Description
Generate nonpreemptive independent positive-integer jobs for identical parallel processors, with an earliest-available list-scheduling witness; no 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 nonpreemptive independent positive-integer jobs for identical parallel processors, with an earliest-available list-scheduling witness; no optimality claim.
When to use this tool
Use this generator to create reproducible multiprocessor 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, processorCount, 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 durations, schedule, processorLoads, makespan. Use the conventions below and keep answer-bearing fields separate when presenting an exercise.
Draw integer job durations and assign each job in input order to the currently least-loaded processor, breaking equal loads by lowest index. Return start/end intervals, processor loads, and the resulting makespan.1
| Input field | Meaning and units | Accepted input |
|---|---|---|
| jobCount | Job Count | Required. Safe integer; minimum 1; maximum 64. |
| processorCount | Processor 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 |
|---|---|---|
| durations | Durations | Required. Array of Safe integer; minimum 1; maximum 9007199254740991.; 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. |
| processorLoads | Processor Loads | Required. Array of Safe integer; minimum 0; maximum 9007199254740991.; minimum items 1; maximum items 16. |
| makespan | Makespan | Required. Safe integer; minimum 1; maximum 9007199254740991. |
| randomness | Replay metadata: source is seed or context; seed is the normalized 32-bit seed or null for a context source. | Required. Object; no unknown keys. |
| randomness.seed | Optional numeric 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": 5,
"processorCount": 2,
"minimumDuration": 2,
"maximumDuration": 9,
"seed": 42
},
"output": {
"durations": [
6,
5,
8,
7,
3
],
"schedule": [
{
"job": 0,
"operation": 0,
"machine": 0,
"start": 0,
"end": 6
},
{
"job": 1,
"operation": 0,
"machine": 1,
"start": 0,
"end": 5
},
{
"job": 2,
"operation": 0,
"machine": 1,
"start": 5,
"end": 13
},
{
"job": 3,
"operation": 0,
"machine": 0,
"start": 6,
"end": 13
},
{
"job": 4,
"operation": 0,
"machine": 0,
"start": 13,
"end": 16
}
],
"processorLoads": [
16,
13
],
"makespan": 16,
"randomness": {
"seed": 42,
"source": "seed"
}
}
}Limitations and assumptions
- This is a feasible list-scheduling witness, not a minimum-makespan solution. Processors are modeled as identical with no precedence, setup, communication, or resource-sharing constraints.
- A replay requires the same complete input and implementation. Randomized construction does not guarantee uniform coverage of every feasible output; finite samples can repeat unless a distinct-selection rule is explicitly stated.
- 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
Similar or alternative tools
- Random Interval Scheduling Challenge Generator
Generate synthetic positive-duration half-open jobs and solve the declared count or weight objective exactly with finish-time dynamic programming, retaining one optimum with exclusion on objective ties.
- Random Job-Shop Scheduling Instance Generator
Generate positive integer processing times and a per-job random machine permutation with a feasible job-priority schedule witness; no makespan optimality claim.