Key Project Tasks

1. Business and Technical Summary

Prepare a concise two-page summary explaining:

• Distributed Order Management

• Why it is a combinatorial optimization problem

• Why advanced optimization methods are relevant

• Trade-offs between solution quality and runtime

• Exact methods versus heuristics

• Hardware versus simulator constraints

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2. Data Understanding and Classical Baselines

Teams should:

• Document each file and field in the data pack.

• Create a default-assignment baseline.

• Create a greedy or sequential-reassignment baseline.

• Report objective value, fill rate, number of reassignments, penalty cost, and shipping cost.

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3. Mathematical Formulation

Formulate the problem as either:

• A deterministic binary optimization model, or

• A weighted exact-cover or column-selection model

Clearly define variables, objective function, constraints, and feasibility requirements.

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4. Solution Implementation

Implement a solution for a tractable subset of the data using a classical, heuristic, quantum, quantum-inspired, or hybrid method.

Compare the best candidate against the classical baselines using the same objective function and feasibility checks.

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5. Scaling Analysis

Teams should:

• Estimate how variables or qubits grow with orders, centers, SKUs, or assignment columns.

• Identify runtime, complexity, and robustness limitations.

• Recommend at least one method for improving scalability.

Possible improvements include:

• Batching

• Column reduction

• Classical preprocessing

• Decomposition

• Parameter tuning

• Improved heuristics