基于供应链的协同生产调度研究发展现状与展望*

供应链的协同生产调度是确保供应链整体运作水平与效率的核心问题,阐述了供应链环境下协同生产调度的模型研究概况以及其关键技术,分别讨论multi-agent技术、协商机制、调度算法等技术研究现状,并分析了目前此类研究中存在的问题和不足。最后展望了供应链环境下协同生产调度问题进一步的研究方向。     

Agent-based modeling of supply chains for distributed scheduling

This paper considers a supply chain that comprises multiple independent and autonomous enterprises (project managers) that seek and select various contractors to complete operations of their project. Both the project managers and contractors jointly determine the schedules of their operations while no single enterprise has complete information of other enterprises. The centralized scheduling approach that can usually obtain good global performance but must share nearly complete information that is difficult or even impractical due to the distributed nature of real-life supply chains. This paper proposes an agent-based supply chain model to support distributed scheduling. A modified contract-net protocol (MCNP) is proposed to enable more information sharing among the enterprises than conventional CNP. Experimental simulation studies are conducted to compare and contrast the performances of the centralized [centralized heuristic (CTR)], conventional CNP, and MNCP approaches. The results show that MCNP outperforms CNP and performs comparably with CTR when project complexity is high in terms of the total supply chain operating cost. Moreover, it is found that although CTR is better than MCNP in terms of global performance, MCNP yields good schedule stability when facing unexpected disturbances.     

钢铁供应链生产计划与调度研究综述

在供应链管理及竞争的背景下,提出钢铁供应链的概念。该钢铁供应链由矿山、钢铁企业、分销商和用户组成,其管理核心是生产计划与调度。分析了钢铁供应链的生产计划与调度研究内容和特点,综述了研究现状,指出了存在的不足。最后,提出了钢铁供应链生产计划与调度工作进一步研究的问题。     

Dynamic supply chain scheduling

Based on a combination of fundamental results of modern optimal program control theory and operations research, an original approach to supply chain scheduling is developed in order to answer the challenges of dynamics, uncertainty, and adaptivity. Both supply chain schedule generation and execution control are represented as an optimal program control problem in combination with mathematical programming and interpreted as a dynamic process of operations control within an adaptive framework. Hence, the problems and models of planning, scheduling, and adaptation can be consistently integrated on a unified mathematical axiomatic of modern control theory. In addition, operations control and flow control models are integrated and applicable for both discrete and continuous processes. The application of optimal control for supply chain scheduling becomes possible by formulating the scheduling model as a linear non-stationary finite-dimensional controlled differential system with the convex area of admissible control and a reconfigurable structure. For this model class, theorems of optimal control existence can be used regarding supply chain scheduling. The essential structural property of this model are the linear right parts of differential equations. This allows applying methods of discrete optimization for optimal control calculation. The calculation procedure is based on applying Pontryagin’s maximum principle and the resulting essential reduction of problem dimensionality that is under solution at each instant of time. The gained insights contribute to supply chain scheduling theory, providing advanced insights into dynamics of the whole supply chains (and not any dyadic relations in them) and transition from a partial “one-way” schedule optimization to the feedback loop-based dynamic and adaptive supply chain planning and scheduling.     

产业互联下面向云平台的智造供应链信息协作

平台的供应链信息协调问题,探索产业资源协作效率路径.本文提出云平台解决智造供应链信息协作的方案,该方案考虑了分布式协作理论应对庞大供应链业务数据的技术优势,构建了分布式下实时动态更新算法和共享数据资源池相结合的供应链企业数据互联互通协作模型,以提升产业制造的“智造”能力.最后以家居产业供应链进行分析论证,验证了云平台模型构建的可行性和有效性.     

敏捷供应链插单调度优化的启发式算法

针对在敏捷供应链已有生产计划基础上进行的插单调度问题,以单工厂和多供应商组成的两级供应链系统为研究对象,采用时间槽表示可用调度时间,以最小化供应链总成本为目标,建立了调度优化问题的整数规划(IP)模型,设计了求解该模型的逐批选优启发式(OOSH)算法。通过算例将其与距离优先和作业周期优先决策算法比较,验证了启发式算法的有效性,显示了敏捷供应链协同调度可以有效提高供应链的市场竞争力。     

基于数据驱动的物资储检配一体化系统研究和应用

针对现有电力物资仓库管理和运营多基于人工作业方式进行,工作内容繁杂,管理内容多样,数据处理量大,难以进行有效的归并和融合分析。再加上物资配送基本以需求部门自提为主,调配难度大;缺乏信息共享机制,无法对规定时期内的物资进行及时质检分析,造成物资供应链管理支撑不足的问题。本文将仓储“储、检、配”多源事件数据进行融合分析。首先识别并解析数据源实体,对于存在的瑕疵数据进行基于知识图谱的修复和整理;接下来结合仓储作业一体化的趋势特点,对仓储运行数据融合多级入出比控制模型,实时动态平衡与调整仓库库存;最后提出基于实时调度配送模型,调度各级入出库数据进行求解优化。经过测试使用,大大提高了仓储信息交互效率,进一步改善了物流的配送效率,节约管理运营成本,提高竞争力。     

改进的哈里斯鹰算法求解农产品供应链调度

针对农产品流通体系的效率低、流通链条协同效率不高、紧急情况下食品供给慢等问题,通过将农产品供应链调度问题建模成混合流水车间调度问题。结合禁忌搜索算法中禁忌表机制,离散化实数编码,提出了一种改进的哈里斯鹰算法来求解农产品供应链调度问题。该方法相比较原始的哈里斯鹰算法,降低了算法陷入局部最优的可能,进一步提高了算法的求解精度。实验结果表明：相比较对比算法,改进的哈里斯鹰算法在提出的农产品供应链调度问题模型上取得了更好的效果。     

基于异类多种群蚁群算法的MC供应链分布式调度研究

分析了目前大规模定制（MC）供应链调度现有调度方法的不足,针对MC供应链调度具有分布式和计算规模大的特点,提出了异类多种群蚁群算法,并设计了MC分布式调度优化的模型。异类多种群蚁群算法由多类不同特性蚁群构成,不同类型蚁群具有不同特质,并优势互补,彼此间具有潜在的合作性。将多个进行寻优的异类蚁群分散到供应链的不同计算节点上多个蚁群进行协同工作,蚁群间既独立也协作,充分发挥并行高效的特点,满足大规模定制供应链调度的要求。此模型算法可根据多订单特点,充分利用供应链上分布的计算资源,对调度规模大而且复杂的供应商选择及企业间合作时序进行寻优,确定优化调度方案。实验结果证明,该算法模型具有较好的有效性、稳定性和订单适应能力。     

混合粒子群算法和多Agent系统的协同生产调度及其组合优化

为了从整体角度优化调度供应链网络的各个环节,研究了在供应链环境下核心制造商与制造商的协同生产调度方案,考虑在满足产品生产时间节点的最少加工时间。建立了多Agent的供应链环境下的协同生产调度模型,针对此模型设计了协同混合粒子群优化算法并进行求解。通过实例研究表明,供应链环境下制造商的协同优化对生产的计划与实行起到了关键的作用。     

供应链协商调度模型与算法

研究了由一个制造商和一个分销商组成的供应链上分销商协商调度问题.此供应链中,制造商比分销商有更强的影响力,先于分销商进行调度.制造商与分销商之间不共享作业处理时间.为了改善分销商调度,建立了基于补偿的分销商协商模型,设计了保留信息私有性的协商调度策略,提出并分析了协商调度下制造商调度算法以及基于生态种群竞争的分销商协同演化调度算法.仿真实验结果表明,分销商协商调度模型与算法能够有效改善分销商调度性能,在不增加制造商调度成本的条件下,可最大程度地削减分销商调度成本超过25％.此外,提出的竞争协同演化算法能够获得比遗传算法、粒子群算法和蚁群算法更好的调度解.     

Multi-plant production scheduling in SMEs

Traditionally, companies used to view themselves as separate entities and did not devote efforts to collaborate with other echelons of the extended enterprise. This even happened at multi-site companies owning different plants that belonged to the same supply chain. However, manufacturing facilities of the same supply chain should intensively share information and coordinate planning and scheduling tasks in order to get a globally optimized solution. This paper discusses some specific characteristics of the planning and scheduling problem in the extended enterprise including an analysis of a case study, and reviews the available state-of-the-art research studies in this field. Most studies suggest that integrated approaches can have a significant impact on the system performance, in terms of lower production costs, and less inventory levels. The paper provides some conclusions and suggestions of future works.     

智能制造系统基于数据驱动的车间实时调度

智能制造系统采用大量先进的信息技术,为车间实时调度提供技术基础.各类信息技术在生产制造过程中的广泛应用使得制造系统积累了大量与生产调度相关的数据,因此,通过利用历史生产调度数据和智能装备收集到的实时生产数据,建立基于数据驱动的生产实时调度方法成为新型制造环境下实现高效调度的新思路.针对智能制造环境下的混合流水车间实时调度问题,提出基于BP神经网络的数据驱动的实时调度方法,从历史近优的调度方案中提取用于调度知识挖掘的样本数据,通过BP神经网络训练学习获取生产系统状态与调度规则的映射关系网络,并将其应用于生产在线实时调度.数值实验表明,所提出的方法优于固定单一调度规则,在不同的调度性能指标下其效果均稳定且良好.     

数据中心巡检机器人信息平台实时任务容错调度算法

为了实现数据中心巡检机器人信息平台实时任务容错性,提出一种基于自适应反馈均衡和码元调制技术的数据中心巡检机器人信息平台实时任务容错调度模型。首先构建路由冲突下的数据中心巡检机器人信息平台传输信道模型,优化数据中心巡检机器人信息传输协议。然后采用模糊C均值聚类的方法进行巡检机器人信息融合,结合自适应反馈均衡方法进行巡检机器人信息传输的信道均衡设计,采用码元调制方法进行信息平台的实时任务容错调度。最后进行仿真实验。结果表明,采用该方法进行数据中心巡检机器人信息平台实时任务调度的容错性较好,信息平台的信道均衡性较强,提高了数据中心巡检机器人信息平台的任务实时调度能力。     

基于双区块链的用电数据收集方案设计

智能电网基于生产计划调度的需要，会对用户实时用电信息进行采集，而细粒度的数据采集会带来用户的隐私暴露问题。针对这一问题，结合区块链技术提出了一个使用双区块链的用电信息存储链（Electricity Consumption Information Storage Blockchain，ECISB）方案，在该方案中无需可信中心，其中一条链用于存储多用户的实时多维聚合用电数据，另一条链用于存储单用户的计费周期内用电量。双链设计既保证了智能电网实时用电数据采集的需要，又解决了传统方案中无法计量单用户用电量的问题。     

Effect of change management capability in real-time environment: an information orientation perspective in supply chain management

Supply chain management (SCM) is an important strategic tool that requires careful planning and management. While the availability of real-time information is a critical aspect of an SCM system, it possesses a potential to disrupt supply chain participants. Thus, the success of an SCM system hinges on how well participants deal with changes taking place in a real-time environment. This study adopts the concept of the real-time enterprise to understand the role of change management on SCM performance based on the concept of the information orientation. The study empirically validated the role of change management on SCM performance.     

基于GIS的动态应急资源调度系统设计与实现*

为提高应急响应能力,利用实时的道路信息、救援状态信息和应急资源配置信息,设计了一个实时、动态的应急资源调度系统。在地理信息系统基础上,实现了利用实时信息动态生成应急资源调度方案。最终通过一个实例应用,结果证明了系统的实用价值,可以为应急资源救援提供科学的决策支持。     

Real-time task scheduling and network device security for complex embedded systems based on deep learning networks

As a hotspot of machine learning research, deep learning is applied in many fields. Embedded systems are becoming more and more complex and networked, so the real-time performance of embedded systems and the security of network embedded devices face severe challenges. Based on this, this paper studies the real-time task scheduling problem for complex embedded systems and the security of embedded network devices. For real-time, this paper proposes a comprehensive task scheduling algorithm. Based on the task classification in the embedded system, different scheduling methods are adopted for different tasks, and the scheduling mode is flexibly changed as the system load changes. A dynamic integrity measurement model is established based on the star trust chain structure, and the hardware implementation mechanism of constructing dynamic trust chain in embedded system is studied. The dynamic reconfigurable hardware design method based on FPGA is applied to the construction of dynamic trust chain, and a verification system is designed to verify the dynamic measurement mechanism. This can solve the security problem of deep network embedded devices to a certain extent.     

Applying pinwheel scheduling and compiler profiling for power-aware real-time scheduling

Energy consumption is a critical design issue in embedded systems, especially in battery-operated systems. Maintaining high performance while extending the battery life is an interesting challenge for system designers. Dynamic voltage scaling and dynamic frequency scaling allow us to adjust supply voltage and processor frequency to adapt to the workload demand for better energy management. Because of the high complexity involved, most solutions depend on heuristics for online power-aware real-time scheduling or offline time-consuming scheduling. In this paper, we discuss how we can apply pinwheel model to power-aware real-time scheduling so that task information, including start times, finish times, preemption times, etc, can be efficiently derived using pinwheel model. System predictability is thus increased and under better control on power-awareness. However, job execution time may be only a small portion of its worst case execution time and can only be determined at runtime. We implement a profiling tool to insert codes for collecting runtime information of real-time tasks. Worst case execution time is updated online for scheduler to perform better rescheduling according to actual execution. Simulations have shown that at most 50% energy can be saved by the proposed scheduling algorithm. Moreover, at most additional 33% energy can be saved when the profiling technique is applied. This paper is an extended version of the paper Power-Aware Real-Time Scheduling using Pinwheel Model and Profiling Technique that appeared in the 11th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications.     

Setting order promising times in a supply chain network using hybrid simulation-analytical approach: An industrial case study

Supply chain management (SCM) involves the management of material and information flow among the members of chain such as vendors, manufacturing plants and distribution centers. The main processes in supply chain are production planning, control, distribution and logistics. Effective management of all these processes provides the manufacturing companies great advantages in time based competition. In order to gain a competitive edge, firms must be able to deliver their products fast and on-time. This can be managed by better production planning, scheduling and due-date setting. This paper suggests a hybrid approach for setting realistic order due dates for a produce-to-order manufacturing company operating within a supply chain environment. The proposed hybrid approach combines mathematical and simulation modeling to bring together the advantages of both approaches.