All production processes produce variance around the desired target value of quality characteristic. This variance affects the product quality level. Accordingly variance reduction needs to be done as the main goal of quality improvement programs. However effort to improve quality of each product unit must take into account to improvement costs. This paper proposes an optimization model for quality improvement in multi-stage processes using a non linear programming model by selecting alternatives process and determining unit of production of each stage to maximize profit as the difference between total income and total relevant cost. Total cost includes manufacturing cost, quality loss cost, rework and scrap cost, and quality improvement implementation cost. This optimization model is implemented in make-to-order manufacturer that produces crimper (a parts of joining plastic packages in packaging machine) which consist of five main stage manufacturing processes. Sensitivity analysis shows that the optimal solution is not sensitive if little changes occur in the constraints scenario. Thus, adding the value constraint on the quality specification, stage capacity, and quality improvement budget will not improve the objective function.

The existences of variances that are very difficult to be removed from manufacturing processes provide significance of tolerance to the product quality characteristics target of customer functional requirement. Furthermore, quality loss incurred due to deviation of quality characteristics of the target with a specified tolerance. This article discusses the development of concurrent engineering optimization model of tolerance design and manufacturing process selection on product with multiple quality characteristics by minimizing total costs in the system, namely total manufacturing cost and quality loss cost as functions of tolerance, also rework and scrap costs. The considered multiple quality characteristics have interrelated tolerance chain. The formulation of proposed model is using mixed integer non linear programming as the method of solution finding. In order to validate of the model, this study presents a numerical example. It was found that optimal solution are achieved from proposed model in the numerical example. Abstrak Rekayasa Simultan Sintesis Toleransi dan Pemilihan Proses untuk Produk dengan Multi-karakteristik Kualitas yang Mempertimbangkan Kapabilitas Proses.Keberadaan variansi yang sangat sulit untuk dihilangkan dalam proses manufaktur memberikan peran penting adanya toleransi terhadap target karakteristik kualitas produk yang menjadi kebutuhan fungsional bagi konsumen. Selanjutnya timbul kerugian kualitas yang disebabkan oleh penyimpangan karakteristik kualitas dari target dengan toleransi yang ditetapkan. Makalah ini membahas pengembangan model optimisasi rekayasa simultan desain toleransi dan pemilihan proses manufaktur pada produk dengan multi karakteristik kualitas untuk meminimasi total ongkos dalam sistem, yaitu total ongkos manufaktur dan ongkos kerugian kualitas yang merupakan fungsi dari toleransi serta ongkos rework dan ongkos scrap. Karakteristik kualitas produk yang diperhatikan dalam penelitian ini memiliki rantai toleransi yang saling berkaitan (interrelated chain). Formulasi model yang dikembangkan menggunakan mixed integer non linear programming sebagai metode pencarian solusi.

Remanufacturing is a key pillar of a circular economy and helps in recovering used products by extending their life cycle via remanufacturing them into new products. A vital aspect in a remanufacturing system is the quality assessment of incoming worn-out products (cores) prior to remanufacturing to ensure that non-conforming cores are discarded at an early stage in order to avoid unnecessary processing. Therefore, quality sorting plays an important role in core acquisition for remanufacturing systems when attempting to mitigate uncertain incoming core quality as an immediate solution. The main problem is that it is difficult to acquire the important information required to decide on the sorting of incoming cores, such as the core quality. The data are also commonly limited, not always available, or inaccurate. Grey systems are powerful methods in decision making when handling uncertainty with small data. In this paper, we consider the usefulness of grey systems for handling uncertain quality information for sorting incoming cores in a remanufacturing system. For this reason, we propose a multi-criteria quality sorting model based on an analytical hierarchy process (AHP)-entropy model that is coupled with grey clustering using possibility functions. The quality criteria for sorting the incoming cores are considered according to the technological, physical, and usage conditions. To demonstrate the practical contribution of this research, a case study of the quality sorting problem with a heavy-duty equipment remanufacturer is presented. The proposed model consistently classifies the quality of used hydraulic cylinders into two grey classes.

This paper presents an optimization of multi response green machining of aluminum 6061 valve. The research is started with study literature and early survey to identify various factors that may likely influence in the green machining.The next step is investigating and collecting experiment data of the control factors (working in 3 levels) for depth of cut, feeding, and cutting speed factor on two responses; power consumption and surface roughness (Ra). The data were evaluated using Taguchi method based on grey relational anaylisis. Statistic tools coupled together with Taguchi design to process the output of the experiment. Finally, the research has successfully to deliver knowledges of the cutting speed and feeding factors have a dominant influence in power consumption and surface roughness of the green machining process