Mathematical model for quality cost optimization

Quality engineering uses robust design in order to improve quality by reducing the effects of variability. Variability of the product can be reduced by two stages. One is parameter design which is adjustable to the nominal value so that output is less sensitive to the cause of variability. Other one is tolerance design which is to reduce the tolerance in order to control variability. All costs incurred in a product life cycle can be divided into two categories—manufacturing cost before the sale to the customer and quality loss after the shipment of the product to the customer. It is very important to find the optimum tolerances for each of the characteristics. A balance between manufacturing cost and quality loss should be arrived at in the tolerance design for quality improvement and cost reduction. For the case of Nominal-The-Best, a mathematical model is developed in order to determine the optimum product tolerance and minimize the total cost which includes the manufacturing cost and the quality loss. Since the process capability index (C_pm) shows the balance of quality responsibility between the design and the manufacturing engineers, this is taken as the basis in developing the functional relationship between the variability of the product and the tolerance. Based on these relationships, the total cost of model can be expressed as a function of product tolerance from which the optimal tolerance limits can be found out. Finally, using this model a tolerance design approach that increases the quality and reduces the cost can be achieved in the early stages of the product process design stage itself.     

An optimization approach for joint pricing and ordering problem in an integrated inventory system with order-size dependent trade credit

Under a business trading environment, it is common for the trade credit to depend on the order size. Therefore, it is important to discuss the single-supplier and single-buyer supply chain problem which includes order-size dependent trade credit. In this study, an integrated inventory model with a price sensitive demand rate, determining jointly economic lot size of the buyer’s ordering and the supplier’s production batch, are developed to maximize the total profit per unit time. An efficient algorithm is provided to obtain the optimal solution, and then numerical examples are presented to illustrate the theoretical results. Finally, the comparison between whether an optimal solution is jointly or independently determined is also provided.     

A grammatical optimization approach for integrated process planning and scheduling

Rapid developments in computerized manufacturing environments and increasing overlapping in the capability of manufacturing resources provoked integration of many manufacturing functions including process planning scheduling. Several approaches have been developed in the literature in order to integrate process planning and scheduling. In this paper a novel approach which makes use of grammatical representation of generic process plans is used within a multiple objective tabu search framework in order to integrate process planning and scheduling effectively. Detailed explanations along with an example problem are presented in the paper. Proposed approach is tested on literature problems and also hypothetically generated flexible job shop scheduling problems with alternative process plans to analyze its performance and efficiency.     

An integrated catchment-coastal modelling system for real-time water quality forecasts

This paper presents a new marine water quality forecasting system for real-time and short-term predictions. The forecasting system comprises an integrated catchment-coastal model and a database management system. The integrated model is validated in an Irish catchment-coastal system using hydrodynamic and water quality data. The forecasting system was then used to provide short-term and real-time forecasts of Escherichia coli (E. coli) and Intestinal Enterococci concentrations (IE) in the near-shore coastal waters of Bray, Ireland. Two hind-cast scenarios were simulated: 5F in which predictions were based on rainfall forecasts only; and I-5F where forecasts of 5F were improved by incorporating real-time data. Results indicate that predictions of E. coli of scenario I-5F are improved. Also predicted IE concentrations by Scenario 5F were comparably higher than the I-5F predications, but due to the wide scatter of observed IE concentrations, the superiority of one scenario over the second could not be definitively determined.     

An integrated CSCW architecture for integrated product/process design and development

Computer Supported Collaborative Work (CSCW) is increasingly being used by engineering design teams to reach a consensus on a range of design issues. CSCW systems are designed to increase the effectiveness of decision-makers by facilitating information exchange, retrieval, sharing and use. They encourage interactive information exchange and have the potential to reduce diseconomies associated with design activities, member dominance, social pressure, inhibition of expression, and other difficulties encountered by project teams. The use of CSCW is expected to have a favorable impact on the group decision-making process and the quality of the resulting decision. In this paper, a general CSCW architecture has been developed to support integrated product/process design and development will be presented. The architecture has been tested extensively on a representative industrial problem. The case study and evaluation of the architecture will also be discussed in this paper. The integrated architecture will facilitate information access, sharing, and analysis among design teams members using the open World Wide Web platforms and resources to make product/process decisions.     

基于成本效益系数的Web服务组合可靠性优化方法

为了解决Web服务组合优化过程计算量大、非线性等问题,提出在给定成本约束下利用成本效益系数(CBC)对Web服务组合可靠性进行优化的方法。首先分析了Web服务组合结构模式及其对应的可靠性函数,进一步给出服务组合的可靠性计算方法,并建立非线性可靠性优化模型;然后,通过组件服务的成本及其可靠性关系计算成本效益系数,进一步确定Web服务组合的优化方案,再根据优化模型计算得到优化结果;最后,以组件服务可靠性数据为基础,通过与传统的优化方法比较,验证了基于成本效益系数的方法可以在给定成本约束时获得更高的可靠性。实验结果表明,所提方法对Web服务组合进行可靠性优化是有效、合理的。     

A new skew generalization of the normal distribution: Properties and applications

A new class of distribution functions with not-necessarily symmetric densities, which contains the normal one as a particular case, is introduced. The class thus obtained depends on a set of three parameters, with an additional one to the classical normal distribution being inserted. This new class of skewed distributions is presented as an alternative to the class of skew-normal and Balakrishnan skew-normal distributions, among others. The density and distribution functions of this new class are given by a closed expression which allows us to easily compute probabilities, moments and related measurements. Certain interesting regularity properties reduce the study of this class to one of a subset of standardized distributions. Finally, some applications are shown as examples.     

An opportunistic and non-anticipating size-aware scheduling proposal for mean holding cost minimization in time-varying channels

In this paper we study how to design a scheduling strategy aimed at minimizing the average holding cost for flows with general size distribution when the feasible transmission rate of each user varies randomly over time. We employ a Whittle-index-based approach in order to achieve an opportunistic and non-anticipating size-aware scheduling index rule proposal. When the flow size distribution belongs to the Decreasing Hazard Rate class, we propose the so-called Attained Service Potential Improvement index rule, which consists in giving priority to the flows with the highest ratio between the current attained-service-dependent completion probability and the expected potential improvement of this completion probability. We further analyze the performance of the proposed scheduler, concluding that it outperforms well-known opportunistic disciplines.     

A novel optimization approach for minimum cost design of trusses

This paper describes new optimization strategies that offer significant improvements in performance over existing methods for bridge-truss design. In this study, a real-world cost function that consists of costs on the weight of the truss and the number of products in the design is considered. We propose a new sizing approach that involves two algorithms applied in sequence – (1) a novel approach to generate a “good” initial solution and (2) a local search that attempts to generate the optimal solution by starting with the final solution from the previous algorithm. A clustering technique, which identifies members that are likely to have the same product type, is used with cost functions that consider a cost on the number of products. The proposed approach gives solutions that are much lower in cost compared to those generated in a comprehensive study of the same problem using genetic algorithms (GA). Also, the number of evaluations needed to arrive at the optimal solution is an order of magnitude lower than that needed in GAs. Since existing optimization techniques use cost functions like those of minimum-weight truss problems to illustrate their performance, the proposed approach is also applied to the same examples in order to compare its relative performance. The proposed approach is shown to generate solutions of not only better quality but also much more efficiently. To highlight the use of this sizing approach in a broader optimization framework, a simple geometry optimization algorithm that uses the sizing approach is presented. This algorithm is also shown to provide solutions better than the existing results in literature.     

An integrated vendor–buyer inventory model with backorder price discount and effective investment to reduce ordering cost

The single-vendor single-buyer integrated production inventory system has been an object of study for a long time, but little is known about the effect of investing in reducing ordering cost on the integrated inventory models with backorder price discount and variable lead time. The purpose of this article is to investigate in the continuous review model with backorder price discount and variable lead time to effectively increase investment and to reduce the joint expected annual total cost. The integrated strategy discussed here is one in which the buyer orders a quantity, then the vendor produces n times order quantity in each production cycle, in order to reduce setup cost. In addition, the buyer offers backorder price discounts to the customers that may motivate the customers’ desire for backorders, and buyer ordering cost can be reduced through effective investment. An integrated inventory model is established to find the optimal solutions of order quantity, ordering cost, backorder price discount, lead time, and the number of shipments from the vendor to the buyer in one production run, so that the joint expected annual total cost incurred has the minimum value. Furthermore, numerical examples are used to demonstrate the benefits of the model.     

An active learning genetic algorithm for integrated process planning and scheduling

In traditional approaches, process planning and scheduling are carried out sequentially, where scheduling is done separately after the process plan has been generated. However, the functions of these two systems are usually complementary. The traditional approach has become an obstacle to improve the productivity and responsiveness of the manufacturing system. If the two systems can be integrated more tightly, greater performance and higher productivity of a manufacturing system can be achieved. Therefore, the research on the integrated process planning and scheduling (IPPS) problem is necessary. In this paper, a new active learning genetic algorithm based method has been developed to facilitate the integration and optimization of these two systems. Experimental studies have been used to test the approach, and the comparisons have been made between this approach and some previous approaches to indicate the adaptability and superiority of the proposed approach. The experimental results show that the proposed approach is a promising and very effective method on the research of the IPPS problem.     

PrivAPP: An integrated approach for the design of privacy‐aware applications

Nowadays, personal information is collected, stored, and managed through web applications and services. Companies are interested in keeping such information private due to regulation laws and privacy concerns of customers. Furthermore, the reputation of a company can be dependent on privacy protection, ie, the more a company protects the privacy of its customers, the more credibility it gets. This paper proposes an integrated approach that relies on models and design tools to help in the analysis, design, and development of web applications and services with privacy concerns. Using the approach, these applications can be developed consistently with their privacy policies to enforce them, protecting personal information from different sources of privacy violation. The approach is composed of a conceptual model, a reference architecture, and a Unified Modified Language Profile, ie, an extension of the Unified Modified Language for including privacy protection. The idea is to systematize the privacy concepts in the scope of web applications and services, organizing the privacy domain knowledge and providing features and functionalities that must be addressed to protect the privacy of the users in the design and development of web applications. Validation has been performed by analyzing the ability of the approach to model privacy policies from real web applications and by applying it to a simple application example of an online bookstore. Results show that privacy protection can be implemented in a model‐based approach, bringing values for the stakeholders and being an important contribution toward improving the process of designing web applications in the privacy domain.     

An effective coding approach for multiobjective integrated resource selection and operation sequences problem

In this paper, we consider an integrated Resource Selection and Operation Sequences (iRS/OS) problem in Intelligent Manufacturing System (IMS). Several kinds of objectives are taken into account, in which the makespan for orders should be minimized; workloads among machine tools should be balanced; the total transition times between machines in a local plant should also be minimized. To solve this multiobjective iRS/OS model, a new two vectors-based coding approach has been proposed to improve the efficiency by designing a chromosome containing two kinds of information, i.e., operation sequences and machine selection. Using such kind of chromosome, we adapt multistage operation-based Genetic Algorithm (moGA) to find the Pareto optimal solutions. Moreover a special technique called left-shift hillclimber has been used as one kind of local search to improve the efficiency of our algorithm. Finally, the experimental results of several iRS/OS problems indicate that our proposed approach can obtain best solutions. Further more comparing with previous approaches, moGA performs better for finding Pareto solutions. Received: May 2005/Accepted: December 2005     

An integrated approach for shape and topology optimization of shell structures

In this paper an automated approach for simultaneous shape and topology optimization of shell structures is presented. Most research in the last decades considered these optimization techniques separately, seeking an initial optimal material layout and refining the shape of the solution later. The method developed in this work combines both optimization techniques, where the shape of the shell structure and material distribution are optimized simultaneously, with the aim of finding the optimum design that maximizes the stiffness of the shell. This formulation involves a variable ground structure for topology optimization, since the shape of the shell is modified in the course of the process. The method has been implemented into a computational model and the feasibility of the approach is demonstrated using several examples.     

A bi-level evolutionary optimization approach for integrated production and transportation scheduling

This paper investigates an integrated production and transportation scheduling (IPTS) problem which is formulated as a bi-level mixed integer nonlinear program. This problem considers distinct realistic features widely existing in make-to-order supply chains, namely unrelated parallel-machine production environment and product batch-based delivery. An evolution-strategy-based bi-level evolutionary optimization approach is developed to handle the IPTS problem by integrating a memetic algorithm and heuristic rules. The efficiency and effectiveness of the proposed approach is evaluated by numerical experiments based on industrial data and industrial-size problems. Experimental results demonstrate that the proposed approach can effectively solve the problem investigated.     

An integrated approach based on execution measures for the continuous improvement of business processes realized by services

ContextOrganizations are rapidly adopting Business Process Management (BPM) as they focus on their business processes (BPs), seeing them to be key elements in controlling and improving the way they perform their business. Business Process Intelligence (BPI) takes as its focus the collection and analysis of information from the execution of BPs for the support of decision making, based on the discovery of improvement opportunities. Realizing BPs by services introduces an intermediate service layer that enables us to separate the specification of BPs in terms of models from the technologies implementing them, thus improving their modifiability by decoupling the model from its implementation.ObjectiveTo provide an approach for the continuous improvement of BPs, based on their realization with services and execution measurement. It comprises an improvement process to integrate the improvements into the BPs and services, an execution measurement model defining and categorizing several measures for BPs and service execution, and tool support for both.MethodWe carried out a systematic literature review, to collect existing proposals related to our research work. Then, in close collaboration with business experts from the Hospital General de Ciudad Real (HGCR), Spain, and following design science principles, we developed the methods and artifacts described in this paper, which were validated by means of a case study.ResultsWe defined an improvement process extending the BP lifecycle with measurement and improvement activities, integrating an execution measurement model comprising a set of execution measures. Moreover, we developed a plug-in for the ProM framework to visualize the measurement results as a proof-of-concept prototype. The case study with the HGCR has shown its feasibility.ConclusionsOur improvement vision, based on BPs realized by services and on measurement of their execution, in conjunction with a systematic approach to integrate the detected improvements, provides useful guidance to organizations.     

Mathematical modeling and evolutionary algorithm-based approach for integrated process planning and scheduling

Traditionally, process planning and scheduling were performed sequentially, where scheduling was implemented after process plans had been generated. Considering their complementarity, it is necessary to integrate these two functions more tightly to improve the performance of a manufacturing system greatly. In this paper, a mathematical model of integrated process planning and scheduling has been formulated. And, an evolutionary algorithm-based approach has been developed to facilitate the integration and optimization of these two functions. To improve the optimized performance of the approach, efficient genetic representation and operator schemes have been developed. To verify the feasibility and performance of the proposed approach, experimental studies have been conducted and comparisons have been made between this approach and some previous works. The experimental results show that the integrated process planning and scheduling is necessary and the proposed approach has achieved significant improvement.     

A multi-objective optimization approach for integrated production planning under interval uncertainties in the steel industry

This paper investigates one of the key decision-making problems referring to the integrated production planning (IPP) for the steelmaking continuous casting-hot rolling (SCC-HR) process in the steel industry. The complexities of the practical IPP problem are mainly reflected in three aspects: large-scale decision variables; multiple objectives and interval-valued uncertain parameters. To deal with the difficulty of large-scale decision variables, we introduce a new concept named “order-set” for modeling. In addition, considering the multiple objectives and uncertainties of the given IPP problem, we construct a multi-objective optimization model with interval-valued objective functions to optimize the throughput of each process, the hot charge ratio of slabs, the utilization rate of tundishes and the additional cost of technical operations. Furthermore, we propose a novel approach based on a modified interval multi-objective optimization evolutionary algorithm (MI-MOEA) to solve the problem. The proposed model and algorithm were tested with daily production data from an iron and steel company in China. Computational experiments demonstrate that the proposed method generates quite effective and practical solutions within a short time. Based on the IPP model and MI-MOEA, an IPP system has been developed and implemented in the company.     

铅锌烧结过程质量产量的智能集成优化控制

针对铅锌烧结过程具有大滞后、多约束的特点,建立烧结块质量产量神经网络预测模型和优化控制模型,提出一种融合聚类搜索粗优化和混沌遗传细优化的智能集成优化控制方法.首先采用模糊聚类算法进行优化样本查询,所得结果作为问题的次优解;然后采用最优保存对简单遗传混沌算法进行二次优化,求取问题的最优解;最后对智能集成方法进行实际验证,系统运行结果表明.该方法较好地实现了高产、优质的生产目标.并且具有全局收敛性和工业有效性,为解决复杂工业过程的优化控制问题提供了一种有效、实用的新思路.