# Complete GUROBIPY implementation

import gurobipy as gp
from gurobipy import GRB

def optimize_editor_journal_assignment():
    """Optimize the assignment of editors to journals to maximize sales."""
    
    # 1. MODEL & DATA SETUP
    model = gp.Model("journal_committee")
    
    # Data: Example data based on the problem context
    editors = [1, 2, 3]
    journals = [101, 102, 103]
    sales = {101: 1200.0, 102: 1800.0, 103: 1600.0}
    qualifications = {
        1: ['Science'],
        2: ['Arts'],
        3: ['Technology']
    }
    journal_themes = {
        101: 'Science',
        102: 'Arts',
        103: 'Technology'
    }
    max_journals_per_editor = {1: 2, 2: 2, 3: 2}
    
    # Validate data lengths
    assert len(editors) == len(max_journals_per_editor), "Editor data length mismatch"
    assert len(journals) == len(sales) == len(journal_themes), "Journal data length mismatch"
    
    # 2. VARIABLES
    # Binary decision variables for editor-journal assignments
    x = model.addVars(editors, journals, vtype=GRB.BINARY, name="x")
    
    # 3. OBJECTIVE FUNCTION
    # Maximize total sales from journal assignments
    model.setObjective(gp.quicksum(sales[j] * x[i, j] for i in editors for j in journals), GRB.MAXIMIZE)
    
    # 4. CONSTRAINTS
    
    # Editor Workload Constraint
    for i in editors:
        model.addConstr(gp.quicksum(x[i, j] for j in journals) <= max_journals_per_editor[i], name=f"workload_{i}")
    
    # Qualification Constraint
    for i in editors:
        for j in journals:
            if journal_themes[j] not in qualifications[i]:
                model.addConstr(x[i, j] == 0, name=f"qualification_{i}_{j}")
    
    # 5. SOLVING & RESULTS
    model.optimize()
    
    if model.status == GRB.OPTIMAL:
        print(f"Optimal value: {model.objVal}")
        for i in editors:
            for j in journals:
                if x[i, j].x > 1e-6:
                    print(f"Editor {i} assigned to Journal {j}")
    elif model.status == GRB.INFEASIBLE:
        print("Problem is infeasible")
    elif model.status == GRB.UNBOUNDED:
        print("Problem is unbounded")
    
    return model

# Run the optimization
optimize_editor_journal_assignment()