基于启发式算法的乘用车物流运输问题分析

伴随着汽车工业的高速崛起,乘用车物流运输问题也快速走进人们的视野。由于现在很多物流公司在制定运输计划时主要依赖调度人员的经验,在面对复杂的运输任务时,往往效率较低且运输成本不尽理想。考虑到影响乘用车物流运输成本的主要因素分别为轿运车的使用数量、轿运车的单价以及行驶里程数等等,本文采用建立逐级目标的模式,应用启发式算法,结合计算机软件,给出了求解乘用车物流运输问题的数学模型。应用此模型求解了2种不同类型的乘用车物流运输问题,提出了合理的运输方案。此项工作对今后物流公司处理此类运输问题提供了重要的参考价值。     

基于改进蚁群算法的一类运输能力约束的生产-运输批量问题求解

针对生产与运输两个过程的联合决策,通过分析一类生产-运输批量优化问题,建立的混合0-1整数规划模型整合了多产品多阶段能力约束批量生产和产品运输。其中运输成本由运输工具使用数量决定,当企业内部运输能力不能满足运输需求时可将运输外包,但需支付更高的运输成本。根据此问题的特点,构造改进蚁群算法求解,令其信息素和启发信息都存在0和1两种状态下的不同取值,通过转移概率确定0-1生产准备矩阵,进一步得到生产矩阵和运输计划。仿真实验结果表明在生产批量决策的同时考虑运输,可以减少运输成本,令总费用最小,通过将实验结果与其他优化算法比较,所构造的蚁群算法寻优概率是100%,平均进化10代,平均耗时小于1 s,稳定性和求解效率均高于其他算法,是求解这类问题一种有效与适用的算法。     

考虑运费的短生命周期产品供应链集成模型

一般的供应链集成问题都是忽略机器故障、产品检查以及运输成本,然而,这些因素对生产计划的制定也是有很大影响的。因此,在考虑机器故障、返修费用和担保费用,以及短生命周期产品库存成本随着时间呈非线关系的情况下,建立了短生命周期产品供应链集成模型,研究分析了运输费用、返修费用和担保费用对最优生产批量和单位时间总成本的影响,同时比较了考虑机器故障与运输费用对短生命周期产品在生产策略上的一些区别,得到了一些有价值的结论。     

考虑价格折扣和运输柔性的库存运输优化研究

在考虑运输能力柔性和销售价格折扣的基础上,建立了零售商主导的分散优化模型、供应商主导的分散优化模型和库存运输联合优化模型,分析得出模型的性质。通过比较研究得出不同决策模式下最优策略和系统绩效的关系。研究表明,零售商主导的分散优化模型的库存成本小于运输库存联合优化模型的库存成本;零售商主导的分散优化模型的运输成本大于运输库存联合优化模型的运输成本;运输库存联合优化供应链的总成本小于零售商主导的供应链总成本。     

遗传算法在水泥矿山卡车运输调度系统中的应用

根据露天矿山运输调度系统的复杂性与非线性特性，建立了实时运输调度系统模型；运用遗传进化算法中的选择、交叉、变异、插入、迁移算子的寻优迭代计算，成功地解决了在开采工艺、产量、质量等多因素约束条件下的实时运输调度优化问题。并将其用于韶峰水泥原料矿山的生产运输调度系统，既降低了矿山运输成本，又协调了开采工艺、质量、产量之间的关系，取得了较好的效果。同时为矿山企业信息化建设和其他物流企业提供了重要的参考价值。     

包装可靠性对运输成本的影响性分析

在运输的过程中,包装可靠性对保护产品起着重要的作用,同时包装可靠性对运输成本也有着很大的影响.在分析包装可靠性和运输成本影响因素的基础上,讨论了包装可靠性和运输成本的内在关系,为实现运输成本最优化提供了参考.     

日本货运飞船的研制设想

一、LTA飞机的研制设想通产省工业技术院作为昭和54年(1979)度大型设汁的新课题,正预备着手“研制混合型LTA飞机”。六年计划预算总额为150亿日元。研制开发LTA飞机的目的是预期改善运输体系、保全环境及节省空运的能源。由于在山间和狭小地带重物运输会使环境受到破坏,运输成本极高;而城市间近距离运输     

配送网络中最佳运输方式的选择研究

在配送网络中,运输方式的选择是在综合考虑运输时间及运输能力的基础上,建立一个最小化运输成本的模型。摒弃传统单节点的算法以及各种运输方式不可理的交汇问题。基于多元网点布局的思想,求解整个配送网络的最佳运输方案。     

多金属矿山工业采掘生产计划模型与优化算法

为了降低多金属矿山工业生产作业成本及保证生产连续性等目标,将矿山生产作业过程中采场至卸矿点间的生产成本归结为采掘生产作业计划优化问题,建立了多金属矿山工业采掘生产计划模型。该模型以采掘和运输作业成本最小为目标,综合考虑了矿石品位波动、采场的出矿量、采掘作业量和矿产资源利用等约束条件。将改进的量子粒子群算法应用于模型中,利用进化速度和聚集度因子对算法参数进行优化,同时提出了双可行域吸引子的粒子搜索策略,提高了算法的全局收敛速度,避免了早熟问题。以某大型多金属矿山为案例,结果表明该算法解算结果要比矿山实际生产指标、非线性规划以及标准粒子群算法结果优于95%以上,算法求解速度提高了65%左右,且该模型与优化算法满足矿山工业采掘生产计划的连续性和均衡性要求。     

分段线性凹成本函数下协同运输路线优化研究

提出了规模经济条件下的协同运输路线优化问题,引入了分段线性凹成本函数表示协同运输成本的规模经济特征,构造了该问题的混合整数规划模型和求解算法,展开了算法测试,并取得了非常好的计算绩效,同时给出了模型与算法在我国中枢航线网络设计中的应用过程.考虑到协同运输容易因合并运输而延长了路线,扩展了模型与算法,增加了总出行距离的限制.     

Two-stage stochastic master production scheduling under demand uncertainty in a rolling planning environment

This paper proposes a scenario-based two-stage stochastic programming model with recourse for master production scheduling under demand uncertainty. We integrate the model into a hierarchical production planning and control system that is common in industrial practice. To reduce the problem of the disaggregation of the master production schedule, we use a relatively low aggregation level (compared to other work on stochastic programming for production planning). Consequently, we must consider many more scenarios to model demand uncertainty. Additionally, we modify standard modelling approaches for stochastic programming because they lead to the occurrence of many infeasible problems due to rolling planning horizons and interdependencies between master production scheduling and successive planning levels. To evaluate the performance of the proposed models, we generate a customer order arrival process, execute production planning in a rolling horizon environment and simulate the realisation of the planning results. In our experiments, the tardiness of customer orders can be nearly eliminated by the use of the proposed stochastic programming model at the cost of increasing inventory levels and using additional capacity.     

Metaheuristics for the Stochastic Hoist Scheduling Problem (SHSP)

Experts look for solutions to increase production delivery, but industrial systems are generally disturbed by random events. In many cases, there is a lack of information about random events consequences on the production time and quality of the manufactured products. Surface treatment lines are robotic cells with time window constraints and so random events can have important consequences on production management. Products could be damaged by constraint violations and rejected by the control quality department. Moreover, owing to the structure of these systems, variations in transportation times can induce unrecoverable situations (deadlock occurrences, for example). Therefore, experts look for schedules to prevent random events consequences. One of the most difficult operational problems in surface treatment lines is the proper coordination of the material-handling resources management and job input scheduling. Previous related research works concern the makespan minimization problem and do not take into account the random events consequences: this problem is denoted by the Hoist Scheduling Problem (HSP). This paper focuses on the Stochastic Hoist Scheduling Problem (SHSP), which is the HSP with random events implying variations in transportation times. It appears that no previous work can be reported concerning such stochastic problems but only deterministic ones. A method is proposed here based on stochastic metaheuristics used to determine the production schedules for which the random events consequences are low. An industrial study that highlights the efficiency of the method is proposed. A company that cannot be named for confidential reasons has provided the test data.     

A block transportation scheduling system considering a minimisation of travel distance without loading of and interference between multiple transporters

Material transportation scheduling problems concerning scheduling optimisation have been extensively investigated by researchers in such fields as industrial engineering and management science. Various algorithms have been proposed to solve such problems. However, the majority of these algorithms cannot be applied to a block transportation problem when a shipyard that uses a transporter, a large vehicle employed for moving weight, is considered. In this study, a hybrid optimisation algorithm is proposed for solving a block transportation problem when multiple transporters are used. With regards to the transporters, a minimisation of the travel distance without loading of and interference between the transporters is considered. A block transportation scheduling system is then developed based on the proposed algorithm. The developed system is applied to an actual block transportation scheduling problem of a shipyard. From the attained results, we demonstrate that the proposed algorithm has the ability to effectively solve the block transportation scheduling problems of a shipyard.     

供应链中生产和运输协调调度问题初探

提出了解决供应链中生产和航空运输协调调度问题的理论框架.基于对生产调度和航空运输调度彼此制约关系的分析,协调调度问题被分解为两个子调度问题.建立了航空运输子调度问题的整数规划模型,并证明了该问题为NP完全问题.提出了基于倒排调度方法(backward scheduling method)的调度算法解单机生产调度子问题.     

Coordinated scheduling of intercell production and intercell transportation in the equipment manufacturing industry

Intercell moves are caused by exceptional parts which need to be processed in multiple cells. Intercell cooperation disrupts the cellular manufacturing philosophy of creating independent cells, but is essential to lower the costs for enterprises. This article addresses an intercell scheduling problem considering limited transportation capability. To solve this problem, a two-stage ant colony optimization approach is proposed, in which pre-scheduling and re-scheduling are performed sequentially. To evaluate and optimize the interaction of production and transportation, a transportation benefit function is presented, according to which the scheduling solutions are adjusted. The computational results show that the transportation benefit function is more effective than other strategies, and the proposed approach has significant advantages over CPLEX in both the production dimension and the transportation dimension.     

Book review of Global Strategic Management,by P. Lasserre,Palgrave Macmillan,Basingstoke, 2003, xx + 454 pp., £24.99, softcover (ISBN 0 333 79375 7)

This paper considers the simultaneous scheduling of material handling transporters (such as automatic guided vehicles or AGVs) and manufacturing equipment (such as machines and workcentres) in the production of complex asembled product. Given the shipping schedule for the end-items, the objective of the integrated problem is to minimize the cumulative lead time of the overall production schedule (i.e. total makespan) for on-time shipment, and to reduce material handling and inventory holding costs on the shop-floor. The problem of makespan minimization is formulated as a transportation integrated scheduling problem, which is NP-hard. For industrial size problems, an effective heuritsic is developed to simultaneouly schedule manufacturing and material handling operations by exploting the critical path of an integrated operation network. The performance of the proposed heuristic is evaluated via extensive numerical studies and compared with the traditional sequential scheduling approach. The superiority of the integrated heuristic is well documented.     

Cash transportation vehicle routing and scheduling under stochastic travel times

Stochastic disturbances occurring in real-world operations could have a significant influence on the planned routing and scheduling results of cash transportation vehicles. In this study, a time–space network flow technique is utilized to construct a cash transportation vehicle routing and scheduling model incorporating stochastic travel times. In addition, to help security carriers to formulate more flexible routes and schedules, a concept of the similarity of time and space for vehicle routing and scheduling is incorporated into the model. The test results show that the model could be useful for security carriers in actual practice.     

Reliability and decomposition techniques to solve certain class of stochastic programming problems

Reliability based techniques has been an area of active research in structural design during the last decade, and different methods have been developed. The same has occurred with stochastic programming, which is a framework for modeling optimization problems involving uncertainty. The discipline of stochastic programming has grown and broadened to cover a wide range of applications, such as agriculture, capacity planning, energy, finance, fisheries management, production control, scheduling, transportation, water management, etc., and because of this, techniques for solving stochastic programming models are of great interest for the scientific community. This paper presents a new approach for solving a certain type of stochastic programming problems presenting the following characteristics: (i) the joint probability distributions of random variables are given, (ii) these do not depend on the decisions made, and (iii) random variables only affect the objective function. The method is based on mathematical programming decomposition procedures and first-order reliability methods, and constitutes an efficient method for optimizing quantiles in high-dimensional settings. The solution provided by the method allows us to make informed decisions accounting for uncertainty.     

Integrated production scheduling and vehicle routing problem with job splitting and delivery time windows

In this paper, we study a production scheduling and vehicle routing problem with job splitting and delivery time windows in a company working in the metal packaging industry. In this problem, a set of jobs has to be processed on unrelated parallel machines with job splitting and sequence-dependent setup time (cost). Then the finished products are delivered in batches to several customers with heterogeneous vehicles, subject to delivery time windows. The objective of production is to minimize the total setup cost and the objective of distribution is to minimize the transportation cost. We propose mathematical models for decentralized scheduling problems, where a production schedule and a distribution plan are built consecutively. We develop a two-phase iterative heuristic to solve the integrated scheduling problem. We evaluate the benefits of coordination through numerical experiments.     

A hybrid VNS-HS algorithm for a supply chain scheduling problem with deteriorating jobs

This paper investigates a coordinated scheduling problem in a two stage supply chain where parallel-batching machine, deteriorating jobs and transportation coordination are considered simultaneously. During the production stage, jobs are processed by suppliers and there exists one parallel-batching machine in each supplier. The actual processing time of a job depends on its starting time and normal processing time. The normal processing time of a batch is equal to the largest normal processing time among all jobs in its batch. During the transportation stage, the jobs are then delivered to the manufacturer. Since suppliers are distributed in different locations, the transportation time between each supplier and the manufacturer is different. Based on some structural properties of the studied problem, an optimal algorithm for minimising makespan on a single supplier is presented. This supply chain scheduling problem is proved to be NP-hard, and a hybrid VNS-HS algorithm combining variable neighbourhood search (VNS) with harmony search (HS) is proposed to find a good solution in reasonable time. Finally, some computational experiments are conducted and the results demonstrate the effectiveness and efficiency of the proposed VNS-HS.