压缩机预选启动与负荷分配自动控制设计开发

长输天然气管道压缩机多机启动时,单靠人工完成一系列操作,耗时时间长,检查判断不准确,多机并行运行能耗较大;为了提高压缩机多机启动效率和启机一次成功率,自动识别启机不满足项,快速投用负荷分配使机组做功效率最大化;利用数据采集与监视控制(SCADA)系统,对多台压缩机设定启动优先级、启动台数和负荷分配自动控制,优化逻辑控制器(PLC)与压缩机组控制系统(UCS)通信;创新实现了多机组一键顺序启机和启机后多机组负荷分配自动并网;经实际应用满足了中俄东线天然气管道黑河首站4台压缩机多机启动要求,降低了人力和能耗,提高了生产运行效率;为多行业多种输送介质的大型机械设备联合自动控制提供了丰富的参考价值,对UCS与SCADA的控制整合具有一定的指导意义.     

Scheduling the truckload operations in automated warehouses with alternative aisles for pallets

In this study, the scheduling of truck load operations in automated storage and retrieval systems is investigated. The problem is an extension of previous ones such that a pallet can be retrieved from a set of alternative aisles. It is modelled as a flexible job shop scheduling problem where the loads are considered as jobs, the pallets of a load are regarded as the operations, and the forklifts used to remove the retrieving items to the trucks are seen as machines. Minimization of maximum loading time is used as the objective to minimize the throughput time of orders and maximize the efficiency of the warehouse. A priority based genetic algorithm is presented to sequence the retrieving pallets. Permutation coding is used for encoding and a constructive algorithm generating active schedules for flexible job shop scheduling problem is applied for decoding. The proposed methodology is applied to a real problem arising in a warehouse installed by a leading supplier of automated materials handling and storage systems.     

Optimal and heuristic scheduling for intermittent gas lift operations

The purpose of this paper is to study the scheduling problem in the intermittent gas lift operations, and to present a mixed integer programming (MIP) formulation for solving it. Some heuristic methods are also developed to gain insight into the gas lift scheduling problem of larger problem size. Finally, the performances of the heuristic methods are compared and contrasted based on the computational efficiency and the solution quality.     

调度问题的形式化描述

本文介绍高层次综合中调度问题的形式化描述。该描述将调度问题形式化为整数规划问题。它全面地考虑了调度问题的时间约束与资源约束、多周期操作与链式操作，流水线功能单元与非流水线功能单元以及分枝操作的调度问题。它为解决调度问题提供了一个较好的数学模型。     

Flexible job shop scheduling with parallel machines using Genetic Algorithm and Grouping Genetic Algorithm

Based on Genetic Algorithm (GA) and Grouping Genetic Algorithm (GGA), this research develops a scheduling algorithm for job shop scheduling problem with parallel machines and reentrant process. This algorithm consists of two major modules: machine selection module (MSM) and operation scheduling module (OSM). MSM helps an operation to select one of the parallel machines to process it. OSM is then used to arrange the sequences of all operations assigned to each machine. A real weapon production factory is used as a case study to evaluate the performance of the proposed algorithm. Due to the high penalty of late delivery in military orders and high cost of equipment investment, total tardiness, total machine idle time and makespan are important performance measures used in this study. Based on the design of experiments, the parameters setting for GA and GGA are identified. Simulation results demonstrate that MSM and OSM respectively using GGA and GA outperform current methods used in practice.     

Fuzzy possibilistic modeling and sensitivity analysis for optimal fuel gas scheduling in refinery

In refinery, fuel gas which is continuously generated during the production process is one of the most important energy sources. Optimal scheduling of fuel gas system helps the refinery to achieve energy cost reduction and cleaner production. However, imprecise natures in the system, such as prediction of production rate of fuel gas, prediction of energy demand of the equipments and cost coefficient in the objective function, make the deterministic optimization method which requires well-defined and precise data cannot be competent for the fuel gas scheduling problem. In this study, fuzzy possibilistic programming (FPP) method is proposed to deal with these imprecise natures by triangular possibility distributions. The fuzzy possibilistic model is transformed into usual mathematical model by definition of necessity measure and the α-level method. Although FPP models have been widely applied to modeling, few research works have been reported on the performance evaluation, namely sensitivity analysis, of these models. Marginal value analysis, which is always used to provide additional economic information, is proposed to give the sensitivity analysis in the paper. This method is demonstrated to be much more flexible than the simulation method. Particularly, the analytical method is adopted to examine how the imprecise natures in the fuel gas system affect the scheduling results.     

软件定义网络架构下基于流调度代价的数据中心网络拥塞控制路由算法

针对传统数据中心网络极易发生拥塞的问题,提出了在软件定义网络（SDN）的架构下设计基于流调度代价的拥塞控制路由算法加以解决。首先,进行拥塞链路上的大小流区分,并对所有大流的各条等价路径进行路径开销权重的计算,选择权重最小的路径作为可用调度路径;然后,使用调度后路径开销变化量和流占用带宽比例来共同定义流调度代价;最终选择调度代价最小的流进行调度。仿真结果表明,所提算法能在网络发生拥塞时降低了拥塞链路上的负荷,并且与仅进行流路径选择的拥塞控制算法相比,提高了链路利用率,减少了流传输时间,使得网络链路资源得到更好的利用。     

一种针对线性循环结构的非线性静态调度策略

现有OpenMP调度策略通常采用动态策略处理程序中的线性循环结构,存在负载不均衡和调度开销大的问题。提出一种针对线性递增或线性递减循环结构的非线性静态调度策略Nonlinear_static。将线性循环负载均匀变化参数与总负载、负载峰值、线程数相结合构建调度模型,计算循环迭代在线程上的映射,使迭代块大小呈非线性递增或递减趋势。将线性循环的负载平均地分配在每个线程上,并在开源OMPi编译器中进行编码。在Adjoint Convolution、Compute Pots、Matrix Multiplication、Mandelbrot Set应用程序上进行多线程调度,实验结果表明,相比静态调度、动态调度、指导调度等策略,Nonlinear_static调度策略在处理线性循环结构时执行时间缩短了5%~10%,且具有无调度开销的优点。     

Using multiple objective tabu search and grammars to model and solve multi-objective flexible job shop scheduling problems

A linguistic-based meta-heuristic modeling and solution approach for solving the flexible job shop scheduling problem (FJSSP) is presented in this study. FJSSP is an extension of the classical job-shop scheduling problem. The problem definition is to assign each operation to a machine out of a set of capable machines (the routing problem) and to order the operations on the machines (the sequencing problem), such that predefined performance measures are optimized. In this research, the scope of the problem is widened by taking into account the alternative process plans for each part (process plan selection problem). Probabilistic selection of alternative process plans and machines are also considered. The FJSSP is presented as a grammar and the productions in the grammar are defined as controls (Baykasolu, 2002). Using these controls and Giffler and Thompson's (1960) priority rule-based heuristic along with the multiple objective tabu search algorithm of Baykasolu et al. (1999) FJSSP is solved. This novel approach simplifies the modeling process of the FJSSP and enables usage of existing job shop scheduling algorithms for its fast solution. Instead of scheduling job shops with inflexible algorithms that cannot take into account the flexibility which is available in the job shop, the present algorithm is developed which can take into account the flexibility during scheduling. Such an approach will considerably increase the responsiveness of the job shops.     

A multi-objective reinforcement learning algorithm for deadline constrained scientific workflow scheduling in clouds

Recently, a growing number of scientific applications have been migrated into the cloud. To deal with the problems brought by clouds, more and more researchers start to consider multiple optimization goals in workflow scheduling. However, the previous works ignore some details, which are challenging but essential. Most existing multi-objective workflow scheduling algorithms overlook weight selection, which may result in the quality degradation of solutions. Besides, we find that the famous partial critical path (PCP) strategy, which has been widely used to meet the deadline constraint, can not accurately reflect the situation of each time step. Workflow scheduling is an NP-hard problem, so self-optimizing algorithms are more suitable to solve it.In this paper, the aim is to solve a workflow scheduling problem with a deadline constraint. We design a deadline constrained scientific workflow scheduling algorithm based on multi-objective reinforcement learning (RL) called DCMORL. DCMORL uses the Chebyshev scalarization function to scalarize its Q-values. This method is good at choosing weights for objectives. We propose an improved version of the PCP strategy calledMPCP. The sub-deadlines in MPCP regularly update during the scheduling phase, so they can accurately reflect the situation of each time step. The optimization objectives in this paper include minimizing the execution cost and energy consumption within a given deadline. Finally, we use four scientific workflows to compare DCMORL and several representative scheduling algorithms. The results indicate that DCMORL outperforms the above algorithms. As far as we know, it is the first time to apply RL to a deadline constrained workflow scheduling problem.     

基于软件定义网络的数据中心网络负载均衡算法研究

针对传统网络的分布式架构使得负载均衡技术难以满足低成本、高灵活性、自适应调整的要求,提出一种基于SDN的数据中心网络负载均衡算法。首先,根据路径当前负载状况和链路负载波动为路径设置了一个权重,并以此作为路径选择依据;其次,设置了一个负载均衡度用于衡量网络负载状况;最后,针对需要调度的流,进一步限定了其流量大小范围,保证了高效的流调度。仿真结果表明,与其他算法相比,所提算法能有效提高网络资源利用率并均衡全网负载。     

An intelligent logistics support system for enhancing the airfreight forwarding business

Abstract: Recent research related to the aircraft container loading and scheduling problem for airfreight forwarding business has seen significant advances in terms of load plan optimization, taking into account the cost and volume of packed boxes. In today's competitive industrial environment, it is essential that freight forwarders are able to collaborate with carriers (airline companies) to achieve the best possible selection of logistics workflow. However, study of contemporary research publications indicates that there is a dearth of articles related to the design and implementation of an intelligent logistics system to support decision‐making on carrier selection, aircraft container loading plans as well as carrier benchmarking. This paper presents an intelligent logistics support system (ILSS) which is able to provide expert advice related to the airfreight forwarding business, enhancing the logistics operations in relevant activities within the value chain of tasks. ILSS comprises a heuristics‐based intelligent expert system which supports carrier searching and cargo trading planning as well as load plan generation. The proposed approach is meant to enhance various operations in the airfreight forwarding business, adopting computational intelligence technologies such as rule‐based reasoning to provide domain advice and heuristics to support the generation of load plans. After potential outcomes are generated by the heuristics‐based intelligent expert system, a neural network engine is applied to support prediction of unexpected events. To validate the viability of this approach, a production system using the ILSS has been developed and subsequently applied in an emulated airfreight forwarding environment. The application results indicate that the operation time from searching for potential carriers to the execution of the order is greatly reduced. In this paper, details related to the structure, design and implementation of the ILSS are also covered with the inclusion of the actual program codes for building the prototype.     

具有线性恶化加工时间的调度问题

讨论了工件具有线性恶化加工时间的调度问题.在这类问题中,工件的恶化函数为线性 函数.对单机调度问题中目标函数为极小化最大完工时间加权完工时间和,最大延误以及最大费 用等问题分别给出了最优算法.对两台机器极小化最大完工时间的Flowshop问题,证明了利用 Johnson规则可以得到最优调度.对于一般情况,如果同一工件的工序的加工时间均相等,则 Flowshop问题可以转化为单机问题.     

On a stochastic sequencing and scheduling problem

We present a framework for solving multistage pure 0–1 programs for a widely used sequencing and scheduling problem with uncertainty in the objective function coefficients, the constraint matrix and the right-hand side. The problem has the following form: given a set of operations to be executed along a time horizon, find a schedule to minimize a function included by the expected operations cost over the scenarios under consideration, subject to a set of constraints. Typical elements are: limited availability of the resources, multiperiod operations, subsets of operations with exclusivity and implicative constraints, precedence relationships in the execution of the operations, etc. The stochasticity is in the resources’ consumption by the operations, their availability and the operations cost along the time horizon. A multistage scenario analysis with complete recourse is used. Given the high dimensions of the problem and its combinatorial nature, it is not realistic to obtain the optimal solution for the problem. Instead, we present the so-called Fix-and-Relax Coordination algorithmic framework to exploit the characteristics of the non-anticipativity constraints for each scenario group in the stochastic model. This exploitation basically consists of selectively exploring the nodes of the branching trees in which the branch-and-bound tree is decomposed while the non-anticipativity constraints are relaxed. The algorithm is specifically designed for coordinating and reinforcing the node pruning, and the branching node and variable selection at each branching tree, such that the non-anticipativity constraints are satisfied. Some computational experience is reported.     

Minimizing fuel cost in gas transmission networks by dynamic programming and adaptive discretization

In this paper, the problem of computing optimal transportation plans for natural gas by means of compressor stations in pipeline networks is addressed. The non-linear (non-convex) mathematical model considers two types of continuous decision variables: mass flow rate along each arc, and gas pressure level at each node. The problem arises due to the presence of costs incurred when running compressors in order to keep the gas flowing through the system. Hence, the assignment of optimal values to flow and pressure variables such that the total fuel cost is minimized turns out to be essential to the gas industry. The first contribution from the paper is a solution method based on dynamic programming applied to a discretized version of the problem. By utilizing the concept of a tree decomposition, our approach can handle transmission networks of arbitrary structure, which makes it distinguished from previously suggested methods. The second contribution is a discretization scheme that keeps the computational effort low, even in instances where the running time is sensitive to the size of the mesh. Several computational experiments demonstrate that our methods are superior to a commercially available local optimizer.     

Multi-phase integrated scheduling of hybrid tasks in cloud manufacturing environment

Cloud manufacturing paradigm aims at gathering distributed manufacturing resources and enterprises to serve for more customized production. Production order which involving several tasks can be taken by distributed suppliers collaboratively at lower cost. The cloud manufacturing platform is responsible for not only arranging reasonable priorities, suitable suppliers, and production processes to multiple orders, but also scheduling hybrid tasks from different orders to manufacturing resources. To maximize the production efficiency and balance the trade-off among different production orders, this paper studies multi-phase integrated scheduling of hybrid tasks in cloud manufacturing environment, which containing order priority assignment, supplier and production process selection, and production line scheduling. Five key objectives are taken into account to analyze the interconnections among different resources and production processes. Six representative multi-objective evolutionary algorithms are adopted to solve the integrated scheduling problem. Experimental results on six production cases show that integrated scheduling is more effective than the traditional step-by-step decision, leading to less production cost and time. In addition, a comparison among the six algorithms is carried out to determine the one best suited for the integrated scheduling problem in different circumstances.     

基于Docker的云资源弹性调度策略

针对云资源弹性调度问题,结合Ceph数据存储的特点,提出一种基于Docker容器的云资源弹性调度策略。首先,指出Docker容器数据卷不能跨主机的特性给应用在线迁移带来了困难,并对Ceph集群的数据存储方法进行改进;然后,建立了一个基于节点综合负载的资源调度优化模型;最后,将Ceph集群和Docker容器的特点相结合,利用Docker Swarm实现了既考虑数据存储、又考虑集群负载的应用容器部署算法和应用在线迁移算法。实验结果表明,与一些调度策略相比,该调度策略对集群资源进行了更细粒度的划分,实现了云平台资源的弹性调度,并在保证应用性能的同时,达到了合理利用云平台资源和降低数据中心运营成本的目的。     

An Optimized Oxygen System Scheduling With Electricity Cost Consideration in Steel Industry

As an essential energy resource in steel industry, oxygen is widely utilized in many production procedures. With different demands of the oxygen amount, a gap between the generation and consumption always occurs. Therefore, its related optimization and scheduling work along with the electricity cost to fill the gap has a great impact on daily production and efficient energy utilization in steel plant. Considering an oxygen system in a steel plant in China, a nonlinear programming model for oxygen system scheduling is proposed in this study, which concerns not only the practical characteristics of the energy pipeline network, but also the electricity cost acquired by a fitting regression modeling between the load of air separation units (ASU) and its corresponding electricity consumption. A set of constraints is formulated for restricting the practical adjusting capacity and filling the imbalance gap of oxygen. To solve the proposed scheduling model with electricity cost consideration, a particle swarm optimization (PSO) algorithm is then adopted. To verify the effectiveness of the proposed approach, a large number of experiments employing the real data from this plant are carried out, both for the fitting regression and the scheduling optimization phases. And the results demonstrate that such a practice-based solution successfully resolves the oxygen scheduling problem and simultaneously minimizes the electricity cost, which will be beneficial for the enterprise.      

基于改进遗传算法的车辆调度模型

随着运输网络复杂程度的不断增加,运输车辆会遇到车祸、拥堵等干扰,传统的车辆调度模型缺少对这种干扰风险的分析,无法建立较为准确的调度模型,造成调度车辆遇到干扰时,调度效率大幅降低。为了避免上述缺陷,提出了一种基于改进遗传算法的车辆路径调度算法,引入扬长避短的思想,对所有的车辆运输路径进行编码,并对所有的路径进行选择、交叉和变异运算,运用模拟退火算法提高算法的寻优性能,形成车辆的高效调度。实验结果表明,利用改进算法进行车辆调度,能够提高运输的效率,从而满足实际运输需求。     

A tabu search heuristic for the quay crane scheduling problem

This paper proposes a tabu search heuristic for the Quay Crane Scheduling Problem (QCSP), the problem of scheduling a fixed number of quay cranes in order to load and unload containers into and from a ship. The optimality criterion considered is the minimum completion time. Precedence and non-simultaneity constraints between tasks are taken into account. The former originate from the different kind of operations that each crane has to perform; the latter are needed in order to avoid interferences between the cranes. The QCSP is decomposed into a routing problem and a scheduling problem. The routing problem is solved by a tabu search heuristic, while a local search technique is used to generate the solution of the scheduling problem. This is done by minimizing the longest path length in a disjunctive graph. The effectiveness of our algorithm is assessed by comparing it to a branch-and-cut algorithm and to a Greedy Randomized Adaptive Search Procedure (GRASP).