关于 distribution-center-network
面向供应链规划的配送中心网络设计技能，涵盖工厂、DC 与客户间的网络方案。它梳理成本和服务约束，并介绍优化模型与启发式方法。
主要功能
- 01多层级网络设计
- 02成本与服务水平建模
- 03设施与容量决策
- 04流量优化方法
- 05聚类与分配启发法
适用场景
- 01比较直运与经 DC 配送
- 02规划 DC 选址与容量
- 03权衡运输、库存和服务成本
安装
复制下面的命令，在终端中运行。
$
npx skills add https:/安装后在 Claude Code 中输入/distribution-center-network确认已加载。
详细介绍
配送中心网络设计
你是一位配送中心网络设计和多级供应链优化领域的专家。你的目标是帮助设计最优配送网络，在满足服务要求的同时最大限度地降低总成本，并考虑货物流从工厂经配送中心流向客户的情况。
初步评估
设计配送中心网络之前，请先了解：
-
网络结构
- 当前网络配置是什么？
- 单级网络（仅配送中心 → 客户）？
- 多级网络（工厂 → 配送中心 → 客户）？
- 是否允许直发？（工厂 → 客户）
- 是否有越库作业？
-
设施层级
- 制造工厂／货源？
- 区域配送中心（RDC）？
- 本地配送中心（LDC）？
- 前置库存点（FSL）？
- 客户地点？
-
产品特征
- 产品种类（SKU）？
- 产品系列或类别？
- 存储要求（常温、冷藏、危险品）？
- 价值密度（单位体积／重量的价值）？
- 保质期和易腐性？
-
流量模式
- 产品从哪里发出？（国内工厂、进口货物、供应商）
- 需求分布在哪里？（客户、市场、零售门店）
- 各线路的货量（起点—目的地组合）？
- 季节性模式？
- 增长预测？
-
成本结构
- 工厂／货源成本（生产、搬运）？
- 配送中心固定成本（租赁、资本、设置）？
- 配送中心运营成本（搬运、人工、公用事业）？
- 运输成本（入库、中心间、出库）？
- 库存成本（周转库存、安全库存）？
- 服务罚金成本（延迟交付、销售损失）？
配送网络设计框架
网络配置选项
1. 直发（0 级）
Plants/Sources → Customers
- 优点：无需中间搬运，运输时间最短
- 缺点：小批量货物的运输成本较高
- 适用场景：高价值、时效性强、大批量订单
2. 单级（1 级）
Plants/Sources → Distribution Centers → Customers
- 优点：可整合货物，享有规模经济
- 缺点：增加搬运环节，需在配送中心存储库存
- 适用场景：大多数 B2B 和零售配送
3. 两级（2 级）
Plants → Regional DCs → Local DCs → Customers
- 优点：市场覆盖更广，交付更快
- 缺点：复杂度更高、库存更多、设施更多
- 适用场景：地理范围广、服务要求高
4. 混合网络
Plants ─→ Regional DCs ─→ Customers
└─→ Direct ────────┘
- 优点：灵活，可按产品／客户优化
- 缺点：规划和执行复杂
- 适用场景：差异化服务策略
关键设计决策
战略层面（长期）：
- 级数
- 配送中心的数量和位置
- 配送中心的规模和容量
- 技术和自动化水平
- 所有权（自营还是外包／第三方物流）
战术层面（中期）：
- 产品与配送中心的分配
- 客户与配送中心的分配
- 库存布局
- 运输方式
- 季节性策略
运营层面（短期）：
- 订单履约分配
- 补货决策
- 车辆路径规划
- 货物整合
数学公式
多级配送中心网络设计模型
集合：
- P：工厂／货源
- D：潜在配送中心位置
- C：客户
- K：产品
参数：
- f_d：启用配送中心 d 的固定成本
- Q_d：配送中心 d 的容量
- c_{pd}：从工厂 p 到配送中心 d 的单位成本
- c_{dc}：从配送中心 d 到客户 c 的单位成本
- c_{pc}：从工厂 p 直接到客户 c 的单位成本（若允许直发）
- d_{ck}：客户 c 对产品 k 的需求量
- α_k：配送中心处产品 k 的单位库存成本
- s_{pk}：工厂 p 对产品 k 的供应／产能
决策变量：
- y_d ∈ {0,1}：若启用配送中心 d，则为 1
- x_{pdk}：产品 k 从工厂 p 到配送中心 d 的流量
- z_{dck}：产品 k 从配送中心 d 到客户 c 的流量
- w_{pck}：产品 k 从工厂 p 直接到客户 c 的流量（若允许直发）
目标函数：
Minimize:
DC fixed costs:
Σ_d f_d × y_d
+ Inbound transportation (plant → DC):
Σ_p Σ_d Σ_k c_{pd} × x_{pdk}
+ Outbound transportation (DC → customer):
Σ_d Σ_c Σ_k c_{dc} × z_{dck}
+ Direct shipment costs (if allowed):
Σ_p Σ_c Σ_k c_{pc} × w_{pck}
+ DC inventory costs:
Σ_d Σ_k α_k × (Σ_c z_{dck})
约束：
1. Demand satisfaction:
Σ_d z_{dck} + Σ_p w_{pck} = d_{ck}, ∀c ∈ C, ∀k ∈ K
2. DC capacity:
Σ_c Σ_k z_{dck} ≤ Q_d × y_d, ∀d ∈ D
3. Flow balance at DCs:
Σ_p x_{pdk} = Σ_c z_{dck}, ∀d ∈ D, ∀k ∈ K
4. Plant capacity:
Σ_d x_{pdk} + Σ_c w_{pck} ≤ s_{pk}, ∀p ∈ P, ∀k ∈ K
5. Serve only from open DCs:
z_{dck} ≤ M × y_d, ∀d ∈ D, ∀c ∈ C, ∀k ∈ K
where M is a large constant (customer demand sum)
6. Non-negativity and binary:
y_d ∈ {0,1}, ∀d ∈ D
x_{pdk}, z_{dck}, w_{pck} ≥ 0
服务水平约束模型
附加参数：
- T_c：客户 c 可接受的最长交付时间
- t_{dc}：从配送中心 d 到客户 c 的交付时间
- SL：最低服务水平（例如，在规定时间内满足 95% 的需求）
服务约束：
1. Each customer must be within service time of at least one DC:
Σ_{d: t_{dc} ≤ T_c} y_d ≥ 1, ∀c ∈ C
2. Or aggregate service level:
Σ_c Σ_{d: t_{dc} ≤ T_c} z_{dck} ≥ SL × Σ_c d_{ck}, ∀k ∈ K
求解方法
1. 多级 MIP 模型
from pulp import *
import numpy as np
import pandas as pd
def solve_dc_network_design(plants, dcs, customers, products,
dc_costs, capacities, inbound_costs,
outbound_costs, demands,
plant_capacities=None, allow_direct=False):
"""
Solve multi-echelon DC network design problem
Args:
plants: list of plant IDs
dcs: list of potential DC IDs
customers: list of customer IDs
products: list of product IDs
dc_costs: dict {dc_id: fixed_cost}
capacities: dict {dc_id: capacity}
inbound_costs: dict {(plant, dc, product): cost}
outbound_costs: dict {(dc, customer, product): cost}
demands: dict {(customer, product): demand}
plant_capacities: optional dict {(plant, product): capacity}
allow_direct: allow direct plant-to-customer shipments
Returns:
optimal network design solution
"""
# Create problem
prob = LpProblem("DC_Network_Design", LpMinimize)
# Decision variables
# y[d] = 1 if DC d is opened
y = LpVariable.dicts("DC", dcs, cat='Binary')
# x[p,d,k] = flow from plant p to DC d for product k
x = {}
for p in plants:
for d in dcs:
for k in products:
x[p,d,k] = LpVariable(f"inbound_{p}_{d}_{k}",
lowBound=0, cat='Continuous')
# z[d,c,k] = flow from DC d to customer c for product k
z = {}
for d in dcs:
for c in customers:
for k in products:
z[d,c,k] = LpVariable(f"outbound_{d}_{c}_{k}",
lowBound=0, cat='Continuous')
# w[p,c,k] = direct flow from plant p to customer c (if allowed)
w = {}
if allow_direct:
for p in plants:
for c in customers:
for k in products:
w[p,c,k] = LpVariable(f"direct_{p}_{c}_{k}",
lowBound=0, cat='Continuous')
# Objective: Minimize total cost
# DC fixed costs
fixed_cost_expr = lpSum([dc_costs[d] * y[d] for d in dcs])
# Inbound transportation costs
inbound_expr = lpSum([
inbound_costs.get((p,d,k), 0) * x[p,d,k]
for p in plants for d in dcs for k in products
])
# Outbound transportation costs
outbound_expr = lpSum([
outbound_costs.get((d,c,k), 0) * z[d,c,k]
for d in dcs for c in customers for k in products
])
prob += fixed_cost_expr + inbound_expr + outbound_expr, "Total_Cost"
# Constraints
# 1. Demand satisfaction
for c in customers:
for k in products:
if (c, k) in demands:
if allow_direct:
prob += (
lpSum([z[d,c,k] for d in dcs]) +
lpSum([w[p,c,k] for p in plants]) ==
demands[c,k],
f"Demand_{c}_{k}"
)
else:
prob += (
lpSum([z[d,c,k] for d in dcs]) == demands[c,k],
f"Demand_{c}_{k}"
)
# 2. DC capacity constraints
for d in dcs:
prob += (
lpSum([z[d,c,k]
for c in customers for k in products]) <=
capacities[d] * y[d],
f"Capacity_DC_{d}"
)
# 3. Flow balance at DCs
for d in dcs:
for k in products:
prob += (
lpSum([x[p,d,k] for p in plants]) ==
lpSum([z[d,c,k] for c in customers]),
f"Flow_Balance_{d}_{k}"
)
# 4. Plant capacity constraints (if specified)
if plant_capacities:
for p in plants:
for k in products:
if (p, k) in plant_capacities:
if allow_direct:
prob += (
lpSum([x[p,d,k] for d in dcs]) +
lpSum([w[p,c,k] for c in customers]) <=
plant_capacities[p,k],
f"Plant_Capacity_{p}_{k}"
)
else:
prob += (
lpSum([x[p,d,k] for d in dcs]) <=
plant_capacities[p,k],
f"Plant_Capacity_{p}_{k}"
)
# 5. Serve only from open DCs (big-M constraint)
for d in dcs:
for c in customers:
for k in products:
if (c, k) in demands:
M = demands[c,k] # Big-M value
prob += (
z[d,c,k] <= M * y[d],
f"Open_DC_{d}_{c}_{k}"
)
# Solve
import time
start_time = time.time()
prob.solve(PULP_CBC_CMD(msg=1, timeLimit=900))
solve_time = time.time() - start_time
# Extract solution
if LpStatus[prob.status] in ['Optimal', 'Feasible']:
open_dcs = [d for d in dcs if y[d].varValue > 0.5]
# Extract flows
inbound_flows = {}
for p in plants:
for d in dcs:
for k in products:
if x[p,d,k].varValue > 0.01:
inbound_flows[p,d,k] = x[p,d,k].varValue
outbound_flows = {}
for d in dcs:
for c in customers:
for k in products:
if z[d,c,k].varValue > 0.01:
outbound_flows[d,c,k] = z[d,c,k].varValue
# Calculate DC utilization
dc_utilization = {}
for d in open_dcs:
total_flow = sum(z[d,c,k].varValue
for c in customers for k in products)
dc_utilization[d] = (total_flow / capacities[d]) * 100
# Cost breakdown
total_fixed = sum(dc_costs[d] for d in open_dcs)
total_inbound = sum(
inbound_costs.get((p,d,k), 0) * x[p,d,k].varValue
for p in plants for d in dcs for k in products
)
total_outbound = sum(
outbound_costs.get((d,c,k), 0) * z[d,c,k].varValue
for d in dcs for c in customers for k in products
)
return {
'status': LpStatus[prob.status],
'total_cost': value(prob.objective),
'fixed_cost': total_fixed,
'inbound_cost': total_inbound,
'outbound_cost': total_outbound,
'open_dcs': open_dcs,
'num_dcs': len(open_dcs),
'inbound_flows': inbound_flows,
'outbound_flows': outbound_flows,
'dc_utilization': dc_utilization,
'solve_time': solve_time
}
else:
return {
'status': LpStatus[prob.status],
'solve_time': solve_time
}
# Example usage
if __name__ == "__main__":
# Network data
plants = ['P1', 'P2', 'P3']
dcs = ['DC1', 'DC2', 'DC3', 'DC4', 'DC5']
customers = [f'C{i}' for i in range(1, 21)] # 20 customers
products = ['ProductA', 'ProductB', 'ProductC']
# DC costs and capacities
dc_costs = {
'DC1': 500000, 'DC2': 450000, 'DC3': 480000,
'DC4': 520000, 'DC5': 470000
}
capacities = {
'DC1': 10000, 'DC2': 8000, 'DC3': 9000,
'DC4': 11000, 'DC5': 8500
}
# Generate random costs (simplified)
np.random.seed(42)
inbound_costs = {}
for p in plants:
for d in dcs:
for k in products:
inbound_costs[p,d,k] = np.random.uniform(10, 50)
outbound_costs = {}
for d in dcs:
for c in customers:
for k in products:
outbound_costs[d,c,k] = np.random.uniform(5, 30)
# Customer demands
demands = {}
for c in customers:
for k in products:
demands[c,k] = np.random.uniform(50, 200)
# Plant capacities
plant_capacities = {}
for p in plants:
for k in products:
plant_capacities[p,k] = 5000
print("="*70)
print("DISTRIBUTION CENTER NETWORK DESIGN")
print("="*70)
print(f"Plants: {len(plants)}")
print(f"Potential DCs: {len(dcs)}")
print(f"Customers: {len(customers)}")
print(f"Products: {len(products)}")
print(f"Total demand: {sum(demands.values()):,.0f} units")
# Solve
result = solve_dc_network_design(
plants, dcs, customers, products,
dc_costs, capacities,
inbound_costs, outbound_costs, demands,
plant_capacities
)
print(f"\n{'='*70}")
print(f"OPTIMAL SOLUTION")
print(f"{'='*70}")
print(f"Status: {result['status']}")
print(f"Total Cost: ${result['total_cost']:,.2f}")
print(f" Fixed Costs: ${result['fixed_cost']:,.2f} "
f"({result['fixed_cost']/result['total_cost']*100:.1f}%)")
print(f" Inbound Transport: ${result['inbound_cost']:,.2f} "
f"({result['inbound_cost']/result['total_cost']*100:.1f}%)")
print(f" Outbound Transport: ${result['outbound_cost']:,.2f} "
f"({result['outbound_cost']/result['total_cost']*100:.1f}%)")
print(f"\nOpen DCs: {result['num_dcs']}")
for dc in result['open_dcs']:
print(f" {dc}: Utilization={result['dc_utilization'][dc]:.1f}%, "
f"Capacity={capacities[dc]:,.0f}")
print(f"\nSolve Time: {result['solve_time']:.2f} seconds")
print(f"\nSample Flows (first 10):")
count = 0
for (d, c, k), flow in result['outbound_flows'].items():
if count >= 10:
break
print(f" {d} → {c} ({k}): {flow:.2f} units")
count += 1
2. 网络流优化
def optimize_network_flows(open_dcs, plants, customers, products,
inbound_costs, outbound_costs, demands,
dc_capacities, plant_capacities):
"""
Optimize flows through given DC network
Given fixed DC locations, optimize product flows
Args:
open_dcs: list of open DC locations
plants, customers, products: network nodes
costs, demands, capacities: network parameters
Returns:
optimal flow pattern
"""
prob = LpProblem("Network_Flow", LpMinimize)
# Variables: flows only (DCs already decided)
x = {} # inbound flows
for p in plants:
for d in open_dcs:
for k in products:
x[p,d,k] = LpVariable(f"inbound_{p}_{d}_{k}",
lowBound=0, cat='Continuous')
z = {} # outbound flows
for d in open_dcs:
for c in customers:
for k in products:
z[d,c,k] = LpVariable(f"outbound_{d}_{c}_{k}",
lowBound=0, cat='Continuous')
# Objective: Minimize transportation costs
prob += (
lpSum([inbound_costs.get((p,d,k), 0) * x[p,d,k]
for p in plants for d in open_dcs for k in products]) +
lpSum([outbound_costs.get((d,c,k), 0) * z[d,c,k]
for d in open_dcs for c in customers for k in products]),
"Total_Transport_Cost"
)
# Constraints
# Demand satisfaction
for c in customers:
for k in products:
if (c, k) in demands:
prob += (
lpSum([z[d,c,k] for d in open_dcs]) == demands[c,k],
f"Demand_{c}_{k}"
)
# DC capacity
for d in open_dcs:
prob += (
lpSum([z[d,c,k] for c in customers for k in products]) <=
dc_capacities[d],
f"DC_Capacity_{d}"
)
# Flow balance
for d in open_dcs:
for k in products:
prob += (
lpSum([x[p,d,k] for p in plants]) ==
lpSum([z[d,c,k] for c in customers]),
f"Balance_{d}_{k}"
)
# Plant capacity
for p in plants:
for k in products:
if (p, k) in plant_capacities:
prob += (
lpSum([x[p,d,k] for d in open_dcs]) <=
plant_capacities[p,k],
f"Plant_{p}_{k}"
)
# Solve
prob.solve(PULP_CBC_CMD(msg=0))
if LpStatus[prob.status] in ['Optimal', 'Feasible']:
inbound_flows = {
(p,d,k): x[p,d,k].varValue
for p in plants for d in open_dcs for k in products
if x[p,d,k].varValue > 0.01
}
outbound_flows = {
(d,c,k): z[d,c,k].varValue
for d in open_dcs for c in customers for k in products
if z[d,c,k].varValue > 0.01
}
return {
'status': LpStatus[prob.status],
'total_cost': value(prob.objective),
'inbound_flows': inbound_flows,
'outbound_flows': outbound_flows
}
return {'status': LpStatus[prob.status]}
启发式方法
1. 基于聚类的配送中心选址
from sklearn.cluster import KMeans
def clustering_based_dc_selection(customer_locations, customer_demands,
potential_dc_locations, num_dcs):
"""
Use demand-weighted clustering to select DC locations
Args:
customer_locations: array of customer coordinates
customer_demands: customer demand weights
potential_dc_locations: candidate DC coordinates
num_dcs: number of DCs to select
Returns:
selected DC indices
"""
# Cluster customers into num_dcs groups
weights = (customer_demands / customer_demands.min()).astype(int)
weighted_locations = []
for i, loc in enumerate(customer_locations):
weighted_locations.extend([loc] * weights[i])
weighted_locations = np.array(weighted_locations)
# K-means clustering
kmeans = KMeans(n_clusters=num_dcs, random_state=42)
kmeans.fit(weighted_locations)
cluster_centers = kmeans.cluster_centers_
# Match cluster centers to nearest potential DC locations
selected_dcs = []
for center in cluster_centers:
# Find nearest potential DC to cluster center
distances = [np.linalg.norm(center - dc_loc)
for dc_loc in potential_dc_locations]
nearest_dc = np.argmin(distances)
if nearest_dc not in selected_dcs:
selected_dcs.append(nearest_dc)
# If we have fewer DCs than requested (due to duplicates),
# add next best options
while len(selected_dcs) < num_dcs:
best_dc = None
best_total_dist = float('inf')
for dc_idx in range(len(potential_dc_locations)):
if dc_idx in selected_dcs:
continue
# Calculate total distance from this DC to all customers
total_dist = sum(
customer_demands[i] *
np.linalg.norm(potential_dc_locations[dc_idx] - customer_locations[i])
for i in range(len(customer_locations))
)
if total_dist < best_total_dist:
best_total_dist = total_dist
best_dc = dc_idx
if best_dc is not None:
selected_dcs.append(best_dc)
else:
break
return selected_dcs
2. 迭代式选址-分配
def iterative_location_allocation(potential_dcs, customers, demands,
transport_costs, dc_costs, capacities,
num_dcs, max_iterations=50):
"""
Iterative heuristic: alternate between location and allocation
Args:
potential_dcs: list of potential DC locations
customers: list of customers
demands: customer demands
transport_costs: outbound transport costs
dc_costs: DC fixed costs
capacities: DC capacities
num_dcs: number of DCs to open
max_iterations: maximum iterations
Returns:
heuristic solution
"""
# Initialize: select random DCs
current_dcs = random.sample(potential_dcs, num_dcs)
def allocate_customers(dcs):
"""Allocate each customer to nearest DC with capacity"""
assignments = {}
remaining_capacity = {d: capacities[d] for d in dcs}
# Sort customers by total demand (largest first)
sorted_customers = sorted(customers,
key=lambda c: demands[c],
reverse=True)
for c in sorted_customers:
# Find nearest DC with capacity
best_dc = None
min_cost = float('inf')
for d in dcs:
if remaining_capacity[d] >= demands[c]:
cost = transport_costs.get((d, c), float('inf'))
if cost < min_cost:
min_cost = cost
best_dc = d
if best_dc:
assignments[c] = best_dc
remaining_capacity[best_dc] -= demands[c]
else:
# No capacity, assign to nearest anyway (infeasible)
best_dc = min(dcs, key=lambda d: transport_costs.get((d,c), float('inf')))
assignments[c] = best_dc
return assignments
def calculate_cost(dcs, assignments):
"""Calculate total cost"""
fixed = sum(dc_costs[d] for d in dcs)
transport = sum(
transport_costs.get((assignments[c], c), 0) * demands[c]
for c in customers if c in assignments
)
return fixed + transport
best_dcs = current_dcs.copy()
best_assignments = allocate_customers(current_dcs)
best_cost = calculate_cost(current_dcs, best_assignments)
for iteration in range(max_iterations):
# Allocate customers to current DCs
current_assignments = allocate_customers(current_dcs)
# Relocate DCs: try swapping each open DC with each closed DC
improved = False
for open_dc in current_dcs:
for closed_dc in potential_dcs:
if closed_dc in current_dcs:
continue
# Test swap
test_dcs = [d if d != open_dc else closed_dc
for d in current_dcs]
test_assignments = allocate_customers(test_dcs)
test_cost = calculate_cost(test_dcs, test_assignments)
if test_cost < best_cost - 1e-6:
best_cost = test_cost
best_dcs = test_dcs.copy()
best_assignments = test_assignments
current_dcs = test_dcs
improved = True
break
if improved:
break
if not improved:
break
return {
'open_dcs': best_dcs,
'assignments': best_assignments,
'total_cost': best_cost,
'method': 'Iterative Location-Allocation'
}
完整配送中心网络求解器
class DCNetworkSolver:
"""
Comprehensive Distribution Center Network Design Solver
"""
def __init__(self):
self.plants = None
self.dcs = None
self.customers = None
self.products = None
self.loaded = False
def load_problem(self, network_data):
"""
Load network design problem
Args:
network_data: dict with all problem data
- plants, dcs, customers, products
- costs, capacities, demands
"""
self.plants = network_data['plants']
self.dcs = network_data['dcs']
self.customers = network_data['customers']
self.products = network_data.get('products', ['Product1'])
self.dc_costs = network_data['dc_costs']
self.capacities = network_data['capacities']
self.inbound_costs = network_data['inbound_costs']
self.outbound_costs = network_data['outbound_costs']
self.demands = network_data['demands']
self.plant_capacities = network_data.get('plant_capacities')
self.loaded = True
print(f"Loaded DC network problem:")
print(f" Plants: {len(self.plants)}")
print(f" Potential DCs: {len(self.dcs)}")
print(f" Customers: {len(self.customers)}")
print(f" Products: {len(self.products)}")
print(f" Total demand: {sum(self.demands.values()):,.0f}")
def solve_exact(self, allow_direct=False, time_limit=900):
"""Solve with exact MIP"""
if not self.loaded:
raise ValueError("Problem not loaded")
print("\nSolving with exact MIP...")
return solve_dc_network_design(
self.plants, self.dcs, self.customers, self.products,
self.dc_costs, self.capacities,
self.inbound_costs, self.outbound_costs,
self.demands, self.plant_capacities,
allow_direct
)
def solve_heuristic(self, method='clustering', num_dcs=None):
"""Solve with heuristic method"""
print(f"\nSolving with {method} heuristic...")
# Methods require different inputs
# Implementation would depend on available data
raise NotImplementedError("Heuristic methods need coordinates")
def analyze_scenarios(self, dc_counts):
"""
Analyze different numbers of DCs
Args:
dc_counts: list of DC counts to test
Returns:
comparison of scenarios
"""
import pandas as pd
results = []
for num_dcs in dc_counts:
print(f"\nAnalyzing scenario with {num_dcs} DCs...")
# This would require solving with a constraint on number of DCs
# or using heuristics
# Placeholder
results.append({
'Num_DCs': num_dcs,
'Total_Cost': None,
'Fixed_Cost': None,
'Transport_Cost': None
})
return pd.DataFrame(results)
def visualize_network(self, solution, coordinates=None):
"""Visualize DC network"""
import matplotlib.pyplot as plt
if coordinates is None:
print("Coordinates required for visualization")
return
# Visualization code
# Similar to previous examples
pass
# Example usage
if __name__ == "__main__":
# Create sample network data
network_data = {
'plants': ['P1', 'P2'],
'dcs': ['DC1', 'DC2', 'DC3', 'DC4'],
'customers': [f'C{i}' for i in range(1, 16)],
'products': ['ProductA', 'ProductB'],
'dc_costs': {
'DC1': 400000, 'DC2': 380000,
'DC3': 420000, 'DC4': 390000
},
'capacities': {
'DC1': 8000, 'DC2': 7000,
'DC3': 9000, 'DC4': 7500
},
'inbound_costs': {},
'outbound_costs': {},
'demands': {},
'plant_capacities': {}
}
# Generate costs and demands
np.random.seed(42)
for p in network_data['plants']:
for d in network_data['dcs']:
for k in network_data['products']:
network_data['inbound_costs'][p,d,k] = np.random.uniform(15, 40)
for d in network_data['dcs']:
for c in network_data['customers']:
for k in network_data['products']:
network_data['outbound_costs'][d,c,k] = np.random.uniform(8, 25)
for c in network_data['customers']:
for k in network_data['products']:
network_data['demands'][c,k] = np.random.uniform(30, 150)
for p in network_data['plants']:
for k in network_data['products']:
network_data['plant_capacities'][p,k] = 3000
# Solve
solver = DCNetworkSolver()
solver.load_problem(network_data)
solution = solver.solve_exact()
print(f"\n{'='*70}")
print(f"OPTIMAL NETWORK CONFIGURATION")
print(f"{'='*70}")
print(f"Total Cost: ${solution['total_cost']:,.2f}")
print(f"DCs Opened: {solution['num_dcs']}")
print(f"DC IDs: {solution['open_dcs']}")
工具与库
Python 库
- PuLP/Pyomo：MIP 建模
- OR-Tools：网络优化
- NetworkX：网络分析
- scikit-learn：聚类
- pandas/numpy：数据处理
商业软件
- Llamasoft：网络设计平台
- SAP IBP：集成规划
- Blue Yonder：供应链套件
- AIMMS：优化建模
常见挑战与解决方案
挑战：大规模网络
解决方案： 分解、聚合、启发式方法
挑战：多产品复杂性
解决方案： 产品聚合、ABC 分析
挑战：需求动态变化
解决方案： 随机模型、鲁棒优化
挑战：服务要求
解决方案： 服务水平约束、惩罚成本
输出格式
网络配置：
- 总成本：$X
- 开放的配送中心：Y 个地点
- 容量利用率：Z%
- 服务水平：W%
需要提出的问题
- 网络范围？（区域、全国、全球）
- 当前网络还是从零开始设计？
- 工厂/货源数量？
- 产品种类和特性？
- 需求地点和数量？
- 服务要求？
- 成本结构？
- 规划周期？
相关技能
- facility-location-problem：一般选址理论
- warehouse-location-optimization：仓库选址详情
- hub-location-problem：枢纽网络
- network-flow-optimization：流量优化
- inventory-routing-problem：集成库存-路径规划
- multi-echelon-inventory：库存配置
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