大二暑假第五周博客

第五周，一些python的代码，有一些bug还没找出来

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 "cells": [
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   "cell_type": "markdown",
   "id": "f427f82c",
   "metadata": {},
   "source": [
    "# 第一题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3bb0e5ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "总的等待时间: 25 h\n",
      "总的延迟时间: 1 h\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# 创建码头的DataFrame\n",
    "ports = pd.DataFrame({'Type': ['Bulk', 'Container', 'Gas'],\n",
    "                      'Number': [3, 2, 1],\n",
    "                      'Capacity': [4, 3, 2],\n",
    "                      'Docking time limit': [8, 12, 16]})\n",
    "\n",
    "# 创建船只的DataFrame\n",
    "ships = pd.DataFrame({'Type': ['Bulk', 'Container', 'Gas'],\n",
    "                      'Arrival': [10, 6, 3],\n",
    "                      'Docking time': [4, 6, 8],\n",
    "                      'Average stay': [6, 8, 10],\n",
    "                      'Priority': [1, 2, 3]})\n",
    "\n",
    "# 模拟船只的到达和离开\n",
    "for i in range(24):  # 对于每一个小时\n",
    "    # 检查是否有船只到达\n",
    "    for j in range(len(ships)):\n",
    "        if np.random.rand() < ships.loc[j, 'Arrival'] / 24:  # 如果类型j的船只到达\n",
    "            # 寻找可用的、优先级最高的码头\n",
    "            available_ports = ports[(ports['Number'] > 0) & (ports['Type'] == ships.loc[j, 'Type'])]\n",
    "            if len(available_ports) > 0:\n",
    "                # 减少可用码头的数量\n",
    "                ports.loc[available_ports.index[0], 'Number'] -= 1\n",
    "                # 增加船只的停靠时间\n",
    "                ships.loc[j, 'Docking time'] += ships.loc[j, 'Average stay']\n",
    "            else:\n",
    "                # 如果没有可用码头，船只需要等待\n",
    "                ships.loc[j, 'Docking time'] += 1\n",
    "\n",
    "    # 检查是否有船只离开\n",
    "    for j in range(len(ships)):\n",
    "        if ships.loc[j, 'Docking time'] > 0:\n",
    "            ships.loc[j, 'Docking time'] -= 1\n",
    "            if ships.loc[j, 'Docking time'] == 0:\n",
    "                # 如果船只完成了停靠，增加可用码头的数量\n",
    "                ports.loc[ports[ports['Type'] == ships.loc[j, 'Type']].index[0], 'Number'] += 1\n",
    "\n",
    "# 计算总的等待时间和延迟时间\n",
    "total_waiting_time = ships['Docking time'].sum()\n",
    "total_delay_time = ships['Docking time'].sum() - ships['Average stay'].sum()\n",
    "\n",
    "print(f'总的等待时间: {total_waiting_time}','h')\n",
    "print(f'总的延迟时间: {total_delay_time}','h')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce5c1f05",
   "metadata": {},
   "source": [
    "# 第二题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cdefc713",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "日总收入(万元): 110\n",
      "运营效率: 1.1956521739130435\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# 创建码头的DataFrame\n",
    "ports = pd.DataFrame({'Type': ['Bulk', 'Container', 'Gas'],\n",
    "                      'Number': [3, 2, 1],\n",
    "                      'Capacity': [4, 3, 2],\n",
    "                      'Docking time limit': [8, 12, 16],\n",
    "                      'Average Load': [0.8, 0.7, 0.6]})\n",
    "\n",
    "# 创建船只的DataFrame\n",
    "ships = pd.DataFrame({'Type': ['Bulk', 'Container', 'Gas'],\n",
    "                      'Arrival': [10, 6, 3],\n",
    "                      'Docking time': [4, 6, 8],\n",
    "                      'Average stay': [6, 8, 10],\n",
    "                      'Priority': [1, 2, 3],\n",
    "                      'Revenue': [15, 20, 25]})\n",
    "\n",
    "# 每日运营成本\n",
    "operating_cost = 100\n",
    "\n",
    "# 计算每日收益\n",
    "daily_revenue = 0\n",
    "\n",
    "# 模拟船只的到达和离开\n",
    "for i in range(24):  # 对于每一个小时\n",
    "    # 检查是否有船只到达\n",
    "    for j in range(len(ships)):\n",
    "        if np.random.rand() < ships.loc[j, 'Arrival'] / 24:  # 如果类型j的船只到达\n",
    "            # 寻找可用的、优先级最高的码头\n",
    "            available_ports = ports[(ports['Number'] > 0) & (ports['Type'] == ships.loc[j, 'Type'])]\n",
    "            if len(available_ports) > 0:\n",
    "                # 减少可用码头的数量\n",
    "                ports.loc[available_ports.index[0], 'Number'] -= 1\n",
    "                # 增加船只的停靠时间\n",
    "                ships.loc[j, 'Docking time'] += ships.loc[j, 'Average stay']\n",
    "                # 增加收益\n",
    "                daily_revenue += ships.loc[j, 'Revenue']\n",
    "            else:\n",
    "                # 如果没有可用码头，船只需要等待\n",
    "                ships.loc[j, 'Docking time'] += 1\n",
    "\n",
    "    # 检查是否有船只离开\n",
    "    for j in range(len(ships)):\n",
    "        if ships.loc[j, 'Docking time'] > 0:\n",
    "            ships.loc[j, 'Docking time'] -= 1\n",
    "            if ships.loc[j, 'Docking time'] == 0:\n",
    "                # 如果船只完成了停靠，增加可用码头的数量\n",
    "                ports.loc[ports[ports['Type'] == ships.loc[j, 'Type']].index[0], 'Number'] += 1\n",
    "\n",
    "# 计算总的等待时间和延迟时间\n",
    "total_waiting_time = ships['Docking time'].sum()\n",
    "total_delay_time = ships['Docking time'].sum() - ships['Average stay'].sum()\n",
    "\n",
    "# 计算运营效率\n",
    "operating_efficiency = daily_revenue / (total_waiting_time + total_delay_time + operating_cost)\n",
    "\n",
    "print(f\"日总收入(万元): {daily_revenue}\")\n",
    "print(f\"运营效率: {operating_efficiency}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ead9f92",
   "metadata": {},
   "source": [
    "# 第三题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6f9e6c4b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "总的等待时间: 13 hours\n",
      "总的延迟时间: 24 hours\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# 定义港口数据\n",
    "ports = pd.DataFrame({\n",
    "    'Type': ['Bulk', 'Container', 'LPG'],\n",
    "    'Number': [3, 2, 1],\n",
    "    'Capacity': [4, 3, 2],\n",
    "    'Time Limit': [8, 12, 16],\n",
    "    'Priority': [1, 2, 3]\n",
    "}).set_index('Type')\n",
    "\n",
    "\n",
    "# 定义船只数据\n",
    "ships = pd.DataFrame({\n",
    "    'Type': ['Bulk', 'Container', 'LPG'],\n",
    "    'Arrival Number': [10, 6, 3],\n",
    "    'Docking Time': [6, 8, 10],\n",
    "    'Priority': [1, 2, 3]\n",
    "}).set_index('Type')\n",
    "\n",
    "# 初始化等待时间和延迟时间\n",
    "waiting_time = 0\n",
    "delay_time = 0\n",
    "\n",
    "# 模拟24小时的运营过程\n",
    "for hour in range(24):\n",
    "        # 检查是否有船只到达\n",
    "    arriving_ships = ships[ships['Arrival Number'] > 0]\n",
    "    if len(arriving_ships) > 0:\n",
    "        selected_ship = arriving_ships.sort_values(by='Priority', ascending=False).iloc[0]\n",
    "           # 减少到达数量\n",
    "        ships.loc[selected_ship.name, 'Arrival Number'] -= 1\n",
    "        \n",
    "         # 检查是否有可用的码头\n",
    "        available_ports = ports[ports['Number'] > 0]\n",
    "        if len(available_ports) > 0:\n",
    "            selected_port = available_ports.sort_values(by='Priority', ascending=False).iloc[0]\n",
    "           # 减少可用码头的数量\n",
    "            ports.loc[selected_port.name, 'Number'] -= 1\n",
    "            \n",
    "           # 计算停靠时间\n",
    "            docking_time = selected_ship['Docking Time']\n",
    "            if docking_time > selected_port['Time Limit']:\n",
    "                delay_time += docking_time - selected_port['Time Limit']\n",
    "                docking_time = selected_port['Time Limit']\n",
    "            ports.loc[selected_port.name, 'Time Limit'] -= docking_time\n",
    "        else:\n",
    "             # 如果没有可用码头，增加等待时间\n",
    "            waiting_time += 1\n",
    "\n",
    "print(f\"总的等待时间: {waiting_time} hours\")\n",
    "print(f\"总的延迟时间: {delay_time} hours\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "451748ea",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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