Development status and prospect of cold chain logistics of fruits,vegetables,and agricultural products based on intelligent technology

The cold chain in the production area of fruits and vegetables is the primary link to reduce product loss and improve product quality,but it is also a weak link.With the application of big data technology in cold chain logistics,intelligent devices,and technologies have become important carriers for improving the efficiency of cold chain logistics in fruit and vegetable production areas,extending the shelf life of fruits and vegetables,and reducing fruit and vegetable losses.They have many advantages in fruit and vegetable pre-cooling,sorting and packaging,testing,warehousing,transportation,and other aspects.This article summarizes the rapidly developing and widely used intelligent technologies at home and abroad in recent years,including automated guided vehicle intelligent handling based on electromagnetic or optical technology,intelligent sorting based on sensors,electronic optics,and other technologies,intelligent detection based on computer vision technology,intelligent transportation based on perspective imaging technology,etc.It analyses and studies the innovative research and achievements of various scholars in applying intelligent technology in fruit and vegetable cold chain storage,sorting,detection,transportation,and other links,and improves the efficiency of fruit and vegetable cold chain logistics.However,applying intelligent technology in fruit and vegetable cold chain logistics also faces many problems.The challenges of high cost,difficulty in technological integration,and talent shortages have limited the development of intelligent technology in the field of fruit and vegetable cold chains.To solve the current problems,it is proposed that costs be controlled through independent research and development,technological innovation,and other means to lower the entry threshold for small enterprises.Strengthen integrating intelligent technology and cold chain logistics systems to improve data security and system compatibility.At the same time,the government should introduce relevant policies,provide necessary financial support,and establish talent training mechanisms.Accelerate the development and improvement of intelligent technology standards in the field of cold chain logistics.Through technological innovation,cost control,talent cultivation,and policy guidance,we aim to promote the upgrading of the agricultural industry and provide ideas for improving the quality and efficiency of fruit and vegetable cold chain logistics.

The Information Modeling and Intelligent Optimization Method for Logistics Vehicle Routing and Scheduling with Multi-objective and Multi-constraint

The vehicle routing and scheduling (VRS) problem with multi-objective and multi-constraint is analyzed, considering the complexity of the modern logistics in city economy and daily life based on the system engineering. The objective and constraint includes loading, the dispatch and arrival time, transportation conditions,total cost,etc. An information model and a mathematical model are built,and a method based on knowledge and biologic immunity is put forward for optimizing and evaluating the programs dimensions in vehicle routing and scheduling with multi-objective and multi-constraints. The proposed model and method are illustrated in a case study concerning a transport network, and the result shows that more optimization solutions can be easily obtained and the method is efficient and feasible. Comparing with the standard GA and the standard GA without time constraint,the computational time of the algorithm is less in this paper. And the probability of gaining optimal solution is bigger and the result is better under the condition of multi-constraint.

New Hybrid Algorithm Based on BicriterionAnt for Solving Multiobjective Green Vehicle Routing Problem

The main objective of this paper is to propose a new hybrid algorithm for solving the Bi objective green vehicle routing problem (BGVRP) from the BicriterionAnt metaheuristic. The methodology used is subdivided as follows: first, we introduce data from the GVRP or instances from the literature. Second, we use the first cluster route second technique using the k-means algorithm, then we apply the BicriterionAntAPE (BicriterionAnt Adjacent Pairwise Exchange) algorithm to each cluster obtained. And finally, we make a comparative analysis of the results obtained by the case study as well as instances from the literature with some existing metaheuristics NSGA, SPEA, BicriterionAnt in order to see the performance of the new hybrid algorithm. The results show that the routes which minimize the total distance traveled by the vehicles are different from those which minimize the CO2 pollution, which can be understood by the fact that the objectives are conflicting. In this study, we also find that the optimal route reduces product CO2 by almost 7.2% compared to the worst route.

A GA approach to vehicle routing problem with time windows considering loading constraints

As a new variant of vehicle routing problem( VRP),a finished vehicle routing problem with time windows in finished vehicle logistics( FVRPTW) is modeled and solved. An optimization model for FVRPTW is presented with the objective of scheduling multiple transport routes considering loading constraints along with time penalty function to minimize the total cost. Then a genetic algorithm( GA) is developed. The specific encoding and genetic operators for FVRPTW are devised.Especially,in order to accelerate its convergence,an improved termination condition is given. Finally,a case study is used to evaluate the effectiveness of the proposed algorithm and a series of experiments are conducted over a set of finished vehicle routing problems. The results demonstrate that the proposed approach has superior performance and satisfies users in practice. Contributions of the study are the modeling and solving of a complex FVRPTW in logistics industry.

A Coordination-Based Algorithm for Dedicated Destination Vehicle Routing in B2B E-Commerce

This paper proposes a solution to the open vehicle routing problem with time windows(OVRPTW)considering third-party logistics(3PL).For the typical OVRPTW problem,most researchers consider time windows,capacity,routing limitations,vehicle destination,etc.Most researchers who previously investigated this problem assumed the vehicle would not return to the depot,but did not consider its final destination.However,by considering 3PL in the B2B e-commerce,the vehicle is required back to the nearest 3PL location with available space.This paper formulates the problem as a mixed integer linear programming(MILP)model with the objective of minimizing the total travel distance.A coordinate representation particle swarm optimization(CRPSO)algorithm is developed to obtain the best delivery sequencing and the capacity of each vehicle.Results of the computational study show that the proposed method provides solution within a reasonable amount of time.Finally,the result compared to PSO also indicates that the CRPSO is effective.

The Application of Automatic Routing Technology in the Logistics System based on Intemet of Things

According to the development trend of intelligent logistics in our country, combining database, communication, GIS （Geography lnfolrnation System） technology, the paper study the key core technology of each module monitoring platform for vehicle monitoring system, and establish logistics transport vehicle monitoring platform that oriented small and medium-sized logistics enterprises, to realize the whole process of real-time monitoring for logistics and transport the vehicle from the warehouse to transportation, promote the informationization of logistics and transport.

Road User Charging for Urban Freight Vehicles: A Systems Approach

Road user charging (RUC) has a long history as a mechanism to recover infrastructure maintenance and capital costs. The present RUC systems are facing issues such as transparency, cross-subsidization, environmental concerns and reducing revenue due to the likes of lessening fuel tax as vehicles become more efficient. Therefore, this paper reviews the strengths and weaknesses of the present RUC mechanisms implemented in the world with respect to stakeholder problems and demands of the freight industry and to describe the need for a more appropriate, practical and sustainable approach that can be used in the future. Finally, a model is proposed that is transparent and considers usage-based charging addressing most of the weaknesses highlighted in the models reviewed. Further, it considers externalities produced by heavy vehicles into account. The model has the potential to provide answers to key stakeholder issues and will lead to a sustainable freight transport system in the future. Encouraging fuel-efficient modes, optimization of loading, routing and logistics systems, and long term land use planning are a few of them.

A hybrid optimization approach for the heterogeneous vehicle routing problem with multiple depots cooperative operation

With the challenge of great growing of transport diversity for the automobile enterprises, the heterogeneous vehicle routing problem with multiple depots, multiple types of finished vehicles and multiple types of transport vehicles in finished vehicle logistics(HVRPMD) is modelled and solved. A multi-objective optimization model for HVRPMD is presented considering loading constraints to minimize the total cost and minimize the number of transport vehicles. Then a hybrid heuristic algorithm based on genetic algorithm and particle swarm optimization(GA-PSO) is developed. Moreover, a case study is used to evaluate the effectiveness of this algorithm. By comparing the GA-PSO algorithm with the traditional GA algorithm, the simulation results demonstrate the proposed GA-PSO algorithm is able to better support the HVRPMD problem in practice. Contributions of the paper are the modelling and solving of a complex HVRPMD in logistics industry.

A Bi-Objective Green Vehicle Routing Problem: A New Hybrid Optimization Algorithm Applied to a Newspaper Distribution

The purpose of this work is to present a methodology to provide a solution to a Bi-objective Green Vehicle Routing Problem (BGVRP). The methodology, illustrated using a case study (newspaper distribution problem) and literature Instances, was divided into three stages: Stage 1, data treatment;Stage 2, “metaheuristic approaches” (hybrid or non-hybrid), used comparatively, more specifically: NSGA-II (Non-dominated Sorting Genetic Algorithm II), MOPSO (Multi-Objective Particle Swarm Optimization), which were compared with the new approaches proposed by the authors, CWNSGA-II (Clarke and Wright’s Savings with the Non-dominated Sorting Genetic Algorithm II) and CWTSNSGA-II (Clarke and Wright’s Savings, Tabu Search and Non-dominated Sorting Genetic Algorithm II);Stage 3, analysis of the results, with a comparison of the algorithms. An optimization of 19.9% was achieved for Objective Function 1 (OF1;minimization of CO2 emissions) and consequently the same percentage for the minimization of total distance, and 87.5% for Objective Function 2 (OF2;minimization of the difference in demand). Metaheuristic approaches hybrid achieved superior results for case study and instances. In this way, the procedure presented here can bring benefits to society as it considers environmental issues and also balancing work between the routes, ensuring savings and satisfaction for the users.

Exploring Hybrid Genetic Algorithm Based Large-Scale Logistics Distribution for BBG Supermarket

In the large-scale logistics distribution of single logistic center,the method based on traditional genetic algorithm is slow in evolution and easy to fall into the local optimal solution.Addressing at this issue,we propose a novel approach of exploring hybrid genetic algorithm based large-scale logistic distribution for BBG supermarket.We integrate greedy algorithm and hillclimbing algorithm into genetic algorithm.Greedy algorithm is applied to initialize the population,and then hill-climbing algorithm is used to optimize individuals in each generation after selection,crossover and mutation.Our approach is evaluated on the dataset of BBG Supermarket which is one of the top 10 supermarkets in China.Experimental results show that our method outperforms some other methods in the field.

农村电商物流下无人机与车辆协同配送路径优化研究

无人机配送正在成为解决物流末端配送难题的重要手段。无人机与车辆协同配送模式克服了无人机配送能力不足、安全性不高的弊端,是无人机参与配送的重要途径之一。针对农村电商物流“最后一公里”配送难、配送贵问题,考虑无人机与车辆协同方式、多无人机多包裹配送等约束,以配送成本最小化为目标构建混合整数规划模型并提出一种两阶段算法对无人机与车辆协同配送路径优化问题进行求解。第一阶段通过带约束的自适应K-means算法确定车辆停靠点范围,第二阶段设计爬山算子与分裂算子改进遗传算法,求得无人机与车辆配送路径。最后,通过算例实验验证了模型和算法的可行性与有效性。研究成果有望为农村电商物流末端配送降本增效提供新思路和参考价值。

AGV路径规划及避障算法研究综述

自动导向车(Automated Guided Vehicle,AGV)目前被广泛应用于物流、仓储、制造业和仓储等领域.AGV的路径规划和避障算法是实现AGV自主导航的关键技术,决定了AGV在复杂环境中能否高效、安全地完成任务,近年来成为AGV领域的重要研究热点之一.本文根据AGV路径规划及避障算法的原理与特点,将主流AGV路径规划及避障算法划分为局部避障路径规划算法、基于几何模型的路径规划算法、智能路径规划算法和混合算法4类,对算法的原理、工作流程、优缺点进行了深入分析,并介绍了相应的改进算法.最后,本文对AGV路径规划及避障的未来发展趋势进行展望,为AGV路径规划及避障算法的研究指出了方向.

低碳自动化码头AGV物流调度和有序充电的联合经济优化

针对现有自动化码头自动导航集卡(AGV)充电策略未能与分布式电源出力充分协同等问题,提出一种自动化码头AGV物流调度和有序充电联合经济优化方法.首先,解析AGV物流调度和充电调度之间的协同关系,构建联合优化框架;其次,针对AGV物流调度模块,提出考虑码头内外集卡道路隔离要求的AGV行驶距离计算方法;再次,针对AGV充电模块,定义AGV充电状态的判断条件和选桩方法;进而以码头购电费用最小为目标,考虑分时电价、风光上网电价、电量平衡约束、终止时刻的剩余电量约束、决策变量的上下限约束、物流调度约束,构建物流调度和有序充电联合优化模型,提出基于改进粒子群优化算法的快速求解方法;最后,通过某码头的实际案例,验证所提方法的有效性和经济性.

带时间窗的时间依赖型同时取送货车辆路径问题研究

针对带时间窗的时间依赖型同时取送货车辆路径问题(Time Dependent Vehicle Routing Problem with Simultaneous Pickup-Delivery and Time Windows,TDVRPSPDTW),本文建立以车辆固定成本、驾驶员成本、燃油消耗及碳排放成本之和为优化目标的数学模型;并在传统蚁群算法的基础上,利用节约启发式构造初始解初始化信息素,改进状态转移规则,引入局部搜索策略,提出一种带自适应大邻域搜索的混合蚁群算法(Ant Colony Optimization with Adaptive Large Neighborhood Search,ACO-ALNS)进行求解;最后,分别选取基准问题算例和改编生成TDVRPSPDTW算例进行实验。实验结果表明:本文提出的ACO-ALNS算法可有效解决TDVRPSPDTW的基准问题;相较于模拟退火算法和带局部搜索的蚁群算法,本文算法求解得到的总配送成本最优值平均分别改善7.56%和2.90%;另外,相比于仅考虑碳排放或配送时间的模型,本文所构建的模型综合多种因素,总配送成本平均分别降低4.38%和3.18%,可有效提高物流企业的经济效益。

考虑分散协作及数量折扣的双目标电动车辆路径优化

当前传统燃油车辆造成了极大的空气污染和资源浪费,电动车辆和协作物流是降低碳排放、提高运输效率的有效途径。本文基于协作物流的思想,建立以运输利润最大及配送任务完成量最大为双目标,考虑分散协作及数量折扣的带时间窗电动车辆路径优化模型。设计将贪婪随机自适应搜索—进化邻域搜索(GRASP-ELS)混合算法与ε-约束法相结合的ε-约束混合进化算法,并通过算例对模型和算法进行测试。实验结果表明:所提出的算法优于多目标优化算法NSGA-Ⅱ;通过灵敏度分析给出管理启示。本文为分散协作情境下电动车辆配送优化提供方法借鉴与决策参考。

一种物流配送机器人路径搜索启发式算法研究

针对带有时间窗和配送机器人的车辆路径问题,提出了一种自适应大邻域搜索启发式算法(ALNS)。首先研究了带时间窗口的车辆路径问题,分析了在调度-等待-检索系统中,时间窗约束引起的同步问题以及两种不同的配送资源在时间问题上的关系,在此基础上提出了改进的自适应大邻域搜索(ALNS)启发式算法。该算法通过破坏和修复部分现有解,在每次迭代时改变大部分解,通过创建初始解决方案,并在自适应机制的基础上获得最优解决方案。通过实验证明了该算法在解决带有时间窗和配送机器人的车辆路径问题上的性能和有效性,最后对配送机器人的应用情况进行了敏感性分析。

基于“分类-调度”优化的应急物资动态调度-重调度算法

由于灾情的不确定性和信息延迟,应急救灾物资配送与车辆调度常面临配送过程中出现需求点信息动态变化的复杂环境,如何在动态变化的环境下快速反应,准确高效地实现应急物资重调度显得尤其重要。针对需求点信息动态变化的复杂环境,建立一个应急物资动态调度模型,并提出基于“分类-调度”的动态调度算法,通过k-means分类将原复杂问题降解为多个简单TSP问题,随着需求点信息的变化可动态调整分类,以实现快速重调度;设计相应的路线调整策略以保证方案的可行性;最后利用遗传算法求解。数值试验结果证明了所提方法的有效性。

客货“共线不共车”模式下地铁货运站点选择

电子商务的蓬勃发展极大增加了城市物流配送和环境保护方面的压力,让地铁承担部分货运功能成为当前解决这一难题的全新思路。本文基于城市地铁运行特征及未来发展趋势,研究客货列车“共线不共车”模式下的地铁货运,并结合地铁系统的运营情况和内部结构,通过建立E-TOPSIS流量筛选模型、集合覆盖模型、混合整数规划的组合模型,对地铁站点进行分析,从而选择合适的站点作为货运站,最终实现地铁客货列车“共线不共车”模式下的货运方案。最后,以北京市通州区为例进行模拟仿真,找出通州区可开展货运的地铁站点,对地铁货运站点选择的方法进行了验证。

两相位信号交叉口右转车辆礼让行人特征分析

为了提高两相位信号交叉口右转车辆与过街行人的通行安全,构建了基于多元有序Logistic模型的礼让意向模型以寻找影响右转车辆礼让行人意向的因素。在现有研究因素是否对右转车辆礼让行为产生影响的基础上,将右转车辆的礼让方式进一步细分为“软礼让”和“硬礼让”,以“不礼让、硬礼让、软礼让”3种礼让程度作为因变量,选取12个因素作为自变量构建礼让意向模型。为分析不同因素对右转车辆礼让行人意向影响的差异性,构建自变量在礼让模型中的优势比k。结果表明:右转车辆车速、人行横道长度、行人过街规模等因素k值大于1,表明这些因素增加了右转车辆对行人主动实施礼让的意向;行人行走在11~20 m长度的人行横道上会在最大程度上促使右转车辆停车礼让行人。