ortools-tutorial.ipynb

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   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 用OR-tools求解JSP\n",
    "https://developers.google.com/optimization/scheduling/job_shop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import collections\n",
    "from ortools.sat.python import cp_model\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "jobs_data = [  # task = (machine_id, processing_time).\n",
    "    [(0, 3), (1, 2), (2, 2)],  # Job0\n",
    "    [(0, 2), (2, 1), (1, 4)],  # Job1\n",
    "    [(1, 4), (2, 3)]  # Job2\n",
    "]\n",
    "\n",
    "machines_count = 1 + max(task[0] for job in jobs_data for task in job)\n",
    "all_machines = range(machines_count)\n",
    "# Computes horizon dynamically as the sum of all durations.\n",
    "horizon = sum(task[1] for job in jobs_data for task in job)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "machines_count = 1 + max(task[0] for job in jobs_data for task in job)\n",
    "all_machines = range(machines_count)\n",
    "# Computes horizon dynamically as the sum of all durations.\n",
    "horizon = sum(task[1] for job in jobs_data for task in job)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = cp_model.CpModel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Named tuple to store information about created variables.\n",
    "task_type = collections.namedtuple('task_type', 'start end interval')\n",
    "# Named tuple to manipulate solution information.\n",
    "assigned_task_type = collections.namedtuple('assigned_task_type',\n",
    "                                            'start job index duration')\n",
    "\n",
    "# Creates job intervals and add to the corresponding machine lists.\n",
    "all_tasks = {}\n",
    "machine_to_intervals = collections.defaultdict(list)\n",
    "\n",
    "for job_id, job in enumerate(jobs_data):\n",
    "    for task_id, task in enumerate(job):\n",
    "        machine = task[0]\n",
    "        duration = task[1]\n",
    "        suffix = '_%i_%i' % (job_id, task_id)\n",
    "        start_var = model.NewIntVar(0, horizon, 'start' + suffix)\n",
    "        end_var = model.NewIntVar(0, horizon, 'end' + suffix)\n",
    "        interval_var = model.NewIntervalVar(start_var, duration, end_var,\n",
    "                                            'interval' + suffix)\n",
    "        all_tasks[job_id, task_id] = task_type(start=start_var,\n",
    "                                               end=end_var,\n",
    "                                               interval=interval_var)\n",
    "        machine_to_intervals[machine].append(interval_var)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 定义限制条件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create and add disjunctive constraints.\n",
    "for machine in all_machines:\n",
    "    model.AddNoOverlap(machine_to_intervals[machine])\n",
    "\n",
    "# Precedences inside a job.\n",
    "for job_id, job in enumerate(jobs_data):\n",
    "    for task_id in range(len(job) - 1):\n",
    "        model.Add(all_tasks[job_id, task_id + 1].start >= all_tasks[job_id, task_id].end)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 定义目标"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Makespan objective.\n",
    "obj_var = model.NewIntVar(0, horizon, 'makespan')\n",
    "model.AddMaxEquality(obj_var, [\n",
    "all_tasks[job_id, len(job) - 1].end\n",
    "    for job_id, job in enumerate(jobs_data)\n",
    "])\n",
    "model.Minimize(obj_var)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 调用求解器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "solver = cp_model.CpSolver()\n",
    "status = solver.Solve(model)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 显示结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solution:\n",
      "Optimal Schedule Length: 11.0\n",
      "Machine 0: job_1_task_0   job_0_task_0   \n",
      "           [0,2]          [2,5]          \n",
      "Machine 1: job_2_task_0   job_0_task_1   job_1_task_2   \n",
      "           [0,4]          [5,7]          [7,11]         \n",
      "Machine 2: job_1_task_1   job_2_task_1   job_0_task_2   \n",
      "           [2,3]          [4,7]          [7,9]          \n",
      "\n"
     ]
    }
   ],
   "source": [
    "if status == cp_model.OPTIMAL or status == cp_model.FEASIBLE:\n",
    "    print('Solution:')\n",
    "    # Create one list of assigned tasks per machine.\n",
    "    assigned_jobs = collections.defaultdict(list)\n",
    "    for job_id, job in enumerate(jobs_data):\n",
    "        for task_id, task in enumerate(job):\n",
    "            machine = task[0]\n",
    "            assigned_jobs[machine].append(\n",
    "                assigned_task_type(start=solver.Value(\n",
    "                    all_tasks[job_id, task_id].start),\n",
    "                                   job=job_id,\n",
    "                                   index=task_id,\n",
    "                                   duration=task[1]))\n",
    "\n",
    "    # Create per machine output lines.\n",
    "    output = ''\n",
    "    for machine in all_machines:\n",
    "        # Sort by starting time.\n",
    "        assigned_jobs[machine].sort()\n",
    "        sol_line_tasks = 'Machine ' + str(machine) + ': '\n",
    "        sol_line = '           '\n",
    "\n",
    "        for assigned_task in assigned_jobs[machine]:\n",
    "            name = 'job_%i_task_%i' % (assigned_task.job,\n",
    "                                       assigned_task.index)\n",
    "            # Add spaces to output to align columns.\n",
    "            sol_line_tasks += '%-15s' % name\n",
    "\n",
    "            start = assigned_task.start\n",
    "            duration = assigned_task.duration\n",
    "            sol_tmp = '[%i,%i]' % (start, start + duration)\n",
    "            # Add spaces to output to align columns.\n",
    "            sol_line += '%-15s' % sol_tmp\n",
    "\n",
    "        sol_line += '\\n'\n",
    "        sol_line_tasks += '\\n'\n",
    "        output += sol_line_tasks\n",
    "        output += sol_line\n",
    "\n",
    "    # Finally print the solution found.\n",
    "    print(f'Optimal Schedule Length: {solver.ObjectiveValue()}')\n",
    "    print(output)\n",
    "else:\n",
    "    print('No solution found.')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gantt Grapht\n",
    "https://plotly.com/python/gantt/  \n",
    "https://blog.csdn.net/weixin_45526117/article/details/124530407"
   ]
  },
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     },
     "metadata": {},
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   "source": [
    "import plotly.express as px\n",
    "import pandas as pd\n",
    "\n",
    "df = []\n",
    "for machine in all_machines:\n",
    "    for task in assigned_jobs[machine]:\n",
    "        start, job, op, duration = task.start, task.job, task.index, task.duration\n",
    "        finish = start + duration\n",
    "        operation = f\"Job_{job}_Op{op}\"\n",
    "        df.append(dict(Machine=machine, Start=start, Finish=finish, Operation=operation, Job=job))\n",
    "df = pd.DataFrame(df)\n",
    "\n",
    "# 处理时间\n",
    "datetime = pd.Timestamp('20230101 00:00:00')\n",
    "for i in range(len(df)):\n",
    "    df['Start'].loc[i] = datetime + pd.Timedelta(minutes = df['Start'].loc[i])\n",
    "    df['Finish'].loc[i] = datetime + pd.Timedelta(minutes = df['Finish'].loc[i])\n",
    "\n",
    "\n",
    "fig = px.timeline(df, x_start=\"Start\", x_end=\"Finish\", y=\"Machine\", color=\"Job\", hover_name=\"Operation\")\n",
    "fig.update_yaxes(autorange=\"reversed\") # otherwise tasks are listed from the bottom up\n",
    "fig.show()\n",
    "        "
   ]
  }
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