一种多产品多约束混合生产线的批量计划模型及其求解

从烧碱以及PVC两条生产线的生产过程提炼出多产品多约束混合生产线批量计划问题。我们针对该问题建立了以产品利润最大化为目的,包含资源、生产工艺和能力等约束的批量计划优化模型。通过微粒群算法进行优化,其计算模型及结果对于工业生产的实际情况具有一定的参考价值。     

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.     

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。     

热轧带钢轧制批量计划优化模型及算法

基于奖金收集车辆路径问题模型建立了热轧带钢生产批量计划多目标优化模型．模型综合考虑了生产工艺约束、用户合同需求以及综合生产指标优化等因素．利用加权函数法将多目标优化模型转换为单目标优化模型,针对模型特点设计了蚁群优化求解算法,算法中嵌入了单向插入和2-opt局部搜索过程．引用某钢铁企业热轧生产轧制批量计划编制的实际问题对模型和算法进行了验证,结果表明模型和算法的优化效果和时间效率是令人满意的．     

基于负载特征的边缘智能系统性能优化

边缘智能指利用人工智能算法为网络边缘设备提供数据分析能力的一种服务形式。然而,边缘计算环境比云计算更加复杂和多变。在构建边缘智能的过程中存在很多问题,例如缺乏量化的评价标准、异构计算平台、复杂的网络拓扑、不断变化的用户需求等,其中比较突出的是算法模型的高资源需求与边缘设备资源储备低之间的矛盾。机器学习是边缘智能的主要工作负载,它需要大量的计算资源,然而边缘设备的计算资源有限,两者的供求关系并不匹配,边缘智能负载的部署和优化成为了一个难题。因此,针对边缘智能负载性能优化问题,文中提出了基于负载特征的边缘智能性能优化CECI(Cloud-Edge Collaborative Inference)策略,从模型选择、批量自适应调整和云边协同方面对不同机器学习负载进行了优化。在模型选择方面,使用基于目标权重的模型自适应选择策略,实现在多个条件约束下,综合权衡多个性能优化目标的效果。在批量自适应调整方面,提出了基于开销反馈的批量自适应调整算法,使得模型在运行时能够达到更好的性能。在云边协同方面,通过结合网络状态和用户时延要求设计出了云边协同策略,进而达到了动态利用云端计算资源的效果。实验结果表明,...     

基于模糊回归的质量屋决策模型

基于带有对称三角形模糊系数的模糊回归及模糊规划理论，提出关联函数及自 相关函数的数学模型，并在系统考虑资源约束影响的基础上，分别建立了基于质量屋的产品 规划精确模型及模糊模型．仿真研究表明，这些模型适合于各种工程设计问题，尤其是在不 确定的、模糊的条件下，能够有效地确定关联函数及自相关函数，帮助开发人员优化顾客需 求的满意水平，在资源约束下使产品的顾客满意度最大．     

基于双层规划的多供应商物料配送期量优化

为有效解决多供应商单制造商构成的二级供应链中供应商配送批量不经济、配送时间无序导致物流成本高和卸货拥堵等问题,建立多供应商配送期量的双层规划模型.上层模型从库存与配送联合优化视角,考虑供应商配送批量经济性,以供应链物流总成本最低为目标,确定最优配送批量;下层模型基于上层优化结果,考虑制造商生产时序性,以供应商时间惩罚成本最小化为目标,确定供应商配送时间序列.采用遗传算法和动态规划相结合的方法求解上下层模型,得到供应商最优配送期量标准.通过实例运用,验证了模型与算法的有效性及可行性.     

基于双层规划的多供应商物料配送期量优化

为有效解决多供应商单制造商构成的二级供应链中供应商配送批量不经济、配送时间无序导致物流成本高和卸货拥堵等问题,建立多供应商配送期量的双层规划模型.上层模型从库存与配送联合优化视角,考虑供应商配送批量经济性,以供应链物流总成本最低为目标,确定最优配送批量;下层模型基于上层优化结果,考虑制造商生产时序性,以供应商时间惩罚成本最小化为目标,确定供应商配送时间序列.采用遗传算法和动态规划相结合的方法求解上下层模型,得到供应商最优配送期量标准.通过实例运用,验证了模型与算法的有效性及可行性.     

两级差分进化算法求解多资源作业车间批量调度问题

以优化生产周期为目标,研究并建立了多资源作业车间批量调度问题模型.提出一种新的两级差分进化算法,采用两级染色体编码来解决批量划分和排序优化问题;设计了基于自适应差分进化算法(DE)的全局搜索操作,并在算法框架中嵌入了基于Interchange邻域结构的局部搜索;基于等量划分原则,为每个工件确定最优批次数及子批次的批量大小,并为各子批次确定最优排序.通过单资源算例和多资源实例仿真表明了模型和算法的可行性和有效性.     

回收不确定下WEEE逆向物流网络鲁棒优化设计

电子电器废弃物（WEEE）存在对环境和人体健康的危害,有效对其进行回收能避免此类危害和提高资源的利用率。WEEE逆向物流回收网络的设计为实现这一目标起到了关键的作用。考虑WEEE逆向物流网络运作的不确定性,引入风险偏好系数和约束背离惩罚系数,建立了WEEE逆向物流网络的鲁棒优化模型。该模型能允许决策者对系统运作的鲁棒水平进行调节,同时能允许决策者对风险偏好进行调节。仿真结果表明建立的模型能有效抑制逆向物流系统运作的不确定性,使系统具有更低的风险。     

A genetic algorithm for permutation flow shop scheduling under make to stock production system

The permutation flow shop scheduling is a well-known combinatorial optimization problem that arises in many manufacturing systems. Over the last few decades, permutation flow shop problems have widely been studied and solved as a static problem. However, in many practical systems, permutation flow shop problems are not really static, but rather dynamic, where the challenge is to schedule n different products that must be produced on a permutation shop floor in a cyclical pattern. In this paper, we have considered a make-to-stock production system, where three related issues must be considered: the length of a production cycle, the batch size of each product, and the order of the products in each cycle. To deal with these tasks, we have proposed a genetic algorithm based lot scheduling approach with an objective of minimizing the sum of the setup and holding costs. The proposed algorithm has been tested using scenarios from a real-world sanitaryware production system, and the experimental results illustrates that the proposed algorithm can obtain better results in comparison to traditional reactive approaches.     

Lot Size Scheduling using Fuzzy Numbers

Due to imprecision that is often inherent in the estimates of future demand for various products in a batch type production system, there are cases where the lot sizing problem may be more naturally treated using fuzzy concepts. Triangular fuzzy sets may be employed in order to represent qualitative estimates that are expressed linguisticaly. In this paper the above approach is introduced in order to derive an appropriate number of production runs and the corresponding lot sizes.     

Optimal lot sizing in an unreliable two-stage serial production–inventory system

This paper deals with a two‐stage lot sizing problem in an unreliable production environment in which the machine at the first stage (stage 1) is failure‐prone while the machine at the final stage (stage 2) is failure‐free. The process goods are obtained in batches by manufacturing and are transferred continuously from stage 1 to stage 2 where the finished goods are produced and then shipped out to customers. If the machine at stage 1 breaks down then the production of the interrupted lot is not resumed. Instead, a new production cycle is initiated after machine repair. The model is formulated assuming that the production rate of the machine at stage 1 is greater than that at stage 2 and the time to machine failure and repair time are arbitrarily distributed. Specific formulation of the model under exponential failure and exponential repair time distributions is derived and a procedure for finding the optimal production policy is presented. The dependence of the optimal production policy on the model parameters is also examined with numerical examples.     

Economic lot sizing in a production system with random demand

An extended economic production quantity model that copes with random demand is developed in this paper. A unique feature of the proposed study is the consideration of transient shortage during the production stage, which has not been explicitly analysed in existing literature. The considered costs include set-up cost for the batch production, inventory carrying cost during the production and depletion stages in one replenishment cycle, and shortage cost when demand cannot be satisfied from the shop floor immediately. Based on renewal reward process, a per-unit-time expected cost model is developed and analysed. Under some mild condition, it can be shown that the approximate cost function is convex. Computational experiments have demonstrated that the average reduction in total cost is significant when the proposed lot sizing policy is compared with those with deterministic demand.     

Optimal lot sizing with maintenance actions and imperfect production processes

Porteus (1986) explored an economic order quantity model with imperfect production processes that the approximate lot size is derived. Basically, he dealt with the lot size problem is rather meaningful. However, for mathematical simplicity, he adopted a truncated Taylor series expansion to present the approximate expected total cost function that results in overvalue of expected total cost. In this paper, we extend Porteus (1986) to present the optimal lot size model for defective items with a constant probability when the system is out-of-control and taking the maintenance cost into account. We show that there exists a unique optimal lot size such that the expected total cost is minimised. In addition, the bounds of optimal lot size are provided to develop the solution procedure. Finally, numerical examples are given to illustrate the theoretical results and compare optimal solutions obtained by using our approach and Porteus's approach. Numerical results show that our approach is better.     

A continuous review inventory model with the controllable production rate of the manufacturer

Optimal operating policy in most deterministic and stochastic inventory models is based on the unrealistic assumption that lead‐time is a given parameter. In this article, we develop an inventory model where the replenishment lead‐time is assumed to be dependent on the lot size and the production rate of the manufacturer. At the time of contract with a manufacturer, the retailer can negotiate the lead‐time by considering the regular production rate of the manufacturer, who usually has the option of increasing his regular production rate up to the maximum (designed) production capacity. If the retailer intends to reduce the lead‐time, he has to pay an additional cost to accomplish the increased production rate. Under the assumption that the stochastic demand during lead‐time follows a Normal distribution, we study the lead‐time reduction by changing the regular production rate of the manufacturer at the risk of paying additional cost. We provide a solution procedure to obtain the efficient ordering strategy of the developed model. Numerical examples are presented to illustrate the solution procedure.     

基于动态批量和提前期的MRP研究

MRP是ERP的关键。论文分析了基于固定批量和提前期来确定计划数量和计划时间的传统MRP算法存在的问题,提出了利用日产量对批量和提前期进行动态调整的方法,并给出了相应的MRP算法。     

Alternative approach for solving replenishment lot size problem with discontinuous issuing policy and rework

Conventional approach for solving the replenishment lot size problem is by using differential calculus on the long-run average production cost function with the need to prove optimality first. Recent studies proposed an algebraic approach to the solution of classic economic order quantity (EOQ) and the economic production quantity (EPQ) models without reference to the use of derivatives. This paper extends it to the solution of a specific EPQ model as was examined by Chiu et al. [Chiu, S. W, Chen, K. -K, Lin, H. -D. Numerical method for determination of the optimal lot size for amanufacturing system with discontinuous issuing policy and rework. International Journal forNumerical Methods in Biomedical Engineering. doi: 10.1002/cnm.1369. (in Press; Published online March-10-2010).]. As a result, optimal replenishment lot size and a simplified optimal production-inventory cost formula for such a particular EPQ model can be derived without derivatives. This alternative approach may enable practitioners who with little knowledge of calculus to understand the realistic production systems with ease.     

An optimal batch size for a production system operating under periodic delivery policy

A manufacturing system that procures raw materials from suppliers in a lot and processes them into a finished product is considered in this research. An ordering policy is proposed for raw materials to meet the requirements of a production facility which, in turn, must deliver finished products demanded by outside buyers at fixed points in time. First, a general cost model is formulated considering both raw materials and finished products. Then, using this model, a simple procedure is developed to determine an optimal ordering policy for procurement of raw materials, and the manufacturing batch size, to minimize the total cost of meeting customer demands in time. The dependent relationships between production batch size and rawmaterial purchasing quantity, and various delivery patterns considered in recent literature are critically reviewed. The quality of the solution is evaluated. Numerical examples are provided.     

Combining an alternative multi-delivery policy into economic production lot size problem with partial rework

This paper combines an alternative multi-delivery policy into an imperfect economic production quantity (EPQ) model with partial rework, with the purpose of reducing supplier’s stock holding cost. We extend the problem examined by Chiu et al. [Chiu, Y.-S. P, Chiu, S. W., Li, C.-Y., & Ting, C.-K. (2009). Incorporating multi-delivery policy and quality assurance into economic production lot size problem. Journal of Scientific and Industrial Research, 68(6), 505-512] by considering an n + 1 delivery policy in lieu of n multi-delivery plan for the specific EPQ model with partial rework. Under such a policy, an initial delivery of perfect (finished) items is distributed to customer for satisfying product demand during manufacturer’s regular production time and rework time. At the end of rework, fixed quantity n installments of the finished products are delivered to customer at a fixed interval of time. As a result, a closed-form optimal replenishment batch size solution to the problem is obtained. A numerical example with analysis and comparison is provided to show practical usage of the proposed model and demonstrate its significant savings in stock holding cost.