基于备件满足率的多级备件库存模型研究

确定充足合理的备件储备是备件供应保障的重要工作,平衡总成本与系统备件满足率间的关系对备件供应保障十分重要.针对寻求多级备件最优储备数量问题建立了备件多级多库存点模型,以最小化备件保障成本和装备备件满足率为目标,采用蒙特卡洛仿真的方法对模型进行求解,基于实际生产数据实验,该模型和方法是有效的,为复杂装备多种类备件供应保障提供支持.     

多级分销网络设计及库存控制整合优化模型

从供应链集成的角度出发,基于多目标规划,根据排队论探讨了随机性需求下多级分销网络设计与库存控制的整合优化问题,提出了多级分销网络设计和库存控制整合优化的多目标规划模型.针对遗传算法收敛速度慢、易陷入局部最优等缺点,采用了基于并列选择法的遗传-模拟退火算法混合优化策略.实验证明,模拟退火算法提高了遗传算法的全局搜索能力,改善了遗传算法的求解性能.     

考虑修复成本的逆向物流多级库存优化研究

通过对逆向物流多级库存的提法引入,提出了逆向物流多级库存总成本最小化问题．在对产品召回的条件下回收系统模型图简化的基础上,提出了考虑修复成本的逆向物流的多级库存成本优化模型,并用MATLAB编程计算实际算例,得出总成本的优化值．研究结果表明,考虑了修复成本的逆向物流多级库存成本优化更具实际应用意义．     

双渠道多级分销网络库存优化配置方法

本文研究企业采用双渠道分销策略时面临的如何保证较低运作成本对多层级、多节点的分销网络中每个节点的线上线下库存进行节点内与节点间协同性配置的问题。针对目前国内外在多级的双渠道分销网络库存优化配置方面的研究甚少，提出了线上库存在多个层级可优化配置方法并将多级保证服务库存模型拓展为双渠道多级分销网络库存优化配置模型。研究结果表明，整合双渠道分销网络将降低企业的运作成本，而可配置的线上库存将提高企业线上商品交易效率并降低分销网络整体运作成本。     

多级库存控制的连锁经营企业配送网络设计

研究了随机需求条件下连锁经营企业配送网络设计及其库存决策的联合优化问题.详细分析了基于POT(power of two)多级库存控制策略的连锁企业多级工作库存及订货成本,给出了门店及配送中心在满足给定服务水平条件下的安全库存成本.在综合考虑运输成本和配送中心选址成本的基础上,建立了以系统总成本最小为目标的配送系统总成本优化模型,并采用遗传算法求解该优化模型,在得到最优配送网络设计方案的同时,确定了配送中心订货周期及门店配送周期.通过算例验证了模型及算法的有效性,并分析了需求、运输距离和选址成本等因素的变化对系统总成本的影响,为连锁经营企业的物流配送网络设计及库存控制提供决策支持.     

产能约束下的多级装配供应链优化配置方法研究

针对具有产能约束的多级装配型供应链优化配置问题,在考虑安全库存与产能约束、净补货时间关系基础上,研究最优供应商选择和交货期设置方法。面向不确定需求,建立对具有最大产能约束的供应链优化配置混合规划数学模型,并提出一种有较高求解精度的改进遗传算法求解。研究结果表明：在价格和时间不具有优势的情况下,供应链应该避免选择剩余产能小的供应商;在需求波动较大的情况下,为节点设定宽松的交货期往往能够降低供应链成本。     

基于碳足迹质量平衡的三级生产-库存协调模型

基于碳足迹质量平衡方程建立多级供应链生产-库存-碳足迹模型,研究多成员之间订货、生产与碳足迹协调问题。数值计算表明:执行最严厉或最宽松的碳排放控制政策,都会产生供应链成本与碳足迹背反,碳交易价格等于零时,供应链成本最低,而碳足迹最高。部分配额免费并结合碳交易,是控排早期可行的选择,价格机制对供应链决策调整更敏感。此外,协调效率曲线突出碳排放供应链协作价值,成本二次分配不仅可以降低运行成本,又能减少供应链碳足迹。     

需求受相关性影响的设备备件库存优化

备件多级库存模型通常基于备件需求相互独立的假设，但随着库存系统层次的增加以及协同管理方式的应用，备件需求的相关性将显著影响库存优化决策。针对需求具有相关性的备件库存问题，以服务响应时间为约束条件，以库存成本及缺货成本最小化为目标建立备件两级库存决策模型。引入Nataf概率变换法，利用已知的备件需求边缘概率密度函数构造满足特定相关性条件的随机需求样本，并将蒙特卡洛仿真与遗传算法相结合求解最优库存分配方案。仿真算例证明，设备备件库存的最优决策随着需求相关性系数的增大而发生变化，根据需求相关性的变化适当地调整库存决策，有利于降低备件库存系统总成本，提高库存系统对顾客需求的响应能力。     

考虑残次品和碳排放的供应链多级库存的EOQ模型

针对供应链多级库存系统,探析残次品与碳排放对该系统的影响作用与供应链库存持有成本、订购成本、运输成本和检查成本的构成函数,建立多级库存的EOQ模型,提供考虑残次品和碳排放的供应链订货经济批量和库存控制策略,并用算例验证了模型的有效性。     

工业互联网场景下运营管理研究综述

工业互联网作为新一代信息技术与工业制造深度融合的全新工业生态,对提高企业运营效率、推动制造业高质量发展具有重要意义。综合分析了工业互联网场景下运营管理相关文献,发现：1)工业互联网平台的研究大多数是通过定性方法分析其在不同行业的应用场景及平台生态,而通过定量方法探究工业互联网平台的深层次运营及协调机制是今后需重点关注的问题;2)通过数字化、网络化和智能化来提高产品质量、降低成本、优化业务流程等,实现价值创造,而工业互联网下制造企业、顾客等多主体的价值共创机理和模式是重要的研究问题;3)现有研究聚焦工业互联网技术和模型算法对生产运营中单一活动进行优化,而工业互联网环境下“研-制-维”一体化协同流程的构建方法以及多级闭环决策体系与智能决策方法需要深入探究;4)现有研究已明确工业互联网下供应链可以通过互联互通、可视性、实时性及可追溯性提升运营绩效,并探讨了技术采纳对其策略选择及协调机制的影响,未来可以将关注点转向下游客户端。     

An effective approach to multi-item capacitated dynamic lot-sizing problems

In this study we solve the multi-item capacitated dynamic lot-sizing problem, where each item faces a series of dynamic demands, and in each period multiple items share limited production resources. The objective is to find the optimal production plan so as to minimise the total cost, including production cost, inventory holding cost, and fixed setup cost. We consider both single-level and multi-level cases. In the multi-level case, some items are consumed in order to produce some other items and therefore items face internally generated demand in addition to external demands. We propose a simple three-stage approach that is applicable to both classes of problems. In the first stage we perform preprocessing, which is designed to deal with the difficulty due to the joint setup cost (a fixed cost incurred whenever production occurs in a period). In the second stage we adopt a period-by-period heuristic to construct a feasible solution, and in the final stage we further improve the solution by solving a series of subproblems. Extensive experiments show that the approach exhibits very good performance. We then analyse how the superior performance is achieved. In addition to its performance, one appealing feature of our method is its simplicity and general applicability.     

Analysing optimum push/pull junction point location using multiple criteria decision‐making for multistage stochastic production system

This research focuses on solving the multistage process push/pull junction point location problem. An aim is to implement a hybrid push/pull production system that can satisfy both high service‐levels and low inventory levels. Simultaneously, we consider sophisticated variability, such as multi‐products, random setup, indiscriminate break‐downs, yield loss, batch processes, and other contingencies. The problem can be solved by a multiple criteria decision‐making (MCDM) method. A technique for order‐preference by similarity‐to‐ideal solution (TOPSIS) is used to select a suitable option. The optimisation involves evaluation of stochastic performance measures within alternative scenarios among candidate junction‐point locations using a discrete event simulation model. A practical thin film transistor‐liquid crystal display (TFT‐LCD) process case‐study is utilised to illustrate the proposed method. After implementing a hybrid push/pull production strategy, simulation results indicate that the inventory level was reduced by over 18% while the service level remained about the same. For another scenario, a 3.4% decrease in service‐level can be paid off by a 46% decrease in inventory level and 34% improvement in lead time.     

不确定环境下复杂机械装配系统设备维修方法

为保障不确定环境下复杂机械装配系统的连续性并降低其维修成本,提出一种以马尔可夫决策理论为基础的设备维修策略动态选择方法。在综合考虑系统运行成本、缓冲库存成本、设备维修成本及停机损失成本的基础上,构建了装配系统可靠性成本模型。该模型以带有中间缓冲区的二级装配系统为研究对象,以设备状态和缓冲库存量为自变量,以可靠性成本为目标函数。分析了装配系统的不同运行状态,利用模拟退火算法和模糊非线性混合整数目标规划对可靠性成本模型求解,制定装配系统最优维修方法。该方法降低了装配系统停机时间,减少了设备维修次数,可为生产线设计和维修计划的制定提供依据。最后,通过算例分析验证了模型的有效性和可行性。     

和声搜索算法优化快速消费品生产配送协调调度

研究具有生产准备环节的快速消费品生产配送问题,考虑工厂和配送中心的库存限制,工厂产能限制和劳动力限制,建立一个多周期、多工厂、多产品、多配送中心、多客户的混合整数线性规划模型,旨在最小化准备成本、生产成本、库存成本和配送成本。通过设计一种遗传和声搜索算法对模型进行求解。最后给出一个算例说明所提模型和算法的可行性和有效性。     

Single-item production–delivery scheduling problem with stage-dependent inventory costs and due-date considerations

This paper studies an integrated scheduling problem for a single-item, make-to-order supply chain system consisting of one supplier, one capacitated transporter and one customer. Specifically, we assume the existence in the production stage of an intermediate inventory that works as a buffer to balance the production rate and the transportation speed. Jobs are first processed on a single machine in the production stage, and then delivered to the pre-specified customer by a capacitated vehicle in the delivery stage. Each job has a due date specified by the customer, and must be delivered to the customer before its due date. Moreover, it is assumed that a job that is finished before its departure date or arrives at the customer before its due date will incur a stage-dependent corresponding inventory cost (WIP inventory, finished-good inventory or customer inventory cost). The objective is to find a coordinated production and delivery schedule such that the sum of setup, delivery and inventory costs is minimised. We formulate the problem as a nonlinear model in a general way and provide some properties. We then derive a precise instance from the general model and develop a heuristic algorithm for solving this precise instance. In order to evaluate the performance of the heuristic algorithm, we propose a simple branch-and-bound (B&B) approach for small-size problems, and a lower bound based on the Lagrangian relaxation method for large-size problems. Computational experiments show that the heuristic algorithm performs well on randomly generated problems.     

Aggregate versus disaggregate production planning: a simulated experiment using LDR and MRP

Within the past several years, considerable research has been devoted to the aggregate production planning problem starting with the pioneering work of Holt el al, (1955) and the resulting Linear Decision Rule (LDR). However, researchers have also recognized that developing optimal aggregate production plans, per se, is not sufficient for solving real problems; these plans have to be disaggregated into specific schedules for specific products. Consequently, the thrust of current research is on the ‘disaggregation’ problem. Simultaneously a number of companies have been installing MRP systems with reports of significant improvements in inventory control, production planning, work force scheduling and production costs. This paper reports on an experiment which was designed and conducted to compare the effectiveness of the ‘aggregate-disaggregate’ and MRP approaches to production planning in a simulation environment. LDR was used as the optimal aggregate technique in the aggregate-disaggregate approach. The results appeared to favour the MRP approach     

Divergent production scheduling with multi-process routes and common inventory

This research considers a scheduling problem in a divergent production system (DPS) where a single input item is converted into multiple output items. Therefore, the number of finished products is much larger than the number of input items. This paper addresses two important challenges in a real-life DPS problem faced by an aluminium manufacturing company. One challenge is that one product can be produced following different process routes that may have slightly different capabilities and capacities. The other is that the total inventory capacity is very limited in the company in the sense that a fixed number of inventory spaces are commonly shared by raw materials, WIP (work-in-process) items and finished products. This paper proposes a two-step approach to solving this problem. In the first step, an integer programming (IP) model is developed to plan the type and quantity of operations. In the second step, a particle swarm optimisation (PSO) is proposed to schedule the operations determined in the first step. The computational results based on actual production data have shown that the proposed two-step solution is appropriate and advantageous for the DPS scheduling problem in the company.     

An optimization-based algorithm for job shop scheduling

Scheduling is a key factor for manufacturing productivity. Effective scheduling can improve on-time delivery, reduce inventory, cut lead times, and improve the utilization of bottleneck resources. Because of the combinatorial nature of scheduling problems, it is often difficult to find optimal schedules, especially within a limited amount of computation time. Production schedules therefore are usually generated by using heuristics in practice. However, it is very difficult to evaluate the quality of these schedules, and the consistency of performance may also be an issue. In this paper, near-optimal solution methodologies for job shop scheduling are examined. The problem is formulated as integer optimization with a “separable” structure. The requirement of on-time delivery and low work-in-process inventory is modelled as a goal to minimize a weighted part tardiness and earliness penalty function. Lagrangian relaxation is used to decompose the problem into individual part subproblems with intuitive appeal. By iteratively solving these subproblems and updating the Lagrangian multipliers at the high level, near-optimal schedules are obtained with a lower bound provided as a byproduct. This paper reviews a few selected methods for solving subproblems and for updating multipliers. Based on the insights obtained, a new algorithm is presented that combines backward dynamic programming for solving low level subproblems and interleaved conjugate gradient method for solving the high level problem. The new method significantly improves algorithm convergence and solution quality. Numerical testing shows that the method is practical for job shop scheduling in industries. This work was supported in part by the National Science Foundation under DMI-9500037, and the Advanced Technology Center for Precision Manufacturing, University of Connecticut.