Fully PCA-based approach to optimization of multiresponse-multistage problems with stochastic considerations

Manufacturing systems have several stages of operations in which different quality characteristics are formed so it is an important objective to ensure that the final products meet the predefined specification limits. Due to the stream of variations in such systems, controlling and improving the product quality level becomes more complicated rather than the single stage ones. To deal with such problems, Response Surface Methodology has received much more attention in recent years. In the context of quality engineering, these problems usually include correlated response variables as well as correlated covariates. This study presents a new framework for product quality improvement in multistage manufacturing systems with multiple correlated responses and covariates in each stage. Stochastic aspects of this model include probabilistic covariates and statistical distributions of estimated parameters in the response surfaces. To cover these considerations, multistage stochastic programming is used together with the Principal Component Analysis technique to make the responses as well as the covariates uncorrelated at each stage of the operations. At the end, a numerical example has been analyzed by the proposed approach and for large-scale cases some meta-heuristic algorithms have been applied to solve the model.     

Managing production with flexible capacity deployment for serial multi-stage manufacturing systems

This paper presents a model for serial multi-stage manufacturing systems facing variability from two sources. One source is demand uncertainty; the other is manufacturing uncertainty associated with all manufacturing stages. A production control policy based on the planned lead time and the manufacturing capacity requirement is developed. It is shown that this production control policy has the effect of reducing the variance of production output for all manufacturing stages. Some specific analyses are provided to illustrate the production control policy developed. The model developed provides a vehicle for examining the interrelationships among the production output, the planned lead time and the actual manufacturing flow time. The risk-pooling value over both demand randomness and manufacturing uncertainty, which is achieved through consolidating some manufacturing capacity and deploying flexible capacity among the manufacturing stages, is analyzed. This risk-pooling value can be realized in the form of either reduced manufacturing flow time or increased effective capacity to meet more demand. It is shown that the risk-pooling value increases as the planned lead time decreases.     

活动长度有限制的多工序批量加工生产计划研究

当前的化工制造中,有很多工厂使用柔性制造设备,并采用批生产模式来组织生产.由于对设备进行准备和清理的成本比较大,加工的排序一般采用多批次加工同一种产品的活动（campaign）模式.在实际的生产中,由于需要保证产品质量和减少库存,应该考虑限制活动的最大长度.本文针对活动长度有限制的多工序批量加工问题进行研究,利用状态-任务-网络概念和层级模型方法,提出了修正的活动计划模型.该模型是基于混合整数线性规划模型,并且以供应网络内总生产成本和物流成本最小化作为目标函数.最后用一个算例来说明所构建模型的有效性.     

Approximate Analysis of Decentralized, Multi-Stage, Pull-Type Production/Inventory Systems

We consider a decentralized, pull-type manufacturing system with each stage having its own input and output stock keeping activities. Material handling between stages is carried out according to a fixed quantity, non-constant withdrawal cycle. We approximate the system behavior using a two-node decomposition approach, which decomposes the system into smaller subsystems. The analysis of two-node subsystems is achieved using a matrix-recursive approach due to phase-type modeling of certain random variables. Our solution algorithm resolved a major difficulty (due to batch transfers) in the analytical approach to study multi-stage manufacturing systems. We also discuss system behavior and suggest several rules-of-thumb to improve system performance.     

A closed queuing maintenance network for automated manufacturing systems

This paper models an unreliable automated manufacturing system (AMS) by a closed queuing network. The AMS consists of a multi-stage network of automated work stations linked by a computer. A closed queuing algorithm is applied to determine the system availability under steady state for the AMS. This algorithm is then integrated into a cost optimization model. By applying the revised genetic algorithm, the optimal (or near-optimal) number of standby units and repair rates for the repair stations are derived by minimizing the total cost. The model is verified by the intuitive results from the sensitivity analysis. A numerical example is used to compare the revised genetic algorithm and the conventional genetic algorithm. The results show that the proposed revised algorithm leads to significant improvement in execution time and lower average total cost.     

Equipment Selection Problems in Just-in-Time Manufacturing Systems

This paper is concerned with the development of a mathematical model for solving the problem of equipment selection in just-in-time (JIT) manufacturing systems. Equipment selection issues are important, particularly during the initial stages of implementing JIT. In this paper a mathematical model is proposed for capacity planning and for selecting equipment in a multi-stage JIT production system. An example situation is considered to explain the application and behaviour of the model.     

A multi-stage financial hedging approach for the procurement of manufacturing materials

This paper addresses the problem of mitigating procurement risk that arises from volatile commodity prices by proposing a hedging strategy within a multi-stage time frame. The proposed multi-stage hedging strategy requires a commodity futures position to be correctly initialised and rebalanced with adequate volumes of short/long positions, so as to reduce the volatility in the total procurement cost that would otherwise be generated by varying commodity spot prices. The novelty in the approach is the introduction of the rebalancing of commodity futures position at defined intermediate stages. To obtain an efficient or near optimal multi-stage hedging strategy, a discrete-time stochastic control model (DSCM) is developed. Numerical experiments and Monte Carlo simulation are used to show that the proposed multi-stage hedging strategy compares favourably with the minimal-variance hedge and the one-stage hedge. A close-form optimal solution is also presented for the case when procurement volume and price are independent.     

A note on scheduling flowshops with flexible stage ordering

We develop an asymptotically optimal heuristic for the m-stage flowshop problem with flexible stage ordering. Our algorithm supplies a job permutation which has the same worst case absolute bound for all possible m! orderings of the m stages. The development of our algorithm is motivated by the observation that it is sometimes beneficial to reverse the processing order in multi-stage manufacturing processes. Our algorithm facilitates the development of a job sequence a priori before the processing order is determined.     

An inventory model with reliability in an imperfect production process

The paper analyzes an economic manufacturing quantity (EMQ) model with price and advertising demand pattern in an imperfect production process under the effect of inflation. If the machine goes through a long-run process, it may shift from in-control state to out-of-control state. As a result, the system produces imperfect items. The imperfect items are reworked at a cost to make it as new. The production of imperfect quality items increases with time. To reduce the production of the imperfect items, the systems have to more reliable and the produced items depend on the reliability of the machinery system. In this direction, the author considers that the development cost, production cost, material cost are dependent on reliability parameter. Considering reliability as a decision variable, the author constructs an integrated profit function which is maximized by control theory. A numerical example along with graphical representation and sensitivity analysis are provided to illustrate the model.     

Analytical approximations to predict performance measures of manufacturing systems with general distributions,job failures and parallel processing

Parallel processing is prevalent in many manufacturing and service systems (i.e. some components may have to wait for other components before the assembly can begin). It is also common to observe manufacturing systems that deal with multiple products, resources shared between different products, and circulation due to random part failures. An example of such a system configuration is observed at a facility equipped to assemble and test web servers. The primary objective of this research was to develop analytical approximations to predict performance measures of a system with the above characteristics and evaluate its accuracy. Manufacturing systems with general distributions, multiple products, job circulation due to failures, resource sharing, and a fork and join system (to model parallel processing of some assembly operations) were studied using the parametric decomposition approach. The different work centers (or stations) in the manufacturing system is modeled as a network of queues and the parametric decomposition approach is applied to decompose the network of queues into individual queues to estimate the performance measure of the system. Existing analytical formulations were modified and appropriate correction terms were added to the approximations to bridge the gap in the error between the analytical approximation and the simulation models. Random instances were generated and the flow times from the approximations and simulation models were compared. The experimental study conducted indicates that the analytical approximations along with the correction terms can serve as a good estimate for the flow times of the manufacturing systems with the above characteristics.     

An economic production lot size model in an imperfect production system

The paper investigates an EPL (Economic Production Lotsize) model in an imperfect production system in which the production facility may shift from an ‘in-control’ state to an ‘out-of-control’ state at any random time. The basic assumption of the classical EPL model is that 100% of produced items are perfect quality. This assumption may not be valid for most of the production environments. More specifically, the paper extends the article of Khouja and Mehrez [Khouja, M., Mehrez, A., 1994. An economic production lot size model with imperfect quality and variable production rate. Journal of the Operational Research Society 45, 1405–1417]. Generally, the manufacturing process is ‘in-control’ state at the starting of the production and produced items are of conforming quality. In long-run process, the process shifts from the ‘in-control’ state to the ‘out-of-control’ state after certain time due to higher production rate and production-run-time.The proposed model is formulated assuming that a certain percent of total product is defective (imperfect), in ‘out-of-control’ state. This percentage also varies with production rate and production-run time. The defective items are restored in original quality by reworked at some costs to maintain the quality of products in a competitive market. The production cost per unit item is convex function of production rate. The total costs in this investment model include manufacturing cost, setup cost, holding cost and reworking cost of imperfect quality products. The associated profit maximization problem is illustrated by numerical examples and also its sensitivity analysis is carried out.     

A dynamic programming approach for batch sizing in a multi-stage production process with random yields

This paper proposes a dynamic programming approach to modeling and determining batch sizes in a single period, multi-stage production process with random yields for each stage. To improve the computational performance of the proposed approach, a statistical bound is developed. A key decision incorporated into the model is whether to continue onto the next stage of processing or to scrap the entire current batch of product. This decision is based on the expected total profit from the remaining items for processing following the removal of all defectives. The decisions involving the locations of test stations after stages are also incorporated into the modeling approach.     

Long term capacity decisions in uncertain markets for advanced manufacturing systems incorporating scope economies

By examining the literature in the field of manufacturing flexibility many researchers have located several form of flexibility by distinguishing them into strategic and operational flexibility forms. Furthermore, many measures and design methods have been proposed for flexible manufacturing systems. Surely scope economies are one of the most important strategic form of flexibility in manufacturing systems and their strategic impact has been acknowledged by the very beginning of the flexible manufacturing era. However, despite several researches are available to address scope economies measurement and design methodologies, very few researches investigate their economic convenience in front of dedicated manufacturing systems depending on the competitive market conditions.This paper proposes a theoretical model whose main aim is the general understanding of the convenience conditions of scope economies-based manufacturing systems. The results of the theoretical model are very interesting, because they locate market conditions that make scope economies manufacturing systems less profitable than dedicated manufacturing ones; moreover, the proposed model sets some general criteria to guide the entrepreneur in making right investment decision regarding this kind of manufacturing investments. Such results can explain the reason of many failures of flexible manufacturing systems and it suggests the use of this kind of approach to investigate other flexibility forms of manufacturing systems. The ultimate goal of this research is the construction of a Decision Support System for supporting the entrepreneur in making decision on Advanced Manufacturing Systems investment decisions.     

Optimization of manufacturing systems using a neural network metamodel with a new training approach

In this study, two manufacturing systems, a kanban-controlled system and a multi-stage, multi-server production line in a diamond tool production system, are optimized utilizing neural network metamodels (tst_NNM) trained via tabu search (TS) which was developed previously by the authors. The most widely used training algorithm for neural networks has been back propagation which is based on a gradient technique that requires significant computational effort. To deal with the major shortcomings of back propagation (BP) such as the tendency to converge to a local optimal and a slow convergence rate, the TS metaheuristic method is used for the training of artificial neural networks to improve the performance of the metamodelling approach. The metamodels are analysed based on their ability to predict simulation results versus traditional neural network metamodels that have been trained by BP algorithm (bp_NNM). Computational results show that tst_NNM is superior to bp_NNM for both of the manufacturing systems.     

The treatment of uncertainty in mineral exploration and exploitation

A sequential method of modeling the increase in precision of expected net revenues for a proposed exploration and exploitation program has been developed. Embedded within a computer simulation model of the exploration process, which incorporates a method of learning about deposit characteristics, is a multi-stage stochastic optimization process model to determine the optimal exploitation pattern of the deposit. This approach stresses the interdependence of the planning of the exploration and exploitation processes.The model can be used to determine the amount of exploration which should be undertaken in an area by more precisely predicting the long-range profitability associated with the amount of exploration. Thus, decision makers are provided a capability which reduces the uncertainty in profitability outcomes over future production periods.Time did not permit us to respond fully to the editorial review process. Accordingly., the authors accept full responsibility for any consequences of this shortcoming.     

Performance analysis of single stage kanban controlled production systems using parametric decomposition

We present a new approach that permits efficient performance analysis of kanban systems with general demand processes, material arrival processes, and service times. The approach is based on parametric characterization of the traffic processes (arrival and departure) in the network and uses two-moment approximations to estimate performance measures at individual stations. We derive traffic flow constraints that are particular to closed queuing networks with synchronization stations and use these to establish relationships between the parameters characterizing arrival and departure processes at the stations in the network. The resultant set of non-linear equations is solved to estimate network performance measures. Numerical studies show that the approach is not only fast but also reasonably accurate when compared to simulation. These studies also provide insights with respect to the impact of different types of variability on the performance of a kanban system. This work also provides a fundamental building block that can be used in the analysis of multi-stage kanban systems. AMS Subject Classifications 68M20, 60K20, 90B05, 90B30     

Benchmarking of maintenance performance: a case study in two manufacturers of furniture

When profit margins of a plant are decreasing, the need forreliable and efficient maintenance policy becomes more important.Measuring maintenance performance is important for companiesto recognize whether their planned goals are achieved or not.Also, such measurements can be utilized for benchmarking, whichis one of the tools for never-ending improvement. But, theseobjectives cannot be achieved without well-documented data ofthe relevant variables. Better data coverage and quality isnecessary for following maintenance performance developmentand it would, in many cases, clarify the ambiguity concerningthe main problem in the context, namely that neither the productionnor maintenance department can show what effect maintenancehas on profitability. A more effective maintenance policy indirectly implies improvementsin product quality and manufacturing process effectiveness.Elongation of the production time, i.e. reducing the downtimedue to failures, planned replacements and repair, in additionto the improvement in the total maintenance activities, arealso some of the results that can be expected when an efficientmaintenance policy is used. Measuring and monitoring maintenanceperformance measures is required partly for detecting, and eventuallytreating as soon as possible, undesirable changes and partlyto make benchmarking with the best in the branch, which savesappreciable economic losses for companies. In this paper, a model for how to identify the measurable variables,which are needed to develop measures for monitoring maintenanceperformance behaviour systematically, is developed. Five maintenanceperformance measures are proposed and applied. An additionalmodel for systematically analysing the trend of maintenanceperformance measures, for an overall assessment of the company'ssituation, is presented. Two case studies in manufacturers offurniture are conducted to verify these models.     

基于面板数据模型的研发投入影响因素区域异质性研究——基于医药制造业样本的实证分析

基于面板数据模型研究了新产品经济产出、税收优惠、人力资本等因素对我国东中西部地区医药制造业研发资金投入强度影响.研究发现:1)东中西部地区在R&D投入影响因素上存在差异,其中人力资本、企业内部研发资金投入、税收优惠因素对东部地区R&D投入的显著作用依次增强;2)政府研发资金投入、人力资本、税收优惠、盈利能力对中部地区R&D投入的显著作用依次增强,而新产品经济产出呈现负向作用;3)企业内部研发资金投入对西部地区R&D投入显著作用强于人力资本因素,企业规模、税收优惠呈现出负向作用.建议我国应因地制宜采取相应对策,有效提高东中西部地区医药制造业R&D研发投入,进一步促进我国医药制造业创新能力和水平.     

A note on “quality improvement and setup reduction in the joint economic lot size model”

In a recent paper, Affisco et al. [J.F. Affisco, M.J. Paknejad, F. Nasri, Quality improvement and setup reduction in the joint economic lot size model, European Journal of Operations Research 142 (2002) 497–508] propose a quality-adjusted joint economic lot size model that considers investments in quality improvement and setup cost reduction. In particular, they consider a single-vendor, single-buyer, deterministic demand economic lot-sizing problem, and they investigate the potential impact of economic investments in the vendor’s quality improvement and setup cost reduction efforts on the system-wide costs. However, the particular form of the investment function that they use to represent the cost of investments in quality improvement does not represent actual practice in many industries. Hence, in this note, we develop modified models for quality improvement and simultaneous quality improvement and setup cost reduction using a modified form of the investment function. Our fundamental results and conclusions are substantially different than those in Affisco et al. (2002).     

Human resources scheduling to improve the product quality according to exhaustion limit

The role of human resources in manufacturing systems is very significant, and without efficient human resources we encounter high-price products with low quality. To improve the efficiency of human resources, we need to provide an optimal working schedule for each worker in production period. In this paper, we proposed a mixed-integer nonlinear model to find the best working schedule based on product quality cost and workers reliability. In this model, if the worker’s exhaustion level reaches a specific limit, the worker can rest to increase his reliability level and an accommodator should work instead of him. Since the proposed model is NP-hard, we used an artificial immune system to provide the best working schedule. The results indicate that this model can provide efficient and effective human resources schedule in manufacturing systems.