Current optimization strategies are based on the increase the productivity and the quality with lower cost in short time. Grey relational analysis “GRA” based on Taguchi design was proposed in this paper for simultaneous improvement of surface quality and productivity. The turning trials based on mixed Taguchi L18 factorial plan were conducted under dry cutting conditions for the machining couple: AISI D3 steel/mixed ceramic inserts (CC650). The machining parameters taken into account during this study are as follow: major cutting edge angle (χr), cutting insert nose radius (r), cutting speed (Vc), feed rate (f), and depth of cut (ap). Significant effects of machining parameters and their interactions were evaluated by the analysis of variance. Through this analysis, it have been found clearly that feed rate and cutting insert nose radius had a big significant effects on surface quality while depth of cut, feed rate followed by cutting speed had a major effect on productivity. The mathematical relationship between the machining parameters and the performance characteristics was formulated by using a linear regression model with interactions. Optimal levels of parametric combination for achieving the higher surface quality with maximum productivity were selected by grey relational analysis which is based on the high value of grey relational grade. Confirmation experiments were carried out to prove the powerful improvement of experimental results and to validate the effectiveness of the multi-optimization technique applied in this paper.

In this research, crash test results from CNG locating method optimization approach for crashworthiness and testing its safety are presented. The locating process is based on principal energy considerations inspired from the current design process in passenger vehicle design development. The potential of the vehicle concept to absorb kinetic energy can be estimated at the very beginning of the design process by the free crash lengths in the different areas of the vehicle and estimates of average forces required in the specific segment and parts of the car body at particular crash phases. Based on the basic principle of vehicle crash analysis using the finite element method, a passenger VAN finite element model was selected to simulate the front and side rear collision test of the VAN, therefore the LS-DYNA software is adopted to calculate the deformation of the car and the acceleration time history curves during the crashing process; the anti-impact capability of the vehicle is evaluated from this simulation. It is important to determine appropriate force distributions and the corresponding loads paths through the whole structure for all relevant crash load in dedicated crash test cases. The results demonstrate that the improvement of local structure and location for the required CNG tanks in safe locations in vehicle chassis can promote the crashworthiness of the car, but the further improvement needs a major change of the vehicle structure. The outcomes are interpreted by using LS-PREPOST to analyze the energy absorption characteristics during crash for different cases at a velocity of 50km/h the duration of 12ms. The result analysis was necessary to derive distinct deformation phases characteristic and following that, the essential crash elements are compared with and without CNG tanks installation in each crash case. At last, the conclusion determines the proposed tank locating model in the selected passenger VAN is within the safe range of crash analysis standards.

Globalization and increasing competition in global markets have forced businesses to provide a high level of service to their customers. Time-sensitive transportation systems which are used in transportation of perishable goods, express mail delivery, and emergency services are playing a very important role in this regard. This paper addresses the problem of uncapacitated multiple allocation p-hub center problem (UMApHCP) which is fundamental in proper functioning of time-sensitive transportation systems. A mixed-integer programming formulation is proposed for the problem and a highly efficient Benders decomposition algorithm is developed for solving it. The proposed algorithm is capable of solving large-scale instances of the problem to optimality in order of seconds.

Forecasting stock index has been received great interest because an accurate prediction of stock index may yield benefits and profits for investors, economists and practitioners. The objective of this study is to develop two efficient forecasting models and compare their performances in one day-ahead forecasting the daily Vietnamese stock index. The model development used the data across 9 years of the trading days. The developed models are based on two artificial intelligence techniques, including adaptive network based fuzzy inference system (ANFIS) and long short-term memory (LSTM). The performance indexes including RMSE, MAPE, MAE and R were used to make comparison of the models. The experimental results reveal that both models successfully forecasted the daily Vietnamese stock index with a high accuracy rate. The comparative results of the two models were then discussed and analyzed. It was found that the LSTM model outperformed the hierarchical ANFIS model in forecasting stock index of the Vietnamese stock market.

Poor urban planning and traffic congestion lead to excessive delays in workers’ transit times and decrease their quality of life, especially in emerging countries. Several medium and large companies have a need to hire a transport service for their staff. In this type of transportation system, there is a heterogeneous fleet of vehicles, which are assigned to a set of predefined routes. However, the total transport delay can be even greater if the private transportation system is inefficient or not controlled. The approach proposed in this study seeks to optimize the private transport service by defining the best allocation of its fleet to its routes. A mathematical model is proposed to minimize user wait times. This approach is validated using real data obtained from a transport company in Co-lombia. The results demonstrate the quantitative benefits that can be achieved when the proposed approach is implemented, represented by a considerable reduction in user wait times.

This article discusses the ecological vehicle routing problem with a stop at a refueling station titled Green-Vehicle Routing Problem. In this problem, the refueling stations and the limit of fuel tank capacity are considered for the construction of a tour. We propose a variable neighborhood search to solve the problem. We tested and compared the performance of our algorithm intensively on datasets existing in the literature.

Financial management faces 3 important decisions, namely funding, investment and dividends. The purpose of this study is to determine the effects of the fundamental factors such as leverage in financial management. Leverage is one of the factors of funding, investment and dividends that interdependently affect the value of the company. The method used in this study uses 2SLS (two-stage least square). There are 4 (four) equations in this study, namely the equation of leverage, investment, dividend and company value. Interdependence is shown based on leverage, investment and dividends. In the interdependence equation leverage, investment and dividends not only on one side act as endogenous variables but also act as exogenous variables on the equation of the company values. The results of the research on leverage equations show that investment affects leverage but dividends do not affect leverage. The investment equation shows the results of both leverage and dividend variables would not affect investment decision. Dividend equation also shows that leverage and investment variables also do not affect dividends. Moreover, the results of the company value equation show that the leverage variable would not affect the value of the company. But in-vestment and dividend variables affect the value of the company. The conclusion of this study is that there was a mutually influential relationship between the three variables of financial management before influencing company value.

Decision making in supply chains is influenced by demand variations, and hence sales, purchase orders and inventory levels are therefore concerned. This paper presents a non-linear optimization model for a two-echelon supply chain, for a unique product. In addition, the model includes the consumers’ maximum willingness to pay, taking socioeconomic differences into account. To do so, the constrained multinomial logit for discrete choices is used to estimate demand levels. Then, a metaheuristic approach based on particle swarm optimization is proposed to determine the optimal product sales price and inventory coordination variables. To validate the proposed model, a supply chain of a technological product was chosen and three scenarios are analyzed: discounts, demand segmentation and demand overestimation. Results are analyzed on the basis of profits, lotsizing and inventory turnover and market share. It can be concluded that the maximum willingness to pay must be taken into consideration, otherwise fictitious profits may mislead decision making, and although the market share would seem to improve, overall profits are not in fact necessarily better.

The purpose of this research is to develop an early warning system model that can anticipate the occurrence of delisting of Islamic stocks (ISSI) using Support Vector Machines (SVM). Financial variables used consist of debt to equity, return on invested capital, asset turn over, quick ratio, current ratio, return on assets, return on equity, leverage, long term debt, and interest coverage. The population of this study is 335 sharia shares registered at ISSI in the period 2012-2017, with a total sample of 102 companies. The results show that the financial variables had a predictive power to the occurrence of delisting of Islamic stocks in the ISSI index. The effect of the independent variable or predictor variable is the financial ratio to the target variable or the dependent variable that is the potential for delisting of Islamic stocks in the ISSI index. With the development of 4 SVM models with different levels of prediction accuracy, SVM Model 1 with an accuracy rate of 71.57%, SVM Model 2 with an accuracy rate of 72.55%, SVM Model 3 with an accuracy rate of 82.35% and SVM Model 4 with an accuracy rate of 100%, it can be concluded that the SVM Model 4 is the best model.

This paper presents a new method for selection of optimal options portfolios. The problem of defining optimal portfolios of real options is formulated as integer programming. The algorithm of generating an optimal portfolio of real options is also presented. The incremental benefit of portfolio of real options is valued using Monte Carlo simulation and modeling the prices and demand as Geometric Brownian Motion. The presented method allows to select optimal portfolios of real options with consideration of statistical and qualitative dependences of options. The results show that real options can generate a significant increase in the net present value (NPV).