Pith Number

pith:KVIR67ET

pith:2026:KVIR67ETIAZV2F4FU277ZEBF7A

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Mixed Integer Goal Programming for Personalized Meal Optimization with User-Defined Serving Granularity

Francisco Aguilera Moreno

Mixed integer goal programming yields whole-serving meal plans that match continuous nutrient optima for typical meal sizes while guaranteeing feasibility.

arxiv:2605.13849 v1 · 2026-03-12 · cs.AI

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\pithnumber{KVIR67ETIAZV2F4FU277ZEBF7A}

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4 Citations open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

For meals with 15+ foods, the integer solution matches the continuous optimum in every benchmark instance. MIGP finds strictly better solutions than GP with post-hoc rounding in 66% of cases (never worse) while maintaining 100% feasibility.

C2weakest assumption

That goal-programming deviation variables sufficiently buffer the integrality gap so that requiring integer servings does not degrade solution quality relative to the continuous relaxation, and that inverse-target normalization produces balanced multi-nutrient trade-offs without additional tuning.

C3one line summary

MIGP uses integer servings and goal deviations to produce feasible, practical meal plans that outperform post-hoc rounding of continuous solutions in 66% of cases while always succeeding.

References

21 extracted · 21 resolved · 0 Pith anchors

[1] Delighting Palates with AI: Reinforcement Learning’s Triumph in Crafting Personalized Meal Plans with High User Acceptance 2024 · doi:10.3390/nu16030346

[2] Integrating Multi-Criteria Decision- Making with Multi-Objective Optimization for Sustainable Diet Design 2025 · doi:10.1016/j.jclepro.2025.145233

[3] Designing Sustainable Diet Plans by Solving Triobjective Integer Programs 2024 · doi:10.1007/s00186-024-00879-8

[4] An Exact Solution Approach for Portfolio Op- timization Problems Under Stochastic and Integer Constraints 2009 · doi:10.1287/opre

[5] Linear Programming: A Mathematical Tool for Analyzing and Op- timizing Children’s Diets During the Complementary Feeding Period 2003 · doi:10.1097/00005176-

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-17T23:39:19.627894Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

55511f7c9340335d1785a6bffc9025f819fafc1f6c4b3f7ea143f0e1dab9d5fc

Aliases

arxiv: 2605.13849 · arxiv_version: 2605.13849v1 · doi: 10.48550/arxiv.2605.13849 · pith_short_12: KVIR67ETIAZV · pith_short_16: KVIR67ETIAZV2F4F · pith_short_8: KVIR67ET

Agent API

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/KVIR67ETIAZV2F4FU277ZEBF7A \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 55511f7c9340335d1785a6bffc9025f819fafc1f6c4b3f7ea143f0e1dab9d5fc

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "b092dfc2bb725ce2f3f9bebcafe3a9881d1c00c1725431b2438b51569914fb61",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.AI",
    "submitted_at": "2026-03-12T14:54:47Z",
    "title_canon_sha256": "64beefada933455b2e7e7a85fcfcf228ac78617a2b346ea9e24dac869e2e0348"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.13849",
    "kind": "arxiv",
    "version": 1
  }
}