时间窗约束下的配送车辆调度问题研究

为解决时间窗约束下的物流配送车辆的多目标调度优化问题,给出了一种基于免疫计算的配送车辆调度优化方案。设计了配送车辆调度问题的数学模型和一种基于非劣邻域支配的多目标调度优化算法,在仿真环境下进行了实验。实验结果表明,算法能够有效地解决物流配送车辆调度问题,具有较好的应用价值。     

基于多目标遗传算法求解时间窗车辆路径问题

有别于传统的单目标方法,将带时间窗约束的车辆路径问题描述成为一个多目标最优化问题,并为之提出了一种多目标遗传算法。在算法中设计了擂台法则作为构造非支配集的方法,提出了可变爬山率的局部爬山法,并通过将组合种群分成多层非支配集来实现精英保留策略。实验结果表明,该算法能有效地求解车辆路径问题并且为决策者提供了强有力的决策支持。     

A Simulated Annealing-based parallel multi-objective approach to vehicle routing problems with time windows

The Capacitated Vehicle Routing Problem with Time Windows (VRPTW) consists in determining the routes of a given number of vehicles with identical capacity stationed at a central depot which are used to supply the demands of a set of customers within certain time windows. This is a complex multi-constrained problem with industrial, economic, and environmental implications that has been widely analyzed in the past. This paper deals with a multi-objective variant of the VRPTW that simultaneously minimizes the travelled distance and the imbalance of the routes. This imbalance is analyzed from two perspectives: the imbalance in the distances travelled by the vehicles, and the imbalance in the loads delivered by them. A multi-objective procedure based on Simulated Annealing, the Multiple Temperature Pareto Simulated Annealing (MT-PSA), is proposed in this paper to cope with these multi-objective formulations of the VRPTW. The procedure MT-PSA and an island-based parallel version of MT-PSA have been evaluated and compared with, respectively, sequential and island-based parallel implementations of SPEA2. Computational results obtained on Solomon’s benchmark problems show that the island-based parallelization produces Pareto-fronts of higher quality that those obtained by the sequential versions without increasing the computational cost, while also producing significant reduction in the runtimes while maintaining solution quality. More specifically, for the most part, our procedure MT-PSA outperforms SPEA2 in the benchmarks here considered, with respect to the solution quality and execution time.     

A hybrid meta-heuristic for multi-objective vehicle routing problems with time windows

The Capacitated Vehicle Routing Problem with Time Windows is an important combinatorial optimization problem consisting in the determination of the set of routes of minimum distance to deliver goods, using a fleet of identical vehicles with restricted capacity, so that vehicles must visit customers within a time frame. A large number of algorithms have been proposed to solve single-objective formulations of this problem, including meta-heuristic approaches, which provide high quality solutions in reasonable runtimes. Nevertheless, in recent years some authors have analyzed multi-objective variants that consider additional objectives to the distance travelled. This paper considers not only the minimum distance required to deliver goods, but also the workload imbalance in terms of the distances travelled by the used vehicles and their loads. Thus, MMOEASA, a Pareto-based hybrid algorithm that combines evolutionary computation and simulated annealing, is here proposed and analyzed for solving these multi-objective formulations of the VRPTW. The results obtained when solving a subset of Solomon’s benchmark problems show the good performance of this hybrid approach.     

Multi-objective vehicle routing problem with time windows using goal programming and genetic algorithm

This paper presents a new model and solution for multi-objective vehicle routing problem with time windows (VRPTW) using goal programming and genetic algorithm that in which decision maker specifies optimistic aspiration levels to the objectives and deviations from those aspirations are minimized. VRPTW involves the routing of a set of vehicles with limited capacity from a central depot to a set of geographically dispersed customers with known demands and predefined time windows. This paper uses a direct interpretation of the VRPTW as a multi-objective problem where both the total required fleet size and total traveling distance are minimized while capacity and time windows constraints are secured. The present work aims at using a goal programming approach for the formulation of the problem and an adapted efficient genetic algorithm to solve it. In the genetic algorithm various heuristics incorporate local exploitation in the evolutionary search and the concept of Pareto optimality for the multi-objective optimization. Moreover part of initial population is initialized randomly and part is initialized using Push Forward Insertion Heuristic and λ-interchange mechanism. The algorithm is applied to solve the benchmark Solomon's 56 VRPTW 100-customer instances. Results show that the suggested approach is quiet effective, as it provides solutions that are competitive with the best known in the literature.     

带时间窗车辆路径问题的文化基因算法

针对物流配送中带时间窗的车辆路径问题（Vehicle Routing Problem with Time Windows,VRPTW）,建立了数学模型,并设计了求解VRPTW的文化基因算法。种群搜索采用遗传算法的进化模式,局部搜索采用禁忌搜索机制,并结合可行邻域结构避免对不可行解的搜索,以提高搜索效率。与单纯的遗传算法和禁忌搜索算法进行对比实验,表明该算法是求解VRPTW的一种有效方法。     

带时间窗限制的车辆调度子路径平衡性研究

带时间窗限制的车辆路径规划问题(VRPTW)是物流领域中一个很重要的问题.路径平衡性作为该问题域新兴的需求,迫切需要得到更深入的研究.本文基于其国际标准测试用例,设计了一个三阶段启发式算法,与已公布的最佳结果比较,该算法以较小的代价获得了更佳质量的近似解.     

A genetic algorithm for the simultaneous delivery and pickup problems with time window

This paper concerns a Simultaneous Delivery and Pickup Problem with Time Windows (SDPPTW). A mixed binary integer programming model was developed for the problem and was validated. Due to its NP nature, a co-evolution genetic algorithm with variants of the cheapest insertion method was proposed to speed up the solution procedure. Since there were no existing benchmarks, this study generated some test problems which revised from the well-known Solomon’s benchmark for Vehicle Routing Problem with Time Windows (VRPTW). From the comparison with the results of Cplex software and the basic genetic algorithm, the proposed algorithm showed that it can provide better solutions within a comparatively shorter period of time.     

带时间窗车辆路径问题的多目标文化基因算法

针对物流配送中带时间窗的车辆路径问题,以最小化车辆使用数和行驶距离为目标,建立了多目标数学模型,提出了一种求解该问题的多目标文化基因算法。种群搜索采用遗传算法的进化模式和Pareto排序的选择方式,局部搜索采用禁忌搜索机制和存储池的结构,协调两者得到的Pareto非占优解的关系。与不带局部搜索的多目标遗传算法和单目标文化基因算法的对比实验表明,本文算法的求解质量较高。     

新型遗传模拟退火算法求解带VRPTW问题

为了克服现有遗传算法不能有效求解时间窗车辆路径问题的缺陷,提出了一种由遗传算法结合模拟退火算法的混合算法求解该问题,并与遗传算法进行了比较。该算法利用了模拟退火算法具有较强的局部搜索能力的特性,有效地克服了传统遗传算法的“早熟收敛”问题。实验结果表明,该算法具有计算效率高、收敛速度快和求解质量优的特点,是解决车辆路径问题的有效方法。