Random Chinese Postman Instance Generator

Description

Generate a connected undirected nonnegative-integer-weight graph and depot for a Chinese postman optimization problem, without claiming an optimal route.

An optimization instance defines choices, constraints, and an objective. A feasible witness demonstrates that its stated constraints can be met; it establishes optimality only when the tool actually computes and verifies an optimum.

Generate a connected undirected nonnegative-integer-weight graph and depot for a Chinese postman optimization problem, without claiming an optimal route.

When to use this tool

Use this generator to create reproducible chinese postman instance cases from the supplied model or bank. Inspect the returned evidence before using cases in a test or exercise.

How to use the tool and how it works

Supply nodeCount, edgeCount, minimumWeight, maximumWeight in the field shapes below. Set seed when you need an explicit replay.
Run the tool and correct any input error. For generated controls, distinguish a successful fixture containing an invalid case from failure to generate the fixture.
Read nodes, edges, componentCount, depot. Use the conventions below. Keep answer-bearing fields separate when presenting an exercise.

Construct a connected simple undirected weighted graph and choose a depot. Return nodes, edges, component evidence, and the starting vertex for a Chinese-postman exercise.^[1]

Input field	Meaning and units	Accepted input
nodeCount	Number of vertices, labeled 0 through nodeCount - 1.	Required. Safe integer; minimum 2; maximum 32.
edgeCount	Exact number of generated edges; feasibility is checked against the selected construction.	Required. Safe integer; minimum 0; maximum 496.
minimumWeight	Safe-integer edge weight in caller-selected consistent units; the inclusive sampling range must also have a safe-integer width.	Required. Safe integer; minimum 0.
maximumWeight	Safe-integer edge weight in caller-selected consistent units; the inclusive sampling range must also have a safe-integer width.	Required. Safe integer; minimum 0.
seed	Optional numeric seed; takes precedence over context randomness. Without either, use the fixed tool.io default seed.	Optional. Safe integer.

Output field	Interpretation	Returned shape
nodes	Nodes	Required. Array of Safe integer; minimum 0.; maximum items 32.
edges	Edges	Required. Array of Array of Safe integer.; minimum items 3; maximum items 3.; maximum items 496.
componentCount	Component Count	Required. Safe integer; exactly 1.
randomness	Replay source and normalized 32-bit seed, or null seed for an advancing context source.	Required. Object; no unknown keys.
randomness.seed	Optional safe-integer replay seed; normalization and precedence are explained below.	Required. Safe integer. or Null.
randomness.source	Source	Required. Text; one of "context", "seed".
depot	Depot	Required. Safe integer; minimum 0; maximum 31.

An explicit safe-integer seed takes precedence over injected context randomness and is normalized to an unsigned 32-bit integer. With no seed and no context source, replay uses the fixed tool.io default seed. A context source must return finite values from zero inclusive to one exclusive; its advancing state must be retained separately. randomness records the source and normalized seed, or null for context. These generators are not cryptographic random sources.

json

{
  "input": {
    "nodeCount": 5,
    "edgeCount": 6,
    "minimumWeight": 2,
    "maximumWeight": 30,
    "seed": 42
  },
  "output": {
    "nodes": [
      0,
      1,
      2,
      3,
      4
    ],
    "edges": [
      [
        0,
        1,
        10
      ],
      [
        0,
        3,
        7
      ],
      [
        0,
        4,
        16
      ],
      [
        1,
        3,
        21
      ],
      [
        2,
        3,
        19
      ],
      [
        2,
        4,
        2
      ]
    ],
    "componentCount": 1,
    "randomness": {
      "seed": 42,
      "source": "seed"
    },
    "depot": 2
  }
}

Limitations and assumptions

This generates an instance only: it does not calculate an edge-covering tour, duplicated traversals, or minimum postman cost. Weights and graph construction are synthetic rather than road routing or delivery guidance.
A replay requires the same complete input and implementation. The stated sampling convention applies within its eligible space. A finite draw does not exhaust all possible outputs; repeats remain possible unless a distinct-selection rule is explicit.
The field tables state structural bounds; cross-field constraints and bounded search or output budgets also apply. Unknown keys are rejected where the object contract disallows them. Input errors are distinct from null fields and deliberate invalid controls in a successful output.

Alternative or Complementary methods

For a supplied example, inspect the returned construction or delegated check directly. To evaluate a consumer, also test cases chosen for its own boundaries; a generated finite corpus does not establish exhaustive coverage.

References

Tool behavior and example: Random Chinese Postman Instance Generator — tool.io

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