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Gasoline Problem


The gasoline problem is an inventory scheduling problem in which deliveries may be reordered but demands must be met in a fixed order. Given nonnegative delivery sizes x_1, x_2, ..., x_n and demands y_1, y_2, ..., y_n with equal totals, choose a permutation pi of the deliveries to minimize the required storage capacity (Lorieau 2024). Each delivery precedes the demand in its time slot, and the initial inventory may be chosen freely.

Writing s_0=0, the inventory relative to its initial value after the jth demand is

 s_j=sum_(i=1)^j(x_(pi(i))-y_i).
(1)

The peaks just after deliveries and the troughs just after demands therefore give the required capacity

 C(pi)=max_(1<=j<=n)(s_(j-1)+x_(pi(j)))-min_(0<=j<=n)s_j.
(2)

The least feasible initial inventory is -min_(j)s_j, and the optimum capacity is OPT=min_(pi)C(pi).

An iterative rounding algorithm fixes the delivery order one position at a time. At each position, it tries each remaining delivery and solves a fractional assignment problem by linear programming, with the chosen prefix fixed. It then chooses a candidate with the smallest fractional optimum. Lorieau's conjecture (Lorieau 2024, Conjecture 3.1.1) asks whether this algorithm always gives a capacity at most twice the optimum when the demands are positive integers and the deliveries have the two possible sizes 1 and K, where K>=2 is an integer.

MathIsEvenEasier (2026) reported the stronger bound

 C_(IR)<=L_(init)+K-2<=OPT+K-2,
(3)

where L_(init) is the initial fractional assignment optimum. The bound permits every exact choice between tied candidates. For K>=3, an instance with 2K-3 time slots has L_(init)=OPT=K and C_(IR)=2K-2 when ties are broken by the original input order. Thus the claimed exact worst-case approximation ratio for that tie rule is 2-2/K, with supremum 2 over K. When K=2, the algorithm is optimal. The result concerns the positive-integer two-size case and does not settle the corresponding conjectures for unrestricted delivery sizes or multiple commodities.

The project reports human-directed proof discovery and Lean formalization by OpenAI Codex using GPT-6 Astra. As of Oct. 5, 2026, recorded Lean builds included the bridge from the fractional assignment problem to the formula used in the proof. Earlier independent kernel checks covered the preceding development, not this added bridge. Independent specialist review of the complete mathematical argument had not been reported (MathIsEvenEasier 2026, VibeMathed 2026).


See also

Linear Programming, Permutation

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References

Lorieau, L. "Iterative Rounding Algorithms for the Extended Gasoline Problem." Master's thesis, 2024. https://perso.limos.fr/~lulorieau/docs/thesis/Master_Thesis.pdf.MathIsEvenEasier. "A Tight Guarantee for Iterative Rounding in the Two-Size Gasoline Problem." 2026. https://github.com/MathIsEvenEasier/gasoline-two-sizes.VibeMathed. "Two-Size Gasoline: The Tight Approximation Guarantee for Iterative Rounding." Oct. 1, 2026. https://vibemathed.com/problem/two-size-gasoline-the-tight-approximation-guarantee-for-iterative-rounding.

Cite this as:

Weisstein, Eric W. "Gasoline Problem." From MathWorld--A Wolfram Resource. https://mathworld.wolfram.com/GasolineProblem.html

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