Week 12: Macro-Event Logistics and Capstone Synthesis
Scaling algorithmic optimization to physical supply chains: payload mathematics, volumetric displacement, and economic synthesis.
Core reading: Scaling Household Purchasing Algorithms to Event Logistics (Fenn, Journal of Applied Consumer Logistics, 2024).
The Lecture#
This final seminar marks the culmination of the SLOP4230 curriculum. An economic optimization that exists solely within a Python terminal is a failure of applied logistics. We must now scale the individual purchasing algorithms generated in Week 10 to satisfy the physical, macro-event requirements of a 50-person cohort.
This requires bridging the gap between digital index maximization and physical supply chain realities. We will calculate vehicle payload constraints, standardizing the volumetric displacement of bulk cask transport against the suspension limits of standard passenger vehicles. Students will finalize their mathematically optimal beverage portfolios, preparing to formally defend their specific exploitation of the WET loophole, spatial retail anomalies, and combinatorial optimization constraints before the faculty.
Applied Computational Lab#
In this final workshop, you will conduct a full physical and financial audit of your Capstone Event Portfolio prior to submission. You will link your finalized CSV dataset to a physical payload modeling spreadsheet.
Your task is to calculate the gross tonnage and spatial volume of your optimized inventory, and distribute it mathematically across a simulated convoy of standard domestic vehicles. An algorithm that successfully maximizes the standard drink yield but inadvertently exceeds the legal axle weight limit of a Toyota Camry will be penalized for logistical failure. You must prove that your inventory can physically transition from the retail floor plan (mapped in Week 7) to the event space.
Mandatory reading#
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Implementation of effective alcohol control strategies is needed at large sports and entertainment events — Lyne & Galloway, Australian and New Zealand Journal of Public Health 36(1), 2012, 55–60. This paper provides empirical observations of thirteen large-scale entertainment events across nine venues. Pay strict attention to the authors’ findings regarding the operational friction of serving four drinks per transaction rather than two. This is a quantified, per-transaction constraint measured in the field. It represents exactly the kind of physical bottleneck your Event Portfolio must mathematically plan around, rather than discovering through operational failure on the day of the event.
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Interventions in sports settings to reduce risky alcohol consumption and alcohol-related harm: a systematic review — Kingsland, Wiggers, Vashum, Hodder & Wolfenden, Systematic Reviews 5(1), 2016. A systematic review delineating which event-scale interventions actually possess an evidence base for mitigating harm, and which are merely performative. As established in Week 11, your hyper-optimized inventory will generate significant externalities. Your final capstone presentation is structurally and academically stronger if your externality mitigation strategy explicitly incorporates a proven intervention documented in this review, rather than relying on unverified assumptions.
Cohort payload against van capacity
The dynamic-programming selection from week 10, budget scaled to a cohort, run through the packaged-density assumption above to estimate a transport payload, then measured against an assumed van capacity of 800 kg.
The objective function here is still total standard drinks under a budget and a variety floor, scaled to a cohort; payload is a logistics consequence of that selection, not a separate objective. It is a description of what the model produces, not a recommendation about how much a cohort should buy — week 11's externality framework is where the cost this objective does not price in is dealt with.
The week 12 lab: Portfolio dry run
Before the lab#
Bring the portfolio as it stands: the deck, the budget spreadsheet, and the externality statement drafted in week 11. Unfinished is expected — this is a dry run, and a pitch with a known gap in it gets more useful questions than a polished one.
In the lab#
Ten minutes each. Pitch the inventory: the budget, the constraint set, the selection, and why it is the selection rather than a cheaper one. The time limit is the assessment’s, and the most common failure in this lab is spending eight minutes on method and two on the answer.
The three standing questions. The room asks each pitch the same three, because a marker will:
- Would a greedy ranking have produced this? If yes, the dynamic-programming requirement has not been met, whatever the method section says.
- Does the spreadsheet reconcile with the slides? Totals that disagree by a few dollars are the single most common defect, and they undermine everything else in the document.
- What does your externality statement forbid? A statement that forbids no inventory is not a position.
Your own two challenges. Each presenter must also take two specific challenges to the inventory from the room and answer them from their own dataset rather than from the literature. “The reading says” is not an answer to “your numbers say”.
Afterwards#
You leave with two answered challenges, a reconciled spreadsheet, and a list of what is still missing — with the portfolio due at the end of the teaching period rather than at the end of this week, which is deliberate.
What you leave the lab able to do
Written so a reader can tell whether it has been met without asking you. The assessments assume it has.
- you have pitched your portfolio to the room inside ten minutes
- you have taken at least two challenges to the inventory and answered them from your own data
- your budget spreadsheet reconciles against the slides you presented
Teaching team
- Idris Fenn — tutor