多资源作业车间的混合自适应GA优化调度

针对多资源约束的车间调度问题,将启发式算法和自适应GA优化方法结合起来,提出了混合自适应GA方法,建立了多资源约束的车间优化调度模型.根据启发式调度算法中优先规则对调度目标的影响,设计了新的编码规则.采用正弦函数作为自适应因子,使得交叉概率和变异概率随群体的适应度自动改变,提高了运算的效率,克服了启发式算法和普通GA的缺陷.通过实例仿真并与其他算法比较结果表明,混合自适应GA算法可以很好的解决作业车间在机床、刀具等多种生产资源约束下的优化调度,并在评价指标上较其他算法更优.     

基于优先规则的模糊资源受限多项目调度

在分析国内外相关研究现状的基础上,结合资源受限多项目调度问题的特点,针对多项目中各个工序资源参数的不确定性,通过对工程项目工序资源需求量的模糊表示,建立了模糊资源受限多项目调度模型,并运用基于优先规则的启发式算法对所建立的模型进行调度计算,最后通过算例验证模型的有效性和可行性,以实现多项目资源的优化配置。     

研发型企业多项目人力资源调度研究——基于蚁群优化的超启发式算法

现阶段,研发型企业的项目处于多项目环境下,为了解决多项目并行时人力资源争夺问题,本文针对该类企业多项目管理中人力资源调度进行优化研究,以考虑项目延期惩罚成本的最小总成本为目标函数,将现实问题抽象建模。基于国内外的研究提出了一种超启发式算法进行求解,该算法将人力资源调度问题分为项目活动分配和人员选择项目活动两个部分,采用蚁群优化作为高层启发式策略搜索低层启发式规则,再进一步根据规则解构造出可行解。最后本研究设计多组仿真实验与启发式规则进行对比,结果表明该算法有较好的搜索性能,为人力资源的调度问题提供了新的解决方案。     

集成启发式规则的混合整数规划调度模型

针对生产调度优化问题,提出了一种集成启发式规则的混合整数规划调度模型。在分析启发式规则逻辑关系的基础上,用布尔逻辑变量表达启发式规则建立了启发式规则的基本逻辑表达式及其等价代数表达式,基于基本表达式给出了启发式规则的数学表达。将启发式规则的代数表达式与混合整数规划相结合,实现了启发式规则的集成。集成启发式规则的混合整数规划调度模型在结合启发式规则的基础上进行数学优化,克服了混合整数规划难以直接利用经验规则和基于规则的调度优化性难以保证的不足。最后以某炼油过程生产调度为例说明了模型的有效性。     

复杂型面智能生产单元任务−刀具联合动态调度策略

以复杂型面智能生产单元为背景,针对多机器的任务?刀具联合动态调度问题展开研究。通过分析复杂型面智能生产单元的作业流程和特征,建立任务?刀具联合调度问题的数学模型。结合通过对问题进行过程分解的方式构建一种组合规则调度算法生产框架,并通过在框架中嵌入启发式规则的方式生成72种组合规则算法。设计大量的算例测试集,通过对比、分析72种算法在差异化环境配置下的仿真结果,对比不同系统指标下算法的表现情况,发现基于FNOP规则构建的算法在75%的场景中均能取得较优的求解质量。研究成果为车间生产管理人员制定任务?刀具调度策略提供一定指导作用。     

基于仿真分析的随机活动工期鲁棒性项目调度优化

在不确定环境下，项目的计划与控制对项目能否顺利实施十分重要。考虑随机活动工期，研究了如何借助仿真分析确定鲁棒性调度优化的最佳项目输入参数组合。首先，对研究问题进行界定；随后，构建鲁棒性项目调度优化模型，并设计仿真分析流程，建立项目输入参数与仿真指标之间的对应关系；接着，开发了禁忌搜索启发式算法对鲁棒性调度优化模型进行求解，并制定了仿真实施流程；最后，通过一个实际案例对研究进行说明。得到结论：进度计划的鲁棒性随项目计划工期与资源可用量的增大而增大，达到一定值后保持稳定；当选取不同的仿真指标时，得到的最佳输入参数的组合不同，管理者可根据实际需求选取相应的指标。研究能够为不确定条件下鲁棒性项目调度提供指导，有助于项目进度计划鲁棒性的提升。     

考虑资源均衡使用的模具项目调度应用

多资源均衡是项目调度的基本问题之一.分析了国内某中型模具企业生产资源的利用现状,建立了模具项目调度的资源均衡使用数学模型,利用求解项目资源均衡使用问题的启发式算法,结合企业生产的实际情况,进行了验证.应用实践表明,该数学模型和启发式算法具有实际可操作性.     

基于优先规则的船舶分段空间调度算法

为有效解决船舶分段的空间调度问题,提出了一种基于优先规则的求解算法。首先利用优先规则和禁忌搜索算法产生可行的分段调度序列,再采用一种启发式定位策略——最下最左填满策略对产生的调度序列进行解码,以评估调度序列的优劣。算法不断迭代,最终可得到近似最优解。对船厂的实际生产数据进行了实证分析,并与现有的算法进行了对比,验证了所提出的算法在空间调度问题上的有效性和优越性。     

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。     

船舶内部物流转运系统仿真

由于船舶内部物流转运系统输送路径复杂,物料分拆的环节多,很难建立数学模型对整个物流系统进行全面的描述或者对物流系统中运动的货物进行实时的跟踪,本文采用系统仿真技术验证船舶内部转运系统设计方案,通过系统仿真确定系统的瓶颈和转运能力.基于仿真分析结果,优化局部转运传输策略,改进了船舶内部转运系统的配置方案,提高了系统的整体转运能力.     

Scheduling of Automated Guided Vehicles in a Decision Making Hierarchy

Scheduling efforts made without considering the special limitations of the material handling system might lead to infeasible results. An analytical model is proposed, first, to incorporate the automated guided vehicle (AGV) system into the overall decision making hierarchy. A mathematical formulation is developed to include interaction between the AGV module and other modules in the system by considering the restrictions of the material handling system. A micro-opportunistic approach is then proposed to solve the AGV scheduling problem. Finally, the proposed method is compared with a number of dispatching rules.     

ROBOT TASK SCHEDULING IN A FLEXIBLE MANUFACTURING CELL

Effective sequencing and scheduling of the material handling system can have a major impact on the productivity of the manufacturing system. This is especially true in the case where material handling times are on par with machine processing times. In a dynamic, real-time environment, the optimal solution of this scheduling problem may be computationally infeasible.

In this paper, we develop a branch and bound approach which is coupled with quick, effective bounds to optimize the movement of a robot which serves the material handling requirements within a manufacturing cell. Computational results are given which explore the tradeoff between computation time and deviation from optimal for different scenarios.     

Development of a simulation-based optimisation for controlling operation allocation and material handling equipment selection in FMS

Flexible manufacturing system (FMS) is described as a set of computerised numerical controlled machines, input–output buffers interconnected by automated material handling devices. This paper develops a bi-objective operation allocation and material handling equipment selection problem in FMS with the aim of minimising the machine operation, material handling and machine setup costs and maximising the machine utilisation. The proposed model is solved by a modified chaotic ant swarm simulation based optimisation (CAS²O) while applying pre-selection and discrete recombination operators is surveyed a capable method to simulate different experiments of FMS problems. A test problem is selected from the literature to evaluate the performance of the proposed approach. The results validate the effectiveness of the proposed method to solve the FMS scheduling problem.     

基于AIS数据的航运物流港口调度优化研究

目的 针对宁波舟山港区的复杂航道水域与密集物流交通流,研究更加有效的调度方案,达成调度时间和等待时间最小化,即效率最大化。方法 分析宁波舟山港区航道的航行情况,提出交会处复杂航道水域存在的问题,以调度时间和等待时间最小为目标的多目标函数,建立复杂航道水域船舶调度模型。针对大量的船舶AIS数据,构建基于神经网络的航道水域调度模型,对不同类型、不同大小的船舶建立速度变化和船舶预测模型,实现对船舶调度状态的预测。设计以传统粒子群算法为基础的改良版船舶调度算法。结果 算法对模型求解表明,根据不同船长与间距可判别交通流拥挤程度进而对船舶进行调度。通过模型预测到可能产生拥挤,则应当选择小型船只走条帚门航道,大型船只走虾峙门航道,并且尽量避免产生拥堵。结论 使用该模型与算法可以有效地提升船舶调度效率,为复杂航运物流港口调度优化研究提供了一定理论基础。     

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.     

An introduction to the segmented flow approach for discrete material flow systems

Due to increases in versatility and the complexity of demands in modern manufacturing systems a growing need has evolved t0 simplify the control of these systems while maintaining a high degree of efficiency and flexibility in operation. The segmented flow approach (SFT) suggested in this paper is a possible solution to these needs. This structure simplifies the control task by reducing the need for dispatching and by eliminating the need for routeing material handling devices and scheduling traffic at intersections. Each task involves only one material handling device and one possible route. This structure maintains a high degree of efficiency by operating material handling devices in a bidirectional mode and eliminates congestion and blocking by operating them on mutually exclusive tracks. This paper introduces a design procedure for an SFT implemented system using a minimum cost objective, and compares the throughput and time in system performance of this system to a conventional system and a tandem type system by means of simulation.     

Hybrid genetic algorithm for test bed scheduling problems

In this paper, we address the scheduling problem for a heavy industry company which provides ship engines for shipbuilding companies. Before being delivered to customers, ship engines are assembled, tested and disassembled on the test beds. Because of limited test bed facilities, it is impossible for the ship engine company to satisfy all customers’ orders. Therefore, they must select the orders that can be feasibly scheduled to maximise profit. An integer programming model is developed for order selection and test bed scheduling but it cannot handle large problems in a reasonable amount of time. Consequently, a hybrid genetic algorithm (GA) is suggested to solve the developed model. Several experiments have been carried out to demonstrate the performance of the proposed hybrid GA in scheduling test beds. The results show that the hybrid GA performs with an outstanding run-time and small errors in comparison with the integer programming model.     

一个改进的单抓钩周期性排序模型及其在自动化学处理线中的应用

计算机控制的抓钩广泛用于自动化学处理线的工件的运送。抓钩的排序直接影响系统的生产率，抓钩排序的目标是对运送进行排序以极大化生产率。当某工序处理时间非常长时，该工序成为瓶颈。为了去除该瓶颈，系统可以为该工序设计多个处理槽，这称为“多重处理槽”问题。本文提出一个改进的混合整数规划模型以求解有“多重处理槽”的单抓钩周期性排序问题的最优解。实例表明所提出的方法是有效的。     

Real-world extensions to scheduling algorithms based on lagrangian relaxation

Recently, efficient scheduling algorithms based on Lagrangian relaxation have been proposed for scheduling parallel machine systems and job shops. In this article, we develop real-world extensions to these scheduling methods. In the first part of the paper, we consider the problem of scheduling single operation jobs on parallel identical machines and extend the methodology to handle multiple classes of jobs, taking into account setup times and setup costs. The proposed methodology uses Lagrangian relaxation and simulated annealing in a hybrid framework. In the second part of the paper, we consider a Lagrangian relaxation based method for scheduling job shops and extend it to obtain a scheduling methodology for a real-world flexible manufacturing system with centralized material handling. This research was supported in part by the Office of Naval Research and the Department of Science and Technology grant N00014-93-1017.