多车场CARP问题的改进遗传算法求解

带有容量限制的弧路径规划问题来源于城市垃圾回收、街道清扫、邮件投递、校车路线安排和洒水车路线安排等实际问题,多车场CARP问题是具有多个车场的CARP问题。提出了一种先划分区域后进行路径规划的方法来求解多车场CARP问题。该方法先将各服务弧按照离车场距离的远近归并到距离最近的车场,从而转化为单车场CARP问题,然后用改进的遗传算法进行求解;在求解过程中,用模拟退火算法对部分服务弧进行局部调整,使服务弧在一定的范围内在不同的车场之间进行调换,从而避免局部收敛,达到全局优化的效果。以洒水车路线安排为实例,实验结果表明,该算法能有效求解一定规模的多车场CARP问题,为实际应用奠定了基础。     

一种求解多车型CARP问题的高效进化算法

对传统遗传算法的染色体编码机制和种群结构进行了改进,并借鉴单亲遗传算法和Memetic Algorithm（MA）算法的优秀思想,设计了一种解决CARP（Capacitated Arc Routing Problem）问题的高效算法HEGA。新算法不但有效解决了使用现有算法无力解决的多车型CARP问题,并且应用于一般的单车型CARP问题在求解效率和求解精度上也比现有MA算法效果更好。结合洒水车路径优化问题,通过一组真实的数据集合对文中算法在该问题上的求解能力做出评测。     

多时间窗车辆路径问题的混合蚁群算法

研究了多时间窗车辆路径问题,建立了多时间窗车辆路径问题的数学模型,并基于蚁群算法设计了一种混合蚁群算法对问题进行了求解。该算法首先利用基本蚁群算法求解,然后采用2-opt算法和元胞自动算法对结果进行优化,同时加入变异算子。实验结果表明该算法可以有效地求解多时间窗车辆路径问题。     

求解VRP问题的一种基于交叉算子的改进型遗传算法

采用基于自然数编码染色体、改进型交叉算子并增加内外扰动策略,构造出一种改进型遗传算法。详细介绍了此算法的基本原理,并进行了代表性算例实验与结果分析。实验表明,该算法收敛速度快,有效地遏制了早熟收敛,防止了进化过程中最优解的退化,改善了遗传算法的性能,提高了算法优化效率,是求解车辆路径问题的一种有效算法。     

基于粒子群算法的非线性方程组求解

将非线性方程组的求解问题转化为无约束极大极小优化问题,并应用一种新的进化计算（EC）方法——粒子群算法（PSO）求解此优化问题。数值实验的结果验证了该方法的可行性和有效性。     

车辆路径问题的改进的双种群遗传算法

提出了一种基于车辆路径问题的改进双种群遗传算法.该改进双种群遗传算法主要通过两个种群同时进行进化操作,并结合新交叉算子和种群交叉策略,以克服传统双种群遗传算法在求解车辆路径问题上所存在的不足.通过仿真实验,将改进的双种群遗传算法与其它几种遗传算法进行比较,改进的双种群遗传算法比其它几种遗传算法显著提高了优化效果.实验结果表明,该算法可以有效求得该问题的优化解,是解决车辆路径问题的好方法.     

求解CVRP问题的混合遗传微粒群算法

采用借鉴遗传算法的编码、交叉和变异操作的遗传微粒群算法对带车辆能力约束的车辆路径优化问题进行求解。设计了符合微粒群算法进化机制的变异算子和改进顺序交叉算子以满足遗传微粒群算法中三条染色体交叉与变异的需要。对多个基准测试实例仿真计算表明算法有效且具有收敛速度快和精度高的优点。     

基于Memetic算法的带时间窗车辆路径问题研究*

提出一种模拟文化进化的Memetic算法求解带时间窗的车辆路径问题。设计了一种实数编码方案,将离散的问题转为连续优化问题。采用邻域搜索帮助具备一定学习能力的个体提高寻优速度;采用禁忌搜索帮助部分个体跳出局部最优点,增强全局寻优性能。实验结果表明,该算法可以更有效地求出优化解,是带时间窗车辆路径问题的一种有效求解算法。     

洒水车作业路线规划的复杂CARP问题求解

作为CARP(Capacitated Arc Routing Problem)的一种实际应用,洒水车路线规划涉及多车型、多路型、转弯限制等因素,属于复杂的CARP问题。对此,本文提出了一种HEGA(High Efficient Genetic Algorithm)算法,以传统遗传算法为基础,对种群机制和染色体结构及交叉操作进行改进,并引入Local Search操作增强算法的寻优能力。根据真实数据进行实验,获得了比人工更优的解决方案;最后的对比实验验证了算法具有很不错的求解精度。     

一种基于基因库求解TSP的单亲遗传算法

研究商品流通路线问题,TSP是组合优化问题的典型代表。针对TSP问题提出了一种改进的遗传算法。以引入＂基因库＂为基础,为了寻找出最优路径,提出一种只使用变异算子和选择算子繁殖后代的单亲遗传算法（PGA）,并设计了一种新的组合算子作为算法的主搜索算子。算法利用基因库指导单亲遗传演化的进化方向,利用设计的组合算子来增强算法的搜索能力,从而很好地仿真了自然界的进化过程。计算结果证明,基因库的PGA算法具有较高的求解质量和求解效率,尤其是在求解Lin318 TSP问题时获得了优于目前最好解最短路径,可为设计提供有效的参考。     

A Global Repair Operator for Capacitated Arc Routing Problem

Capacitated arc routing problem (CARP) has attracted much attention during the last few years due to its wide applications in real life. Since CARP is NP-hard and exact methods are only applicable for small instances, heuristics and metaheuristic methods are widely adopted when solving CARP. This paper demonstrates one major disadvantage encountered by traditional search algorithms and proposes a novel operator named global repair operator (GRO) to address it. We further embed GRO in a recently proposed tabu search algorithm (TSA) and apply the resultant repair-based tabu search (RTS) algorithm to five well-known benchmark test sets. Empirical results suggest that RTS not only outperforms TSA in terms of quality of solutions but also converges to the solutions faster. Moreover, RTS is also competitive with a number of state-of-the-art approaches for CARP. The efficacy of GRO is thereby justified. More importantly, since GRO is not specifically designed for the referred TSA, it might be a potential tool for improving any existing method that adopts the same solution representation.     

Memetic Algorithm With Extended Neighborhood Search for Capacitated Arc Routing Problems

The capacitated arc routing problem (CARP) has attracted much attention during the last few years due to its wide applications in real life. Since CARP is NP-hard and exact methods are only applicable to small instances, heuristic and metaheuristic methods are widely adopted when solving CARP. In this paper, we propose a memetic algorithm, namely memetic algorithm with extended neighborhood search (MAENS), for CARP. MAENS is distinct from existing approaches in the utilization of a novel local search operator, namely Merge-Split (MS). The MS operator is capable of searching using large step sizes, and thus has the potential to search the solution space more efficiently and is less likely to be trapped in local optima. Experimental results show that MAENS is superior to a number of state-of-the-art algorithms, and the advanced performance of MAENS is mainly due to the MS operator. The application of the MS operator is not limited to MAENS. It can be easily generalized to other approaches.     

Design of an improved quantum-inspired evolutionary algorithm for a transportation problem in logistics systems

Logistics faces great challenges in vehicle schedule problem. Intelligence Technologies need to be developed for solving the transportation problem. This paper proposes an improved Quantum-Inspired Evolutionary Algorithm (IQEA), which is a hybrid algorithm of Quantum-Inspired Evolutionary Algorithm (QEA) and greed heuristics. It extends the standard QEA by combining its principles with some heuristics methods. The proposed algorithm has also been applied to optimize a problem which may happen in real life. The problem can be categorized as a vehicle routing problem with time windows (VRPTW), which means the problem has many common characteristics that VRPTW has, but more constraints need to be considered. The basic idea of the proposed IQEA is to embed a greed heuristic method into the standard QEA for the optimal recombination of consignment subsequences. The consignment sequence is the order to arrange the vehicles for the transportation of the consignments. The consignment subsequences are generated by cutting the whole consignment sequence according to the values of quantum bits. The computational result of the simulation problem shows that IQEA is feasible in achieving a relatively optimal solution. The implementation of an optimized schedule can save much more cost than the initial schedule. It provides a promising, innovative approach for solving VRPTW and improves QEA for solving complexity problems with a number of constraints.     

A lower bound for the Node,Edge, and Arc Routing Problem

The Node, Edge, and Arc Routing Problem (NEARP) was defined by Prins and Bouchenoua in 2004, although similar problems have been studied before. This problem, also called the Mixed Capacitated General Routing Problem (MCGRP), generalizes the classical Capacitated Vehicle Routing Problem (CVRP), the Capacitated Arc Routing Problem (CARP), and the General Routing Problem. It captures important aspects of real-life routing problems that were not adequately modeled in previous Vehicle Routing Problem (VRP) variants. The authors also proposed a memetic algorithm procedure and defined a set of test instances called the CBMix benchmark. The NEARP definition and investigation contribute to the development of rich VRPs. In this paper we present the first lower bound procedure for the NEARP. It is a further development of lower bounds for the CARP. We also define two novel sets of test instances to complement the CBMix benchmark. The first is based on well-known CARP instances; the second consists of real life cases of newspaper delivery routing. We provide numerical results in the form of lower and best known upper bounds for all instances of all three benchmarks. For three of the instances, the gap between the upper and lower bound is closed. The average gap is 25.1%. As the lower bound procedure is based on a high quality lower bound procedure for the CARP, and there has been limited work on approximate solution methods for the NEARP, we suspect that a main reason for the rather large gaps is the quality of the upper bound. This fact, and the high industrial relevance of the NEARP, should motivate more research on approximate and exact methods for this important problem.     

A new and efficient ant-based heuristic method for solving the traveling salesman problem

Abstract: In this paper, we present an efficient metaheuristic approach for solving the problem of the traveling salesman. We introduce the multiple ant clans concept from parallel genetic algorithms to search solution space using different islands to avoid local minima in order to obtain a global minimum for solving the traveling salesman problem. Our simulation results indicate that the proposed novel traveling salesman problem method (called the ACOMAC algorithm) performs better than a promising approach named the ant colony system. This investigation is concerned with a real life logistics system design which optimizes the performance of a logistics system subject to a required service level in the vehicle routing problem. In this work, we also concentrate on developing a vehicle routing model by improving the ant colony system and using the multiple ant clans concept. The simulation results reveal that the proposed method is very effective and potentially useful in solving vehicle routing problems.     

Approximate solutions for the capacitated arc routing problem

The capacitated arc routing problem (CARP), is a capacitated variation of the arc routing problems in which there is a capacity constraint associated with each vehicle. Due to the computational complexity of the problem, recent research has focussed on developing and testing heuristic algorithms which solve the CARP approximately. In this paper, we review some of the existing solution procedures, analyze their complexity, and present two modifications of the existing methods to obtain near-optimal solutions for the CARP. Extensive computational results are presented and analyzed.     

Exploiting sparsity in pricing routines for the capacitated arc routing problem

The capacitated arc routing problem (CARP) is a well-known and fundamental vehicle routing problem. A promising exact solution approach to the CARP is to model it as a set covering problem and solve it via branch-cut-and-price. The bottleneck in this approach is the pricing (column generation) routine. In this paper, we note that most CARP instances arising in practical applications are defined on sparse graphs. We show how to exploit this sparsity to yield faster pricing routines. Extensive computational results are given.     

Heuristics for the periodic capacitated arc routing problem

The Capacitated Arc Routing Problem (CARP) involves the routing of vehicles to service a set of arcs in a network. This NP-hard problem is extended to a multiperiod horizon, giving a new tactical problem called the Periodic CARP (PCARP). This problem actually occurs in municipal waste collection. Its objective is to assign arcs to periods and to compute the trips in each period, to minimize an overall cost on the horizon. An integer linear program, two insertion heuristics and a two-phase heuristic are developed. These very first algorithms for the PCARP are evaluated on PCARP instances derived from standard CARP benchmarks from literature: the insertion heuristics are very fast but the two-phase method yields better solution costs.