c题.rar 2023全国大学生数学建模比赛C题代码 打开即用，学习使用

import random from ortools.linear_solver import pywraplp # 船只类型和数量 ships = { '散货船': 30, '集装箱船': 20, '液化气船': 10 } # 码头类型和数量 docks = { '散货码头': {'容量': 4, '停靠时间限制': 8}, '集装箱码头': {'容量': 3, '停靠时间限制': 12}, '液化气码头': {'容量': 2, '停靠时间限制': 16} } # 港口的运营成本和收益 operating_cost = 100000 # 每日运营成本 earnings = { '散货船': 150000, '集装箱船': 200000, '液化气船': 250000 } # 船只的到达和离开时间表 arrival_departure_table = { '散货船': {'到达': 10, '离开': 6}, '集装箱船': {'到达': 6, '离开': 8}, '液化气船': {'到达': 3, '离开': 10} } # 船只之间的优先级 priority_order = ['散货船', '集装箱船', '液化气船'] # 根据船只到达和离开的时间表，生成船只的到达和离开时间 def generate_schedule(ship_type): arrival_time = random.randint(1, arrival_departure_table[ship_type]['到达']) departure_time = arrival_time + random.randint(1, arrival_departure_table[ship_type]['离开']) return arrival_time, departure_time # 计算总等待时间和总延迟时间 def calculate_waiting_delay(schedule, ship_type, dock_type): dock_capacity = docks[dock_type]['容量'] time_limit = docks[dock_type]['停靠时间限制'] arrival_time, departure_time = schedule[ship_type][dock_type] waiting_time = max(0, arrival_time - dock_capacity) delay_time = max(0, departure_time - time_limit) return waiting_time, delay_time # 定义线性规划模型 solver = pywraplp.Solver.CreateSolver('SCIP') # 定义变量 waiting_vars = {} delay_vars = {} load_vars = {} earnings_vars = {} for ship_type in ships: for dock_type in docks: waiting_vars[(ship_type, dock_type)] = solver.IntVar(0, solver.infinity(), 'waiting_{0}_{1}'.format(ship_type, dock_type)) delay_vars[(ship_type, dock_type)] = solver.IntVar(0, solver.infinity(), 'delay_{0}_{1}'.format(ship_type, dock_type)) load_vars[(ship_type, dock_type)] = solver.IntVar(0, solver.infinity(), 'load_{0}_{1}'.format(ship_type, dock_type)) earnings_vars[(ship_type, dock_type)] = solver.BoolVar('earnings_{0}_{1}'.format(ship_type, dock_type)) # 定义约束条件 for ship_type in ships: for dock_type in docks: dock_capacity = docks[dock_type]['容量'] solver.Add(load_vars[(ship_type, dock_type)] <= dock_capacity) solver.Add(load_vars[(ship_type, dock_type)] >= ships[ship_type] - dock_capacity) solver.Add(waiting_vars[(ship_type, dock_type)] >= 0) solver.Add(waiting_vars[(ship_type, dock_type)] >= ships[ship_type] - dock_capacity) solver.Add(delay_vars[(ship_type, dock_type)] >= 0) solver.Add(delay_vars[(ship_type, dock_type)] >= ships[ship_type] - dock_capacity) solver.Add(earnings_vars[(ship_type, dock_type)] * earnings[ship_type] >= operating_cost) # 定义目标函数 objective = solver.Objective() for ship_type in ships: for dock_type in docks: waiting_time_coefficient = 1 # 控制等待时间的权重 delay_time_coefficient = 1 # 控制延迟时间的权重 earnings_coefficient = 1 # 控制收益的权重 objective.SetCoefficient(waiting_vars[(ship_type, dock_type)], waiting_time_coefficient) objective.SetCoefficient(delay_vars[(ship_type, dock_type)], delay_time_coefficient) objective.SetCoefficient(earnings_vars[(ship_type, dock_type)], -earnings_coefficient) objective.SetMinimization() # 求解线性规划模型 status = solver.Solve() # 打印结果 if status == pywraplp.Solver.OPTIMAL: print('总等待时间：', sum(waiting_vars[(ship_type, dock_type)].solution_value() for ship_type in ships for dock_type in docks)) print('总延迟时间：', sum(delay_vars[(ship_type, dock_type)].solution_value() for ship_type in ships for dock_type in docks)) print('总收益：', -objective.Value()) print('每个码头的使用情况：') for dock_type in docks: total_load = sum(load_vars[(ship_type, dock_type)].solution_value() for ship_type in ships) print(dock_type, ':', total_load, '船只') else: print('无法找到最优解') # 打印每个船只的到达和离开时间 print('每个船只的到达和离开时间：') schedule = {} for ship_type in ships: schedule[ship_type] = {} for dock_type in docks: schedule[ship_type][dock_type] = generate_schedule(ship_type) arrival_time, departure_time = schedule[ship_type][dock_type] print(ship_type, dock_type, ':', '到达:', arrival_time, '离开:', departure_time)