210 lines
7.1 KiB
Python
210 lines
7.1 KiB
Python
"""
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Task 3 - Step 2: 共生站点配对筛选与选择
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=======================================
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输入: 01_distance.xlsx (距离矩阵 + 站点信息)
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输出: 02_pairing.xlsx (筛选后的配对列表 + 最终选择)
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配对筛选条件:
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1. 距离约束: l_ij ≤ l_max (50英里)
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2. 容量约束: μ_i + μ_j ≤ 450
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3. 稳定性约束: CV_i ≤ 0.5 且 CV_j ≤ 0.5
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配对价值函数:
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V_ij = α * (μ_i + μ_j) / Q - β * l_ij / l_max - γ * (σ_i² + σ_j²) / (μ_i + μ_j)²
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配对选择: 贪心算法，每个站点最多配对一次
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"""
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import pandas as pd
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import numpy as np
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# ============================================
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# 参数设置
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# ============================================
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INPUT_FILE = '01_distance.xlsx'
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OUTPUT_FILE = '02_pairing.xlsx'
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# 约束参数
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L_MAX = 50 # 距离阈值 (英里)
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MU_SUM_MAX = 450 # 需求和上限
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CV_MAX = 0.5 # 变异系数上限
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Q = 400 # 卡车容量
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# 价值函数权重
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ALPHA = 1.0 # 容量利用率权重
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BETA = 0.3 # 距离惩罚权重
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GAMMA = 0.5 # 风险惩罚权重
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# ============================================
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# 读取数据
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# ============================================
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print("=" * 60)
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print("Task 3 - Step 2: 共生站点配对筛选与选择")
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print("=" * 60)
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# 读取站点信息
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sites = pd.read_excel(INPUT_FILE, sheet_name='sites')
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print(f"\n读取站点数据: {len(sites)} 个站点")
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# 读取距离矩阵
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dist_matrix = pd.read_excel(INPUT_FILE, sheet_name='distance_matrix', index_col=0)
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print(f"读取距离矩阵: {dist_matrix.shape}")
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# 计算变异系数
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sites['cv'] = sites['sigma'] / sites['mu']
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print(f"\nCV范围: [{sites['cv'].min():.3f}, {sites['cv'].max():.3f}]")
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print(f"CV > {CV_MAX} 的站点数: {(sites['cv'] > CV_MAX).sum()}")
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# ============================================
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# 配对筛选
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# ============================================
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print(f"\n" + "-" * 40)
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print("配对筛选")
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print("-" * 40)
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candidates = []
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n = len(sites)
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for i in range(n):
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for j in range(i + 1, n): # 只考虑上三角
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site_i = sites.iloc[i]
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site_j = sites.iloc[j]
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# 获取距离
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dist = dist_matrix.iloc[i, j]
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# 约束检查
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# 1. 距离约束
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if dist > L_MAX:
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continue
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# 2. 容量约束
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mu_sum = site_i['mu'] + site_j['mu']
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if mu_sum > MU_SUM_MAX:
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continue
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# 3. 稳定性约束
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if site_i['cv'] > CV_MAX or site_j['cv'] > CV_MAX:
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continue
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# 通过所有约束，计算配对价值
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sigma_sq_sum = site_i['sigma']**2 + site_j['sigma']**2
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# 价值函数
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value = (ALPHA * mu_sum / Q
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- BETA * dist / L_MAX
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- GAMMA * sigma_sq_sum / mu_sum**2)
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candidates.append({
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'site_i_id': site_i['site_id'],
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'site_j_id': site_j['site_id'],
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'site_i_name': site_i['site_name'],
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'site_j_name': site_j['site_name'],
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'distance': dist,
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'mu_i': site_i['mu'],
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'mu_j': site_j['mu'],
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'mu_sum': mu_sum,
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'sigma_i': site_i['sigma'],
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'sigma_j': site_j['sigma'],
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'cv_i': site_i['cv'],
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'cv_j': site_j['cv'],
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'k_i': site_i['k'],
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'k_j': site_j['k'],
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'value': value
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})
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df_candidates = pd.DataFrame(candidates)
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print(f"通过约束的候选配对数: {len(df_candidates)}")
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if len(df_candidates) > 0:
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print(f"配对价值范围: [{df_candidates['value'].min():.3f}, {df_candidates['value'].max():.3f}]")
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print(f"平均距离: {df_candidates['distance'].mean():.2f} 英里")
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print(f"平均需求和: {df_candidates['mu_sum'].mean():.1f}")
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# ============================================
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# 贪心配对选择
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# ============================================
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print(f"\n" + "-" * 40)
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print("贪心配对选择")
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print("-" * 40)
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# 按价值降序排序
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df_candidates_sorted = df_candidates.sort_values('value', ascending=False).reset_index(drop=True)
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# 贪心选择
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selected_pairs = []
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used_sites = set()
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for _, row in df_candidates_sorted.iterrows():
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site_i = row['site_i_id']
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site_j = row['site_j_id']
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# 检查是否已被使用
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if site_i not in used_sites and site_j not in used_sites:
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selected_pairs.append(row.to_dict())
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used_sites.add(site_i)
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used_sites.add(site_j)
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df_selected = pd.DataFrame(selected_pairs)
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print(f"最终选择配对数: {len(df_selected)}")
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print(f"涉及站点数: {len(used_sites)} (占总站点 {len(used_sites)/n*100:.1f}%)")
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print(f"未配对站点数: {n - len(used_sites)}")
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if len(df_selected) > 0:
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print(f"\n选中配对的价值范围: [{df_selected['value'].min():.3f}, {df_selected['value'].max():.3f}]")
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print(f"选中配对的距离范围: [{df_selected['distance'].min():.2f}, {df_selected['distance'].max():.2f}] 英里")
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print(f"选中配对的需求和范围: [{df_selected['mu_sum'].min():.1f}, {df_selected['mu_sum'].max():.1f}]")
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# ============================================
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# 显示选中的配对
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# ============================================
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print(f"\n" + "-" * 40)
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print("选中的配对列表")
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print("-" * 40)
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if len(df_selected) > 0:
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display_cols = ['site_i_name', 'site_j_name', 'distance', 'mu_sum', 'k_i', 'k_j', 'value']
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print(df_selected[display_cols].to_string(index=False))
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# ============================================
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# 保存结果
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# ============================================
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with pd.ExcelWriter(OUTPUT_FILE, engine='openpyxl') as writer:
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# Sheet 1: 站点信息（含CV）
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sites.to_excel(writer, sheet_name='sites', index=False)
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# Sheet 2: 所有候选配对
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df_candidates_sorted.to_excel(writer, sheet_name='all_candidates', index=False)
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# Sheet 3: 最终选择的配对
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df_selected.to_excel(writer, sheet_name='selected_pairs', index=False)
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# Sheet 4: 参数记录
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params = pd.DataFrame({
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'parameter': ['L_MAX', 'MU_SUM_MAX', 'CV_MAX', 'Q', 'ALPHA', 'BETA', 'GAMMA'],
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'value': [L_MAX, MU_SUM_MAX, CV_MAX, Q, ALPHA, BETA, GAMMA],
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'description': ['距离阈值(英里)', '需求和上限', 'CV上限', '卡车容量',
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'容量利用率权重', '距离惩罚权重', '风险惩罚权重']
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})
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params.to_excel(writer, sheet_name='parameters', index=False)
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# Sheet 5: 汇总统计
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summary = pd.DataFrame({
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'metric': ['total_sites', 'candidate_pairs', 'selected_pairs',
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'paired_sites', 'unpaired_sites', 'avg_distance', 'avg_mu_sum'],
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'value': [n, len(df_candidates), len(df_selected),
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len(used_sites), n - len(used_sites),
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df_selected['distance'].mean() if len(df_selected) > 0 else 0,
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df_selected['mu_sum'].mean() if len(df_selected) > 0 else 0]
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})
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summary.to_excel(writer, sheet_name='summary', index=False)
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print(f"\n结果已保存至: {OUTPUT_FILE}")
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print(" - Sheet 'sites': 站点信息（含CV）")
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print(" - Sheet 'all_candidates': 所有候选配对")
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print(" - Sheet 'selected_pairs': 最终选择的配对")
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print(" - Sheet 'parameters': 参数记录")
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print(" - Sheet 'summary': 汇总统计")
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print("\n" + "=" * 60)
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