Cost models for lot streaming in a multistage flow shop

Lot streaming is a technique used to split a processing batch into several transfer batches. In this way, overlapping operations can be performed in different manufacturing stages, and production can be accelerated. This paper proposes two cost models for solving lot streaming problems in a multistage flow shop. The purpose is to determine the optimal processing batch size and the optimal number of transfer batches that minimize the total annual cost in each model. In the first model, a more complete and accurate method is developed to compute the costs of raw materials, work-in-process, and finished-product inventories. The total cost includes the setup cost, the transfer batch movement cost, the three-type inventory holding cost, and the finished-product shipment cost. The second model contains not only the four costs in the first model, but also the imputed cost associated with the makespan time. The total annual cost functions in both models are shown to be convex, and two solution approaches are suggested. An experiment consisting of three phases was conducted to explore the effect on the optimal solution when changing the value of one parameter at a time. The results indicate that three parameters have significant effects on the optimal solution.     

Processing in a multi-machine batch manufacturing system with sequence dependent cost

Many workcells in batch manufacturing systems are populated with multiple, nonidentical machines that perform similar tasks. Because of the size of a batch when a job arrives, it may be uneconomical to set up two or more machines to process the same job simultaneously. An economic decision has to be made as regards which machine in the cell to assign the job. Likewise, many multi-operation jobs can be processed using one of several feasible operation sequences that may lead to different total manufacturing costs. The cost differences are the result of several factors, among which are processing time and cost dependencies between operations, fixturing requirements, and material handling requirements. When the workcell machine selection decision is considered along with the operation sequencing decision, determination of the best machine in a cell and the best operation sequence for the batch is a non-trivial task. In this paper, we address the problem of selecting the best machine within a cell and the best operation sequence for a batch when operation cost is machine and sequence dependent. The problem is modeled mathematically and solved using a heuristic algorithm. The performance of the algorithm is compared with that of an exact solution procedure.     

Setups and effective capacity: the impact of lot sizing techniques in an MRP environment

This study investigates how lot sizing techniques influence the profit performance, inventory level, and order lardiness of an assembly job shop controlled by MRP. Four single-level lot sizing techniques are compared by simulation analysis under two levels of master schedule instability and two levels of end item demand. A second analysis investigates the influence of a multilevel lot sizing technique, the generalized constrained-K (GCK) cost modification, on the four single-level techniques at low demand and low nervousness. The analyses reveal a previously unreported phenomenon. Given the same inventory costs, the single-level lot sizing techniques generate substantially different average batch sizes. The lot sizing techniques maintain the following order of increasing average batch size (and decreasing total setup time):

economic order quantity (EOQ)

period order quantity (POQ)

least total cost (LTC)

Silver-Meal heuristic (SML)

The causes for different average batch sizes among the lot sizing techniques are analysed and explained. Demand lumpiness, inherent in multilevel manufacturing systems controlled by MRP, is found to be a major factor. The number of setups each lot sizing technique generates is the primary determinant of profit performance, inventory level, and order tardiness. EOQ, a fixed order quantity technique, is less sensitive to nervousness than the discrete lot sizing techniques. EOQ_, however, generates the smallest average batch size, and, therefore, the most setups. Since setups consume capacity, EOQ, is more sensitive to higher demand. SML generates the largest average batch sizes, and is, therefore, less sensitive to increased demand. At low demand, the other lot sizing techniques perform better on all criteria. They generate smaller batches and, therefore, shorter actual lead times. The GCK cost modification increases the average batch size generated by each lot sizing technique. GCK improves the profit and customer service level of EOQ the lot sizing technique with the smallest batches. GCK causes the other lot sizing techniques to generate excessively large batches and, therefore, excessively long actual lead times.     

基于遗传算法的生产批量优化问题

针对生产费用、生产准备费用和库存费用综合指标最小的、最优的能力约束生产批量问题,建立基于遗传算法的数学模型,通过模拟试验及实际问题计算,验证算法的可行性。     

An Optimal Model for Cell Formation Decisions

This study develops an approach to cell formation decisions for cellular manufacturing layouts in group technology settings. An optimal 0–1 integer programming model is used to provide an analysis for determining which machines and parts should be assigned to cells in cellular manufacturing layouts. This approach minimizes the cost of manufacturing exceptional parts outside the cellular system, subject to machine capacity constraints. Part–machine matrices are partitioned into disconnected cells and use far fewer 0–1 variables than earlier approaches. Formulation of the model is described with a numerical example and computer solutions to realistic problems are obtained. The characteristics of computer run times, model performance, and applications of the model are discussed.     

Analysis of a batch machine fed by an unreliable discrete machine

This paper analyses the behaviour of a manufacturing line consisting of two machines in series where the first machine processes lots one at a time, and is subject to failure and takes a random amount of time to repair when it fails, and the second machine is a perfectly reliable batch machine. A control limit policy is adopted to determine lot sizes for the batch machine. When the batch machine completes processing, if the number of lots in the buffer is greater than or equal to the critical number (Q?), all the lots in the buffer are loaded immediately, otherwise the batch machine waits until Q lots are accumulated. An embedded discrete time Markov-chain approach is proposed, and recursive approaches are developed to derive necessary performance measures. A numerical example explains how to obtain the optimal value of a critical number minimizing the cost functions.     

Development of an algorithm for the application of Group Technology in the design of Manufacturing Systems

The Group Technology theory identifies and groups similar parts into families and machines into cells, advantages being in the similarity of the parts made in each cell, and designing and manufacturing. The benefits are: simple material flow, cost reduction in material handling, reduction in work in progress, reduction in the cycle time and set-up time, increased manufacturing flexibility, increased quality, and increased job satisfaction. The objective of the work presented in this paper was to develop a Group Technology (GT) algorithm by means of cell analysis for the design of the productive system to batch production, e.g. water heater manufacturing, system manufacturing, lathes manufacturing, etc. The TGIP algorithm allows the definition of technical and economical parameters for the application of Group Technology, also taking into account the demand for the performance of the algoof heating gasrithm. The benefits of the algorithm developed are the focus on solving the case of multiple machines and the assignment of the parts to the groups related to the load of the machines; also it allows the definition of technical and economical parameters to differentiate the machines that do the same operation or even the consideration of the demand for a certain period of time.     

A DISCRETE PRODUCTION SWITCHING RULE FOR AGGREGATE PLANNING

Aggregate planning (AP) is a necessary activity for manufacturing and services alike. A shift toward high-volume batch and continuous flow processes within American manufacturing has given rise to increasing numbers of crew-loaded facilities. A majority of AP approaches incorporate continuous decision variables and require frequent adjustments to both production and work-force settings. Despite the availability and diversity of these approaches, few significant applications have been reported. This paper presents the detailed development of a discrete AP switching rule that can be applied to a variety of cost environments. Inventory costs are estimated using an interval approach rather than traditional point estimates. The model allows incorporation of overtime options and is interactive in nature. Decision variables from the model can be disaggregated and linked directly to lower-level planning activities. Actual results of model implementation are reported. An overview of the model's incorporation into the larger context of hierarchical production planning is found in [21].     

EXPLAINING PLANT-LEVEL DIFFERENCES IN MANUFACTURING OVERHEAD: STRUCTURAL AND EXECUTIONAL COST DRIVERS IN THE WORLD AUTO INDUSTRY

Using data from 62 automobile assembly plants worldwide, we examine the extent to which various structural cost drivers (plant scale, automation, and product mix complexity) and exe-cutionalcost drivers (product manufacturability, management policies, and production practices) account for plant-level differences in manufacturing overhead. Our analysis of structural drivers finds that overhead hours per vehicle are negatively associated with production volumes, consistent with economies of scale in overhead functions. However, automation appears to have little influence on overhead levels. Product mix complexity's effect on overhead requirements varies with the complexity's form and the plant's manufacturing capabilities. Option and parts complexity (measures of peripheral and intermediate product variety) both exhibit adverse effects on overhead, reflecting the considerable logistical, coordination, and supervisory challenges that accompany an increased number of parts and more complex manufacturing tasks. In contrast, model mix complexity (a measure of fundamental variety) appears to have little impact on direct or overhead labor requirements in auto assembly plants. Our analysis of executional drivers provides empirical support for the claim that advanced manufacturing practices such as the reduction of buffers, multiskilled production workers, and the use of teams can lower overhead costs. We also find preliminary evidence that the lower overhead costs in Japanese auto assembly plants are due primarily to the use of multiskilled work teams and the shifting of traditional overhead activities to production workers. Overall, our results indicate that manufacturing overhead is a function not only of the structural cost drivers that have dominated the academic literature but also of executional cost drivers that are harder to duplicate and therefore potentially more valuable for achieving competitive advantage.     

Optimal batch size and investment in multistage production systems with scrap

In this paper, we study the problem of selecting the optimum production batch size in multistage manufacturing facilities with scrap and determining the optimal amount of investment. We analyse the effect of investment for quality improvement on the reduction of the proportion of defectives, and the effect of this reduction on processing cost, setup cost, holding cost, and profit loss. The quality characteristic of the product manufactured is assumed to be normally distributed with a mean equal to the target value. The purpose of the investment is to reduce the variance of the quality characteristic and hence the proportion of defectives. The model assumes known demand, which must be satisfied completely, scrappage at each stage and profit loss due to scrap. Using this model, the optimal values of the production quantity and the proportion of defectives for minimizing the total cost are obtained. The optimal investment is then obtained using the relationship between the investment and the proportion of defectives.     

面向并行制造的多生产单元协同调度研究

为提升多生产单元制造系统整体效率,在其系统内开展面向并行制造的协同调度研究,在考虑运输、换线等时间的基础上,构建多生产单元并行协同调度模型,采用并行分段协同遗传算法求解;在此基础上,将所研究协同调度方法应用于某复杂机电产品多生产单元制造车间,并与变批量调度与等批量调度比较。研究表明,所提的并行协同调度方法可以显著提升生产单元效率,提高生产单元设备和人员利用率。     

A COMPARISON OF DIRECT COST SAVINGS BETWEEN FLEXIBLE AUTOMATION AND LABOR WITH LEARNING

This paper compares flexible automation with labor‐intensive manufacturing processes in a batch production environment and considers learning, forgetting, inventory carrying costs, setup costs, production demand volume, previous production experience, and the proportion of material to labor cost. While flexible automation typically can reduce setup times, and therefore inventory carrying costs through smaller optimal batch sizes, the results of this research show that the effect of forgetting on relative cost savings is difficult to predict in some situations. When using optimal lot sizes in both the automated and labor environments, cost savings from flexible automation may be smaller than expected or may occur in different ways than anticipated.     

A SYSTEMS PERSPECTIVE ON MATERIAL REQUIREMENTS PLANNING*

For many companies, the implementation of material requirements planning systems has failed to produce the expected improvements in manufacturing efficiency. This paper shows that MRP failure can occur in the common situation in which MRP is installed in a manufacturing environment that has evolved around manual methods of material control. A system dynamics simulation model is used to interrelate decision functions (policies) of a manufacturing firm. Simulation runs of the model operating with manual methods of material control show six- to seven-year fluctuations in production, ordering, and labor. A modified version of the model, changed only by the introduction of a requirements explosion to represent MRP, shows that MRP can actually cause more severe production fluctuations, resulting in lower average labor productivity and higher manufacturing costs. The major practical implication of the analysis is that the organizational environment that suits MRP (and utilizes its inherent strengths) is different from the environment that is likely to prevail at the time of implementation.     

An Integrated Framework for Measuring Product Development Performance in High Technology Industries

We present an integrated framework for measuring product development performance. The framework consists of a three stage model for exploring the relationships between metrics used by design, manufacturing, marketing functions, and overall commercial success. Using a cross‐sectional survey of 383 product development professionals working on 38 product development projects in the high‐tech electronic assembled goods manufacturing sector, we provide empirical evidence of the proposed framework. The findings indicate that in the high‐tech manufacturing sector (1) commercial success of new product development projects is primarily determined by market share, (2) gain in market share is primarily driven by lower unit cost and not by technical performance, and (3) reduction in unit cost is primarily driven by the increased speed of new product development and not by the R&D budget. The study failed to identify any significant association between R&D budget and technical performance, and development speed and technical performance.     

爱尔朗型按订单装配系统最优生产——库存控制策略研究

本文研究具有复杂装配结构的爱尔朗型按订单装配(ATO)系统的组件生产与库存优化控制问题。系统涉及多种组件,一个最终产品和多类客户需求。在此系统中,各种组件的生产时间服从爱尔朗分布,各类客户的需求为泊松到达过程。针对不同客户需求类型:产品需求与独立组件需求且同为销售损失型,建立基于马尔可夫决策过程(MDP)的平均总成本模型,应用动态规划方法求解最优策略。仿真模拟方法实现最优策略,并通过数值实验分析多生产阶段和系统参数对最优策略的影响。研究结果表明,爱尔朗型生产时间ATO系统的最优策略为状态依赖型策略,即组件的生产与库存分配由动态基础库存水平值和动态库存配给水平值控制。对于任一组件,其基础库存水平值和库存配给水平值均随着生产阶段的增加而降低,且生产阶段对基础库存水平值和平均总成本的影响较显著。     

考虑分时电价的多目标批调度问题蚁群算法求解

对同时优化电力成本和制造跨度的多目标批处理机调度问题进行了研究,设计了两种多目标蚁群算法,基于工件序的多目标蚁群算法(J-PACO,Job-based Pareto Ant Colony Optimization)和基于成批的多目标蚁群算法(B-PACO,Batch-based Pareto Ant Colony Optimization)对问题进行求解分析。由于分时电价中电价是时间的函数,因而在传统批调度进行批排序的基础上,需要进一步确定批加工时间点以测定电力成本。提出的两种蚁群算法分别将工件和批与时间线相结合进行调度对此类问题进行求解。通过仿真实验将两种算法对问题的求解进行了比较,仿真实验表明B-PACO算法通过结合FFLPT(First Fit Longest Processing Time)启发式算法先将工件成批再生成最终方案,提高了算法搜索效率,并且在衡量算法搜索非支配解数量的Q指标和衡量非支配集与Pareto边界接近程度的HV指标上,均优于J-PACO算法。     

A joint economic lot size model for raw material ordering,manufacturing setup,and finished goods delivering

In this research we consider a single-manufacturer single-buyer supply chain problem where the manufacturer orders raw materials from its supplier, then using its manufacturing processes converts the raw materials to finished goods, and finally delivers the finished goods to its customer. The manufacturer produces the product in batches at a finite rate and periodically delivers the finished goods at a fixed lot size to its customer, who has a constant demand rate. An integrated inventory control model, making joint economic lot sizes of manufacturer's raw material ordering, production batch, and buyer's ordering, is developed to minimize the mean total cost per unit time of the raw materials ordering and holding, manufacturer's setup and finished goods holding, the buyer's ordering, and inventory holding. Numerical examples are also setup to illustrate that jointly considering the inventory costs above results in less mean total cost than that of considering them separately.     

Integrated Order Scheduling and Packing

We consider an integrated production–distribution scheduling model in a make‐to‐order supply chain consisting of one supplier and one customer. The supplier receives a set of orders from the customer at the beginning of a planning horizon. The supplier needs to process all the orders at a single production line, pack the completed orders to form delivery batches, and deliver the batches to the customer. Each order has a weight, and the total weight of the orders packed in a batch must not exceed the capacity of the delivery batch. Each delivery batch incurs a fixed distribution cost. The problem is to find jointly a schedule for order processing and a way of packing completed orders to form delivery batches such that the total distribution cost (or equivalently, the number of delivery batches) is minimized subject to the constraint that a given customer service level is guaranteed. We consider two customer service constraints—meeting the given deadlines of the orders; or requiring the average delivery lead time of the orders to be within a given threshold. Several problems of the model with each of those constraints are considered. We clarify the complexity of each problem and develop fast heuristics for the NP‐hard problems and analyze their worst‐case performance bounds. Our computational results indicate that all the heuristics are capable of generating near optimal solutions quickly for the respective problems.     

基于碳标签制度的两级供应链协调机制研究

以市场需求随机的视角建立Stackelberg博弈模型,研究基于碳标签制度的供应商和零售商组成的两级供应链协调策略。模型引入产品碳足迹e₁作为决策变量,分别研究了在收益共享与成本共担契约下集中决策和分散决策中决策变量最优解及最优利润的变化情况。研究结果表明,在考虑碳排因素时集中决策相比于分散决策具有明显的优势;在参数满足一定条件下零售商通过与供应商共担碳减排成本,可以实现利润的增长,初步验证模型的契约设计的有效性。     

Manufacturing feature-based cost management system: a case study in Brazil

Feature costing is a topic of recent discussion related to cost management systems (CMSs) aimed at understanding how product features influence production process costs. It raises as a key issue in mass customisation environments where a single product model can present hundreds of menu options. In this study, we explore the concept of feature costing beyond the use of activity-based costing (ABC). We propose a CMS based on features and common elements as costing objects instead of products. In our model, we use the unit of production effort method to organise the direct manufacturing costs, ABC method to address the indirect cost and the standard methods to compute raw materials costs. We illustrate the method in the production of bus seats in a Brazilian manufacturing company. This study gives different perspectives to the international academic community on the use of a different CMS used by many Brazilian companies.