Description
Generate positive integer processing times in a common machine order with a feasible job-priority schedule witness; no makespan optimality claim.
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 positive integer processing times in a common machine order with a feasible job-priority schedule witness; no makespan optimality claim.
When to use this tool
Use this generator to create reproducible flow shop scheduling 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 jobCount, machineCount, minimumDuration, maximumDuration 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 processingTimes, machineOrder, schedule, makespan. Use the conventions below. Keep answer-bearing fields separate when presenting an exercise.
Draw processing times for every job and operation and use the same ascending machine order for all jobs. In job order, start each operation at the later of job readiness and machine availability; return intervals and makespan.1
| Input field | Meaning and units | Accepted input |
|---|---|---|
| jobCount | Job Count | Required. Safe integer; minimum 1; maximum 64. |
| machineCount | Machine Count | Required. Safe integer; minimum 1; maximum 16. |
| minimumDuration | Minimum Duration | Required. Safe integer; minimum 1; maximum 9007199254740991. |
| maximumDuration | Maximum Duration | Required. Safe integer; minimum 1; maximum 9007199254740991. |
| 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 |
|---|---|---|
| processingTimes | Processing Times | Required. Array of Array of Safe integer; minimum 1; maximum 9007199254740991.; minimum items 1; maximum items 16.; minimum items 1; maximum items 64. |
| machineOrder | Machine Order | Required. Array of Array of Safe integer; minimum 0; maximum 15.; minimum items 1; maximum items 16.; minimum items 1; maximum items 64. |
| schedule | Schedule | Required. Array of Object; no unknown keys.; minimum items 1; maximum items 512. |
| schedule[].job | Job | Required. Safe integer; minimum 0; maximum 63. |
| schedule[].operation | Operation | Required. Safe integer; minimum 0; maximum 15. |
| schedule[].machine | Machine | Required. Safe integer; minimum 0; maximum 15. |
| schedule[].start | Start | Required. Safe integer; minimum 0; maximum 9007199254740991. |
| schedule[].end | End | Required. Safe integer; minimum 1; maximum 9007199254740991. |
| makespan | Makespan | Required. Safe integer; minimum 1; maximum 9007199254740991. |
| 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". |
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.
{
"input": {
"jobCount": 2,
"machineCount": 3,
"minimumDuration": 2,
"maximumDuration": 7,
"seed": 42
},
"output": {
"processingTimes": [
[
5,
4,
7
],
[
6,
3,
5
]
],
"machineOrder": [
[
0,
1,
2
],
[
0,
1,
2
]
],
"schedule": [
{
"job": 0,
"operation": 0,
"machine": 0,
"start": 0,
"end": 5
},
{
"job": 0,
"operation": 1,
"machine": 1,
"start": 5,
"end": 9
},
{
"job": 0,
"operation": 2,
"machine": 2,
"start": 9,
"end": 16
},
{
"job": 1,
"operation": 0,
"machine": 0,
"start": 5,
"end": 11
},
{
"job": 1,
"operation": 1,
"machine": 1,
"start": 11,
"end": 14
},
{
"job": 1,
"operation": 2,
"machine": 2,
"start": 16,
"end": 21
}
],
"makespan": 21,
"randomness": {
"seed": 42,
"source": "seed"
}
}
}Limitations and assumptions
- The returned flow-shop schedule is feasible, not minimum-makespan. Duration columns correspond to operation positions in the shared machine order. Setup, failures, preemption, and extra resources are not modeled.
- 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
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