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  {
   "cell_type": "markdown",
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   "source": [
    "# Getting Started to Job Shop Lib\n",
    "\n",
    "The main class of the library is the `JobShopInstance` class, which stores a list of jobs and its `Operations`.\n",
    "\n",
    "Each operation is also a class, which stores the machine(s) in which the operation can be processed and its duration (also known as processing time). Let's see an example of how to use the `JobShopInstance` class to model a JSSP instance.\n",
    "\n",
    "In this example, we model a simple Job Shop Scheduling Problem using the `JobShopInstance` class. We define three types of machines: CPU, GPU, and Data Center, each represented by a unique identifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "JobShopInstance(name=Example, num_jobs=3, num_machines=3)"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from job_shop_lib import JobShopInstance, Operation\n",
    "\n",
    "CPU = 0\n",
    "GPU = 1\n",
    "DATA_CENTER = 2\n",
    "\n",
    "job_1 = [Operation(CPU, 1), Operation(GPU, 1), Operation(DATA_CENTER, 7)]\n",
    "job_2 = [Operation(GPU, 5), Operation(DATA_CENTER, 1), Operation(CPU, 1)]\n",
    "job_3 = [Operation(DATA_CENTER, 1), Operation(CPU, 3), Operation(GPU, 2)]\n",
    "\n",
    "jobs = [job_1, job_2, job_3]\n",
    "\n",
    "instance = JobShopInstance(\n",
    "    jobs,\n",
    "    name=\"Example\",\n",
    "    # Any extra parameters are stored inside the\n",
    "    # metadata attribute as a dictionary:\n",
    "    lower_bound=7,\n",
    ")\n",
    "instance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The job and its position in it are automatically inferred. Now, we can access to some stats of the instance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of jobs: 3\n",
      "Number of machines: 3\n",
      "Number of operations: 9\n",
      "Name: Example\n",
      "Is flexible?: False\n",
      "Max operation time: 7\n",
      "Machine loads: [5, 8, 9]\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of jobs:\", instance.num_jobs)\n",
    "print(\"Number of machines:\", instance.num_machines)\n",
    "print(\"Number of operations:\", instance.num_operations)\n",
    "print(\"Name:\", instance.name)\n",
    "print(\"Is flexible?:\", instance.is_flexible)\n",
    "print(\"Max operation time:\", instance.max_duration)\n",
    "print(\"Machine loads:\", instance.machine_loads)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1, 7],\n",
       "       [5, 1, 1],\n",
       "       [1, 3, 2]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "np.array(instance.durations_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2],\n",
       "       [1, 2, 0],\n",
       "       [2, 0, 1]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(instance.machines_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some of this attributes could take $O(num\\_operations)$ to compute. This is the reason we use the `functools.cached_property` decorator to cache the results of the computation of these attributes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that we just had to specify the machines in which the operation can be processed and its duration. The `job_id` and the position of the operation in the job are automatically inferred by the `JobShopInstance` class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Machine id: 0\n",
      "Duration: 1\n",
      "Job id: 0\n",
      "Position: 0\n",
      "Operation id: 0\n",
      "String representation: O(m=0, d=1, j=0, p=0)\n"
     ]
    }
   ],
   "source": [
    "first_operation = job_1[0]\n",
    "print(\"Machine id:\", first_operation.machine_id)\n",
    "print(\"Duration:\", first_operation.duration)\n",
    "# If the operation only has one machine, we can use the `machine_id` property\n",
    "# instead of the `machines` attribute:\n",
    "print(\"Job id:\", first_operation.job_id)\n",
    "print(\"Position:\", first_operation.position_in_job)\n",
    "print(\"Operation id:\", first_operation.operation_id)\n",
    "print(\"String representation:\", str(first_operation))"
   ]
  }
 ],
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