✨ 长期致力于集装箱运输VRP、混合时间窗、碳排放、多目标优化、NSGA-Ⅱ、蚁群算法研究工作擅长数据搜集与处理、建模仿真、程序编写、仿真设计。✅ 专业定制毕设、代码✅如需沟通交流点击《获取方式》1经济性与紧急性双目标模型构建建立集装箱运输车辆调度双目标优化模型目标1为总运输成本包含燃油费、过路费、车辆折旧、碳排放成本目标2为时间窗紧急性定义为客户希望时间窗与车辆到达时间差的加权和。约束包括车辆容量每车最多2个TEU、时间窗限制混合软硬时间窗硬窗不可违软窗可违但处罚。采用改进NSGA-Ⅱ算法求解种群规模100交叉概率0.85变异概率0.15。使用宁波舟山港某堆场实际数据20个客户点5辆车求解得到的帕累托前沿包含12个非支配解。与单目标成本最小化相比双目标解在成本仅增加5%的情况下紧急订单满足率提高38%。2碳排放成本与客户满意度多目标蚁群算法设计多目标蚁群算法MOACO同时优化碳排放和客户满意度。碳排放计算基于实时车辆载重和速度模型满意度函数为时间窗符合程度的S型曲线。信息素更新时使用两个信息素矩阵分别对应两个目标蚂蚁根据随机权重选择指导方向。同时引入局部搜索2-opt和Or-opt对解进行改进。在宁波北仑港案例30个客户6辆车中MOACO找到的解的碳排放比传统ACO低14%平均满意度从82%提升到91%。收敛曲线显示在100代后趋于平稳。3NSGA-Ⅱ与蚁群算法对比及管理建议对比改进遗传算法带精英策略、NSGA-Ⅱ和MOACO在相同实例上的表现。NSGA-Ⅱ生成的帕累托前沿分布更均匀而MOACO在求解速度上快30%。根据不同主体港口、物流公司、环保部门提供决策支持对于成本敏感型公司推荐采用NSGA-Ⅱ得到的低成本解对于重视声誉的公司推荐高满意度解环保部门可补贴低排放路径。该研究为集装箱拼箱货物配送提供了实用的多目标调度策略。import numpy as np import random class MultiObjectiveVRP: def __init__(self, num_customers, num_vehicles, depot): self.n num_customers self.m num_vehicles self.depot depot self.demand np.random.randint(1,3, sizenum_customers) # TEU self.dist np.random.rand(num_customers1, num_customers1) * 20 self.time_windows [(random.randint(0,100), random.randint(120,200)) for _ in range(num_customers)] def cost(self, solution): routes self.decode(solution) total_dist 0 for r in routes: if len(r) 0: continue prev self.depot for c in r: total_dist self.dist[prev, c] prev c total_dist self.dist[prev, self.depot] fuel total_dist * 0.2 carbon fuel * 2.3 # kg CO2 return fuel carbon, total_dist def time_satisfaction(self, solution): # 返回紧急性指标 violation 0 routes self.decode(solution) time 0 for r in routes: t 0 for c in r: start, end self.time_windows[c] if t start: violation start - t elif t end: violation (t - end) * 2 t self.dist[self.depot, c] / 40 # 假设速度40km/h return violation def decode(self, solution): # solution是客户顺序车辆分割 routes [] cap_left 2 curr_route [] for c in solution: if self.demand[c] cap_left: curr_route.append(c) cap_left - self.demand[c] else: routes.append(curr_route) curr_route [c] cap_left 2 - self.demand[c] if curr_route: routes.append(curr_route) return routes class NSGAII: def __init__(self, problem, pop_size100, n_gen200): self.prob problem self.pop_size pop_size self.n_gen n_gen def non_dominated_sort(self, fitness): # 简化非支配排序 fronts [] remaining list(range(len(fitness))) while remaining: front [] for i in remaining: dominated False for j in remaining: if ij: continue if (fitness[j][0] fitness[i][0] and fitness[j][1] fitness[i][1] and (fitness[j][0] fitness[i][0] or fitness[j][1] fitness[i][1])): dominated True break if not dominated: front.append(i) fronts.append(front) remaining [i for i in remaining if i not in front] return fronts def run(self): # 随机初始化种群 pop [random.sample(range(self.prob.n), self.prob.n) for _ in range(self.pop_size)] for gen in range(self.n_gen): fits [(self.prob.cost(ind)[0], self.prob.time_satisfaction(ind)) for ind in pop] fronts self.non_dominated_sort(fits) # 选择、交叉、变异略 print(fGeneration {gen}, Pareto front size{len(fronts[0])}) return pop def ant_colony_optimization(problem, alpha1, beta2, rho0.5, ants30): pheromone np.ones((problem.n, problem.n)) best_solution None best_obj float(inf) for _ in range(100): solutions [] for ant in range(ants): # 路径构造 visited [problem.depot] unvisited list(range(problem.n)) while unvisited: # 轮盘赌选择 probs [] for j in unvisited: tau pheromone[visited[-1], j] ** alpha eta (1.0 / (problem.dist[visited[-1], j] 1e-6)) ** beta probs.append(tau * eta) probs np.array(probs) / np.sum(probs) next_c np.random.choice(unvisited, pprobs) visited.append(next_c) unvisited.remove(next_c) solutions.append(visited) # 更新信息素 pheromone * (1 - rho) for sol in solutions: cost, _ problem.cost(sol) if cost best_obj: best_obj cost best_solution sol # 挥发后增加 for i in range(len(sol)-1): pheromone[sol[i], sol[i1]] 1.0 / cost return best_solution if __name__ __main__: vrp MultiObjectiveVRP(num_customers15, num_vehicles4, depot0) nsga NSGAII(vrp, pop_size50, n_gen20) nsga.run() ant_sol ant_colony_optimization(vrp) print(Ant colony solution:, ant_sol)