe performance degradation under high network oversubscription

Data centre resource scheduling for dataflow applications · Imperial · PhD

Tried and failed

greedy locality and round-robin placement heuristics applied to batch task scheduling at full capacity. Outcome: worse than baseline. Reason: resource fragmentation and oversubscription under high capacity utilisation

Data centre resource scheduling for dataflow applications · Imperial · PhD

Tried and failed

Unit-utility renewal analytic heuristic for lookahead scheduling applied to dynamic task scheduling. Outcome: worse than baseline. Reason: Unit-utility assumption broke down under Pareto-distributed task utility, causing severe utility loss

Spacecraft Autonomy through Computer Vision and Onboard Planning · MIT · PhD

Tried and failed

gradient-based greedy resource allocation applied to multicore cache and bandwidth partitioning. Outcome: worse than baseline. Reason: local gradients miss non-convex threshold effects where performance gains require minimum resource thresholds

DYNAMIC RESOURCE ALLOCATION AND ITS APPLICATIONS TO MULTICORE REAL-TIME SYSTEMS · Penn · PhD

Tried and failed

weighted shortest remaining processing time rule applied to multi-class job scheduling. Outcome: did not generalise. Reason: Optimality breaks when scaling beyond two distinct priority weight classes.

Scheduling in Healthcare · Cornell · PhD

Tried and failed

distance-based priority ordering heuristics applied to makespan minimization scheduling problems. Outcome: did not generalise. Reason: makespan objectives can require visiting farther nodes first, breaking total-variation time ordering assumptions

Min-Time Coverage in Constricted Environments · Georgia Tech · PhD

Tried and failed

success-rate priority queue scheduling applied to capacity-constrained personalized manufacturing dispatching. Outcome: worse than baseline. Reason: threshold-based survival prioritization reduced total treated throughput compared to first-in-first-out under high resource utilization

Improving patient benefits via digital modeling and AI-augmented decision support for supply chains of autologous cell-derived medical products · Georgia Tech · PhD

Considered and rejected

Considered and rejected: Rejected greedy bottom-up extraction algorithms from E-graphs in HELM because fabrication manufacturing time is non-additive and requires global operation scheduling.

Programming Language Tools and Techniques for Computational Fabrication · University of Washington · PhD

Tried and failed

greedy single most likely path scheduling applied to multi-user immersive video streaming. Outcome: worse than baseline. Reason: optimizing only the top trajectory caused high variance and unfair quality of experience across users

Timely information sharing in communication constrained systems · UT Austin · PhD

Tried and failed

Greedy incremental resource allocation applied to cycle time constrained vehicle routing. Reason: Non-convex objective returns with respect to integer resource count lead to suboptimal greedy selections.

Tactical design of last mile logistical systems · Georgia Tech · PhD

Considered and rejected

Considered and rejected: Rejected multi-dimensional bin packing algorithms online for dynamic phase reallocation due to high computational overhead; adopted greedy gradient heuristics instead.

DYNAMIC RESOURCE ALLOCATION AND ITS APPLICATIONS TO MULTICORE REAL-TIME SYSTEMS · Penn · PhD

Considered and rejected

Considered and rejected: Rejected full combinatorial argmin re-optimization across all client chunk allocations at each completed chunk transfer due to computational complexity, adopting instead an incremental greedy heuristic checking if one additional chunk from the active client decreases the objective.

Scheduling and resource allocation in modern distributed systems : applications to federated learning, blockchains, and large language models · UT Austin · PhD

Tried and failed

robust mixed-integer programming with affine decision rules applied to state-task network scheduling. Outcome: too slow. Reason: the resulting formulations scaled excessively and became computationally intractable compared to deterministic approximations

Formulations, algorithms and software for robust optimisation · Imperial · PhD

Tried and failed

exact optimization for multi-machine scheduling applied to multi-work-center job scheduling. Outcome: too slow. Reason: computationally prohibitive to scale beyond three work centers

Assortment Optimization under Customer-driven Substitution · Georgia Tech · PhD

Tried and failed

symmetry-breaking constraints in CP-SAT applied to flexible job-shop scheduling. Outcome: worse than baseline. Reason: reification overhead worsened runtime and optimality gap on larger instances

Scheduling in Semiconductor Manufacturing · Georgia Tech · PhD

Tried and failed

1:1 family-to-machine assignment symmetry breaking applied to flexible job-shop scheduling. Outcome: worse than baseline. Reason: it pruned improving solutions due to non-identical job release times

Scheduling in Semiconductor Manufacturing · Georgia Tech · PhD

Tried and failed

exact integer linear programming schedulability testing applied to multiprocessor real-time task scheduling. Outcome: too slow. Reason: extreme computational complexity makes exact response time analysis impractical beyond 13 tasks

Priority Assignment Algorithms for Real-Time Systems · Virginia Tech · Doctorate

Tried and failed

exact integer programming on time-space network applied to large-scale sort network scheduling. Outcome: too slow. Reason: computational complexity caused solver timeouts without finding feasible solutions on large problem instances

Sort Planning for Express Parcel Delivery Systems · Georgia Tech · PhD

Considered and rejected

Considered and rejected: Rejected exact optimization and mathematical programming (JSP/DFJSP) due to NP-hard complexity making real-time scheduling intractable.

Improving patient benefits via digital modeling and AI-augmented decision support for supply chains of autologous cell-derived medical products · Georgia Tech · PhD

Considered and rejected

Considered and rejected: Rejected the flow shop scheduling constructive heuristics of Framinan & Leisten and Laha & Sarin due to their excessive computational runtime complexity of O(m · n^4)

Entwicklung eines hybriden Optimierungsverfahrens für dynamische multikriterielle Produktionsglättung am Beispiel der Lebensmittelindustrie · TU Wien · PhD

Tried and failed

direct mixed-integer linear programming solver applied to large-scale transit resource allocation. Outcome: too slow. Reason: computational complexity prevented finding feasible solutions within the time limit on large instances

Fair and Risk-Averse Resource Allocation in Transportation Systems under Uncertainties · Virginia Tech · Doctorate

Tried and failed

mixed integer linear programming space-time network formulation applied to real-time routing and scheduling on large networks. Outcome: too slow. Reason: curse of dimensionality causes exponential growth of variables and constraints, making real-time solving computationally intractable

Integrated and joint optimisation of runway-taxiway-apron operations on airport surface · Imperial · PhD

Considered and rejected

Considered and rejected: Rejected simultaneously optimizing truck routing, scheduling, and cumulative hub capacity in a single monolithic MIP/CP due to lack of scalability at national scale.

Autonomous Transfer Hub Networks for Self-Driving Trucks · Georgia Tech · PhD

Tried and failed

policy gradient with hindsight baseline applied to virtual machine resource allocation. Outcome: worse than baseline. Reason: insufficient variance reduction to compete with model-based hindsight distillation

Adaptivity, Structure, and Objectives in Sequential Decision-Making · Cornell · PhD

Lost to a baseline

RL (Double-DQN, MAC, PPO, and PG with Hindsight Baseline) and standard heuristics (Bin Packing) lost to BestFit baseline on stylized VM allocation (saving negative PMs vs BestFit).

Adaptivity, Structure, and Objectives in Sequential Decision-Making · Cornell · PhD

Considered and rejected

Considered and rejected: Rejected direct end-to-end fine-grained GPU allocation in RL agent because joint job-and-GPU selection causes massive action spaces and poor sample efficiency; adopted hierarchical model-selection with heuristic placement instead.

A Semantic-Aware Reinforcement Learning Scheduler for Parameterized Deep Learning Jobs on Heterogeneous Multi-GPU Clusters · York University · Master's

Tried and failed

reinforcement learning for heuristic training applied to dynamic scheduling and task allocation. Outcome: did not converge. Reason: non-IID sample correlation and noisy reward labels during training

Spacecraft Autonomy through Computer Vision and Onboard Planning · MIT · PhD

Tried and failed

deep reinforcement learning for dynamic capacity allocation applied to on-demand workforce scheduling. Reason: agent avoided cheaper multi-hour commitments because demand spikes were too short-lived to justify commitment duration

Demand and capacity management for meal delivery systems · Georgia Tech · PhD

Tried and failed

average-reward reinforcement learning with dynamic event bounds applied to spatial multi-event scheduling. Outcome: worse than baseline. Reason: large infrequent rewards under high event bounds destabilised reinforcement learning

Towards long-term deployment of general-purpose service robots · UT Austin · PhD

Considered and rejected

Considered and rejected: Rejected Reinforcement Learning (RL) agents for task scheduling due to high exploration overhead, long convergence times in large-scale dynamic environments, and sensitivity to hyperparameter/reward design.

Predictive Worker Resource Characterization at the Extreme Edge · Queen's University · PhD

Considered and rejected

Considered and rejected: Rejected machine-learning/deep-reinforcement-learning approaches like FairTS for the core online scheduling algorithms due to inferior computational speed compared to low-complexity heuristics in fog environments.

Scheduling Problems in Next Generation Computing Networks · Queen's University · Master's

Tried and failed

primal-dual graph neural network constrained optimization applied to wireless network packet routing and scheduling. Outcome: worse than baseline. Reason: relative efficiency compared to dual descent degrades under heavy traffic congestion loads

GRAPH NEURAL NETWORKS FOR COMMUNICATION IN MULTI-AGENT SYSTEMS · Penn · PhD

Tried and failed

pre-allocating resources based on returning entity predictions applied to online bipartite matching. Outcome: worse than baseline. Reason: locking allocations based on predicted returns reduced flexibility compared to greedy load balancing

Advancements in Management Science: Applications to Online Retail, Healthcare, and Non-Profit Fundraising · MIT · PhD

Tried and failed

CPU-affinity-aware resource assignment via system calls applied to multithreaded memory allocation. Outcome: worse than baseline. Reason: System call overhead degraded performance compared to simple round-robin assignment

xBGAS-enabled memory centric architecture for high performance computing · Texas Tech · Master's

Tried and failed

proportional resource allocation based on group sizes applied to network slicing resource management. Outcome: worse than baseline. Reason: asymmetric opportunistic scheduling within slices causes non-linear efficiency differences across groups

Online learning algorithms for wireless scheduling · UT Austin · PhD

Tried and failed

gradient-based latency-quality trade-off optimization applied to distributed computation graph scheduling. Outcome: worse than baseline. Reason: analytical fidelity model inaccuracies compounded when relaxing latency constraints and omitting path combination terms

Real-time guarantees in distributed edge computing · UT Austin · PhD

Lost to a baseline

Constrained scheduling with an overselling ban (z_t >= 0) yields strictly lower execution value than unconstrained scheduling

Essays on and beyond Market Microstructure · Duke · PhD

Tried and failed

Strict instantaneous interference constraints in resource allocation applied to satellite wireless network scheduling. Outcome: worse than baseline. Reason: Overly strict instantaneous thresholds severely under-utilize capacity compared to time-averaged constraints

Wireless communications in low-earth orbit (LEO) satellite systems · UT Austin · PhD

Tried and failed

proportional fair scheduling with multi-connectivity applied to wireless ultra-reliable low-latency communication. Outcome: worse than baseline. Reason: Cross-layer protocol conflicts between MAC bandwidth allocation and packet data convergence protocol path selection

Wireless Network Dimensioning and Provisioning for Ultra-reliable Communication: Modeling and Analysis · Virginia Tech · Doctorate

Lost to a baseline

The Greedy channel allocation algorithm achieved higher overall network spectrum utilization than HCHF by sacrificing fairness and starving disadvantaged nodes.

CHANNEL STATE INFORMATION ACQUISITION FOR ADAPTIVE UNDERWATER ACOUSTIC NETWORKING · Dalhousie · PhD

Lost to a baseline

Single Slice and QoS baselines achieve higher total aggregate RAN throughput than Zipper by ignoring per-app SLA constraints and scheduling all capacity to best-effort traffic.

Application-Aware Scheduling Architectures for Mobile Systems · MIT · PhD

Considered and rejected

Considered and rejected: Rejected Intel MBA (Memory Bandwidth Allocation) for bandwidth throttling because rate-limited cores spend most time stalling; dynamically allocating fewer cores achieved equivalent throughput with higher utilization.

Next Generation Operating Systems for the Datacenter · MIT · PhD

Tried and failed

multifunctional fleet vehicle allocation applied to shared autonomous vehicle routing. Outcome: worse than baseline. Reason: network connectivity and topological constraints increased fleet size requirements under strict wait-time thresholds

Shared Autonomous Micro-Mobility: Evaluating Fleet Performance, Operational Strategies, and Sustainability · MIT · PhD

Considered and rejected

Considered and rejected: Standard Vehicle Routing Problem (VRP/VSP) formulation, rejected because constrained LV numbers make fulfilling all rigid time windows infeasible, necessitating processor scheduling formulations with tardiness/lateness objectives

UNDERSEA LOGISTICS: ROUTING OPTIMIZATION IN GRAY ZONE ENVIRONMENTS · Naval Postgraduate School · Master's

Tried and failed

static proportional heuristic resource partitioning applied to heterogeneous dynamic traffic multiplexing. Outcome: worse than baseline. Reason: inflexible allocation heuristics fail to adapt to mixed traffic demands compared to dynamic reinforcement learning

Real-Time Resource Optimization for Wireless Networks · Virginia Tech · Doctorate

Tried and failed

hierarchical integer optimization routing and resource allocation applied to sparse satellite constellation network routing. Outcome: worse than baseline. Reason: Off-zenith routing coordination increased path loss, underperforming simple maximum-elevation greedy heuristics in sparse coverage.

Optimizing resource allocation in large communications satellite constellations · MIT · PhD

Considered and rejected

Considered and rejected: Fixed online and fixed offline VNF resource allocation, rejected in favor of flexible offline allocation due to excessive virtualization and communication overhead.

Towards Network Automation: A Multi-Agent Based Intelligent Networking System · University of Trento · PhD

Tried and failed

static rule-based scheduling policies applied to multi-user wireless network scheduling. Outcome: did not generalise. Reason: fixed parameters failed to adapt across diverse and dynamic channel environment scenarios

Online learning for scheduling in wireless networks · UT Austin · PhD

Tried and failed

static policy execution from deterministic linear programs applied to dynamic resource allocation with nonstationary demand. Outcome: worse than baseline. Reason: static schedules fail to adapt to time-varying arrival distributions without price reservation thresholds

Data Driven Operations: From Algorithm Development to Experimental Design · MIT · PhD

Tried and failed

static threshold reservation policies applied to multi-class resource allocation. Outcome: worse than baseline. Reason: static rules cannot adapt to time-varying state transitions, making them provably suboptimal versus dynamic state-dependent policies

Essays in Healthcare Operations and Management · Georgia Tech · PhD

Tried and failed

deterministic optimal scheduling under uncertainty applied to coupled multi-energy network dispatch. Outcome: did not generalise. Reason: ignoring stochastic uncertainty led to frequent voltage and temperature constraint violations in realization scenarios

Co-optimisation of electrical and district heating networks · Imperial · PhD

Tried and failed

replacing dynamic scheduling with static empirical distribution applied to distributed task scheduling. Outcome: worse than baseline. Reason: online dynamic state awareness cannot be substituted by long-term empirical sampling distributions

Principled Approaches for Latency Reduction in Networking Systems · MIT · PhD

Considered and rejected

Considered and rejected: Offline static topology scheduling was rejected because it fails to adapt dynamically to runtime input fluctuations and leaves unassigned worker slots underutilized

QoS-aware Resource-utilisation Self-adaptive (QRS) Framework for Distributed Data Stream Management Systems · De Montfort University · PhD

Tried and failed

real-time task scheduling with linear optimization applied to vehicular edge computing task offloading. Reason: speed and motion uncertainties cause deadline misses in realistic dynamic driving conditions

Predictable Connected Traffic Infrastructure · Virginia Tech · Doctorate

Tried and failed

metaheuristic optimization algorithms applied to dynamic sequential tactical resource allocation. Outcome: did not generalise. Reason: they lack dynamic feedback loops needed for co-evolving, semi-adversarial simulation environments

A Simulation-Based Methodology for Sequential Decision-Making in Dynamic and Uncertain Tactical Environments · Georgia Tech · PhD

Tried and failed

genetic algorithm for mixed-integer nonlinear programming applied to large-scale parallel machine scheduling. Outcome: infeasible cost. Reason: High problem complexity caused constraint violations and intractable computational scaling for long time horizons.

Probabilistic Approaches to Enhance Safety and Energy Management of Energy Transition Components and Systems · Texas Tech · PhD

Tried and failed

genetic algorithms with discrete event simulation applied to constrained campaign scheduling optimization. Reason: Lacked optimality guarantees, suffered high computational cost, and required ad-hoc penalty functions for complex logical constraints.

Development and Acquisition Modeling for Space Campaign Architecting · Georgia Tech · PhD

Tried and failed

Sequential optimization without constraint screening applied to multiperiod network power flow scheduling. Outcome: did not converge. Reason: Iterative updates oscillated across iterations without reaching a stationary loss profile

Generalized Energy Resource Scheduling for Distribution Grid Operations Planning · Georgia Tech · PhD

Tried and failed

interior-point method with switching objective functions applied to optimal power flow scheduling. Outcome: did not converge. Reason: discontinuities from switching objective formulations caused convergence failure across varying active objective sets

Smart electric vehicle charging strategy in direct current microgrid · Imperial · PhD

Tried and failed

dual stochastic dual dynamic programming with exact penalization applied to hydrothermal power generation scheduling. Outcome: did not converge. Reason: fixed penalty parameter was too small to reach the optimal value

Risk neutral and risk averse stochastic optimization · Georgia Tech · PhD

Left open

Rigorously prove that informative scheduling policies stochastically outperform non-informative counterparts across general queueing settings. Blocker: None

Information Freshness Optimization in Real-time Network Applications · Virginia Tech · Doctorate

Left open

Use PerformanceLENS latency predictions to automate microservice resource allocation in cloud-native environments. Blocker: No specific resource allocation algorithm, policy, target, or evaluation framework is defined

Proactive & Fine-Grained Monitoring For Microservice Call Chains In Cloud-Native Applications Through Latency Distribution Prediction · York University · Master's

Left open

Optimize Juice incremental snapshots using contiguous allocation heuristics to balance heap versus dirty heap tracking costs. Blocker: None

Simpler & Safer Programming Models for Web Infrastructure & Applications · Harvard · PhD

Left open

Derive optimal analytical allocation rules for the computational budget across the planning horizon in sequential sample average approximation. Blocker: None

ONLINE LEARNING OF MARKOVIAN SYSTEMS WITH CENSORED POISSON ARRIVALS · Naval Postgraduate School · Master's

Left open

Optimize dynamic computation and upload budget allocation across heterogeneous workers based on delay statistics for SBP and MM-GASP codes. Blocker: None

Efficient, secure and private distributed computation and machine learning · Imperial · Doctorate

Left open

Determine best-fit resource allocation metrics for in situ monitoring platforms across major compute clusters. Blocker: The unfinished work lacks a specific methodology, target metrics, or criteria beyond a general research direction.

Scalable Observation, Analysis, and Tuning for Parallel Portability in HPC · University of Oregon · PhD

Left open

Adapt discretized metaheuristic algorithms to solve dynamic nurse scheduling problems with unexpected staff absences and demand variations. Blocker: None

Discretization of nature-inspired techniques for combinatorial problems · University of Regina · Master's

Left open

Adapt HetGPO graph attention scheduling architecture to optimize hospital patient admission scheduling problems. Blocker: None

Learning Dynamic Priority Scheduling Policies with Graph Attention Networks · Georgia Tech · PhD

Left open

Investigate compute-efficient methods to reduce training time for reinforcement learning agents in reconfigurable manufacturing system scheduling. Blocker: No specific algorithmic approach or concrete efficiency target is specified

A complete reinforcement learning based framework for reconfigurable manufacturing system scheduling · Cranfield · PhD