Six Sigma is the concept of improving the quality by reducing process variations, making con-tinuous improvements, reducing defect rates and improving the processes. Initially, the concept of Six Sigma focused on defect reduction, cost reduction and value addition. Fundamentally the basic idea of Six Sigma is to improve the process-capability and making the process more relia-ble along with reducing wastes within industries. Evaluation the implication of applying Six Sigma over the small and medium-sized enterprises is the main purpose of this research work taking a particular case of automobile industries. In the present work, DMAIC methodology of Six Sigma is used to a small seat slider lock nut manufacturing unit to reduce the play issue in K2 seat slider lock in automobile units by reducing defects inherent in the process. After apply-ing Six Sigma methodology, it was found that manufacturing units could earn profits in terms of reducing the wastage as well as improving the quality standard of the product by controlling the play issue in seat slider lock nut mechanism. Result shows that with the application of Six Sig-ma, process, sigma level brought up to 5.53σ from 1.59σ by varying the seat slider lock nut di-mensions/size.

Education is the future. Education is the only way for a country to start developing and reducing poverty. In countries with medium incomes like Peru, the resources to spend on education is not unlimited. Therefore, it is necessary to have quality in investment. However, risks and uncertainty can make a project surpass its initial budget. Therefore, statistic based methods like Monte Carlo simulation is a powerful tool to forecast possible events that might endanger the profitability and sustainability of a project. Although there is not plenty of academic literature about Monte Carlo empirical usage, many projects employ this method to manage the possible risks the project could have. In consequence, the current research analyzed both risk and sensitivity of an elementary school building project. Both analyses showed that this project had huge probabilities to surpass the current profit and return estimations. However, the sensitivity analysis portrayed that the project could be endangered because of infrastructure overspending. Moreover, it indicated that students’ attendance is also a critical factor to ensure the sustainability of the project.

Polyester short cut fiber is a textile industry which is rarely explored or researched. This research explains the necessary steps of improvement using Six Sigma method to reduce the nonconform-ing products in a polyester short cut fiber manufacturing in Indonesia. An increased noncon-forming products in the shortcut fiber production process created some quality problems from January to May, 2015. Define, measure, analysis, improve, control (DMAIC) steps were im-plemented to determine root cause of the problems and to improve production process using sta-tistical approach. The results of Six Sigma improvement has indicated that the process capability was increased from 2.2 to 3.1 sigma, savings $18,394.2 USD per-month.

The integration between production and distribution to minimize total elapsed time is an important issue for industries that produce products with a short lifespan. However, the literature focus on production environments with a single stage. This paper enhances the complexity of the production system of an integrated production and distribution system by considering flowshop environment decisions integrated with a vehicle routing problem decision. In this case, each order is produced in a permutation flowshop subsystem and then shipped to its destination by a capacitated vehicle, and the objective is to sequence these orders to minimize the makespan of the schedule. This paper uses two approaches to address this integrated problem: a mixed-integer formulation and an Iterated Greedy algorithm. The experimentation shows that the Iterated Greedy algorithm yields results with a 0.02% deviation from the optimal for problems with five jobs, and is a viable option to be used in practical cases due to its short computational time.

This research presents detailed notes on the penetrating geological formations of the wellbore. Logs can be presented in two forms of good geological logs, means visual inspection of surface-carrying samples, and geophysical logs, means physical measurements by instruments that are lowered into holes. Logs that are created during drilling are called real-time logs. LWD tool is produced from real-time data transmitted to the surface computer from downhole. Three common services are Natural Gamma Ray, Resistivity, and Porosity and Bulk Density. The output of an LWD service is a log. A log is a graphical representation of the properties of the formation. Some factors that affect data quality are Depth Calculation, Sensor Malfunction and LWD Tools Measurements Fault. During drilling, borehole path monitoring is the important thing to be maintained because the quality of LWD data is critical for the success of any LWD job. Therefore, the field engineer has to understand the factors that affect data quality. Quality Control Process is the key to ensure that the tools are working properly.

We investigated a variant of the customer order scheduling problem taking into consideration due dates to minimize the total tardiness. Since the problem under study is NP-hard, we propose an efficient size reduction algorithm (SR). We perform an extensive computational experience and compare our proposition with JPO-20 matheuristic, the best existing algorithm for the problem under study. We use the Relative Deviation Index (RDI) and the Success Rate (SRa) as the statistical indicators for the performance measure. We must emphasize that SR presented the lowest average RDI (around 15.5 %), whereas the JPO-20 presented an average RDI approximately three times higher (around 52.5 %). Furthermore, the proposed SR presented a higher average SRa (around 66.9%), whereas the JPO-20 presented a lower average success (around 25.7%). Our proposal used a lower computational effort, resulting in a reduction for the computation times of approximately 22%. The obtained results point to the superiority of the proposed SR in comparison with the JPO-20.

The no-wait flow shop scheduling problem (NWFSP) plays a crucial role in the allocation of resources in multitudinous industries, including the steel, pharmaceutical, chemical, plastic, electronic, and food processing industries. The NWFSP consists of n jobs that must be processed in m machines in series, and no job is allowed to wait between consecutive operations. This project deals with NWFSP with sequence-dependent setup times for minimizing earliness and tardiness. From the literature review of the last five years in NWFSP, it is noticeable that only around 1.92% of the researchers have studied that multi-objective function, which could help to improve the productivity of industries where methods such as just in time are considered. Besides, there is no information about previous researchers that have solved this problem with sequence-dependent setup times. Firstly, a MILP model is proposed to solve small instances, and secondly, a genetic algorithm (GA) is developed as a solution method for medium and large instances. Compared with the mathematical model for small instances, the GA obtained the optimal solution in 100% of the cases. For medium and large instances, the GA improves in an average of 31.54%, 38.09%, 44.58%, 47.72%, and 37.33% the MDD, EDDP, ATC, SPT, and LPT dispatching rules, respectively.

The main aim of this paper is to present a novel multi-objective gray wolf optimization (MOGWO) by utilizing the Kriging meta-model. To this end, surrogate models are used in Multi-Objective Gray Wolf Optimizer as the fitness function. The meta-model is obtained based on exact analysis and numerical simulations. Inheritable Latin Hypercube Design (ILHD) is used as the design of experiments for generation and testing the Kriging model. Then, sensitivity analysis is done to evaluate the effect of design parameter on system responses. The sensitivity analysis leads to appropriate selection of optimization design variables. Hence, the MOGWO algorithm is applied to the problem, the set of non-dominated optimal points are obtained as Pareto Front and one optimal point is selected based on the minimum distance approach. The most important purpose of the methodology is to improve the time consuming in multi-objective optimization problems. In conclusion, for the design of hydrazine catalyst bed was utilized from the proposed methodology. In case, design variables are catalyst bed pellet diameter, loading factor, thrust chamber pressure and Reaction efficiency and objective functions are increasing performance and reducing mass and pressure drop. The results of optimal catalyst bed parameters and also corresponding value of objective functions are shown the performance of methodology in the space propulsion system applications.

This work presents a stress-strain state analytical estimation of restored tooth hard tissues with consideration of their contact interactions with a monolithic filling or a dental onlay. The tooth with a defect is simulated as a continuous isotropic cylindrical body with a non-through hole, and the filling or onlay is simulated as an elastic deformable cylinder. The contact between the tooth and the fillings (onlay) is simulated as the ideal contact. The final strength estimation of the restored tooth is carried out according to the energy criterion. The authors suggest a linear index of tooth damage for practical application of the obtained results. This index can be easily obtained by direct measurement. The differential approach is developed to select a restoring method of the tooth with the defect to provide a reliable restoration of the anatomical crown of the tooth.

Composite materials show different mechanical properties in different directions which leads to complications in the prediction of their behavior. In addition to being inhomogeneous, behavior of composite materials is highly dependent on the curing process and many studies have tried to address these issues. Residual stress can induce high errors in prediction of composite material properties and leads to a difference between theoretical and experimental results. It has been observed that the humidity of environment and the chemical properties of composite materials are also important. Fewer studies have addressed thick layer composites and no damage theory has been developed for these materials. In this paper residual stress in thick layer composites have been studied. Using numerical analysis, the curing process has been modeled and mechanical properties of a thick layer composite are obtained and the induced residual stress in these composites has been assessed. Unlike thin layer composites, the stress in each layer is not specific and a tensile and compressive stress field can exist simultaneously in thick layer composites.