冷轧生产调度模型及算法

针对冷轧生产线调度问题的复杂性, 将该问题规划为拼卷优化和轧制批量计划编制两个部分. 将拼卷优化问题归结为一个多容器装箱问题, 采用一种新的智能搜索算法——离散微分进化 (DDE) 对该问题进行求解; 对于轧制批量计划编制建立了一种特殊的双旅行商问题模型, 采用基于进化策略和邻域搜索的混合启发式方法求解模型. 最后通过上海宝钢生产实际数据对所提方法进行了试验, 试验结果显示本文给出的生产调度方法是有效的.     

基于混合遗传算法的热轧生产调度优化方法

热轧生产调度是一个复杂的约束组合优化问题,其生产约束包括连续轧制板坯的宽度、厚度和硬度跳变要求,轧制单元的最大长度,产品库存及交货期等.基于多旅行商模型,建立了热轧生产批量调度问题的优化模型,并提出一种混合遗传算法(遗传算法、局部搜索)求解该问题.通过应用串行边重组和并行边重组的遗传交叉算子,算法在优化过程中可以很好地处理调度约束.针对工业数据的仿真结果证明该调度模型和混合遗传算法的并行求解策略可以有效地解决热轧生产批量调度问题.     

需求损失下两产品联合生产动态批量决策及预测时阈

研究需求损失下两产品联合生产(采购)动态批量决策问题.在各周期成本变动情形下分析多周期动态批量决策的预测时阈和决策时阈,构建包含联合启动成本、两产品的单独启动成本、库存持有成本、变动生产成本和需求损失成本在内的成本最小化模型.在最优解结构特性的基础上,设计出前向动态规划算法求解问题,通过建立两产品生产点的单调性和建立生产集,给出求解预测时阈和决策时阈的充分条件.通过数值算例分析预测时阈求解的具体过程,表明所构建模型的有效性.     

带批量分割的多级批处理调度自组织优化

针对一类带批量分割的多级批处理调度典型问题的特点,提出一种自下而上的自组织优化方法.模拟人类群体"业务办理"机制,构建了带批量分割的批处理过程调度的自组织优化模型,分析了由批次转换和批量分割引起的复杂性,提出了基于友好度的自组织选择策略和基于最小响应的批量分割策略,在此基础上,给出了自组织调度优化算法.该方法能够和短时间内获得问题的最优解或近优解,并通过调度实例求解结果验证了该方法的有效性和优越性.     

带最小批量约束的计划问题及其拉格朗日松弛算法

针对一类带最小批量约束的计划问题, 提出了基于拉格朗日松弛策略求解算法. 通过拉格朗日松弛策略, 将原问题转为一系列带最小批量约束的动态经济批量W-W(Wagner-Whitin)子问题. 提出了解决子问题且其时间复杂度O(T³)的最优前向递推算法. 对于拉格朗日对偶问题, 用次梯度算法求解, 获得原问题的下界. 若对偶问题的解是不可行的, 通过固定装设变量, 求解一个剩余的线性规划问题来进行可行化处理. 最后, 数据仿真验证了算法的有效性.     

一种利用切比雪夫逼近求解库存最优控制的方法

针对供应链中库存随着需求的变化可能导致的积压和对生产(或采购)产生的不利影响,为更好地协调生产(或采购)并减少产品库存,研究了一类基于库存约束和动态时变需求下的多品种、多周期、多循环的生产与库存的最优控制模型.结合最优控制理论,给出一种采用切比雪夫多项式逼近和高斯-切比雪夫数值积分对库存最优控制问题进行数值求解的方法.实例分析表明该方法是可行的.     

基于MLD模型的预测控制可行性与约束优先级研究

混合逻辑动态(MLD)框架为处理约束预测控制不可行和优先级问题提供了新的方法,但其优化算法求解计算量大,求解时间长.通过在目标函数中引入新的惩罚项改进优化算法,将混合整数二次规划(MIQP)问题转化为二次规划(QP)进行求解,减少了计算工作量,提高了求解效率.通过在Wood-Berry塔上的仿真分析,获得了良好的控制性能,分析结果表明了该方法的有效性.     

蚁群算法在服务器集群批量任务调度中的应用

服务器集群中的负载均衡和作业调度是影响系统性能的重要因素.本文描述服务器集群批量任务的作业调度问题,对该问题建立了基于图的模型.由于使用一般的启发式算法或动态规划算法解决该问题具有局限性,本文引入蚁群算法进行求解,并针对该问题具体求解提出了启发式距离合适的计算方法.最后在仿真的基础上,讨论了算法的优化效果和收敛性,结果表明蚁群算法解决该问题具有优异的性能.     

多级生产批量规划问题的柔性惯量反捕食粒子群算法

多级生产批量规划(MLLS)是原料需求计划(MRP)中主生产计划(MPS)的关键决策问题,具有广泛的工业应用;已被证明是NP-hard类型的组合优化问题.反捕食粒子群算法(APSO)是最近提出的一种与粒子群算法(PSO)密切相关的亚启发式算法.本文提出带柔性惯性权重的反捕食粒子群算法(WAPSO)对具有指定装配结构而无约束的MLLS问题进行了求解.本算法对12个小规模benchmark数据集和1个随机产生的较大规模数据进行了测试.测试结果与遗传算法(GA)和Wagner-Whitin(WW)动态规划算法的结果进行了比较.结果表明了WAPSO算法的有效性和适用性.     

随机供应中断和退货环境下库存的应急控制

随机供应中断和退货环境下库存变化, 失去传统上的单调性, 呈现复杂的随机波动状态, 从而, 极大地增加了控制难度. 为解决系统库存的短缺和超储问题, 本文提出一个应急控制(包括应急采购和应急处理)策略. 在库存水平的动态变化表示为Lévy过程条件下, 利用连续时间Markov链、更新过程和鞅理论, 构建了系统期望折扣总利润模型, 并设计了交叉熵法确定最优控制策略. 仿真结果表明, 中断强度和类型及退货批次和批量, 对最优应急处理水平和应急采购量均有较大影响. 而退货类型仅影响最优应急处理水平, 对最优应急采购量影响较小.     

Single-Item Dynamic Lot-Sizing Models With Bounded Inventory and Outsourcing

This paper addresses a real-life lot-sizing problem which can be considered a single-item dynamic lot-sizing problem with bounded inventory. The particularity is that the demand of a period can be entirely or partially outsourced with an outsourcing cost. The goal is to minimize the total cost of production, setup, inventory holding, and outsourcing. The cost functions are linear but time-varying. We assume that the unit production cost is constant or nonincreasing over time. The problem is shown to be solvable in a strongly polynomial time with a dynamic-programming approach. The proposed algorithm can solve problems of sizes of up to 400 periods in less than 2 ms on a 1.4-GHz Pentium IV processor.     

结合批量问题的多目标矩形件优化排样

设计多目标启发式进化算法,研究了一种考虑批量问题的二维矩形件排样问题,建立了含有原材料成本最小化和零件库存成本最小化的多目标优化模型。先用启发式算法初始化下料方式,再用改进的快速非支配排序算法进行优化求解,确定下料方案。通过实验结果以及与其他算法的对比表明,在中等规模的矩形件排样问题中,该算法能够在较快的时间内既保证较高的原料利用率,又能降低该问题的总成本,证明了该算法的有效性。     

Modeling and analysis of single item multi-period procurement lot-sizing problem considering rejections and late deliveries

Integer linear programming approach has been used to solve a multi-period procurement lot-sizing problem for a single product that is procured from a single supplier considering rejections and late deliveries under all-unit quantity discount environment. The intent of proposed model is two fold. First, we aim to establish tradeoffs among cost objectives and determine appropriate lot-size and its timing to minimize total cost over the decision horizon considering quantity discount, economies of scale in transactions and inventory management. Second, the optimization model has been used to analyze the effect of variations in problem parameters such as rejection rate, demand, storage capacity and inventory holding cost for a multi-period procurement lot-sizing problem. This analysis helps the decision maker to figure out opportunities to significantly reduce cost. An illustration is included to demonstrate the effectiveness of the proposed model. The proposed approach provides flexibility to decision maker in multi-period procurement lot-sizing decisions through tradeoff curves and sensitivity analysis.     

The multi-product,economic lot-sizing problem in flow shops: the powers-of-two heuristic

This paper presents a new and efficient heuristic to solve the multi-product, multi-stage, economic lot-sizing problem. The proposed heuristic, called the powers-of-two method, first determines sequencing decisions then lot sizing and scheduling decisions are determined. This method assumes that cycle times are integer multiples of a basic period and restricts these multiples to the powers of two. Once time multiples are chosen, we determine for each basic period of the global cycle the set of products to be produced and the production sequence to be used. Then a non-linear program is solved to simultaneously determine lot sizes and a feasible production schedule. To evaluate its performance, the powers-of-two method was compared to both the common cycle method and a reinforced version of the job-splitting heuristic. Numerical results show that the powers-of-two method outperforms both of these methods.Scope and purposeThe multi-product, multi-stage, economic lot-sizing problem studied in this paper is the problem of making sequencing, lot-sizing and scheduling decisions for several products manufactured through several stages in a flow shop environment so as to minimize the sum of setup and inventory holding costs while a given demand is fulfilled without backlogging. This problem and similar problems are met in many different industries like the food canning industry, the appliance assembly facilities or in beverage bottling companies. The most commonly used approach to deal with this problem is the common cycle approach where a lot of each product is produced each cycle. A few other approaches are also proposed. In this paper we propose a new and more efficient solution approach that assigns different cycle times to different products.     

价格波动下钢铁企业原材料采购多期优化模型

考虑钢铁企业原材料需求、采购提前期及价格波动的影响,建立原材料采购的多期优化模型,最小化单位采购成本并保证原材料的供应。由于模型求解困难,将其分解为多个子模型,并采用改进的粒子群算法进行求解。最后以某钢铁企业铁矿石采购优化为案例,表明多期优化决策比单期最优经济采购决策和以补充库存为目的的采购策略更优。     

高科技易逝品的三层联合定价与订货决策

研究高科技产业背景下三级供应链系统中原材料供应商的最优定价决策、制造商和零售商的最优定价和订货决策. 围绕高科技易逝品的售价和零部件购买价格随时间递减的特点, 分别建立以零售商和供应商为主导的三层联合定价和订货模型, 并设计了三层粒子群算法对模型进行求解. 数值实验验证了所提出算法的有效性. 对模型特点的分析结果表明, 零售商主导下的三层供应链系统能更好地提高整个供应链的效率.

     

Lot-sizing heuristics for continuous time-varying demand and shortages

In this paper we propose two heuristic procedures for the inventory lot-sizing problem with continuous time-varying demands and shortages. The first heuristic is an extension of the Silver-Meal solution method to general continuous demands. The key idea of the second heuristic is to balance the sum of the holding and backorder costs over each replenishment cycle with the ordering cost. In the case of linearly time-varying demand, the two heuristics procedures are evaluated according to three measures of cost performance and two measures of computation efficiency over 10000 test problems. The results revealed that the modified least-cost approach is generally more effective than the generalized Silver-Meal. It generated comparable cost performance in problems with shortages and a superior cost advantage in problems with infinite shortage cost. Moreover, both heuristics are faster than the exact procedure in execution.     

A hybrid solution approach for fuzzy multiobjective dual supplier and material selection problem of carton box production systems

A novel optimization problem of carton box manufacturing industries is introduced in this paper. A mixed integer linear formulation with multiple objective functions is developed in order to determine the value of some criteria of carton raw sheets such as size, amount, and supplier under simultaneous minimization of multiple goals such as purchasing cost of raw sheets under discount policy, wastage remained from raw sheets, and quantity of surplus of carton boxes. In order to cope with the unstable market of this sector, some parameters of the proposed formulation such as demand value of the products and price given for raw sheets are assumed to be fuzzy numbers. To tackle such fuzzy multiobjective problem, first, the fuzzy problem is converted to a crisp form using the concepts of necessity‐based chance‐constrained modelling approach. Then a new hybrid form of the fuzzy programming approach is proposed to solve the obtained crisp multiobjective problem effectively. Computational experiments on a real case given by a carton box factory show the superior result of the proposed solution approach compared with the well‐known multiobjective solution methods taken from the literature.     

Multiple lot-sizing decisions with an interrupted geometric yield and variable production time

This study examines a multiple lot-sizing problem for a single-stage production system with an interrupted geometric distribution, which is distinguished in involving variable production lead-time. In a finite number of setups, this study determined the optimal lot-size for each period that minimizes total expected cost. The following cost items are considered in optimum lot-sizing decisions: setup cost, variable production cost, inventory holding cost, and shortage cost. A dynamic programming model is formulated in which the duration between current time and due date is a stage variable, and remaining demand and work-in-process status are state variables. This study then presents an algorithm for solving the dynamic programming problem. Additionally, this study examines how total expected costs of optimal lot-sizing decisions vary when parameters are changed. Numerical results show that the optimum lot-size as a function of demand is not always monotonic.