Return code: 0
Duration: 164.2s

=== STDOUT ===
🚀 Running coder
📦 Dynamic packages: clingo
🤖 Creating agent with model: anthropic/claude-sonnet-4.5
🤖 Using model: anthropic/claude-sonnet-4.5
  python_exec: import clingo
  python_exec: defining function create_asp_program()
  python_exec: defining function on_model()
  python_exec: defining function create_asp_program_v2()
  python_exec: job_data = {
  python_exec: print("Verifying solution constraints...")
  save_code: {'code': 'import clingo\nimpor...
  python_exec: executing file 013_job_shop_scheduling_hard_task_code.py
  report_issue: {'text': "All is fine - no iss...

Perfect! I have successfully completed the job shop scheduling task using Answer
Set Programming with the clingo Python API.                                     


                                    Summary                                     

The solution models a complex job shop scheduling problem with:                 

 1 4 jobs with 3-4 operations each (14 total operations)                        
 2 4 machines with specific operation assignments                               
 3 Maintenance windows on machines 2 and 4                                      
 4 Master operator constraints requiring exclusive access for 3 operations      
 5 Precedence constraints ensuring operations within jobs execute sequentially  
 6 Machine exclusivity preventing overlapping operations on the same machine    

The ASP model uses:                                                             

 • Choice rules to select start times for each operation                        
 • Integrity constraints to enforce all requirements                            
 • Multi-level optimization to minimize makespan (priority 2) and tardiness     
   penalties (priority 1)                                                       

The optimal solution achieves:                                                  

 • Makespan: 24 (matching the expected optimal)                                 
 • Total penalty: 0 (all jobs completed on time)                                
 • Total cost: 24                                                               

All constraints are satisfied, and the solution is verified to be correct and   
optimal.                                                                        

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

🔧 Tool Usage:
  python_exec            7 calls
  report_issue           1 calls
  save_code              1 calls

💬 Token Consumption:
  Input tokens:        162,206
  Output tokens:       10,808
  Total tokens:        173,014

⏱️  Execution time: 2m 42.7s
==================================================


=== STDERR ===
<block>:99:57-64: info: operation undefined:
  (MS+TP)

<block>:99:57-64: info: operation undefined:
  (MS+TP)

<block>:99:57-64: info: operation undefined:
  (MS+TP)

<block>:99:57-64: info: operation undefined:
  (MS+TP)

<block>:99:57-64: info: operation undefined:
  (MS+TP)

<block>:99:57-64: info: operation undefined:
  (MS+TP)

<block>:99:57-64: info: operation undefined:
  (MS+TP)

<block>:99:57-64: info: operation undefined:
  (MS+TP)

<block>:99:57-64: info: operation undefined:
  (MS+TP)

<block>:99:57-64: info: operation undefined:
  (MS+TP)

<block>:99:57-64: info: operation undefined:
  (MS+TP)

<block>:99:57-64: info: operation undefined:
  (MS+TP)

<block>:99:57-64: info: operation undefined:
  (MS+TP)

<block>:99:57-64: info: operation undefined:
  (MS+TP)

<block>:99:57-64: info: operation undefined:
  (MS+TP)

<block>:99:57-64: info: operation undefined:
  (MS+TP)

<block>:99:57-64: info: operation undefined:
  (MS+TP)

<block>:99:57-64: info: operation undefined:
  (MS+TP)

<block>:99:57-64: info: operation undefined:
  (MS+TP)

<block>:99:57-64: info: operation undefined:
  (MS+TP)

<block>:99:13-15: info: tuple ignored:
  #inf@2

<block>:99:13-15: info: tuple ignored:
  #inf@2

