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。     

《调度：原理、算法和系统（第2版）》

“国外大学优秀教材——工业工程系列（影印版）”之一。该书以生产调度的基本理论及其应用为主，介绍了与调度相关的若干基本算法模型及其在实际中的应用。内容涵盖了确定性调度模型、随机调度模型、调度理论的应用等三部分。内容及难度适合工业工程、物流工程、企业管理等专业的高年级本科生或研究生及相关的工程技术人员。     

面向无人机物流的分布式任务调度

为提高无人机物流任务调度系统的实时性、可靠性和扩展性,本文提出一种面向通用航空无人机物流的分布式自主任务调度系统,并阐述了该系统的概念、架构、机制和运行模式;为实现物流任务的高效分配,适用于常规物流及紧急物流任务,结合集中调度及边缘自主两种调度模式。实验证明：该系统可为通用航空无人机物流提供一种有效途径,成为未来无人机物流的主要调度模式之一。     

浅析物流车辆GPS监控调度项目质量管理方案设计

现代化物流最主要的表现特征已经不仅仅是物流设备的先进,而是对物流进行科学化的管理,在科学化管理中最为重要的一个环节就是对物流车辆的有效监控调度。在物流企业中引入项目质量管理理念,就能保证物流车辆监控调度项目的质量,实时地对物流车辆进行调度、管理与监控,使物流管理的效率提高、监控调度项目质量提升,同时使项目管理的成本降低。本文通过对物流车辆GPS监控调度项目质量管理的目标以及项目质量管理方案设计的实例进行简单论述。     

基于HTCPN的分段生产物流系统建模与仿真:案例研究

船舶分段生产物流系统是一个典型的资源约束下的多项目调度复杂系统。根据S企业分段生产流程和资源的配置情况,利用层次赋时有色佩特里网(HTCP-net),结合提出的一种适合多项目调度的启发式规则,建立了分段生产物流系统模型。通过CPN tools进行仿真,验证了模型的正确性,发现了流程中的瓶颈,提出了合理的资源配置数量,通过对比证明了本启发式规则较适合船厂的多项目调度。文中的研究方法对复杂的造船流程的建模、仿真和分析具有一定参考价值。     

家电行业生产中的现代物流系统设计

分析了家电行业生产系统及其相应遥物流系统的特点，提出了复杂物流系统布局的基本原则，并给出了一布局示例；同时，根据大批量生产对物流计算机监控和管理系统的要求，提出了采用分布式调度管理的思想来构造系统的结构及确定系统运行方式的方案，并设计出了基于多进程的监控与管理系统软件的结构。     

海南烟草现代物流系统设想

本设想是建设进一步实现仓储管理系统、分拣系统、物流智能调度系统等信息共享，满足现代化物流配送中心作业和管理的需要，在物流作业过程控制的过程中，实现全面和精细的成本管理，并采取考核体系进行绩效考核，以实现烟草公司现代化流通企业管理模式，同时发挥省级烟草公司宏观管理的作用，建立以物流运营监控、分析为基础的省级海南烟草物流管理信息系统。     

光伏电池片生产车间智能物流调度研究

为实现光伏电池片生产车间物流的智能化改造,提出以最大化瓶颈工序机台产能的方式来最大化生产车间产能,并设计相关数学模型和智能物流调度算法。首先,通过对光伏电池片生产车间的问题描述与分析,建立以瓶颈工序机台产能最大化（瓶颈工序机台总停机时间最短）为目标的数学模型。然后,设计了嵌入模型约束规则的车间智能物流调度算法,包括物料调度算法、AGV选择与路径规划、AGV碰撞避免策略设计等,并提出了另一种物料调度算法作为对照方案。最后,通过仿真实验与分析,证明了所设计模型及智能物流调度算法的高效性及适用性,给予车间管理人员相应的管理启示。     

第三方物流联盟中物流任务的优化调度

为了提高物流服务水平、降低物流运作成本,针对由多个第三方物流服务商组建而成的第三方物流联盟中物流任务与物流服务资源的优化调度问题展开研究,综合考虑各第三方物流服务商资源节点提供物流活动服务成本和物流服务总时间,以时间最短和成本最低为优化目标,提出了基于时间和成本的多目标优化调度模型,针对目前物流任务调度优化模型中只考虑各物流服务资源节点本身的服务成本和时间,而未考虑执行各个物流活动之间的物流资源节点之间的衔接时间与衔接成本的问题,提出一种计算不同物流服务资源节点之间的物流服务衔接时间和衔接成本的方法,在模型中,考虑了物流资源服务时间窗限制问题.最后提出了一个改进的遗传算法进行模型求解,并通过算例验证了研究的有效性.     

基于混沌烟花算法的多车型供应链物流运输调度方法

由于传统调度方法以动态供应链的全局化角度出发,及时性较差,无法适应多车型物流运输环境,因此该文提出基于混沌烟花算法的多车型供应链物流运输调度方法。通过计算空间兼容量得到各车型的调度能力与运输能力,并基于混沌烟花算法的全局搜索寻优能力实现运输路径决策,设计了一个以运输路径畅通、运输成本较低、车辆油耗较低为目标的多车型供应链物流运输调度模型。利用该文方法求解1个供应商与20个零售网点的多车型供应链物流运输调度问题,用到3种车型,总运输距离为5081km,总油耗为525.84L,经验证该方法具有较强的寻优能力。     

Resource-Constrained Scheduling of Projects with Variable-Intensity Activities

We consider resource-constrained scheduling of projects including activities whose resource loading is flexible. We propose a model of project-oriented production in which the application rates of the various resources required by an activity are indexed by the performance speed or intensity of the activity. Heuristic algorithms are introduced for the assignment of activity intensities through time in lieu of traditional, fixed-intensity start-time scheduling. On test problems from the literature, the assignment algorithms are shown to outperform even optimal scheduling algorithms for the fixed-intensity case, yet the assignment algorithms are cornputationally practical for scheduling actual, large-scale industrial projects. We also extend the model and the assignment algorithms to admit production-like workflow dependencies and to interface with aggregate models for multi-project planning.     

Comparison of multi-objective evolutionary approaches for task scheduling in distributed computing systems

Parallel and distributed systems play an important part in the improvement of high performance computing. In these type of systems task scheduling is a key issue in achieving high performance of the system. In general, task scheduling problems have been shown to be NP-hard. As deterministic techniques consume much time in solving the problem, several heuristic methods are attempted in obtaining optimal solutions. This paper presents an application of Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) and a Non-dominated Sorting Particle Swarm Optimization Algorithm (NSPSO) to schedule independent tasks in a distributed system comprising of heterogeneous processors. The problem is formulated as a multi-objective optimization problem, aiming to obtain schedules achieving minimum makespan and flowtime. The applied algorithms generate Pareto set of global optimal solutions for the considered multi-objective scheduling problem. The algorithms are validated against a set of benchmark instances and the performance of the algorithms evaluated using standard metrics. Experimental results and performance measures infer that NSGA-II produces quality schedules compared to NSPSO.     

Adaptive opportunistic fair scheduling in power-controlled code division multiple access systems

The authors treat the multiuser scheduling problem for practical power-controlled code division multiple access (CDMA) systems under the opportunistic fair scheduling (OFS) framework. OFS is an important technique in wireless networks to achieve fair and efficient resource allocation. Power control is an effective resource management technique in CDMA systems. Given a certain user subset, the optimal power control scheme can be derived. Then the multiuser scheduling problem refers to the optimal user subset selection at each scheduling interval to maximise certain metric subject to some specific physical-layer constraints. The authors propose discrete stochastic approximation algorithms to adaptively select the user subset to maximise the instantaneous total throughput or a general utility. Both uplink and downlink scenarios are considered. They also consider the time-varying channels where the algorithm can track the time-varying optimal user subset. Simulation results to show the performance of the proposed algorithms in terms of the throughput/ utility maximisation, the fairness, the fast convergence and the tracking capability in time-varying environments are presented.     

物料需求计划不稳定性的模拟研究

介绍物料需求计划不稳定性的基本概念和模拟研究方法。在不确定性需求的流动式计划环境下,研究冻结参数和计划算法在不同生产条件下对物料需求计划不稳定性的影响。通过设计模拟实验和大量模拟计算及统计分析表现：费用结构、预测模式、冻结比例、计划周期和计划算法对物料需求计划不稳定性有较大影响,且交互作用显著。研究结果对减小物料需求计划的不稳定性有一定指导意义。     

生物计算网格中的在线调度技术研究

研究了在异构网格环境下的生物应用集成,定义了服务的提供者、部署者和使用者三种用户角色,设计了网格环境下的服务和资源整合机制,重点实现了用户管理以及作业调度控制等功能.根据计算资源的异构特点,设计了多种启发式调度算法.考虑到生物计算应用的不同类型,提出了自适应调度算法,该算法根据应用的特点动态选择启发式调度算法.实验表明,非阻塞调度优于阻塞调度方式;自适应调度算法比静态的在线调度算法有更好的性能,而在异构的网络中,带宽优先调度算法的性能比其他静态调度算法性能更好.     

港口业船舶优化调度模型设计

以港口船舶计划调度为研究背景,分析了港口计划调度的特征,提出了生产计划调度的系统框架,并在此基础上建立了以船舶拖期惩罚费用为最小,多种因素约束下的调度模型,将人工智能技术应用到实际生产调度中,实现分层次,为分研究多种资源约束条件下的计划调度和优化。     

A mixed-integer linear programming model for integrated production and preventive maintenance scheduling in the capital goods industry

The scheduling literature is extensive, but much of this work is theoretical and does not capture the complexity of real world systems. Capital goods companies produce products with deep and complex product structures, each of which requires the coordination of jobbing, batch, flow and assembly processes. Many components require numerous operations on multiple machines. Integrated scheduling problems simultaneously consider two or more simultaneous decisions. Previous production scheduling research in the capital goods industry has neglected maintenance scheduling and used metaheuristics with stochastic search that cannot guarantee an optimal solution. This paper presents a novel mixed integer linear programming model for simultaneously solving the integrated production and preventive maintenance scheduling problem in the capital goods industry, which was tested using data from a collaborating company. The objective was to minimise total costs including: tardiness and earliness penalty costs; component and assembly holding costs; preventive maintenance costs; and set-up, production, transfer and production idle time costs. Thus, the objective function and problem formulation were more extensive than previous research. The tool was successfully tested using data obtained from a collaborating company. It was found that the company’s total cost could be reduced by up to 63.5%.     

Heuristic Algorithms for Multistage Flowshop Scheduling Problem

This paper describes three heuristic algorithms for seeking a quick and near optimal solution to the n-job M-machine flowshop scheduling problem where jobs are processed on all machines in the same order, and the objective is to minimize either the mean flowtime or the maximum flowtime (make-span). The proposed heuristic algorithms are extensions of the heuristic rules of the author and are comparatively more effective in finding the optimal or near optimal solution to the problem. Computational experience pertaining to the effectiveness of proposed heuristic algorithms is discussed, indicating that the solutions obtained by proposed heuristic algorithms are compared to the Campbell-Dudek-Smith algorithm.     

Application and modeling of resource service trust-QoS evaluation in manufacturing grid system

Resource scheduling is the bottleneck in MGrid; the current research on resource scheduling strategy is mainly based on resource performance-QoS (quality of service), but the factor of trust-QoS is ignored, which will result in unreasonable and unpractical scheduling results. In order to enhance the validity and success rate of resource scheduling in a manufacturing grid (MGrid) system, provide high credible resource service abilities and results to the user, the concept of resource service trust-QoS is presented; trust-QoS was introduced into MGrid resource service scheduling and the important roles it plays emphasized. The trust problems existing in the resource service transaction between resource service demanders (RSD) and resource service providers (RSP) are put forward. The trust-QoS relationship model which is capable of capturing a comprehensive range of trust relationships which exist in the MGrid system is put forward. Then a two-layer resource service trust-QoS evaluation model is put forward, including an intra-domain trust-QoS evaluation model and an inter-domain trust-QoS evaluation model. The quantitative evaluating algorithms of trust-QoS degree value are proposed and described in detail, as well as the value of real-time and dynamic updating algorithms of trust-QoS degree. Finally, an application prototype, namely MBRSPP-MGrid, is developed. The experimental results of the case study show that the proposed models and algorithms are effective and useful.     

Group production scheduling for minimum total tardiness Part(I)

This paper considers the problem of group scheduling on a single stage to minimize total tardiness. It is assumed that jobs are classified into several groups on the basis of group technology. Optimal decision as to scheduling sequences will be made as to product group and specific job. This paper proves basic theorems that establish the relative order in which pairs of groups are processed in an optimal schedule. In general, scheduling problems of moderate size may be at least partially ordered so that very few schedules remain to be searched. Two practical algorithms for determining the optimal group schedule and the near optimal group schedule are proposed. Numerical examples are presented in detail.