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arxiv: 2605.23592 · v1 · pith:SVVKKFNGnew · submitted 2026-05-22 · 💻 cs.AI

Solving the Aircraft Disassembly Scheduling Problem

Charles Thomas , Pierre Schaus This is my paper

Pith reviewed 2026-05-25 04:08 UTC · model grok-4.3

classification 💻 cs.AI
keywords aircraft disassemblyscheduling problemconstraint programmingmixed integer programmingprecedence constraintsbalance constraintscertification requirements
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The pith

Constraint programming and mixed-integer programming models solve aircraft disassembly scheduling problems with up to 1450 tasks from real data.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper defines the aircraft disassembly scheduling problem as a large-scale task scheduling challenge with constraints on technician certifications, task precedences, maintaining aircraft balance, and limited workspace capacity. It develops a constraint programming model and a mixed-integer programming model to generate feasible schedules. These models are tested on instances of different sizes based on operational data, with the largest having 1450 tasks. If successful, such models would enable efficient planning that supports the profitability of sustainable aircraft dismantling operations.

Core claim

The constraint programming model and the mixed-integer programming model solve the aircraft disassembly scheduling problem on instances of varying sizes involving up to 1450 tasks based on real operational data.

What carries the argument

The Constraint Programming model and the MIP model that encode the certifications, precedence relations, balance requirements, and space limits for the disassembly tasks.

If this is right

  • Feasible schedules can be produced for real-world sized disassembly problems.
  • The models handle varying instance sizes from small to 1450 tasks.
  • Both CP and MIP approaches are viable for this application domain.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Similar modeling techniques could extend to disassembly of other complex machinery like ships or vehicles.
  • Integration with real-time data could allow dynamic rescheduling during the process.
  • The balance constraint might inspire similar stability requirements in other assembly or disassembly domains.

Load-bearing premise

The listed constraints on certifications, precedence, balance, and space, along with the generated instances, fully represent the practical challenges of aircraft disassembly operations.

What would settle it

Running the models on a new set of instances derived from actual disassembly records and verifying whether the produced schedules can be executed without violating any operational rules in practice.

read the original abstract

Dismantling aircrafts reaching their end of life is a complex endeavour that is necessary in terms of sustainability but yields small income margins for air transport companies. An efficient scheduling of the disassembly procedure is thus crucial to ensure the profitability of the process and incentivize practice. This is a large scheduling problem that involves thousands of tasks and many different constraints: Extracting parts that are destined to be reused requires technicians with specific certifications and equipment. Extraction operations might be subject to precedence relations. Furthermore, the aircraft must be kept balanced during the whole process. Finally, some of the locations of the aircraft have a limited space that caps the number of technicians able to work there concurrently. This article presents the problem in details and proposes two approaches to solve the problem: a Constraint Programming model and a MIP model. The models are tested on instances of varying sizes involving up to 1450 tasks, which are based on real operational data provided by an industrial partner.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 0 minor

Summary. The manuscript formulates the aircraft disassembly scheduling problem incorporating four constraint types (technician certifications and equipment, task precedence relations, aircraft balance throughout the process, and space limits on concurrent technicians at locations). It introduces a Constraint Programming model and a Mixed-Integer Programming model, states that both are tested on instances of varying sizes derived from real operational data (up to 1450 tasks), and claims these models solve the problem.

Significance. If the models were shown via quantitative experiments to produce feasible, high-quality schedules in reasonable time on the largest instances, the work would offer a practical optimization approach for a sustainability-relevant industrial process with thin margins. The grounding in real data from an industrial partner is a positive element, though the absence of any reported metrics prevents evaluation of whether the contribution is incremental or substantial relative to standard CP/MIP applications.

major comments (3)
  1. [Abstract] Abstract: the claim that 'the models are tested on instances of varying sizes involving up to 1450 tasks, which are based on real operational data' supplies no performance metrics, runtimes, optimality gaps, solution-quality comparisons, or validation against manual schedules. This omission is load-bearing for the central claim that the models solve the problem.
  2. [Problem description] Problem description (constraints section): the four enumerated constraints are asserted to define the problem, yet no argument or evidence is given that they are necessary and sufficient to capture the industrial partner's full operational rules; if omitted rules exist, formally feasible solutions may be unusable in practice.
  3. [Instance generation] Instance generation: no details or validation are provided on how the generator reproduces the statistical and structural properties of the partner's actual cases rather than simplified proxies, which directly affects whether model solutions translate to usable schedules.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below and outline revisions to enhance the manuscript's clarity regarding performance reporting, constraint justification, and instance details.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim that 'the models are tested on instances of varying sizes involving up to 1450 tasks, which are based on real operational data' supplies no performance metrics, runtimes, optimality gaps, solution-quality comparisons, or validation against manual schedules. This omission is load-bearing for the central claim that the models solve the problem.

    Authors: The abstract is intentionally concise, with full experimental results (including runtimes and feasibility outcomes on instances up to 1450 tasks) detailed in the computational experiments section. We agree the abstract would benefit from summary metrics to better support the claim. We will revise it to include key indicators such as average runtimes and solution feasibility rates across sizes. Direct validation against manual schedules was outside the scope of this work, which focused on model formulation and scalability. revision: yes

  2. Referee: [Problem description] Problem description (constraints section): the four enumerated constraints are asserted to define the problem, yet no argument or evidence is given that they are necessary and sufficient to capture the industrial partner's full operational rules; if omitted rules exist, formally feasible solutions may be unusable in practice.

    Authors: The four constraints were identified through direct collaboration with the industrial partner as the core operational requirements. We acknowledge the need for explicit justification. In revision, we will add a subsection explaining the rationale for each constraint, their derivation from partner input, and any noted assumptions or potential extensions for additional rules. revision: yes

  3. Referee: [Instance generation] Instance generation: no details or validation are provided on how the generator reproduces the statistical and structural properties of the partner's actual cases rather than simplified proxies, which directly affects whether model solutions translate to usable schedules.

    Authors: Instances are derived by scaling real operational data from the partner while preserving task distributions, precedence, certifications, and spatial limits. We will expand the instance generation section with a more detailed description of the scaling procedure and any structural checks performed to maintain fidelity to the original data. revision: yes

Circularity Check

0 steps flagged

No circularity: standard models applied to explicitly stated constraints

full rationale

The paper presents a Constraint Programming model and a MIP model for an aircraft disassembly scheduling problem defined by four explicit constraint families (certifications, precedence, balance, space limits) and tested on generated instances up to 1450 tasks drawn from industrial data. No equations, derivations, or predictions are offered that reduce by construction to fitted parameters, self-definitions, or self-citations; the models are described as direct encodings of the listed constraints using standard CP and MIP solvers. Instance generation and constraint enumeration are presented as modeling choices whose fidelity to operations is an external assumption, not a load-bearing internal derivation. The central claim therefore rests on the independent correctness of the solvers and the explicit problem statement rather than any self-referential reduction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No free parameters, axioms, or invented entities are described in the abstract; the work relies on standard CP and MIP modeling assumptions that are not enumerated here.

pith-pipeline@v0.9.0 · 5681 in / 1091 out tokens · 20482 ms · 2026-05-25T04:08:46.755733+00:00 · methodology

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Reference graph

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