Solving material distribution routing problem in mixed manufacturing systems with a hybrid multi-objective evolutionary algorithm

The material distribution routing problem in the manufacturing system is a complex combinatorial optimization problem and its main task is to deliver materials to the working stations with low cost and high efficiency. A multi-objective model was presented for the material distribution routing problem in mixed manufacturing systems, and it was solved by a hybrid multi-objective evolutionary algorithm (HMOEA). The characteristics of the HMOEA are as follows: 1) A route pool is employed to preserve the best routes for the population initiation; 2) A specialized best?worst route crossover (BWRC) mode is designed to perform the crossover operators for selecting the best route from Chromosomes 1 to exchange with the worst one in Chromosomes 2, so that the better genes are inherited to the offspring; 3) A route swap mode is used to perform the mutation for improving the convergence speed and preserving the better gene; 4) Local heuristics search methods are applied in this algorithm. Computational study of a practical case shows that the proposed algorithm can decrease the total travel distance by 51.66%, enhance the average vehicle load rate by 37.85%, cut down 15 routes and reduce a deliver vehicle. The convergence speed of HMOEA is faster than that of famous NSGA-II.

GREEDY NON-DOMINATED SORTING IN GENETIC ALGORITHM-ⅡFOR VEHICLE ROUTING PROBLEM IN DISTRIBUTION

Vehicle routing problem in distribution （VRPD） is a widely used type of vehicle routing problem （VRP）, which has been proved as NP-Hard, and it is usually modeled as single objective optimization problem when modeling. For multi-objective optimization model, most researches consider two objectives. A multi-objective mathematical model for VRP is proposed, which considers the number of vehicles used, the length of route and the time arrived at each client. Genetic algorithm is one of the most widely used algorithms to solve VRP. As a type of genetic algorithm （GA）, non-dominated sorting in genetic algorithm-Ⅱ （NSGA-Ⅱ） also suffers from premature convergence and enclosure competition. In order to avoid these kinds of shortage, a greedy NSGA-Ⅱ （GNSGA-Ⅱ） is proposed for VRP problem. Greedy algorithm is implemented in generating the initial population, cross-over and mutation. All these procedures ensure that NSGA-Ⅱ is prevented from premature convergence and refine the performance of NSGA-Ⅱ at each step. In the distribution problem of a distribution center in Michigan, US, the GNSGA-Ⅱ is compared with NSGA-Ⅱ. As a result, the GNSGA-Ⅱ is the most efficient one and can get the most optimized solution to VRP problem. Also, in GNSGA-Ⅱ, premature convergence is better avoided and search efficiency has been improved sharply.

Models for Location Inventory Routing Problem of Cold Chain Logistics with NSGA-Ⅱ Algorithm

In this paper,a novel location inventory routing(LIR)model is proposed to solve cold chain logistics network problem under uncertain demand environment. The goal of the developed model is to optimize costs of location,inventory and transportation.Due to the complex of LIR problem( LIRP), a multi-objective genetic algorithm(GA), non-dominated sorting in genetic algorithm Ⅱ( NSGA-Ⅱ) has been introduced. Its performance is tested over a real case for the proposed problems. Results indicate that NSGA-Ⅱ provides a competitive performance than GA,which demonstrates that the proposed model and multi-objective GA are considerably efficient to solve the problem.

Method of Searching for Earthquake Disaster Evacuation Routes Using Multi-Objective GA and GIS

This study treats the determination of routes for evacuation on foot in earthquake disasters as a multi-objective optimization problem, and aims to propose a method for quantitatively searching for evacuation routes using a multi-objective genetic algorithm (multi-objective GA) and GIS. The conclusions can be summarized in the following three points. 1) A GA was used to design and create an evacuation route search algorithm which solves the problem of the optimization of earthquake disaster evacuation routes by treating it as an optimization problem with multiple objectives, such as evacuation distance and evacuation time. 2) In this method, goodness of fit is set by using a Pareto ranking method to determine the ranking of individuals based on their relative superiorities and inferiorities. 3) In this method, searching for evacuation routes based on the information on present conditions allows evacuation routes to be derived based on present building and road locations.?Further, this method is based on publicly available information;therefore, obtaining geographic information similar to that of this study enables this method to be effective regardless of what region it is applied to, or whether the data regards the past or the future. Therefore, this method has high degree of spatial and temporal reproducibility.

A Multi-Objective Scheduling and Routing Problem for Home Health Care Services via Brain Storm Optimization

At present,home health care(HHC)has been accepted as an effective method for handling the healthcare problems of the elderly.The HHC scheduling and routing problem(HHCSRP)attracts wide concentration from academia and industrial communities.This work proposes an HHCSRP considering several care centers,where a group of customers(i.e.,patients and the elderly)require being assigned to care centers.Then,various kinds of services are provided by caregivers for customers in different regions.By considering the skill matching,customers’appointment time,and caregivers’workload balancing,this article formulates an optimization model with multiple objectives to achieve minimal service cost and minimal delay cost.To handle it,we then introduce a brain storm optimization method with particular multi-objective search mechanisms(MOBSO)via combining with the features of the investigated HHCSRP.Moreover,we perform experiments to test the effectiveness of the designed method.Via comparing the MOBSO with two excellent optimizers,the results confirm that the developed method has significant superiority in addressing the considered HHCSRP.

A Region Enhanced Discrete Multi-Objective Fireworks Algorithm for Low-Carbon Vehicle Routing Problem

A constrained multi-objective optimization model for the low-carbon vehicle routing problem(VRP)is established.A carbon emission measurement method considering various practical factors is introduced.It minimizes both the total carbon emissions and the longest time consumed by the sub-tours,subject to the limited number of available vehicles.According to the characteristics of the model,a region enhanced discrete multi-objective fireworks algorithm is proposed.A partial mapping explosion operator,a hybrid mutation for adjusting the sub-tours,and an objective-driven extending search are designed,which aim to improve the convergence,diversity,and spread of the non-dominated solutions produced by the algorithm,respectively.Nine low-carbon VRP instances with different scales are used to verify the effectiveness of the new strategies.Furthermore,comparison results with four state-of-the-art algorithms indicate that the proposed algorithm has better performance of convergence and distribution on the low-carbon VRP.It provides a promising scalability to the problem size.

电动汽车-无人机联合救援系统协调调度模型

电动汽车(EV)和无人机(UAV)的迅速发展为紧急状态下的人员搜救与物资配送提供了新的技术手段.提出一种电动汽车-无人机(EV-UAV)联合救援系统.其中,无人机以电动汽车作为充电和维护基站,为待救援对象提供紧急救援服务,而电动汽车能够利用各类分布式电源获得多元化的电能补充,从而提高EV-UAV系统在紧急状态下的适应能力及续航水平.以混合整数线性规划形式建立该EV-UAV联合救援系统的协调调度模型,综合考虑电动汽车和无人机的电能消耗、电能补充、装载容量、配送路径以及配送时窗等因素.算例仿真验证了所建立模型的有效性,对比了EV-UAV型与地面车辆(GV)-UAV型联合救援系统,显示了EV-UAV联合救援系统在紧急求援中的技术特性和应用潜力.

ELRP多目标优化模型及其混合算法

以时间效益最大化为主要目标、成本最小化为次要目标,考虑灾后纵多不确定因素,基于系列假设和约束条件构建ELRP多目标优化模型;采用先定位分配,再安排路线的思路,首先根据时间窗、距离和路阻等因素进行应急中转站定位和救援点分配,再设计ACO-GA混合启发式算法进行全局和局部路径寻优;运用SOLOMON标准测试数据测试模型和算法的可行性,最后将其用于求解以江苏地震灾害为背景的仿真实例。研究结果表明,优化模型和改进算法具有较好性能,解的质量和稳定性有明显改进,其运算结果可以作为地方政府应急救援决策的理论支持。

机场需求响应式应急救援车辆调度优化

为快速应援受损机场接驳车辆,提出机场接驳车辆受损情况下应急救援服务决策模型和算法。以最小化车辆运行成本为目标函数,建立接驳车辆预排班模型。在此基础上,明确一般需求响应式接驳与受损情况下应急救援机场接驳之间的联系,以最小化运行时间和救援等待时间为目标建立应急救援模型,该模型通过设置救援等待约束和虚拟车场发车时间约束提升救援可靠性;运用列生成算法求解应急救援模型,采用优化求解器与混合编程获得精确解;最后以无锡市苏南国际硕放机场和无锡市部分地区为例,随机选取2个时间点为接驳车辆受损时刻,验证了模型与算法的有效性。

低空安全监测飞艇介绍及其行驶路线问题研究

在地震救灾中,利用低空安全监测飞艇进行灾情监测是了解灾情的有效手段。由于飞艇信号传输具有距离限制,如何结合救灾需要和飞艇技术参数提前设计飞艇的行驶路线是一个急需解决的问题,同时也是飞艇救援指挥系统的重要组成部分。该文首先介绍了低空安全监测飞艇的优势和功能,然后结合应急救灾实际情况,将低空安全监测飞艇的路线问题转化为带条件的旅行商问题,并通过仿真实验详细分析比较了蚁群算法、"最近邻居"算法、模拟退火算法、遗传算法四种算法在解决该问题时的效果。结果表明,蚁群算法得到的最优解次数最多、计算时间最稳定,于是将蚁群算作为原问题的核心算法,提出了该问题的解决方案。

行驶时间延迟下配送车辆调度的干扰管理模型与算法

针对行驶时间延迟下配送车辆调度的干扰管理问题,给出了车辆调度人员实际操作中的一系列"救援模式",并将其提炼为计算机可以理解并处理的形式化知识;按照车辆调度人员的"救援路线列举→救援路线选择"两阶段的思维方式,以顾客时间窗偏离程度最小化和配送成本最小化为目标,建立了问题的数学模型及其求解算法.通过初步的实验,确定了算法的参数配置;在Solomon提出的标准算例上对算法的鲁棒性、求解结果的质量、以及求解性能等几项指标进行了测试及与文献算法的比较;最后对算法进行了实时化的处理.实验结果表明,本文算法不仅达到了多目标优化的效果,而且可以满足实时应用的要求.

车辆路径问题的受扰救援策略

分析了带时间窗服务型车辆路径问题中车辆受损的救援需求,基于干扰管理思想建立了服务型车辆路径问题扰动恢复模型;对车辆受损的带时间窗服务型车辆路径问题提出了两种救援策略,并研究了该策略在处理集货型和送货型问题的转换方法;最后对实施这两种策略的效果进行了分析和比较.

考虑异质物资合车运输的灾后救援选址-路径-配给优化

研究灾后应急救援中的双层选址-路径-配给问题.针对灾害发生之后第一时间内各类型救援物资供给受限的情况,以救援及时性、综合满意度和物资供给公平性为优化目标,建立多工厂节点、多品种物资的考虑异质物资合车运输的多目标双层选址-路径-配给优化模型.采用融合差分进化和约束优化的方法,将多目标优化问题分解为三个单目标子迭代过程和一个多目标迭代过程,同时优化车辆行驶路线和需求节点物资分配方案.实验表明,采用合车运输的策略能够有效减少派出车辆的数量和车辆行驶时间.

考虑“安全-时间”的自然灾害多地点应急救援路线优化

研究有多个地点受灾等待接受救援的应急救援路线规划问题。针对可同时派出多组救援人员,且有固定救援出救点和救灾补给点的情况,建立了综合考虑安全风险和时间花费的救援路线优化模型。基于进化多目标优化思想,设计了求解模型的遗传算法。提出的交叉和变异算子确保算法迭代过程中得到的路径始终是可行的,回路清除算法清除所有非有益回路,精英保留策略在各代Pareto最优解中优中选优。实验证明该算法有较好的运行结果和效率。综上,本文提出了路网具有安全风险和时间花费两个属性的多受灾地点多救援队伍应急救援路线优化问题,并设计了相应的求解算法。