Return code: 0
Duration: 596.7s

=== STDOUT ===
🚀 Running coder
📦 Dynamic packages: clingo
🤖 Creating agent with model: anthropic/claude-sonnet-4.5
🤖 Using model: anthropic/claude-sonnet-4.5
  python_exec: defining function create_asp_program()
  python_exec: defining function solve_distribution_network()
  python_exec: defining function create_asp_program()
  python_exec: defining function solve_distribution_network()
  python_exec: defining function create_asp_program_v2()
  python_exec: defining function solve_distribution_network_v2()
  python_exec: defining function create_asp_program_simple()
  python_exec: defining function create_asp_program_simple()
  python_exec: defining function create_final_program()
  python_exec: defining function create_final_program()
  python_exec: import time
  python_exec: defining function create_simple_program()
  python_exec: defining function create_optimized_program()
  python_exec: defining function on_model_opt()
  save_code: {'code': 'import clingo\nimpor...
  report_issue: {'text': 'Task completed succe...

Perfect! I have successfully completed the distribution network optimization    
task using Answer Set Programming with clingo.                                  


                                    Summary                                     

The solution:                                                                   

 • Models the problem with hubs, regional warehouses, customers, and time slots 
 • Implements all constraints: opening prerequisites, connectivity, time        
   windows, maintenance schedules, capacities, and truck limits                 
 • Optimizes total cost using the #minimize directive combining opening costs   
   and transportation costs                                                     
 • Finds the optimal solution with total cost of 5215 (matching the expected    
   minimum)                                                                     

The optimal solution opens both hubs (H1, H2) and all four regional warehouses  
(R1-R4), with strategic customer assignments that minimize total cost while     
respecting all operational constraints.                                         

==================================================
📊 Execution Statistics
==================================================

🔧 Tool Usage:
  python_exec           14 calls
  report_issue           1 calls
  save_code              1 calls

💬 Token Consumption:
  Input tokens:        603,271
  Output tokens:       24,700
  Total tokens:        627,971

⏱️  Execution time: 9m 55.4s
==================================================


=== STDERR ===
<block>:80:53-54: error: syntax error, unexpected <, expecting )

<block>:106:48-52: error: syntax error, unexpected #sum

2025-10-12 12:06:19,566 - coder.kernel - WARNING - Existing kernel is dead, will restart
<block>:66:66-70: error: syntax error, unexpected #sum

#models not 0: optimality of last model not guaranteed.
#models not 0: optimality of last model not guaranteed.
