Mathematical model and discrete artificial Bee Colony algorithm for distributed integrated process planning and scheduling

With the development of the globalization of economy and manufacturing industry, distributed manufacturing mode has become a hot topic in current production research. In the context of distributed manufacturing, one job has different process routes in different workshops because of heterogeneous manufacturing resources and manufacturing environments in each factory. Considering the heterogeneous process planning problems and shop scheduling problems simultaneously can take advantage of the characteristics of distributed factories to finish the processing task well. Thus, a novel network-based mixed-integer linear programming (MILP) model is established for distributed integrated process planning and scheduling problem (DIPPS). The paper designs a new encoding method based on the process network and its OR-nodes, and then proposes a discrete artificial bee colony algorithm (DABC) to solve the DIPPS problem. The proposed DABC can guarantee the feasibility of individuals via specially-designed mapping and switching operations, so that the process precedence constraints contained by the network graph can be satisfied in the entire procedure of the DABC algorithm. Finally, the proposed MILP model is verified and the proposed DABC is tested through some open benchmarks. By comparing with other powerful reported algorithms and obtaining new better solutions, the experiment results prove the effectiveness of the proposed model and DABC algorithm successfully.     

Real-time integrated production-scheduling and maintenance-planning in a flexible job shop with machine deterioration and condition-based maintenance

The introduction of modern technologies in manufacturing is contributing to the emergence of smart (and data-driven) manufacturing systems, known as Industry 4.0. The benefits of adopting such technologies can be fully utilized by presenting optimization models in every step of the decision-making process. This includes the optimization of maintenance plans and production schedules, which are two essential aspects of any manufacturing process. In this paper, we consider the real-time joint optimization of maintenance planning and production scheduling in smart manufacturing systems. We have considered a flexible job shop production layout and addressed several issues that usually take place in practice. The addressed issues are: new job arrivals, unexpected due date changes, machine degradation, random breakdowns, minimal repairs, and condition-based maintenance (CBM). We have proposed a real-time optimization-based system that utilizes a modified hybrid genetic algorithm, an integrated proactive-reactive optimization model, and hybrid rescheduling policies. A set of modified benchmark problems is used to test the proposed system by comparing its performance to several other optimization algorithms and methods used in practice. The results show the superiority of the proposed system for solving the problem under study. The results also emphasize the importance of the quality of the generated baseline plans (i.e., initial integrated plans), the use of hybrid rescheduling policies, and the importance of rescheduling times (i.e., reaction times) for cost savings.     

Bicriteria scheduling of a material handling robot in an m-machine cell to minimize the energy consumption of the robot and the cycle time

This study considers a flowshop type production system consisting of m machines. A material handling robot transports the parts between the machines and loads and unloads the machines. We consider the sequencing of the robot moves and determining the speeds of these moves simultaneously. These decisions affect both the robot’s energy consumption and the production speed of the system. In this study, these two objectives are considered simultaneously. We propose a second order cone programming formulation to find Pareto efficient solutions. We also develop a heuristic algorithm that finds a set of approximate Pareto efficient solutions. The conic formulation can find robot schedules for small cells with less number of machines in reasonable computation times. Our heuristic algorithm can generate a large set of approximate Pareto efficient solutions in a very short computational time. Proposed solution approaches help the decision-maker to achieve the best trade-off between the throughput of a cell and the energy efficiency of a material handling robot.     

成像卫星星地联合成像调度

成像卫星星地联合调度问题,涉及调度对象众多,约束条件复杂,需要考虑任务的观测、回传2个过程,是一个具有两层时间窗口约束的双层优化问题,统一建模困难。根据问题的特点,采用基于阶段优化的方式,降低了问题的复杂性。把问题分为观测调度阶段和数据回传调度阶段,分别给出了优化目标和约束条件,建立了基于阶段优化的成像卫星星地联合调度模型,实现了从任务观测到数据回传的全过程调度。仿真实例表明,该方法能够有效解决多星多站的协同任务调度问题。     

Adaptive load balancing algorithm for multiple homing mobile nodes

In places where mobile users can access multiple wireless networks simultaneously, a multipath scheduling algorithm can benefit the performance of wireless networks and improve the experience of mobile users. However, existing literature shows that it may not be the case, especially for TCP flows. According to early investigations, there are mainly two reasons that result in bad performance of TCP flows in wireless networks. One is the occurrence of out-of-order packets due to different delays in multiple paths. The other is the packet loss which is resulted from the limited bandwidth of wireless networks. To better exploit multipath scheduling for TCP flows, this paper presents a new scheduling algorithm named Adaptive Load Balancing Algorithm (ALBAM) to split traffic across multiple wireless links within the ISP infrastructure. Targeting at solving the two adverse impacts on TCP flows, ALBAM develops two techniques. Firstly, ALBAM takes advantage of the bursty nature of TCP flows and performs scheduling at the flowlet granularity where the packet interval is large enough to compensate for the different path delays. Secondly, ALBAM develops a Packet Number Estimation Algorithm (PNEA) to predict the buffer usage in each path. With PNEA, ALBAM can prevent buffer overflow and schedule the TCP flow to a less congested path before it suffers packet loss. Simulations show that ALBAM can provide better performance to TCP connections than its other counterparts.     

Distributed constraint optimization with MULBS: A case study on collaborative meeting scheduling

This paper introduces MULBS, a new DCOP (distributed constraint optimization problem) algorithm and also presents a DCOP formulation for scheduling of distributed meetings in collaborative environments. Scheduling in CSCWD can be seen as a DCOP where variables represent time slots and values are resources of a production system (machines, raw-materials, hardware components, etc.) or management system (meetings, project tasks, human resources, money, etc). Therefore, a DCOP algorithm must find a set of variable assignments that maximize an objective function taking constraints into account. However, it is well known that such problems are NP-complete and that more research must be done to obtain feasible and reliable computational approaches. Thus, DCOP emerges as a very promising technique: the search space is decomposed into smaller spaces and agents solve local problems, collaborating in order to achieve a global solution. We show with empirical experiments that MULBS outperforms some of the state-of-the-art algorithms for DCOP, guaranteeing high quality solutions using less computational resources for the distributed meeting scheduling task.     

供电局调度生产管理系统的设计与实现

针对延安供电局电网调度生产管理方面存在的问题,设计了一套调度生产管理系统（OMS）。通过对企业内部业务需求的详细分析,建立了系统的体系结构和功能,详细阐述了系统的设计过程,该系统现已投入使用,性能良好。     

Continuous process control using neural networks

We present some adaptive control strategies based on neural networks that can be used for designing controllers for continuous process control problems. Specifically, a learning algorithm has been formulated based on reinforcement learning, a weakly supervised learning technique, to solve set-point control and control scheduling for continuous processes where the process cannot be modeled easily. It is shown how reinforcement learning can be used to learn the control strategy adaptively based on exploration of the control space without making assumptions about the process model. A new learning scheme, handicapped learning, was developed to learn a control schedule that specifies a schedule of set points. Applications studied include the control of a nonisothermal continuously stirred tank reactor at its unstable state and the learning of the daily time-temperature schedule for an environment controller. Experimental results demonstrate good learning performance, indicating that the learning algorithm can be used for solving transient startup and boundary value control problems.     

FMS的模型参考自适应调度

用运筹学研究生产线调度问题已有几十年的历史,但由此得出的调度策略的实时性很差.1986年自动控制工作者提出了“PMS动态自适应计算机调”一词.令人感到十分新奇.可惜.从未见到任何有关的论文.本文研究FMS的模型参考自适应调度.本文采用我们提出的考虑了有限缓冲器容量的极大代数模型.环境的影响反映于对象参数的摄动,本文采用我们提出的摄动计算方法.调度器的设计采用次梯度法,以求解连续变量和离散变量混合的优化问题,附有数字实例,计算和仿真表明:FMS的模型参考自适应调度是可行的,是值得继续深入研究的。     

Some efficient solutions to the affine scheduling problem. Part II. Multidimensional time

This paper extends the algorithms which were developed in Part I to cases in which there is no affine schedule, i.e. to problems whose parallel complexity is polynomial but not linear. The natural generalization is to multidimensional schedules with lexicographic ordering as temporal succession. Multidimensional affine schedules, are, in a sense, equivalent to polynomial schedules, and are much easier to handle automatically. Furthermore, there is a strong connection between multidimensional schedules and loop nests, which allows one to prove that a static control program always has a multidimensional schedule. Roughly, a larger dimension indicates less parallelism. In the algorithm which is presented here, this dimension is computed dynamically, and is just sufficient for scheduling the source program. The algorithm lends itself to a divide and conquer strategy. The paper gives some experimental evidence for the applicability, performances and limitations of the algorithm.