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Author: arnobt78

README.md

@@ -1,12 +1,3 @@
-# vrptw-solver-comparison
-Solving Vehicle Rounting Problem with Time Windows using multiple Classic Heuristic, Metaheuristic algorithms (Hybrid Genetic Search (HGS), Guided Local Search (GLS), Ant Colony Optimisation (ACO), Simulated Annealing (SA)), then comparing the results with each one's results and represent it in the graph. This was my master's thesis project.
-
-# keywords
-Vehicle Routing Problems with Time Window (VRPTW), Hybrid Genetic Search (HGS), Guided Local Search (GLS), Ant Colony Optimization (ACO), Simulated Annealing (SA), MACS-VRPTW, Genetic Algorithm (GA), Exact, Heuristics, Metaheuristics, Machine Learning Algorithms, GRASP, Local Search (LS), Neighborhood Search, OR-Tools, VRP, VRPTW, Vehicle Routing Problems (VRP), pyVRP.
-
-# Note
-Using the Python Jupyter Notebook is highly advised. While compiling, each model executes independently. However, if you run the code straight from main.py , the application may crash and display errors due to the ACO procedure’s use of multiple threads! However, it functions perfectly now that I’m using the Python Jupyter Notebook code.
-
 # Development
 
 ## Create venv
@@ -25,3 +16,4 @@ python -m venv .venv
 
 ```sh
 pip install -r requirements.txt
+```

bks.py

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+from vrplib import read_solution
+
+def bks_solution(bks_path):
+ BKS = read_solution(bks_path)
+ BKS_ROUTES = BKS["routes"]
+ BKS_COST = BKS["cost"]
+ print("BKS cost:", BKS_COST)
+ print("BKS solution:")
+ for i, route in enumerate(BKS_ROUTES, start=1):
+ print(f"Route #{i}: {' '.join(str(node) for node in route)}")
+ print()
+
+ return BKS_ROUTES, BKS_COST

main.py

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+import time
+from matplotlib import pyplot as plt
+from tabulate import tabulate
+from aco.solve import solve_with_aco
+from bks import bks_solution
+from hgs.solve import solve_with_hgs
+from gls.solve import solve_with_gls
+from plot import plot_my_solution
+from sa.solve import solve_using_sa
+from pyvrp import read
+
+dataset = "r211"
+INPUT_PATH = f"data/{dataset}.txt"
+BKS_PATH = f"data/{dataset}.sol"
+RUNTIME = 120 # seconds
+
+INSTANCE = read(INPUT_PATH, instance_format="solomon", round_func="trunc1")
+
+result = {
+ "bks": {},
+ "hgs": {},
+ "gls": {},
+ "aco": {},
+ "sa": {},
+}
+print("Running Algorithms on dataset:", dataset)
+result["bks"]["routes"], result["bks"]["cost"] = bks_solution(BKS_PATH)
+
+start = time.time()
+result["hgs"]["routes"], result["hgs"]["cost"] = solve_with_hgs(INPUT_PATH, RUNTIME)
+result["hgs"]["runtime"] = time.time() - start
+
+start = time.time()
+result["gls"]["routes"], result["gls"]["cost"] = solve_with_gls(INPUT_PATH, RUNTIME)
+result["gls"]["runtime"] = time.time() - start
+
+start = time.time()
+result["aco"]["routes"], result["aco"]["cost"] = solve_with_aco(INPUT_PATH)
+result["aco"]["runtime"] = time.time() - start
+
+start = time.time()
+result["sa"]["routes"], result["sa"]["cost"] = solve_using_sa(INPUT_PATH)
+result["sa"]["runtime"] = time.time() - start
+
+_, ax = plt.subplots(figsize=(10, 10))
+plot_my_solution(result["hgs"], INSTANCE, ax=ax, dataset=dataset, algo="HGS")
+
+_, ax = plt.subplots(figsize=(10, 10))
+plot_my_solution(result["gls"], INSTANCE, ax=ax, dataset=dataset, algo="GLS")
+
+_, ax = plt.subplots(figsize=(10, 10))
+plot_my_solution(result["aco"], INSTANCE, ax=ax, dataset=dataset, algo="ACO")
+
+_, ax = plt.subplots(figsize=(10, 10))
+plot_my_solution(result["sa"], INSTANCE, ax=ax, dataset=dataset, algo="SA")
+
+gap = lambda bks_cost, algo_cost: round(100 * (algo_cost - bks_cost) / bks_cost, 2)
+header = ["Algorithms", "No. of Routes", "Costs", "Gap(%)", "Runtime(seconds)"]
+rows = [
+ ["BKS", len(result["bks"]["routes"]), result["bks"]["cost"], "-", "-"],
+ [
+ "HGS",
+ len(result["hgs"]["routes"]),
+ result["hgs"]["cost"],
+ gap(result["bks"]["cost"], result["hgs"]["cost"]),
+ result["hgs"]["runtime"],
+ ],
+ [
+ "GLS",
+ len(result["gls"]["routes"]),
+ result["gls"]["cost"],
+ gap(result["bks"]["cost"], result["gls"]["cost"]),
+ result["gls"]["runtime"],
+ ],
+ [
+ "ACO",
+ len(result["aco"]["routes"]),
+ result["aco"]["cost"],
+ gap(result["bks"]["cost"], result["aco"]["cost"]),
+ result["aco"]["runtime"],
+ ],
+ [
+ "SA",
+ len(result["sa"]["routes"]),
+ result["sa"]["cost"],
+ gap(result["bks"]["cost"], result["sa"]["cost"]),
+ result["sa"]["runtime"],
+ ],
+]
+print("Algorithm results on dataset:", dataset)
+tabulate(rows, header, tablefmt="html")

plot.py

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+from typing import Optional
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+from pyvrp import ProblemData
+
+
+def plot_my_solution(
+ solution: any,
+ data: ProblemData,
+ ax: Optional[plt.Axes] = None,
+ dataset: str = "c101",
+ algo: str = "HGS",
+):
+ """
+ Plots the given solution.
+
+ Parameters
+ ----------
+ solution
+ Solution to plot.
+ data
+ Data instance underlying the solution.
+ plot_clients
+ Whether to plot clients as dots. Default False, which plots only the
+ solution's routes.
+ ax
+ Axes object to draw the plot on. One will be created if not provided.
+ """
+ if not ax:
+ _, ax = plt.subplots()
+
+ dim = data.num_clients + 1
+ x_coords = np.array([data.client(client).x for client in range(dim)])
+ y_coords = np.array([data.client(client).y for client in range(dim)])
+
+ # This is the depot
+ kwargs = dict(c="tab:red", marker="*", zorder=3, s=500)
+ ax.scatter(x_coords[0], y_coords[0], label="Depot", **kwargs)
+
+ for idx, route in enumerate(solution["routes"], 1):
+ x = x_coords[route]
+ y = y_coords[route]
+
+ # Coordinates of clients served by this route.
+ ax.scatter(x, y, label=f"Route {idx}", zorder=3, s=75)
+ ax.plot(x, y)
+
+ # Edges from and to the depot, very thinly dashed.
+ kwargs = dict(ls=(0, (5, 15)), linewidth=0.25, color="grey")
+ ax.plot([x_coords[0], x[0]], [y_coords[0], y[0]], **kwargs)
+ ax.plot([x[-1], x_coords[0]], [y[-1], y_coords[0]], **kwargs)
+
+ ax.grid(color="grey", linestyle="solid", linewidth=0.2)
+
+ ax.set_title(f"Solution - {dataset} - {algo} - {solution['cost']:.1f}")
+ ax.set_aspect("equal", "datalim")
+ ax.legend(frameon=False, ncol=2)

requirements.txt

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+ortools==9.8.3296
+pandas==2.1.3
+pydantic==2.5.1
+pyvrp==0.6.3
+tabulate==0.9.0