T67

[hyunjimoon]

todo:

• add multithread of each particle
• finalize optimization formulation to argue under original formulation, one could never adopt AV

migrating from #161, closer to operations to prove prob.prog competence

first starting from airport performance (sailer's innovation odyssey). this will synthesize CIVA #249 with existing formulations of airport problems in below
koo20_airport_gate_assignment.pdf
kim_incheon airport.pdf

[hyunjimoon]

using #249 's SAFE comparison interface

Aspect	Flight A	Flight B	Flight C
Type	Long-haul	Short-haul	Long-haul
Purpose	Passenger	Passenger	Cargo
Special Consideration	Many transfer passengers	Crew nearing duty time limits	Critical medical supplies
Scheduled Departure	In 45 minutes	In 30 minutes	In 50 minutes
Priority Factor	Transfer passengers	Crew time constraints	Medical urgency
Flight Duration	Long	Short	Long
Cargo Type	Regular passenger luggage	Regular passenger luggage	Critical medical supplies
Potential Impact of Delay	Missed connections for passengers	Potential crew overtime or flight cancellation	Delayed delivery of critical medical supplies

[hyunjimoon]

dashborad using improving changi airpot operations cld

Entities	Attributes	Operations	Events	Constraints	Probabilistic Elements
Flight, Crew, Passenger, Gate, Aircraft	Departure Time, Flight Type (Long-haul/Short-haul), Purpose (Passenger/Cargo), Transfer Passenger Count, Crew Time Remaining, Medical Urgency	Prioritize Flights, Assign Gates, Manage Crew Schedules, Handle Passenger Connections	Flight Arrival, Flight Departure, Delay Occurrence, Medical Emergency	Gate Availability, Crew Duty Time Limits, Minimum Connection Times, Aircraft Turnaround Times	Weather-related Delays, Unscheduled Maintenance, Air Traffic Control Restrictions, Passenger Boarding Times

[hyunjimoon]

building on this, i made This interactive visualization demonstrates the decision boundary between modular and integrated learning strategies for entrepreneurial testing, displayed in a 3D space defined by market potential (μ_ϕ), market certainty (1/σ_ϕ), and implementation capability (μ_θ). The key insight revealed is that startups with lower values across these parameters (shown in green) typically benefit from modular learning - testing the market first before committing to implementation. As these parameters increase and especially when there's higher correlation (ρ) between market potential and implementation capability, the decision boundary shifts to favor integrated learning (shown in blue), suggesting that testing market and implementation together becomes more valuable. The interactive ρ slider allows entrepreneurs to explore how the strength of this correlation affects their optimal testing strategy, providing a practical tool for strategic decision-making in early-stage venture development.

using Adapting Airport Allocation Visualization to Entrepreneurial Framework cld

[hyunjimoon]

using autogp, Advancing ChiExpert's Probabilistic Programming for Data Science cld , gist code with ops_innov_samp_opt.jl controlling the use of samp_var and opt_sch_alloc. samp_var first estimates variability of each subtask when adopting AV_baggage and opt_sch_alloc optimizes given the estimated effect of adopting innovation online.

size_of_plane	number_of_passengers	weather	distance_to_travel	servicing_time_aircraft_arrival_parking	servicing_time_baggage_unloading	servicing_time_passenger_crew_disembarkation	servicing_time_aircraft_interior_cleaning	servicing_time_fueling	servicing_time_catering	servicing_time_lavatory_services	servicing_time_passenger_crew_boarding	servicing_time_baggage_uploading_first_batch	servicing_time_baggage_uploading_second_batch	servicing_time_baggage_uploading_loose_luggage	servicing_time_connect_tug	servicing_time_final_ramp	servicing_time_push_back	servicing_time_gate_close	departure_urgency	passenger_connections	crew_duty_limits
small	73	clear	13002	14.7234321	11.0794615	17.3740904	29.4206184	67.4125118	37.2851701	13.698317	30.7277828	17.8776168	16.3956617	14.3600475	8.29635909	12.3343297	12.7976293	6.01185514	0.06328094	77	0.09073149
medium	320	clear	11140	7.92457319	14.0362445	15.4837668	21.9544612	78.9739716	32.1940815	13.7693444	28.7402693	18.5003868	10.4710035	12.2867553	7.24458883	7.18032014	9.21123354	11.0436605	0.52353481	29	0.78121967
medium	134	stormy	14529	6.40743854	11.8772588	19.1385668	20.7365195	78.3571782	36.3891498	10.8560653	32.5345277	23.569235	15.5402212	7.69580446	10.4067701	10.7340378	10.294141	5.40462777	0.67392858	16	0.15400962
large	253	clear	10007	6.88586482	11.9428627	16.224446	22.0218331	66.6867407	30.6611875	10.8970219	26.7896668	22.9797549	17.8940791	14.1049319	8.84757035	10.9695892	13.5192748	5.43743246	0.71108259	0	0.26492808

...

[hyunjimoon]

using auto_bagg_handling perp

incorporating autonomous baggage vehicles:

"Create an optimization model for airport resource allocation that incorporates the adoption of autonomous baggage vehicles. The model should:

1. Define key decision variables related to autonomous vehicle deployment, traditional vehicle usage, and human labor allocation.

2. Specify an objective function that minimizes total operational costs while maximizing efficiency.

3. Include constraints on:

   • Total number of bags that must be processed

   • Available autonomous and traditional vehicles

   • Labor hours and shift restrictions

   • Charging/refueling requirements for vehicles

   • Safety protocols and regulatory compliance

4. Account for variability in:

   • Flight schedules and passenger loads

   • Vehicle performance and reliability

   • Integration challenges during the adoption phase

5. Incorporate parameters for:

   • Costs of autonomous vehicles vs. traditional equipment

   • Labor costs for different roles

   • Energy/fuel costs

   • Maintenance requirements

6. Consider both short-term optimization (daily operations) and long-term planning (fleet composition over time).

7. Suggest methods to handle uncertainty in the model, such as stochastic programming or robust optimization techniques.

8. Propose key performance indicators to evaluate the effectiveness of the optimized solution.

Provide a mathematical formulation of this model and explain how it could be solved and implemented in practice."

Citations:

[1] https://www.airport-technology.com/contractors/groundequipment/tcr-international/pressreleases/autonomous-baggage-handler-tested/

[2] https://www.changiairport.com/corporate/media-centre/changijourneys/the-airport-never-sleeps/autonomous_tractor.html

[3] https://aurrigo.com/aurrigo-to-introduce-four-new-autonomous-baggage-handling-vehicles-at-changi-airport/

[4] https://www.passengerterminaltoday.com/news/technology/cincinnati-northern-kentucky-airport-deploys-autonomous-baggage-handling-vehicle.html

[5] https://www.internationalairportreview.com/news/195996/testing-of-autonomous-baggage-handling-technology-continues-at-cvg-airport/

[6] https://www.autonomousvehicleinternational.com/news/testing/aurrigo-conducts-autonomous-baggage-and-cargo-tractor-trial-at-stuttgart-airport-2.html

[7] https://www.airport-technology.com/features/how-new-technology-is-improving-baggage-handling-performance/

[8] https://www.futuretravelexperience.com/2024/04/avinors-high-level-strategy-for-future-baggage-handling-infrastructure-focused-on-robotics-autonomous-vehicles-and-smart-systems-using-ai/

[hyunjimoon]

charlie mentioned synergy marine group in
charlie_ai_om_strat.txt otter

Capt. Anurag Gupta seemed to be at mendaki workshop and introduced their group is evaluating or thinking about how AI can improve ownership operation (didn't hear the rest)

[hyunjimoon]

angie reflection (summary below)

1. Re-identified Tom and Abdullah's research alignment, which was one motivation for taking Abdullah's class

Tom and Abdullah's paper When Are Combinations of Humans and AI Useful? accepted in nature was introduced.

I gave three comments:

1. I’m sure T6 would already know but https://gilbrethnetwork.tripod.com/therbligs.html therblig notation from early 1980 classified and optimized physical task operations
2. What is the format of data input from CAG and synergy-marine? Do they give database? I expect some difficulty from language matching (column names) and wonder what your plans are
3. Perhaps of interest from https://arxiv.org/abs/2408.03943 that compares machine as tool vs thought partner and I think it is important to have a communicable symbolic representation of world model of both human and machine so that assumptions can be checked.
   For 1, Robert Laubacher and Jie Gao both verified therblig would be "very useful". let alone usefulness and history of therblig (industrial engineering sci.mng 101), i hypothesize starting sentence with "I'm sure you'd know" creates some fear, increasing learning efficiency

2. Charlie's observation on T67 connection - dynamic

There are useful connections between T7 and T6 Changi issues. T6 needs to understand dynamics of airplane assignments to gates based on servicing (food, baggage, fuel, etc) capacity and coordination. Both T6 and T7 need to look at passenger needs. Both need to consider dynamic stochastic nature of scheduling and rescheduling.

I generally agree. One improvement may be, instead of stochastic nature, agent's need for reasoning probabilistically is more useful. But as "nature" is not an agent we can control, I decided to train myself to translate every observation (what's happening) into the language of agent (airport institution, staff, airline, passenger)'s reaction (what agents do in response to new observation given its world model). Moreover, I believe stochasticity is subsumed by dynamics given all agents have the same model of the world. It's lack of agent's modeling resource that abstracts unknown/outside-model as stochasticity (detail in process vs feature noise in SD).

Moving forward, how can "useful connection" between task analysis in T6 and resource allocation algorithm in T7 be designed? I view the former as the computation/theory level that defines the system components and goal of our intervention. Once this mental model on system component and goal are aligned among different stakeholders, different (probabilistic inference / optimization) algorithms can be assembled to implement the update of system component in the direction of given goal.

3. CAG's AV adoption process

Discussed AV implementation with CAG: Angie reached out to her friend from CAG (Yanling Liu) for a quick question on how autonomous vehicle adoption affects baggage handling operations. Key points:

Initial Exchange:

• You inquired about CAG's AV implementation in baggage handling
• Yanling shared that AVs are in early adoption (pre-scale), mimicking human drivers, with impact not yet measurable. "The very simplistic thing now is to say the AV mimics the human driven vehicle so it assumes they will deliver the bags in line with our current planning norm"

Context & Proposal:

• You shared your thesis work and T7's current operations research

• Connected this to Hyundai EV project experience

• Proposed leveraging T6 (human-AI collaboration) insights for T7 (airport operations)

• Asked "Would CAG be interested in exploring how human-machine collaboration framework (how AVs integrate into existing operations - not just mimicking human drivers, but potentially identifying new optimization opportunities in baggage handling processes)can help measure and optimize AV integration as you scale AV adoption?"

• This connects to Charlie's observation about T6/T7 synergies, particularly regarding baggage handling capacity and coordination in airport operations (human-machine collaboration)

• takeaway: evaluation is what matters eventually

---

less subjective summary

T6 Updates (Human-AI collaboration):

Paper "When Are Combinations of Humans and AI Useful?" accepted in Nature Human Behavior
Working on taxonomy of human-AI interaction modes and online platform for ontology content
Collaborating with Changi and Synergy Marine for data collection

T7 Updates (Airport Operations):

Modeling passenger activity and gate assignments
Developed static assignment algorithm with multiple objectives:

Passenger-oriented: minimize walking distance, optimize transfer times
Airport/airline-oriented: minimize tow operations, satisfy gate preferences

Built visualization tool showing flight connectivity, arrival/departure patterns, and gate utilization

meeting_saved_chat.txt

[hyunjimoon]

based on chat with Jie (T6 member), I updated my belief and desire with the following steps. using Modeling Angie's Evolving Beliefs and Desires in T67 Environment cld

I'll note synergy marine as SM, changi as CAG.

Here's the updated explanation that matches the detail level of your enhanced table:

Based on the chat with Jie (T6 member), I updated my belief and desire with the following steps:

1. 🫀🧠 Desire & Belief: Entered with desire to program operations and innovation management solutions. Initial beliefs modeled different ROIs: cost-saving potential (p_c ~ Beta(2,2)) for investing in dashboard development, and revenue-growth potential (p_r ~ Beta(1,2)) for investing in market expansion through Charlie's T6 connection. Despite personal preference against long SG stays, remained positive about M3S market based on team alignment (Tom-Abdullah-Angie correlation).

2. 👁️ Observation: Collected multiple signals from both dimensions:

   • Cost-saving (2/2 positive): Lower data acquisition cost for SM vs CAG (C1); T6's established process mapping and bottleneck identification aligns with my approach (C2)
   • Revenue-growth (4/4 positive): SM's aggressive fleet expansion plan (R1); procurement solution's replicability beyond SM (R2); Jie's positive response to CAG dashboard's applicability to SM (R3); SM's openness to parallel procurement experimentation (R4)

3. 🧠🔄 Reasoning: Updated beliefs proportional to signal intensity:

   • Cost ROI posterior strengthened: p_c|d_c ~ Beta(2+2,2)
   • Revenue ROI posterior strengthened more: p_r|d_r ~ Beta(1+4,2)
   • Both updates reflected successful observations (all binary signals = 1)

4. 📍 Planning: Optimization shifted from cost-saving (🟩) to revenue-growth (💜) focus as posterior ratio comparison suggested higher ROI (4/6 < 5/7). Action choice maximizes utility increase (Δu) per unit investment, incorporating both updated ROI beliefs and available actions in cost-saving (a_c) and revenue-growth (a_r) dimensions.

Section	Language	Math	Symbol
1. 🫀🧠 Desire & Belief	Desire: Program ops & innovation mgmt Belief: • T67 is interesting for both market/collaboration • Tom-Abdullah-Angie interests correlated • SG/M3S promising (despite location preference)	$p_c\sim Beta(2,2)$ $p_c$: cost-saving ROI (saved cost if investing in cost-saving action e.g. develop dashboard further) $p_r\sim Beta(1,2)$ $p_r$: revenue-growth ROI (gained revenue if investing in revenue gaining action e.g. sync charlie on new info on T6)	🟩🔴Beta(1,1) 🔴💜Beta(1,2)
2. 👁️ Observation	Cost signals: C1: Jie shared data acquisition cost is lower for SM (vs CAG) and angie needs data to validate her desire's product. C1: T6's procurement process map (Jie's poster) and its bottleneck identifying approach is aligned with Angie's desire Revenue signals: R1: SM plans to increase ships (600→1000) in three years R2:Jie emphasized developed procurement AI-human collaboration is highly replicable outside SM R3:Jie liked Angie's dynamic prioritization dashboard (developed for CAG) and verified its applicability for SM R4:Jie liked Angie's idea on proceeding parallel procurement contract and confirmed SM would be open to this experimentation	$d_c=$ 2 out of 2 $d_c$: observation on cost ROI (1 if positive, else 0) $d_r=$ 4 out of 4 $d_r$: observation on revenue ROI (1 if positive, else 0)	👁️🟩🔴_1 = 1 👁️🟩🔴_2 = 1 👁️🔴💜_1 = 1 👁️🔴💜_2 = 1 👁️🔴💜_3 = 1 👁️🔴💜_4 = 1
3. 🧠🔄 Reasoning	Belief on cost & revenue ROI updated propositional to intensity of cost and revenue side signal	$p_c | d_c \sim Beta(2+2,2)$ $p_r|d_r \sim Beta(1+4,2)$ $p_c | d_c , p_r | d_r$: posterior on cost, revenue ROI	🟩🔴|👁️Beta(4,1) 🔴💜|👁️Beta(4,2)
4. 📍Planning	Action chosen based on - cost, revenue ROI posterior - utility function - action set (new options appear)	$a_c$ = $\underset{a \in a_c, a_r} {argmax} ; \Delta u(p_c |d_c, p_r|d_r, a)= p_c |d_c * a_c + p_r|d_r * a_r$ $\Delta u$: utility increase per unit investment $p_c, p_r$: cost, revenue ROI posterior $a_c$: cost-saving action $a_r$: revenue-growth action	📍changed from 🟩 to 💜 as $\frac{2}{4}>\frac{1}{3} \rightarrow$ $\frac{4}{6} < \frac{5}{7}$