以瓶颈为中心多层次负荷集成的订单交货期预测方法

针对MTO(make-to-order)生产环境下的订单交货期预测问题,提出了一种集成订单接收、订单投放与车间调度等多层次负荷,以瓶颈为中心的订单交货期预测(bottleneck driven due-date forecasting, BDDF)方法。该方法先根据订单是否通过瓶颈,分类估计“鼓链”与“非鼓链”订单在不同层次的负荷,同时利用Little’s法则估计各个层次的通过时间,求和得到订单的交货期。运用FlexSim建模仿真比较了BDDF方法与两种经典交货期预测方法在不同订单池排序规则、车间调度规则以及保护产能水平组合下的系统绩效。结果表明,BDDF方法在拖期率、平均拖期以及延期标准差指标上均优于两种经典方法。同时,订单池排序规则、调度规则以及保护产能水平都会影响BDDF方法的系统绩效,需要根据实际情况选择合适的参数,以更好地控制订单交货期,避免订单延期。     

基于优先准则的电子产品订单交货期决策

如何安排订单生产使得企业在获得最大效益的同时,也要满足客户对订单的交货期要求,这已经成为当今企业的一项重要决策问题。研究了多装配线情况下的订单交货期决策问题,建立了电子产品订单排产成本模型。通过对客户重要性、交货紧迫性和订单效益的分析计算,确定订单的优先执行顺序准则。基于订单优先准则,考虑装配资源约束和交货期约束,提出了多装配线条件下的装配模拟排产方法,根据订单的重要与否分为两类订单,分别采用后向排产方法和前向排产方法。应用实例的计算分析表明提出方法的有效性,企业获得较大效益并且能够满足重要客户的交货期要求。     

基于WLC的钢铁企业多工序、多路径订单投放方法

针对按订单生产(MTO)下的多工序、多路径的钢铁生产中订单投放问题建立数学模型,提出了度量订单交货提前/拖期程度、负荷均衡程度以及工序生产紧密衔接程度的3个指标.针对问题的模型及其特点,提出了基于负荷均衡的订单投放算法.首先基于负荷控制的思想为订单池中的订单指定生产路径;然后基于瓶颈工序优先均衡的原则调整工序负荷;最后对非瓶颈工序进行负荷均衡.基于企业实际生产背景的仿真实验结果验证了模型和算法的有效性.     

考虑订单扰动因素的热轧重调度分布估计算法

热轧工序作为钢铁生产的核心环节,具有严格的生产连续性和复杂的产品工艺要求,而紧急订单的随机到达和紧急交货期要求会对生产连续性和质量稳定性产生不利影响。针对这类紧急订单插入的动态事件,提出一种热轧重调度优化方法。首先,分析了订单扰动因素对调度方案的影响,并以最小化订单拖期惩罚和板坯跳跃惩罚加权和为优化目标,建立了热轧重调度问题的数学模型。然后,设计了热轧重调度分布估计算法（EDA）。该算法针对紧急订单的插入式处理方式,提出一种基于插入位置的整数编码方案;结合模型特征设计了概率模型;并综合考虑目标与约束,定义了基于惩罚值的适应度函数。通过实际生产数据进行仿真实验,验证了模型和算法的可行性和有效性。     

钢铁生产中的可用能力承诺模型与算法

针对钢铁企业订单排产中的可用能力承诺问题,建立了以对合同交货期的提前和拖期惩罚最小化为目标,考虑钢铁生产中工序间最大和最小间隔等特殊约束的数学模型;针对所建立的模型,结合问题本身的特点设计了基于遗传算法的求解方法,并通过仿真实验验证了该算法的可行性和有效性.     

多行程多交货期的成品油配送优化

研究多行程多交货期的成品油配送优化问题,已知油库使用带运输时间窗的多舱车辆配送各加油站的多个订单,每个加油站具有各自的优先级,且加油站的各个订单带有交货期.综合考虑客户优先级、订单交货期和车辆运输时间窗等因素,以配送收益最大化为目标,建立多行程多交货期的成品油配送优化模型,并设计带交货期移除算子的改进变邻域搜索算法进行求解.基于前向插入启发式算法构造初始解,设计基于订单交货期的邻域扰动算子和基于单位时间收益最大化的贪婪策略,以增强算法的局部寻优能力,并提出基于逆序访问的后期优化策略,从而在保证解的质量情况下加快算法收敛速度.通过不同规模下的仿真实验验证了所提出模型和算法在最大化配送收益的同时,能够有效地提高配送及时性.     

带有交货期的比例流水车间调度问题的合作博弈

针对具有多个客户订单的比例流水车间调度问题,在考虑有交货期及提前和拖期惩罚下,以客户支出成本为优化指标,在客户通过合作结成联盟的方式下,以联盟内成员进行重新调度所获得的最大成本节省为联盟的价值,建立合作博弈模型.该合作博弈是具有无外部性的平衡博弈,从而有非空核.考虑到客户对提前加工和延迟加工的迫切程度不同,提出基于提前及拖期惩罚的β规则分配方法,该方法能得到带有交货期的比例流水车间调度合作博弈的一个核分配.通过混合差分进化算法求解最优调度顺序,实验结果验证了基于合作博弈模型的调度方法及成本分配方法的有效性.     

面向装配的多agent排产模型研究

拖期罚金带来的成本问题使中小规模离散制造企业在竞争中面临巨大压力.为此,采用多agent技术设计面向装配的三层排产框架模型;利用多agent的自治和协同,结合经验规则,将复杂的大规模排产逐层分解,转化为可用算法优化的子问题,再自底向上归并更新,迭代寻优完成生产排产,并可依计划执行情况进行动态调度.以合作企业凹印机历史订单数据进行排产仿真,结果表明,通过对设备空闲时间分布的有效管理,此三层模型能够有效解决订单拖期问题,对中小企业的成本控制具有重要意义.     

顺序依赖的调整时间和拖期的无缝钢管热轧批量调度算法

无缝钢管热轧生产存在一类特殊的顺序依赖机器调整时间,调整时间依赖于相邻轧制批量间的规格切换,与批量间规格呈线性函数关系.针对具有此类调整时间的热轧批量调度问题,进一步考虑交货期要求,探讨了调整时间与交货期之间的性质特征,并以最小化总机器调整时间和最小化总拖期为目标,基于进化算法框架设计了快速重排序邻域搜索多目标算法(f...     

混合流水车间提前/拖期调度问题的DE优化解

针对JIT生产模式下的混合流水车间调度问题特点,提出了采用DE算法与指派规则联合调度策略求解流水车间提前/拖期调度问题。构建了混合流水车间的提前/拖期调度模型。详细论述了DE算法的实施流程和关键问题。在算法实施过程中,首先,采用DE算法进行全局寻优,完成生产任务指派,确定某个工件在某个工序在哪个工位加工;然后采用局部指派规则来确定工件在该工序的开工时间。在满足目标完成时间(交货期)的前提下,使提前惩罚费用与拖期惩罚费用之和最小。数值计算结果证明了该算法的有效性。     

Genetic algorithm for supply planning in two-level assembly systems with random lead times

This paper examines supply planning for two-level assembly systems under lead time uncertainties. It is supposed that the demand for the finished product and its due date are known. The assembly process at each level begins when all necessary components are in inventory. If the demand for the finished product is not delivered at the due date, a tardiness cost is incurred. In the same manner, a holding cost at each level appears if some components needed to assemble the same semi-finished product arrive before beginning the assembly at this level. It is assumed also that the lead time at each level is a random discrete variable. The expected cost is composed of the tardiness cost for finished product and the holding costs of components at levels 1 and 2. The objective is to find the release dates for the components at level 2 in order to minimize the total expected cost. For this new problem, a genetic algorithm is suggested. The proposed algorithm is evaluated with a variety of supply chain settings in order to verify its robustness across different supply chain scenarios. Moreover, the effect of a local search on the performance of the Genetic Algorithm in terms of solution quality, convergence and computation time is also investigated.     

Scheduling rules to minimize total tardiness in a parallel machine problem with setup and calendar constraints

Quality control lead times are one of most significant causes of loss of time in the pharmaceutical and cosmetics industries. This is partly due to the organization of laboratories that feature parallel multipurpose machines for chromatographic analyses. The testing process requires long setup times and operators are needed to launch the process. The various controls are non-preemptive and are characterized by a release date, a due date and available routings. These quality processes lead to significant delays, and we therefore evaluate the total tardiness criterion. Previous heuristics were defined for the total tardiness criterion, parallel machines, and setup such as apparent tardiness cost (ATC) and ATC with setups (ATCS). We propose new rules and a simulated annealing procedure in order to minimize total tardiness.     

Optimization of Order Fulfillment in Distribution Network Problems

This paper focuses on optimization of order due date fulfillment reliability in multi-echelon distribution network problems with uncertainties present in the production lead time, transportation lead time, and due date of orders. Reliability regarding order due date fulfillment is critical in customer service, and customer retention. However, this reliability can be seriously influenced by supply chain uncertainties, which may induce tardiness in various stages throughout the supply chain. Supply chain uncertainty is inevitable, since most input values are predicted from historical data, and unexpected events may happen. Hence, a multi-criterion genetic integrative optimization methodology is developed for solving this problem. The proposed algorithm integrates genetic algorithms with analytic hierarchy process to enable multi-criterion optimization, and probabilistic analysis to capture uncertainties. The optimization involves determination of demand allocations in the network, transportation modes between facilities, and production scheduling in manufacturing plants. A hypothetical three-echelon distribution network is studied, and the computation results demonstrated the reliability of the proposed algorithms. Received: October 2004 / Accepted: September 2005     

用模拟退火法解任务的加工时间为模糊区间数的单机提前/拖期调度问题

研究了单机作业下任务的加工时间为模糊区间数的提前／拖期调度问题。     

A simulation analysis of part launching and order collection decisions for a flexible manufacturing system

In a dynamic flexible manufacturing system (FMS) environment jobs arrive randomly, and in most of the existing studies the due date for a single part is set individually. However, when the due date is set for an order that consists of multiple parts, some completed parts may have to wait for the rest of the order to be completed. This paper studied the scheduling problem in the FMS in which orders require the completion of different parts in various quantities. The orders arrive randomly and continuously, and all have predetermined due dates. Two scheduling decisions were considered in this study: launching parts into the system for production, and determining the order sequence for collecting the completed parts. A new part-launching rule, named the Tardiness Estimating Method (TEM) was proposed. A discrete-event simulation model of the FMS was developed and used as a test-bed for experiments under various system conditions. The proposed part launch rule was capable of providing good performance regarding minimum mean tardiness and maximum service level, but provided only a moderate flow time when compared with the other five rules commonly used in the literature. In addition, three order collection rules were tested in the experiments. Collecting parts for the order with the earliest due date (EDD) was found better than the other rules for tardiness related measures.     

Minimizing total earliness and tardiness through unrelated parallel machine scheduling using distributed release time control

Using unrelated parallel machine scheduling to minimize the total earliness and tardiness of jobs with distinct due dates is a nondeterministic polynomial-hard problem. Delayed customer orders may result in penalties and reduce customer satisfaction. On the other hand, early completion creates inventory storage costs, which increase the total cost. Although parallel machines can increase productivity, machine assignments also increase the complexity of production. Therefore, the challenge in parallel machine scheduling is to dynamically adjust the machine assignment to complete the job within the shortest possible time. In this paper, we address an unrelated parallel machine scheduling problem for jobs with distinct due dates and dedicated machines. The objective is to dynamically allocate jobs to unrelated parallel machines in order to minimize the total earliness and tardiness time. We formulate the problem as a mixed integer linear programming (MILP) model and develop a modified genetic algorithm (GA) with a distributed release time control (GARTC) mechanism to obtain the near-optimal solution. A preliminary computational study indicates that the developed GARTC not only provides good quality solutions within a reasonable amount of time, but also outperforms the MILP model, a classic GA and heuristic approaches described in the literature.     

An advanced order acceptance model for hybrid production strategy

Competitors stand out through commitment to providing excellent customer service. Many organizations strive to promote their order promising system to generate more reliable quotes with quantity and due date constraints. This paper develops a Capable-to-Promise (CTP) based Order Acceptance Model (OAM) for a Hybrid Production Strategy (HPS) based on the positioning of the Customer Order Decoupling Points. The proposed model allocates the uncommitted availability and planned production receipts to current and anticipation of future needs. A Mixed-Integer Linear Programming (MILP) is developed to determine the optimal order quantities based on the resource availability. The proposed model efficiently mitigates the risk of not being reliable in the commitments due to discrepancies between the real and unused quantities. The CTP based OAM is compatible with a HPS with both make-to-order (MTO) and make-to-stock (MTS). The presented model encompasses four steps. First, demands for MTO products are collected in batches on daily basis and a forecasting model is applied to predict orders for MTS products. Secondly, the quantity-based Revenue Management approach is used to prioritize orders. Afterwards, the optimization model assesses the availability of resources in order to produce collected orders. The final step is to accept valuable orders based on the resource availability. In order to illustrate the applications of the modeling approach, two case studies are provided.     

Due-date assignment for multi-server multi-stage assembly systems

In this paper, we attempt to present a constant due-date assignment policy in a multi-server multi-stage assembly system. This system is modelled as a queuing network, where new product orders are entered into the system according to a Poisson process. It is assumed that only one type of product is produced by the production system and multi-servers can be settled in each service station. Each operation of every work is operated at a devoted service station with only one of the servers located at a node of the network based on first come, first served (FCFS) discipline, while the processing times are independent random variables with exponential distributions. It is also assumed that the transport times between each pair of service stations are independent random variables with generalised Erlang distributions. Each product's end result has a penalty cost that is some linear function of its due date and its actual lead time. The due date is calculated by adding a constant to the time that the order enters into the system. Indeed, this constant value is decided at the beginning of the time horizon and is the constant lead time that a product might expect between the time of placing the order and the time of delivery. For computing the due date, we first convert the queuing network into a stochastic network with exponentially distributed arc lengths. Then, by constructing an appropriate finite-state continuous-time Markov model, a system of differential equations is created to find the manufacturing lead-time distribution for any particular product, analytically. Finally, the constant due date for delivery time is obtained by using a linear function of its due date and minimising the expected aggregate cost per product.     

一种新的遗传算法在并行机调度问题中的应用

针对相同交货期窗口非等同并行机提前/拖后调度问题,设计了一个基于向量组编码的遗传算法.此算法的编码方法简单,能有效地反映实际调度方案,收敛速度快.为适应调度实时性和解大型此类问题的需要,基于遗传算法自然并行性特点的基础上,实现了主从式控制网络模式下并行遗传算法.仿真结果表明,此算法是有效的,优于普通的遗传算法,具有较高的并行性.