考虑具有不同交付选择的物流配送路径问题研究

为了满足城市配送中顾客对交付方式及特定时间的个性化需求,引入顾客偏好概念刻画送货上门与自提服务的交付需求,以总运营成本最小化为优化目标建立了具有不同交付选择的车辆路径优化模型。考虑到模型的复杂性引入多种算子,设计并改进自适应大邻域搜索算法对模型进行求解。最后结合重庆市南岸区某配送案例进行实例分析,验证了该模型与算法的有效性。     

基于小生境遗传算法的物流配送路径优化研究

通过对物流车辆配送过程的分析,建立了带时间窗约束的物流配送路径优化问题的数学模型。针对遗传算法具有早熟的缺点,将小生境技术引入遗传算法,构建小生境遗传算法。最后,将小生境遗传算法应用于所建立的物流配送路径优化模型的求解,实验结果表明小生境遗传算法在一定程度上可以避免标准遗传算法早熟现象的发生,提高了其求解物流配送路径优化问题的效率。     

考虑供应不足的应急物流车辆路径优化模型及算法

灾害发生后的关键救援期内,应急物资有限且受灾点对应急物资的需求具有不确定性,为提高应急物流工作效率,需同时对应急资源分配和运输车辆路径进行优化决策。针对救援关键期内应急物资可能供应不足的情况,在假设物资需求为随机其服从正态分布的前提下,以最小化供应不足和供应过量所带来的损失、运输成本和车辆使用成本等为优化目标,考虑服务时间窗和车辆装载能力等约束,建立了随机需求环境下应急物流车辆路径问题的优化模型,并基于遗传算法设计了模型的求解方法。算例分析表明,本文所提出的优化方法运算快捷且结果合理,可为相关决策者提供科学的决策依据。     

考虑时效性和品质性满意度约束的易腐品冷链物流配送

针对易腐品冷链配送环节存在的成本高、碳排放量大、客户满意度低等问题,从易腐品配送的时效性和品质性两方面度量客户满意度,并以此为约束考虑配送过程中的固定成本、运输成本、货损成本、制冷成本、惩罚成本以及碳排放成本,构建以总成本最小为目标的易腐品冷链配送车辆路径优化模型,设计改进遗传算法求解优化模型,分析求解算法的复杂度.数值实验结果表明,所设计的求解算法总能获得总成本更低、产品新鲜度更高以及碳排放量更少的配送方案,同时表明改进的遗传算法相比于传统遗传算法在成本节约以及客户满意度提高方面具有一定优势,在一定程度上验证了所建模型的合理性及求解算法的有效性.     

时间窗约束下农产品物流配送路径优化研究

农产品需求量增加对物流配送提出较高挑战,基于此提出时间窗约束下农产品物流配送路径优化方法研究。依客户预期服务时间需求,取混合时间窗约束函数确定时间窗、物流配送车辆最大载重、配送路径长度与物流配送车辆约束条件,构建农产品物流配送路径优化模型;基于农产品物流配送需求改进传统遗传算法,求解构建模型,即获农产品物流配送路径优化结果。实验结果显示：相较生鲜农产品多车型冷链物流车辆路径优化,所提方法最优农产品物流配送路径获取迭代次数更少、配送路径长度更短、总成本更低,应用性能更佳。     

配送选址-多车型运输路径优化问题及求解算法

考虑到不同车型、车辆容量、时间窗等约束,研究了配送选址-多车型运输路径优化问题,采用分解法进行问题分析,建立数学模型.首先应用改进聚类分析模型确定配送中心的最佳位置与服务客户群,然后设计遗传算法进行求解.算法比较及算例测试表明它是求解选址-多车型运输路径优化问题的一种有效方法.     

一类非确定性车辆路径问题模型及其算法设计

提出一类路网结构未定、需求随机的非确定性车辆路径问题(N-DVRP),通过分析路网结构变动和需求随机双重不确定性对车辆路径选择的影响,建立N-DVRP的优化模型,并设计求解该问题的动态加速自适应遗传算法.仿真实验结果验证了该问题模型及其求解算法的合理性和有效性.     

模糊时间窗VRP的动态规划和禁忌搜索混合算法

为优化具有模糊时间窗的车辆路径问题,以物流配送成本和顾客平均满意度为目标,建立了多目标数学规划模型。基于Pareto占优的理论给出了求解多目标优化问题的并行多目标禁忌搜索算法,算法中嵌入同时优化顾客满意度的动态规划方法,运用阶段划分,把原问题分解为关于紧路径的优化子问题。对模糊时间窗为线性分段函数形式和非线性凹函数形式的隶属度函数,分别提出了次梯度有限迭代算法和次梯度中值迭代算法来优化顾客的最优开始服务时间。通过Solomon的标准算例,与次梯度投影算法的比较验证了动态规划方法优化服务水平的有效性,与主流的NSGA-II算法的对比实验表明了该研究提出的多目标禁忌搜索算法的优越性。     

考虑随机旅行时间与二维装载约束的越库配送车辆路径优化

面向越库配送模式下二维装载和车辆路径联合优化,考虑现实配送过程的不确定性因素,提出考虑随机旅行时间和二维装载约束的越库配送车辆路径问题.基于蒙特卡洛模拟与场景分析方法,建立以运输成本、车辆固定成本以及时间窗期望惩罚成本之和最小化为目标的带修正随机规划模型.继而根据问题特征,设计改进的自适应禁忌搜索算法和基于禁忌搜索的多重排序最佳适应装箱算法进行求解.其中,改进的自适应禁忌搜索算法在禁忌搜索算法的基础上引入自适应机制,对不同邻域算子进行动态选择,并提出基于移除-修复策略的多样性机制以增强算法的寻优能力.数值实验表明,所提出的模型与方法能够有效求解考虑随机旅行时间和二维装载约束的越库配送车辆路径问题,自适应与多样性机制能一定程度上增强算法的全局搜索能力.     

垃圾分类收运模式下车辆路径问题建模与仿真

建立与垃圾种类相匹配的垃圾分类收运体系是确保垃圾分类制度有效实施的重要一步.生活垃圾分类收运问题属于车辆路径问题范畴.在垃圾分类收运模式下,考虑垃圾种类-车辆类型匹配关键因素,研究多种类垃圾多车型车辆路径优化问题.以车辆启动成本、运输成本之和最小化为目标,建立混合整数规划模型,利用遗传算法予以求解.通过算例仿真验证了模型及算法的有效性,在践行垃圾分类制度上具有实际应用价值.     

用于求解混合车辆路径问题的混合进化算法

文中研究了具有NP难度的混合车辆路径问题(Mixed Capacitated General Routing Problem,MCGRP),其是在基本车辆路径问题(Vehicle Routing Problem,VRP)的基础上通过添加限载容量约束及弧上的用户需求而衍生的。给定一列车辆数不限的车队,使车辆从站点出发向用户提供服务,服务完用户需求后仍返回站点;规定每辆车的总载重不能超过其载重量,且每个需求只能被一辆车服务且仅服务一次。MCGRP旨在求解每辆车的服务路线,使得在满足以上约束条件的情况下所有车辆的旅行消耗之和最小。混合车辆路径问题具有较高的理论价值和实际应用价值,针对该问题提出了一种高效的混合进化算法。该算法采用基于5种邻域算符的变邻域禁忌搜索来提高解的质量,并通过一种基于路径的交叉算符来继承解的优异性,从而有效地加速算法的收敛。在一组共计23个经典算例上的实验结果表明,该混合进化算法在求解混合车辆路径问题时是非常高效的。     

Artificial intelligence heuristics in solving vehicle routing problems with time window constraints

This paper describes the authors’ research on various heuristics in solving vehicle routing problem with time window constraints (VRPTW) to near optimal solutions. VRPTW is NP-hard problem and best solved to near optimum by heuristics. In the vehicle routing problem, a set of geographically dispersed customers with known demands and predefined time windows are to be served by a fleet of vehicles with limited capacity. The optimized routines for each vehicle are scheduled as to achieve the minimal total cost without violating the capacity and time window constraints. In this paper, we explore different hybridizations of artificial intelligence based techniques including simulated annealing, tabu search and genetic algorithm for better performance in VRPTW. All the implemented hybrid heuristics are applied to solve the Solomon's 56 VRPTW with 100-customer instances, and yield 23 solutions competitive to the best solutions published in literature according to the authors’ best knowledge.     

Discrete firefly algorithm with compound neighborhoods for asymmetric multi-depot vehicle routing problem in the maintenance of farm machinery

We introduce a new variant of the vehicle routing problem, that is, the asymmetric multi-depot vehicle routing problem in the maintenance of farm machinery. When providing door-to-door service for farm machinery maintenance, there exists not only node service, (e.g., part replacement), but also directed arc service, (e.g., pulling the breakdown farm machinery from the farm location to the specified maintenance station). In the problem, there are multiple constraints, including the customer’s time window, maximum repairman working duration, fleet size, and vehicle capacity, etc. A mathematical programming model is formulated with the minimum total costs by transforming the problem into the asymmetric multi-depot vehicle routing problem with time windows. Discrete firefly algorithm with compound neighborhoods, presenting new neighborhood methods, is proposed to solve it. New procedures to evaluate the duration infeasibility are suggested with the reduced additional computational complexity. Computational results demonstrate that the proposed approach performs better than CPLEX solver, especially for large designed instances. Moreover, the proposed approach is superior to the other algorithms on solving benchmark instances of multi-depot vehicle routing problem with time windows. This study can provide decision support to door-to-door service for the maintenance of farm machinery.     

The multi-shift vehicle routing problem with overtime

In this paper, we study a new variant of the vehicle routing problem (VRP) with time windows, multi-shift, and overtime. In this problem, a limited fleet of vehicles is used repeatedly to serve demand over a planning horizon of several days. The vehicles usually take long trips and there are significant demands near shift changes. The problem is inspired by a routing problem in healthcare, where the vehicles continuously operate in shifts, and overtime is allowed. We study whether the tradeoff between overtime and other operational costs such as travel cost, regular driver usage, and cost of unmet demands can lead to a more efficient solution. We develop a shift dependent (SD) heuristic that takes overtime into account when constructing routes. We show that the SD algorithm has significant savings in total cost as well as the number of vehicles over constructing the routes independently in each shift, in particular when demands are clustered or non-uniform. Lower bounds are obtained by solving the LP relaxation of the MIP model with specialized cuts. The solution of the SD algorithm on the test problems is within 1.09–1.82 times the optimal solution depending on the time window width, with the smaller time windows providing the tighter bounds.     

A planning model and solution algorithm for multi-trip split-delivery vehicle routing and scheduling problems with time windows

This study proposes a daily vehicle routing model for minimizing the total cost of replenishing inventory within a supply chain. The first major contribution of this research is to allow multiple use of vehicles in a split delivery vehicle routing problem with time windows (SDVRPTW), which is more realistic for various real-life applications. The multi-trip SDVRPTW (MTSDVRPTW) is formulated using the time–space network technique, which provides greater flexibility for formulating the complicated interactions between vehicles and products when multi-trip, split delivery, and delivery time windows are simultaneously considered. The resulting formulation of the MTSDVRPTW can be categorized as an integer multi-commodity network flow problem with side constraints. A two-step solution algorithm is proposed to solve this NP-hard problem, which is the second major contribution of this research. Finally, a real-world scale numerical example is performed to demonstrate and to test the methodology. The results indicate that these vehicle routing problems can be solved effectively and efficiently and that the proposed methodology has great potential for inventory replenishment scheduling where split deliveries and multiple trips for a single vehicle are allowed and time window constraints are imposed.     

A multilevel approach for modelling vehicle routing problem with uncertain travelling time

Vehicle routing problem is concerned with finding optimal collection or delivery routes in a transportation network, beginning and ending at a central depot, for a fleet of vehicles to serve a set of customers under some constraints. Assuming the travel times between two customers are uncertain variables, this paper proposes an uncertain multilevel programming model for a vehicle routing problem, of which the leader’s object is to minimize the total waiting times of the customers, and the followers’ objects are to minimize the waiting times of the vehicles for the beginning moments of the customers’ time windows. The uncertain multilevel programming model is transformed into a determinacy programming model, and an intelligent algorithm is designed for solving the crisp model.     

A branch-and-price-and-check model for the vehicle routing problem with location congestion

This paper considers a vehicle routing problem with pickup and delivery, time windows and location congestion. Locations provide a number of cumulative resources that are utilized by vehicles either during service (e.g., forklifts) or for the entirety of their visit (e.g., parking bays). Locations can become congested if insufficient resources are available, upon which vehicles must wait until a resource becomes available before proceeding. The problem is challenging from a computational standpoint since it incorporates the vehicle routing problem and the resource-constrained project scheduling problem. The main contribution of this paper is a branch-and-price-and-check model that uses a branch-and-price algorithm that solves the underlying vehicle routing problem, and a constraint programming subproblem that checks the feasibility of the location resource constraints, and then adds combinatorial nogood cuts to the master problem if the resource constraints are violated. Experimental results show the benefits of the branch-and-price-and-check approach.     

An ACO hybrid metaheuristic for close–open vehicle routing problems with time windows and fuzzy constraints

The close–open vehicle routing problem is a realistic variant of the “classical” vehicle routing problem where the routes can be opened and closed, i.e. all the vehicles are not required to return to the depot after completing their service. This variant is a planning model that is a standard practice in business nowadays. Companies are contracting their deliveries to other companies that hire vehicles, and payment is made based on the distance covered by the vehicles. Available information on parameters in real world situations is also imprecise, and must be included in the optimization model and method. The aims of this paper are to formulate a model of this novel variant with time windows and imprecise constraints and to propose a fuzzy optimization approach and a hybrid metaheuristic for its solutions. The full proposal is applied to a real route planning problem with outsourcing, obtaining promising practical results. Customer demands and travel times are imprecise, thus capacity and time windows constraints are considered flexible and modelled as fuzzy constraints.     

A multi-parametric evolution strategies algorithm for vehicle routing problems

Vehicle routing problems are at the heart of most decision support systems for real-life distribution problems. In vehicle routing problem a set of routes must be determined at lowest total cost for a number of resources (i.e. fleet of vehicles) located at one or several points (e.g. depots, warehouses) in order to efficiently service a number of demand or supply points. In this paper an efficient evolution strategies algorithm is developed for both capacitated vehicle routing problem and for vehicle routing problem with time window constraints. The algorithm is based on a new multi-parametric mutation procedure that is applied within the 1 + 1 evolution strategies algorithm. Computational testing on six real-life problems and 195 benchmark problems demonstrate that the suggested algorithm is efficient and highly competitive, improving or matching the current best-known solution in 42% of the test cases.     

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.