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reasoning-datasets-competition
combinatorial optimization
LLM evaluation
google OR tools
test dataset
optimization
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Browse filesreasoning-datasets-competition
- README.md +48 -3
- llm_combinatorial_test.json +0 -0
README.md
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# Combined Combinatorial Optimization Dataset
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## Overview
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This dataset is created for evaluating the effectiveness of Large Language Models (LLMs) on various combinatorial optimization problems. Each instance in the dataset represents a problem instance and includes all the necessary attributes to learn and evaluate problem solutions generated by Google's OR Tools.
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## Problem Types and Attributes
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Each instance in the dataset is expected to have the following attributes:
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- **input**: The actual problem data (e.g., graph data for VRP, jobs and machines for JSSP, etc.).
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- **instruction**: A general description of the problem. This attribute contains problem-specific instructions or details (for example, the number of machines and jobs in a jssp problem). The description varies for each problem type.
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- **output_list_of_list**: Expected feasible solution provided as a list of lists.
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- **output_starjob**: Expected solution in a human-readable format. This includes detailed explanations such as how the makespan is calculated in JSSP or how the knapsack capacity is updated step by step.
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Additionally, instances are tagged with a specific **problem_type** (e.g., 'vrp', 'jssp', etc.) which may have additional attributes, depending on the specific requirements of the problem type. For example, instances where `problem_type` equals `'vrp'` might include extra attributes relevant only to Vehicle Routing Problems.
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## Dataset Statistics
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- **Total Instances**: 150
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### Instance Counts by Problem Type
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| Problem Type | Count |
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|--------------|-------|
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| vrp | 30 |
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| knapsack | 30 |
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| tsp | 30 |
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| binpack | 30 |
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| jssp | 30 |
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## Attribute Details
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Below are the attributes extracted from the first instance along with their inferred data types. Note that the dataset may contain additional problem-specific attributes that vary by problem type:
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```
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{'capacity': 'int',
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'city_size': 'int',
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'demands': 'list',
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'input': 'str',
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'instance_id': 'int',
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'instruction': 'str',
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'max_interval': 'int',
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'num_cities': 'int',
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'num_vehicles': 'int',
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'output_list_of_list': 'str',
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'output_starjob': 'str',
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'paired_distances': 'str',
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'problem_type': 'str',
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'time': 'float',
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'vehicle_count': 'int'}
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```
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## Conclusion
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This dataset provides both structured problem definitions and two types of solution representations to facilitate a comprehensive evaluation of LLM-based approaches across different combinatorial optimization problems.
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llm_combinatorial_test.json
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