混流无等待总装分装流水线任务排序研究

为解决混流产品在无等待多条流水线生产条件下,由于产品生产节拍不一致导致总装分装系统中生产连续性较差的问题,研究总装分装任务排序优化方法,实现在保证批量生产、部件齐套供应前提下,使订单能够按期交货.以最小化总加工时间、最小化总提前/拖期和产品转换惩罚为优化目标,建立了优化数学模型,并设计了改进多种群蚁群算法求解该优化模型.以某机床厂某月生产任务为例进行仿真实验,与多种群蚁群算法、传统蚁群算法对比,验证了该算法性能较好.并与现行的调度方法进行对比,验证了该任务排序方法在混流节拍不一致的多条装配线生产上,能够有效地缩短产品生产周期、降低生产成本,提高订单的准时交付率.     

基于有限缓存区的多任务并行分拣调度研究

通过对某物流车间的实际调研,将自动化立体仓库出货台空间限制作为优化问题的约束条件,建立订单并行分拣模式下堆垛机调度问题的模型,并采用蚁群算法进行求解。在求解过程中,根据问题假设设定了算法相关的状态转移概率公式,并采用动态更新信息素浓度的改进型方式避免传统蚁群算法早熟的情况。最后根据工厂的实际订单信息给出了算例,并通过两种不同算法和不同参数设置的比较,说明通过蚁群算法求解该优化问题的有效性。数值试验显示该蚁群算法相比传统优化算法效率提升了10.5%。     

集成预防性维护计划的单机调度蚁群优化研究

尽管生产调度与预防性维护计划密切相关,且其共有目标都是提高机床的利用率,但是存在着调度优化上的冲突.为了综合考虑单机情形下的生产调度与预防性维护计划,提出了一种改进的蚁群优化算法,用于解决以总计作业加权完成时间和总计维护成本最小为双目标的生产调度与预防性维护计划的集成模型.同时进行了大量的仿真实验,比较结果表明提出的蚁...     

一种改进的蚁群算法在工艺规划与车间调度集成优化中的应用

改进标准蚁群算法的执行策略,可提高工艺规划和调度集成问题的求解质量和效率。通过节点集、有向弧/无向弧集、AND/OR关系,建立了基于AND/OR图的工艺规划和调度集成优化模型。提出一种求解工艺规划与车间调度集成问题的改进蚁群优化算法,采用了信息素动态更新策略避免收敛过慢和局部收敛,利用多目标优化策略提高求解质量。仿真结果证明了该算法的有效性。     

基于改进蚁群算法的一类运输能力约束的生产-运输批量问题求解

针对生产与运输两个过程的联合决策,通过分析一类生产-运输批量优化问题,建立的混合0-1整数规划模型整合了多产品多阶段能力约束批量生产和产品运输。其中运输成本由运输工具使用数量决定,当企业内部运输能力不能满足运输需求时可将运输外包,但需支付更高的运输成本。根据此问题的特点,构造改进蚁群算法求解,令其信息素和启发信息都存在0和1两种状态下的不同取值,通过转移概率确定0-1生产准备矩阵,进一步得到生产矩阵和运输计划。仿真实验结果表明在生产批量决策的同时考虑运输,可以减少运输成本,令总费用最小,通过将实验结果与其他优化算法比较,所构造的蚁群算法寻优概率是100%,平均进化10代,平均耗时小于1 s,稳定性和求解效率均高于其他算法,是求解这类问题一种有效与适用的算法。     

基于规则的钢铁订单归并及炉料结构优化方法

针对订单生产型钢铁企业的组炉计划和原料配方优化问题,综合考虑钢铁订单的产品结构属性、加工过程特征和客户交货要求等多约束条件,提出了基于规则的订单归并及炉料结构优化方法.首先利用订单组批合炉冶炼规则,建立以炼钢余材量最小和拖期/提前惩罚最小为目标的订单归并优化模型;然后利用精炼阶段合金投料配方规则,建立以投入原料成本最小和合金元素目标成分偏差最小为目标的炉料结构优化模型;并分别设计了求解上述两模型的启发式微粒群算法.案例企业的实际数据验证结果表明,基于该方法形成的组炉计划和原料配方方案,能够在满足订单交货期的前提下有效地减少炼钢余材量,合理地降低铁合金等原材料的投入成本.     

应用改进蚁群算法求解柔性作业车间调度问题

将蚁群算法信息素更新规则进行改进,规定只有产生至今最优解的蚂蚁才能释放信息素,且只更新全局信息素,减少了传统蚁群算法的时间复杂度,提高了问题的求解效率。分析了柔性作业车间调度的特点,选取三个性能指标作为求解目标,设定其求解优先级,并建立相应的调度模型。将改进蚁群算法应用于柔性作业车间调度算例的求解,与其它算法比较,平均解有很大提高,表明了该算法求解柔性作业车间调度问题的有效性。     

一种流程型企业主生产计划优化方法研究

为了解决传统的流程型生产管理方法不适应现代市场需求的问题,提出了一种基于生产柔性提高的主生产计划优化方法.建立了流程型企业的产品需求与生产能力平衡的最优化模型,讨论了模型的求解方法--混合启发式算法.经研究表明,该方法在流程型生产管理理论和实践方面具有一定的参考意义.     

多品种小批量订单型企业生产调度优化

目的研究多品种小批量订单型企业的生产调度优化问题,方法针对S公司的生产现状,应用遗传算法思想设计调度优化方案,采用不等长矩阵的编码方式实现订单的批量生产及车间排产的方案。结果通过仿真分析和S公司生产调度的实际应用,验证了该算法的可行性及有效性。结论基于遗传算法的调度优化算法实现了多品种小批量流程型生产企业生产调度优化,达到了缩短生产周期、有效利用生产资源的目的。     

钢铁企业订单动态交货期承诺模型与算法

针对钢铁企业交货期承诺问题的动态特征,建立了动态约束满足模型.该模型综合考虑了设备利用率和订单的提前拖期惩罚.模型求解属于NP-hard问题,很难用精确算法在可行时间内求解的特点,因此结合启发式规则和约束满足一致性技术的优点,提出了求解该问题的算法.通过实验验证了模型及算法的可行性和有效性.     

混合型生产方式中动态调度问题的研究

讨论了介于按库存制造和按定单装配之间的混合型生产方式的车间层生产动态调度问题,探讨了调度过程中的信息及其处理。对实际企业生产计划的控制过程进行分析后,归纳出“虚拟发装”的概念,并将其作为一种动态调度方案。最后应用信息技术,实现虚拟发装的工段动态调度,以达到提高决策的透明度,增强生产管理信息系统的实施效用的目的。     

Finite-capacity production planning algorithms for a semiconductor wafer fabrication facility

We consider the problem of production planning for a semiconductor wafer fabrication facility producing application-specific integrated circuits (ASICS) to customer order. Using a simple planning algorithm based on forward scheduling for WIP and backward scheduling for new orders, the effects of the level of detail at which the production process is modelled was examined. A simulation study showed that considering all near-constraint workcentres explicitly as having finite-capacity gives the best results. Also examined were the effects of undercapacity planning, and it was shown that when coupled with a shop-floor scheduling procedure driven by the planned completion date rather than customer due dates, significant improvements in both delivery performance and system predictability were obtained.     

Integrated process planning and scheduling – multi-agent system with two-stage ant colony optimisation algorithm

In this paper, a two-stage ant colony optimisation (ACO) algorithm is implemented in a multi-agent system (MAS) to accomplish integrated process planning and scheduling (IPPS) in the job shop type flexible manufacturing environments. Traditionally, process planning and scheduling functions are performed sequentially and the actual status of the production facilities is not considered in either process planning or scheduling. IPPS is to combine both the process planning and scheduling problems in the consideration, that is, the actual process plan and the schedule are determined dynamically in accordance with the order details and the status of the manufacturing system. The ACO algorithm can be applied to solve IPPS problems. An innovative two-stage ACO algorithm is introduced in this paper. In the first stage of the algorithm, instead of depositing pheromones on graph edges as in common ant algorithms, ants are directed to deposit pheromones at the nodes to select a set of more favourable processes. In the second stage, the set of nodes not selected in the first stage will be ignored, and pheromones will be deposited along the graph edges while the ants traverse the paths connecting the selected set of nodes.     

混装线投产序列和工位任务的协同调度机理

柔性汽车混装线需根据订单和生产需求调整当月工位任务规划和车型投产序列.通过分析汽车行业装配生产现状,提出高效而精准的调度目标,指出面向车型投产序列规划和工位任务规划的协同调度思想;说明针对不同目标采用的投产序列和工位规划方法,陈述了协同调度的策略框架,并用实际事例递进演示了协同调度机理.实验 证明协同调度能达到混装生产作业的高效精准过程控制的目的.     

Remanufacturing-oriented process planning and scheduling: mathematical modelling and evolutionary optimisation

Remanufacturing has been widely studied for its potential to achieve sustainable production in recent years. In the literature of remanufacturing research, process planning and scheduling are typically treated as two independent parts. However, these two parts are in fact interrelated and often interact with each other. Doing process planning without considering scheduling related factors can easily introduce contradictions or even infeasible solutions. In this work, we propose a mathematical model of integrated process planning and scheduling for remanufacturing (IPPSR), which simultaneously considers the process planning and scheduling problems. An effective hybrid multi-objective evolutionary algorithm (HMEA) is presented to solve the proposed IPPSR. For the HMEA, a multidimensional encoding operator is designed to get a high-quality initial population. A multidimensional crossover operator and a multidimensional mutation operator are also proposed to improve the convergence speed of the algorithm and fully exploit the solution space. Finally, a specific legalising method is used to ‘legalise’ possible infeasible solutions generated by the initialisation method and mutation operator. Extensive computational experiments carried out to compare the HMEA with some well-known algorithms confirm that the proposed HMEA is able to obtain more and better Pareto solutions for IPPSR.     

Master production scheduling in capacitated sequence-dependent process industries

Traditional approaches to planning and control of manufacturing (MRPII) focus on discrete parts manufacturing industries (e.g. automotive). The chemical industry, however, presents unique challenges. Cross-contamination of production is a key issue among some chemical facilities. A considerable amount of capacity is lost as a result of changeovers which involve performing thorough clean-ups to wash away the impurities which may contaminate the next product to be produced. Therefore, planning for sequence-dependent changeovers becomes crucial and complicates the master production scheduling process. This paper shows how improved master production scheduling performance can be obtained by using a two-level master production schedule (MPS) to focus on key plant processes, and by incorporating a scheduling heuristic which considers sequence-dependent changeovers and capacity constraints. This approach is illustrated using actual operating data from a chemical firm typical of many process industry operations. Simulation experiments are reported that test the performance of the proposed master scheduling method in a single-stage sequence-dependent process. The experimental factors include both the introduction of the two-level MPS with the scheduling heuristic, and the effect of changes in the MPS batch size. The results demonstrate that important simultaneous improvements in process changeover time and delivery performance can be achieved using the proposed MPS scheduling approach against a more traditional (single-level) MPS approach which does not consider sequence-dependent changeovers. Further, we find that delivery performance is relatively insensitive to adjustments in the MPS batch size when using the two-level MPS approach.     

Research on Fuzzy Decision of Resources Selection in Job-shop Scheduling for a One-of-a-Kind and Order-Oriented Production System

In a one-of-a-kind and order-oriented production corporation, job shop scheduling plays an important role in the production planning system and production process control. Since resource selection in job shop scheduling directly influences the qualities and due dates of products and production cost, it is indispensable to take resource selection into account during job shop scheduling. By analyzing the relative characteristics of resources, an approach of fuzzy decision is proposed for resource selection. Finally, issues in the application of the approach are discussed.     

A new multi-objective optimization method for master production scheduling problems using simulated annealing

Tactic planning or master production scheduling focuses on time and spatial decomposition of the aggregate planning targets and forecasts, as well as, forecast and provision of needed resources. This process becomes extremely hard and time consuming with the increase of number of products, resources and periods considered. In face of such obstacles, this work shows a study of an Artificial Intelligence technique called Simulated Annealing applied to the optimization of production planning problem, more specifically, Master Production Scheduling. This work reviews some of the fundamental theory of simulated annealing, the methodology for master production scheduling calculation, the applicability of simulating annealing to planning problems, most important results and suggestions for further studies.     

Development and challenges of planning and scheduling for petroleum and petrochemical production

Production planning and scheduling are becoming the core of production management, which support the decision of a petrochemical company. The optimization of production planning and scheduling is attempted by every refinery because it gains additional profit and stabilizes the daily production. The optimization problem considered in industry and academic research is of different levels of realism and complexity, thus increasing the gap. Operation research with mathematical programming is a conventional approach used to address the planning and scheduling problem. Additionally, modeling the processes, objectives, and constraints and developing the optimization algorithms are significant for industry and research. This paper introduces the perspective of production planning and scheduling from the development viewpoint.     

Advanced production scheduling for batch plants in process industries

An Advanced Planning System (APS) offers support at all planning levels along the supply chain while observing limited resources. We consider an APS for process industries (e.g. chemical and pharmaceutical industries) consisting of the modules network design (for long–term decisions), supply network planning (for medium–term decisions), and detailed production scheduling (for short–term decisions). For each module, we outline the decision problem, discuss the specifi cs of process industries, and review state–of–the–art solution approaches. For the module detailed production scheduling, a new solution approach is proposed in the case of batch production, which can solve much larger practical problems than the methods known thus far. The new approach decomposes detailed production scheduling for batch production into batching and batch scheduling. The batching problem converts the primary requirements for products into individual batches, where the work load is to be minimized. We formulate the batching problem as a nonlinear mixed–integer program and transform it into a linear mixed–binary program of moderate size, which can be solved by standard software. The batch scheduling problem allocates the batches to scarce resources such as processing units, workers, and intermediate storage facilities, where some regular objective function like the makespan is to be minimized. The batch scheduling problem is modelled as a resource–constrained project scheduling problem, which can be solved by an efficient truncated branch–and–bound algorithm developed recently. The performance of the new solution procedures for batching and batch scheduling is demonstrated by solving several instances of a case study from process industries.