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Heuristics based ant colony optimization for vehicle routing problem

机译:基于启发式的蚁群算法求解车辆路径问题

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Vehicle routing problem is a combinatorial optimization problem and is known as NP-complete. Among many proposed schemes, meta-heuristic algorithms are prospective for solving NP problems. Hence, a modified ant colony optimization (ACO) is proposed to solve a type of vehicle routing problem named capacitated vehicle routing problem (CVRP) which involves minimization of the total routing distance by each vehicle plus the total services time on the customer nodes. The studied CVRP is treated as a two phase problem; customer/vehicle assignment and makespan minimization phases. The modified ACO involving a semi-greedy heuristic in state transition rule is used in the clustering phase. Meanwhile, a NEH heuristic is conducted to update the visiting customers' order of each vehicle in the makespan minimization phase. The experimental results indicate that the proposed scheme is sound for solving the CVRP type problems.
机译:车辆路径问题是组合优化问题,被称为NP完全问题。在许多提议的方案中,元启发式算法有望解决NP问题。因此,提出了一种改进的蚁群优化算法(ACO),以解决一种称为“容量化车辆路径问题”(CVRP)的车辆路径问题,该问题涉及使每辆车辆的总路径距离最小化,并将客户节点上的总服务时间最小化。所研究的CVRP被视为两阶段问题。客户/车辆分配和最小化制造周期。在聚类阶段使用了涉及状态转换规则中的半贪婪启发式的修改后的ACO。同时,在使制造期最小化阶段,进行NEH启发式更新每辆车的拜访客户的订单。实验结果表明,该方案对于解决CVRP类型问题是合理的。

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