混合蝙蝠算法在包装件配送中的应用研究

目的使蝙蝠算法(BA)适应包装件配送车辆路径问题(VRP)的求解,并提高该算法的求解性能。方法在标准BA算法的基础上提出混合蝙蝠算法(HBA)。首先,设计改进的蝙蝠算法(IBA),使其能够适用于包装件配送VRP问题的求解。其次,引入混沌系统,对IBA算法进行混沌初始化。然后,设计裂变算子和变异算子。在IBA算法迭代前半段,将蝙蝠种群中较差的一半蝙蝠重新混沌初始化,以提高种群多样性。在IBA算法迭代后半段,对陷入局部最优解的蝙蝠进行鲶鱼扰动。最后,提出HBA算法并对企业实例进行仿真测试。结果 HBA算法求得的最优配送距离为773.01 km,相对于GA算法(781.25 km)和IBA算法(786.04 km)分别节约了8.24 km和13.03 km。结论与IBA算法和GA算法相比,HBA算法求解包装件配送VRP问题的全局优化能力更强、收敛速度更快。     

包装废弃物回收车辆路径问题的改进遗传算法

目的采用优化传统遗传算法(GA)研究包装废弃物回收车辆路径问题(VRP)的性能。方法提出改进遗传算法(IGA)。首先,设计基于贪婪算法的初始种群生成算子,提高初始种群质量;其次,设计根据适应度值大小、进化代数等自适应调整的交叉和变异概率;然后,设计最大保留交叉算子,保证种群的多样性;最后,对企业实例和标准算例进行仿真测试。结果采用IGA算法、蚁群算法(ACO)能求得算例最优解,且IGA算法运行速度快于ACO算法,分支界定算法(BBM)、传统GA算法无法求得算例最优解。结论与BBM算法、传统GA算法和ACO算法相比,IGA算法求解包装废弃物回收VRP问题的整体性能更优。     

Study on the vehicle routing problem considering congestion and emission factors

To meet the requirement of greening transportation in poor traffic condition, vehicle routing problem (VRP) with consideration of fuel consumption and congestion is studied. We formulated a time-dependent green vehicle routing problem (TD-GVRP) model with minimised total cost as the objective function which includes fuel consumption cost, and the measurement of fuel consumption is based on the Comprehensive Modal Emissions Model (CMEM). In the model, the situation of waiting at customer nodes to avoid bad traffic is defined. To solve this model, a Response Surface Method (RSM)-based hybrid algorithm (HA) that combines genetic algorithm (GA) and particle swarm optimisation (PSO) is constructed. Finally, using instances from PRPLIB database, the following experiments are carried out and the corresponding conclusions are drawn. (i) Comparison of the proposed objective and traditional VRP objectives shows that fuel consumption can be greatly reduced by introducing fuel consumption factor into the objective function. (ii) Sensitivity analysis of congestion duration provides the influence of congestion duration on fuel consumption and travel time. (iii) Experiments based on different waiting time reveal that the optimisation of departure time can reduce fuel consumption and total cost to some extent.     

Solving the heterogeneous fixed fleet open vehicle routing problem by a combined metaheuristic algorithm

The vehicle routing problem (VRP) is a well-known combinatorial optimisation problem and holds a central place in logistics management. Many exact, heuristic and metaheuristic approaches have been proposed to solve VRP. An important variant of the VRP arises when a ?eet of vehicles is fixed and characterised by different capacities for distribution activities. The problem is known as the heterogeneous fixed fleet VRP (HFFVRP). The HFFVRP is a natural generalisation of the VRP with several vehicle types, each type being defined by a capacity, a fixed cost and a cost per distance unit, and can cover more practical situations in transportation. This problem consists of determining a set of vehicle trips of minimum total length in which a set of customers is to be satisfied in the demand constraints using identical vehicles with limited capacity. If open routes instead of closed ones are considered in the HFFVRP, the problem becomes a heterogeneous fixed fleet Open VRP (HFFOVRP) which has numerous applications in industrial and service problems. In this paper, a bone route algorithm which uses the tabu search as an improved procedure is utilised to solve the HFFOVRP. The proposed algorithm was tested empirically on a 24 of generated VRPs, and compared with elite ant system and ant colony system. In all cases, the proposed algorithm finds the best-known solutions within a reasonable time.     

A review on the electric vehicle routing problems: Variants and algorithms

Over the past decade, electric vehicles (EVs) have been considered in a growing number of models and methods for vehicle routing problems (VRPs). This study presents a comprehensive survey of EV routing problems and their many variants. We only consider the problems in which each vehicle may visit multiple vertices and be recharged during the trip. The related literature can be roughly divided into nine classes: Electric traveling salesman problem, green VRP, electric VRP, mixed electric VRP, electric location routing problem, hybrid electric VRP, electric dial-a-ride problem, electric two-echelon VRP, and electric pickup and delivery problem. For each of these nine classes, we focus on reviewing the settings of problem variants and the algorithms used to obtain their solutions.     

VRP模块化技术

“变化减少方法（ＶＲＰ）”是制造业处理产品多样化的一种崭新的方法，已经日益得到工业界的重视。ＶＲＰ模块化技术的主要目标是，企业在保证产品质量和提供多样化产品的同时，通过模块化设计和制造来降低产品的零件数量和工艺数目，最大限度地降低成本，达到控制产品成本以适应激烈市场竞争的目的。本文探讨了ＶＲＰ模块化技术，着重研究了模块的分解、优化和组合技术，最后提供了一套系统的ＶＲＰ模块化思路和方法。     

A Hybrid Genetic Algorithm for Vehicle Routing Problem with Complex Constraints

Most research on the Vehicle Routing Problem （VRP） is focused on standard conditions, which is not suitable for specific cases. A Hybrid Genetic Algorithm is proposed to solve a Vehicle Routing Problem （VRP） with complex side constraints. A novel coding method is designed especially for side constraints. A greedy algorithm combined with a random algorithm is introduced to enable the diversity of the initial population, as well as a local optimization algorithm employed to improve the searching efficiency. In order to evaluate the performance, this mechanism has been implemented in an oil distribution center, the experimental and executing results show that the near global optimal solution can be easily and quickly obtained by this method, and the solution is definitely satisfactory in the VRP application.     

A robust optimization approach for the capacitated vehicle routing problem with demand uncertainty

In this paper we introduce a robust optimization approach to solve the Vehicle Routing Problem (VRP) with demand uncertainty. This approach yields routes that minimize transportation costs while satisfying all demands in a given bounded uncertainty set. We show that for the Miller-Tucker-Zemlin formulation of the VRP and specific uncertainty sets, solving for the robust solution is no more difficult than solving a single deterministic VRP. Our computational results on benchmark instances and on families of clustered instances show that the robust solution can protect from unmet demand while incurring a small additional cost over deterministic optimal routes. This is most pronounced for clustered instances under moderate uncertainty, where remaining vehicle capacity is used to protect against variations within each cluster at a small additional cost. We compare the robust optimization model with classic stochastic VRP models for this problem to illustrate the differences and similarities between them. We also observe that the robust solution amounts to a clever management of the remaining vehicle capacity compared to uniformly and non-uniformly distributing this slack over the vehicles.     

Chronic toxicity of verapamil on juvenile rainbow trout (Oncorhynchus mykiss): effects on morphological indices, hematological parameters and antioxidant responses

In this study, the toxic effects of verapamil (VRP) were studied on juvenile rainbow trout, Oncorhynchus mykiss, by chronic semi-static bioassay. Fish were exposed to sublethal concentrations of VRP (0.5, 27 and 270 μg/L) for 0, 21 and 42 d. Multiple biomarkers were measured, including morphological indices, hematological parameters and antioxidant responses of different tissues (brain, gill, liver, muscle and intestine). Based on the results, there was no significant change in all parameters measured in fish exposed to VRP at environmental related concentration, but VRP-induced stress in fish exposed to higher concentrations reflected the significant changes of physiological and biochemical responses. Through principal component analysis and integrated biomarker response assessment, effects induced by VRP-stress in each test group were distinguished. Additionally, all parameters measured in this study displayed various dependent patterns to VRP concentrations and exposure time using two-way ANOVA statistic analysis. In short, the multiple responses in fish indicated that VRP induced physiological stress and could be used as potential biomarkers for monitoring residual VRP in aquatic environment; but molecular and genetic mechanisms of these physiological responses in fish are not clear and need to be further studied.     

应用蜜蜂繁殖进化型粒子群算法求解车辆路径问题

为了提高粒子群算法求解车辆路径问题时收敛速度和全局搜索能力,将蜜蜂繁殖进化机制与粒子群算法相结合,应用到CVRP问题的求解。该算法中,最优的个体作为蜂王与通过选择机制选择的雄蜂以随机概率进行交叉,增强了最优个体信息的应用能力;同时,随机产生一部分雄蜂种群,并将其与蜂王交叉增加了算法的多样性。实例分析表明该算法具有较好的全局搜索能力,验证了该算法的可行性。     

A branch and cut algorithm for the VRP with satellite facilities

An important aspect of the vehicle routing problem (VRP) that has been largely overlooked is the use of satellite facilities to replenish vehicles during a route. When possible, satellite replenishment allows the drivers to continue making deliveries until the close of their shift without necessarily returning to the central depot. This situation arises primarily in the distribution of fuels and certain retail items. When demand is random, optimizing customer routes a priori may result in significant additional costs for a particular realization of demand. Satellite facilities are one way of safeguarding against unexpected demand. This paper presents a branch and cut methodology for solving the VRP with satellite facilities subject to capacity and route time constraints. We begin with a mixed-integer linear programming formulation and then describe a series of valid inequalities that can be used to cut off solutions to the linear programming relaxation. Several separation heuristics are then outlined that are used to generate the cuts. Embedded in the methodology is a VRP heuristic for finding good feasible solutions at each stage of the computations. Results are presented for a set of problems derived from our experience with a leading propane distributor.     

Stability,viscosity, thermal conductivity,and electrical conductivity enhancement of multi-walled carbon nanotube nanofluid using gum arabic

This experimental investigation discussed on the stability and rheological behavior of multi-wall carbon nanotubes (MWCNTs) nanofluids with and without gum arabic (GA). The stability of MWCNT in the presence of GA dispersant in solar glycol is systematically investigated by taking into account the combined effect of different parameters, such as sonication time, temperature, dispersant and particle concentration. The concentrations of MWCNT and GA have been varied from 0.2 to 0.6% volume concentration and from 0.25 to 1.25 wt%, respectively, and the sonication time has been varied in between 30 and 120 min. The effect of sonication time on viscosity was discussed. It was perceived that the shear thinning behavior is exhibited by all the nanofluid samples. The stability of nanofluid is measured in terms of MWCNT concentration as a function of sediment time using UV-Vis spectrophotometer. Rheological behavior of MWCNT nanofluids is measured using Bohlin CVO Rheometer in the temperature range of 30–50°C, with step sizes of 5°C. Optimal GA concentration is obtained for the entire range of MWCNT concentration and 0.25–1.25 wt% of GA is found to be sufficient to steady all MWCNT range in solar glycol. Rapid sedimentation of MWCNTs is observed at higher GA concentration and sonication time. The presence of MWCNT and GA enhanced the thermal conductivity of the nanofluids by 30.59% at 0.6 vol.% particle concentration and 1.25 GA wt% at 50°C. The electrical conductivity is enhanced in a linear manner with respect to the loading of MWCNT and GA. Nevertheless, the electrical conductivity is increased linearly with increasing the temperature of the nanofluid. At particle concentration of 0.6 vol.% of MWCNT and 1.25 wt% of GA, the electrical conductivity of the nanofluid is improved by 190.57% at a temperature of 50°C.     

An enhanced genetic algorithm for structural topology optimization

Genetic algorithms (GAs) have become a popular optimization tool for many areas of research and topology optimization an effective design tool for obtaining efficient and lighter structures. In this paper, a versatile, robust and enhanced GA is proposed for structural topology optimization by using problem‐specific knowledge. The original discrete black‐and‐white (0–1) problem is directly solved by using a bit‐array representation method. To address the related pronounced connectivity issue effectively, the four‐neighbourhood connectivity is used to suppress the occurrence of checkerboard patterns. A simpler version of the perimeter control approach is developed to obtain a well‐posed problem and the total number of hinges of each individual is explicitly penalized to achieve a hinge‐free design. To handle the problem of representation degeneracy effectively, a recessive gene technique is applied to viable topologies while unusable topologies are penalized in a hierarchical manner. An efficient FEM‐based function evaluation method is developed to reduce the computational cost. A dynamic penalty method is presented for the GA to convert the constrained optimization problem into an unconstrained problem without the possible degeneracy. With all these enhancements and appropriate choice of the GA operators, the present GA can achieve significant improvements in evolving into near‐optimum solutions and viable topologies with checkerboard free, mesh independent and hinge‐free characteristics. Numerical results show that the present GA can be more efficient and robust than the conventional GAs in solving the structural topology optimization problems of minimum compliance design, minimum weight design and optimal compliant mechanisms design. It is suggested that the present enhanced GA using problem‐specific knowledge can be a powerful global search tool for structural topology optimization. Copyright © 2005 John Wiley & Sons, Ltd.     

A hybrid genetic algorithm for the multi-depot open vehicle routing problem

This paper studies the multi-depot open vehicle routing problem (MDOVRP), a variant of the vehicle routing problem (VRP), in which vehicles start from several depots and are not required to return to the depot. Despite the vast amount of literature about VRPs, the MDOVRP has received very little attention from researchers. In this paper, a new hybrid genetic algorithm is presented for finding the routes that minimize the traveling cost of the vehicles. Computational results on a number of test instances indicate the proposed algorithm dominates the CPLEX solver and the existing approach in the literature. Meanwhile, experiments are conducted on multi-depot VRP benchmarks, and the results are compared with a sophisticated tabu search approach and an exact method.     

N-octyl-N-arginine-chitosan (OACS) micelles for gambogic acid oral delivery: preparation,characterization and its study on in situ intestinal perfusion

Context: Gambogic acid (GA) can inhibit the growth of various cancer cells. However, the low bioavailability caused by insolubility, limits its clinical application. L-arginine is always used with GA to form a complex to obtain the higher solubility. Moreover, guanidyl group from arginine, which can facilitate the cellular uptake, was identified.

Objective: In this study, L-arginine and chitosan (CS) were used for the first time to prepare N-octyl-N-arginine CS (OACS), a novel amphiphilic carrier for GA with solubility- and absorption-enhancing functions; the characterization of the GA loaded OACS micelles (GA-OACS) and its absorption-enhancing effect were also investigated.

Materials and methods: GA-OACS were prepared by the dialysis method. The formed micelles were characterized and evaluated by atomic force microscope (AFM), dynamic light scattering, differential scanning calorimeter (DSC), solubility test, in vitro release and in situ intestinal perfusion.

Results: The GA-OACS micelles were successfully prepared attaining a 35.3% drug loading and 82.2% entrapment efficiency. GA-OACS had a homogeneous particle size of 160.3?nm; +21.8?mv zeta potential with smooth continuous surface was observed by using AFM. DSC diagram suggested that GA was encapsulated in the micelles. Meanwhile, GA encapsulated in micelles exhibited a desirable slow release in vitro experiment. The solubility of GA in OACS micelles was increased up to 3.16?±?0.13?mg/mL, 2320 times than that of free GA. The single pass perfusion showed that the absorption of GA-OACS micelles was enhanced 3.6-fold, 2.1-fold and 2.2-fold for jejunum, ileum and colon, respectively.

Discussion and conclusion: OACS provided excellent ability of drug loading, increasing solubility and enhanced absorption for GA, which indicated that OACS micelles as an oral drug delivery carrier may have potential research and application values.     

Improved genetic algorithm for design optimization of truss structures with sizing,shape and topology variables

This paper presents an improved genetic algorithm (GA) to minimize weight of truss with sizing, shape and topology variables. Because of the nature of discrete and continuous variables, mixed coding schemes are proposed, including binary and float coding, integer and float coding. Surrogate function is applied to unify the constraints into single one; moreover surrogate reproduction is developed to select good individuals to mating pool on the basis of constraint and fitness values, which completely considers the character of constrained optimization. This paper proposes a new strategy of creating next population by competing between parent and offspring population based on constraint and fitness values; so that lifetime of excellent gene is prolonged. Because the initial population is created randomly and three operators of GA are also indeterminable, it is necessary to check whether the structural topology is desirable. An improved restart operator is proposed to introduce new gene and explore new space, so that the reliability of GA is enhanced. Selected examples are solved; the improved numerical results demonstrate that the enhanced GA scheme is feasible and effective. Copyright © 2005 John Wiley & Sons, Ltd.     

基于鱼骨型仓库布局的多车拣选路径问题优化

为了推动鱼骨型仓库在实际场景下的应用,针对鱼骨型仓库布局下的拣货路径优化问题,构建待拣货点距离计算模型和以有载重、容积限制的多车拣货距离最短为总目标的拣选路径优化模型。考虑遗传算法(GA)全局搜索能力强、粒子群算法(GAPSO)收敛速度快以及蚁群算法(ACO)较强的局部寻优能力,提出一种解决拣选路径优化模型的混合算法(GA-PSO-ACO)。通过不同订单规模的仿真实验,得出该混合算法在适应度值、迭代次数、收敛速度等方面均优于GA算法和GAPSO算法,且在订单规模较大时,平均适应度值约降低8%,有效缩短了总拣选距离,验证了混合算法在解决鱼骨型仓库布局下的拣货路径问题的先进性和有效性,为解决此类仓库内部的拣货路径问题提供新的解决方法和思路。     

求解VRP问题的混沌模拟退火萤火虫算法

目的使萤火虫优化算法(GSO)能够适用于车辆路径问题(VRP)的求解,同时提高该算法的求解性能。方法通过对GSO算法的改进,提出求解VRP问题的混沌模拟退火萤火虫优化算法(CSAGSO)。首先,设计改进的GSO算法(IGSO)使IGSO算法能够适应VRP问题的求解;其次,在IGSO算法中引入模拟退火机制,提出模拟退火萤火虫优化算法(SAGSO),使IGSO算法可有效避免陷入局部极小并最终趋于全局最优。然后,在SAGSO算法中引入混沌机制,提出CSAGSO算法,对SAGSO算法的荧光素浓度值进行混沌初始化和混沌扰动;最后,对标准算例集进行仿真测试。结果与遗传算法、蚁群算法和粒子群算法相比,CSAGSO算法的全局寻优能力、收敛速度及稳定性均改善了50%以上。结论对GSO算法的改进是合理的,且CSAGSO算法的全局优化能力、收敛速度和稳定性均优于遗传算法、蚁群算法和粒子群算法。     

An optimization algorithm for a capacitated vehicle routing problem with time windows

In this paper, vehicle routing problem (VRP) with time windows and real world constraints are considered as a real-world application on google maps. Also, tabu search is used and Hopfield neural networks is utilized. Basic constraints consist of customer demands, time windows, vehicle speed, vehicle capacity and working hours. Recently, cost and on-time delivery are the most important factors in logistics. Thus, the logistic applications attract attention of companies. In logistic management, determining the locations of delivery points and deciding the path are the vital components that should be considered. Deciding the paths of vehicles provides companies to use their vehicles efficiently. And with utilizing optimized paths, big amounts of cost and time savings will be gained. The main aim of the work is providing the best path according to the needs of the customers, minimizing the costs with utilizing the VRP and presenting an application for companies that need logistic management. To compare the results, simulated annealing is used on special scenarios. And t-test is performed in the study for the visited path in km with p-value of 0.05.     

基于专家控制调整的机器人手臂系统模糊控制器设计

 对一种复杂的控制系统，简单的模糊控制器不能形成很好的控制效果，将专家知识应用到模糊控制器上，构成一种综合集成的智能专家模糊控制器，采用一种直接对隶属度函数参数进行矩阵式个体编码的遗传算法，对模糊控制器中隶属函数进行寻优，大大提高了系统的控制效果，同时由于专家知识的引入改善了系统对环境和机构参数变化的适应性。