考虑排产约束的汽车零部件出库订单排序优化北大核心CSSCI

针对TPL仓库面向汽车零部件企业的产前物料拣选和配送作业环节,研究了JIT生产模式下配送送达时间限定和生产线缓冲库容量有限条件下的出库作业拣选订单排序的优化模型及算法。在分析现有拣选订单排序模式的基础上,提出了融合现有模式优点的新作业模式,建立了该模式下拣选订单排序优化模型,并针对这一整数组合优化模型,设计了一套基于贪心理论的算法对其进行求解。通过实际案例分析表明,该模型及算法可以达到减少拣选作业空余时间、提高作业效率、降低作业成本的目的。     

基于机会约束规划的弹性供应链多目标优化

研究供应商失效风险、零部件价格与制造商产能波动并存环境下,采用多源采购策略的弹性供应链成本与服务水平多目标优化问题,根据不确定理论构建了多目标机会约束规划模型,并采用标准化正规化约束法(Normalized normal constraint method)求解模型的Pareto前沿。通过数值算例求解验证了所提出模型及其求解算法的有效性,并对模型中的关键参数进行了敏感性分析,能够为企业管理人员开展供应商选择、零部件订单分配及顾客订单排序决策提供借鉴与参考。     

考虑绿色制造和外协方式的多周期订单安排模型研究

针对多品种小批量企业的多周期订单安排问题,提出一种以绿色制造和外协方式为研究视角的优化方法模型。综合考虑企业的设备操作成本、人工成本、库存成本、外协成本等竞争性指标以及设备能耗、噪声等绿色性指标,构建考虑绿色制造和外协方式的多周期订单安排模型,并基于遗传算法设计了模型的求解方法;最后以一个机加工企业的四周期订单需求为例,应用该方法给出了各阶段订单的安排结果以及企业制造资源的分配方案,既验证了模型和算法的有效性,又在绿色制造和外协方式的研究视角上为多周期订单的安排方式提供理论方法支持。     

考虑加班因素的订单接受问题优化模型与算法

加班是制造商在面对客户需求增多时经常会采取的一种临时扩大产能的方法。本文将加班因素引入订单接受问题,考虑到加班容易导致生产线的工位间同时存在产能不足与产能过剩的情况,建立了以总利润最大化和生产线产能均衡为优化目标的数学模型。根据模型特征,本文采用两种分布式估计算法,即基于节点矩阵抽样算法和基于节点的偶合算法来实现问题求解。实验结果表明,基于节点的偶合算法具有良好的求解效果,并且产能均衡这一优化目标的设定可以在保证生产线产能平衡的同时有效地提高产品利润。     

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.     

带时间窗口的多式联运模型与算法

以带时间窗的多式联运模型为基础,考虑节点间的运输时间、节点处的运输方式转换时间以及可能发生的货运延迟,以包括运输成本、转运成本和时间偏离惩罚成本在内的总成本最小为目标函数,建立基于满意时间路径的多式联运模型.在算法上,构造了基于K最短路的改进遗传算法,通过3个算例说明多式联运的运输路径与运输方式搭配可以不是唯一的,为多式联运的相关研究工作提供了理论基础.     

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.     

基于众包的外卖配送订单选择研究

各外卖平台均提供了兼职配送员参与众包服务的渠道。与专职配送员相比,兼职配送员有着“路径开放、时间有限、最终目的地确定”等诸多不同的特点。基于兼职配送特点,为了提高众包配送员的接单效率,提高兼职收益,对众包模式下的订单选择及订单执行路径进行深入分析,提出将二者进行统一考虑的双层算法：在底层建立众包外卖配送路径规划模型,并使用改进的遗传算法求解;第2层利用贪心算法调用底层模型,通过比较配送收益进行订单选择,使得兼职人员的配送收益最大。通过算例实验,验证模型及算法的合理性及有效性条件,发现算法的计算时间随备选订单数量增加线性增加。在现实应用中,需要通过对备选订单进行打分排序,控制“订单池”规模,则能在可接受时间内得到较高质量的选择结果。     

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%。     

考虑排产约束的汽车零部件出库订单排序优化

针对TPL仓库面向汽车零部件企业的产前物料拣选和配送作业环节,研究了JIT生产模式下配送送达时间限定和生产线缓冲库容量有限条件下的出库作业拣选订单排序的优化模型及算法。在分析现有拣选订单排序模式的基础上,提出了融合现有模式优点的新作业模式,建立了该模式下拣选订单排序优化模型,并针对这一整数组合优化模型,设计了一套基于贪心理论的算法对其进行求解。通过实际案例分析表明,该模型及算法可以达到减少拣选作业空余时间、提高作业效率、降低作业成本的目的。     

Integrated job release and shop-floor scheduling to minimize WIP and meet due-dates

We address the problem of releasing jobs to the factory floor while meeting delivery dates and minimizing the work-in-process inventory. Heuristic algorithms were developed that used an efficient, detailed shop-floor scheduling model to determine the release times of new jobs. The process starts with the current shop floor conditions and determines the sequencing of both in-process and new jobs on machines in order to minimize the maximum lateness (L max ). The approach is tractable for industrial-sized problems and provides solutions close to a calculated lower bound for WIP. Computational experience is given.     

An optimal ordering strategy for a third-party managed supply chain

The rapid pace of new product introduction and global sourcing of raw materials and manufacturing facilities have created a complex and fragmented supply chain. Retailers in many consumer product industries are relying on third-party supply-chain managers (SCMs) to manage the global supply chain cost-effectively with short production lead times. To meet delivery requirements, the SCM must start the production process before receiving a firm order from retailers. We assume a two-stage production process where, in stage 1, a base product is produced. In stage 2, the base product is customised. The SCM absorbs the penalties associated with over- or underestimating retailers’ demand. Hence, the SCM has to decide on (a) the optimal production quantity of the base products and (b) the number of base products to be customised without delay. We develop a profit maximisation model for this strategy using a nonlinear objective function with two decision variables and one constraint. Applying Kuhn–Tucker conditions, closed-form solutions are obtained for the two decision variables. Also, formulations are given for two additional strategies: postpone customisation of all products and produce customised products only. An example illustrates the use of our model. We also examine the impact of demand variability on the effectiveness of the three strategies. Results show that postponement may not always be the best strategy for an SCM.     

Sequence-dependent batch chemical scheduling with earliness and tardiness penalties

Production volume in the specialized agricultural chemical industry is typically too small to justify the capital expenditure required for continuous processing. As such, there is a trend towards building chemical processing plants for this market segment that are batch plants. Scheduling of this type of chemical plant under just-in-time operations, where both earliness and lateness penalties are included, is critical to the efficient operation of these plants. In this paper, a heuristic scheduling procedure is developed for the problem of minimizing the total weighted earliness and tardiness costs as well as the total set-up cost for the single-stage batch chemical manufacturing environment.     

Scheduling with generalized batch delivery dates and earliness penalties

In this paper, we study some single machine scheduling problems with generalized batch delivery dates and earliness penalties. The generalized delivery dates are given a-priori before any jobs are processed. They are unrelated to the jobs and the processing order. Each specific delivery batch contains jobs completed but undelivered until the specific delivery date. We consider scheduling problems to minimize two types of earliness penalties: one is the total earliness; the other is the maximum earliness. For these two problems, first we show that they are NP-hard in the strong sense for general cases; then we prove that they are still NP-hard even if there are only two generalized batch dates. We also prove that they are solved in polynomial time for general earliness penalty function if all processing times are equal, and give an O(n log(n)) algorithm to solve the weighted earliness cases.     

Modelling and integration of customer flexibility in the order commitment process for high mix low volume production

With increasing product variety and dynamic demand fluctuation, manufacturing industry is moving towards a high product mix and low order volume production environment. Consequently, the order commitment process is becoming one of the most important processes for manufacturing firms to meet individual customer's needs with limited resources. However, demands for shortened delivery lead time, diverse customer requirements and more frequent customer orders have made the order commitment task more challenging. This paper attempts to tackle these new challenges by incorporating not only manufacturing flexibility but also flexibility from the demand side. Customer flexibility is characterised by customer indifference to certain product attributes and/or delivery schedules. Intuitively, with the consideration of customer flexibility, both manufacturers’ and customers’ interests can be better served since the solution space of matching demand and supply can be extended beyond the traditional domain purely from a manufacturing perspective. To this end, a systematic approach is developed to characterise and model customer flexibility. A mixed-integer-programming model is formulated to provide optimal order commitment decisions.     

Evaluating order throughput time with variable time window batching

Given increased pressure to provide short delivery times, minimising customer order throughput time is a very important objective in warehousing operations. There are many factors that may affect the performance of an order picking system, such as layout of the warehouse, the storage strategy, the routing policy, the zoning method and the batching policy. In this study, we propose a simple travel time model and analyse the effect of order batching on the expected customer order throughput time with variable time window batching. In addition, we discussed the impact of some parameters on the batch size and the expected customer order throughput time. In the practice of Wuhan Dong Hon Logistics co. Ltd, the storage rack in the warehouse has two levels: the low storage level and the high storage level. These two levels are visited by pickers and forklifts, respectively. The classification improves the performance of order picking. In this study, we build the model based on this practice.     

MTO运营模式的订单交货期决策模型

承诺交货期过晚,没有时间优势,容易丢失订单;承诺交货期过早,企业超负荷生产,增加生产成本,可能出现亏损。针对基于订单式生产(MTO)的中小制造企业的客户订单交货期预报问题,综合考虑价格、订单需求量、提前完工和延期交货损失、加班、外协和损耗率因素等对任务完工期的影响,以收益最大化为目标,建立了订单任务交货期决策模型,并运用Matlab编程和计算工具对实例进行分析,验证了模型的有效性。模型对MTO生产企业交货期决策具有一定的参考意义。     

Order release in a workload controlled flow-shop with sequence-dependent set-up times

In this paper, we report a simulation study on the role of sequence-dependent set-up times in decision making at the order release level of a workload controlled make-to-order flow-shop. The study evaluates the potential for set-ups savings, dependent on the level of workload in the shop, for two alternative strategies, namely considering set-up times centrally, within the release decision or locally, within the dispatching decision. These strategies are compared and assessed on the basis of two main performance measures namely time in system and standard deviation of the job lateness. Results indicate that the local strategy, which has been traditionally adopted in practice and in most of the studies dealing with sequence-dependent set-up times, does not always give the best results. The release frequency and the shop workload appear critical to the selection of the strategy to adopt, strongly influencing system performance.     

A genetic algorithm-based approach to machine assignment problem

Over the last few decades, production scheduling problems have received much attention. Due to global competition, it is important to have a vigorous control on production costs while keeping a reasonable level of production capability and customer satisfaction. One of the most important factors that continuously impacts on production performance is machining flexibility, which can reduce the overall production lead-time, work-in-progress inventories, overall job lateness, etc. It is also vital to balance various quantitative aspects of this flexibility which is commonly regarded as a major strategic objective of many firms. However, this aspect has not been studied in a practical way related to the present manufacturing environment.

In this paper, an assignment and scheduling model is developed to study the impact of machining flexibility on production issues such as job lateness and machine utilisation. A genetic algorithm-based approach is developed to solve a generic machine assignment problem using standard benchmark problems and real industrial problems in China. Computational results suggest that machining flexibility can improve the overall production performance if the equilibrium state can be quantified between scheduling performance and capital investment. Then production planners can determine the investment plan in order to achieve a desired level of scheduling performance.     

A local multiple proper generalized decomposition based on the partition of unity

It is well known that model order reduction techniques that project the solution of the problem at hand onto a low-dimensional subspace present difficulties when this solution lies on a nonlinear manifold. To overcome these difficulties (notably, an undesirable increase in the number of required modes in the solution), several solutions have been suggested. Among them, we can cite the use of nonlinear dimensionality reduction techniques or, alternatively, the employ of linear local reduced order approaches. These last approaches usually present the difficulty of ensuring continuity between these local models. Here, a new method is presented, which ensures this continuity by resorting to the paradigm of the partition of unity while employing proper generalized decompositions at each local patch.