From informs-journal-on-computing-skills
Positions an IJOC manuscript's computational/methodological contribution against OR/MS and CS prior art, identifying the right baselines and frontier.
How this skill is triggered — by the user, by Claude, or both
Slash command
/informs-journal-on-computing-skills:ijoc-literature-positioningThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
- Reviewers say "the contribution over existing methods is unclear" or "this is incremental"
An IJOC paper usually advances against two literatures at once: the OR/MS literature that owns the problem and the computing literature that owns the method. Your positioning must make clear which frontier you push and by how much, in computational terms. The decisive move is to identify the state-of-the-art method you must beat or match, cite it precisely, and commit to it as an experimental baseline. Vague positioning ("little work exists") reads as not having read the field and is a fast path to desk rejection by an Area Editor who knows it well.
| Your claim type | The prior art you must engage | The baseline this implies |
|---|---|---|
| New exact method, larger instances | best published exact method for this problem | re-run or cite its reported results on shared instances |
| New formulation, tighter bounds | strongest existing formulation / relaxation | root-gap and node-count comparison |
| New heuristic, better quality/time | the leading heuristic and the best exact bound | gap-to-optimal and time-to-target |
| ML-for-OR, learns to solve faster | both the OR baseline and prior learning approaches | beat the OR method and prior learning |
| New simulation/estimation method | prior estimators for the same estimand | variance/cost at equal accuracy |
| Software/tooling | prior tools and the methods they implement | feature/performance comparison, not just existence |
IJOC reviewers are active researchers in the chosen area; they will know the last two years of work. Cite the most recent competing methods, not only the classics, and state honestly where a competitor is still better (e.g., "Method X remains faster on dense instances; we win on sparse and large"). Honest scoping is more credible than a blanket "we outperform all." When a competitor's code is in the IJOC GitHub repository or a public repo, plan to actually run it rather than quoting stale numbers from different hardware.
Position so the reader sees why this is IJOC and not a sibling: emphasize the computational/methodological delta. If the related work reads like an Operations Research model survey, the computing contribution is buried; if it reads like a CS algorithms paper with no OR task, the OR relevance is missing. The synthesis — "here is the OR problem, here is the computing frontier, here is the gap we close" — is the IJOC signature.
Two underused positioning moves are specific to IJOC. First, the IJOC Software and Data Repository means many recent competing methods ship with runnable code; cite those and, where feasible, re-run them on your instances rather than quoting heterogeneous published numbers — a re-run comparison is far more persuasive to a referee who knows the field. Second, IJOC's "Test of Time" award and its published archive signal which methods the area considers canonical; engaging those anchors your claim in the literature the Area Editor and reviewers actually hold as the bar. Position against the strongest reproducible competitor, not merely the most cited.
【Journal】INFORMS Journal on Computing
【Skill】ijoc-literature-positioning
【SOTA to beat/match】named method + citation
【Frontier(s) advanced】OR/MS problem / computing method / both
【Claimed delta】the computational gap closed, in measurable terms
【Honest scoping】where a competitor still wins
【Sibling boundary】why IJOC and not OR / MS / MPC / IJOO / CS
【Next skill】ijoc-methods
npx claudepluginhub brycewang-stanford/awesome-journal-skills --plugin informs-journal-on-computing-skillsSharpens the computational/methodological claim of an IJOC manuscript by framing the contribution in a falsifiable one-sentence claim. Use when the contribution is not sharp or reads as application rather than computing.
Places an OR/MS manuscript against prior literature by specifying exact technical deltas in model assumptions, results, and algorithmic guarantees. Use when reviewers need a precise comparison to closest papers.
Guides authors on positioning algorithmic/optimization/ML-for-OR manuscripts for INFORMS Journal on Computing, including scope fit, method evidence bar, house style, and desk-reject heuristics.