The objective of this study is to propose solutions to attract investment capital for tourism development in Thanh Hoa province, Vietnam. Based on the theoretical framework following eclectic or “OLI model” developed by Dunning and Narula (1996) [Dunning, J., & Narula, R. (2003). Foreign direct investment and governments: catalysts for economic restructuring. Routledge.] and data of 660 questionnaires collected from a survey of tourism investors in Thanh Hoa provinces, we found some important influential factors affecting the attraction of investment for tourism development in Thanh Hoa province. In particular, the local government’s policy of attracting capital appears to have the highest level of importance. The study has some policy implications to increase the attractiveness of investment capital in tourism development in the area.

This paper considers the capacity determination in a closed-loop supply chain network when a queueing system is established in the reverse flow. Since the queueing system imposes costs on the model, the decision maker faces the challenge of determining the capacity of facilities in such a way that a compromise between the queueing costs and the fixed costs of opening new facilities could be obtained. We develop a De Novo programming approach to determine the capacity of recovery facilities in the reverse flow. To this aim, a mixed integer nonlinear programming (MINLP) model is integrated with the De Novo programming and the robust counterpart of this model is proposed to cope with the uncertainty of the parameters. To solve the model, an interactive fuzzy programming approach is combined with the hard worst case robust programming. Numerical results show the performance of the developed model in determining the capacity of facilities.

In today’s dynamic business environment, the product mix and demand may vary from period to period. Therefore, the same configuration of cellular systems will not be optimal in different periods. In this paper, a new mixed integer non-linear mathematical programming model is presented to design dynamic cellular manufacturing systems and combines several design features including multi-period production planning, alternate routings, system reconfiguration, duplicate machines, lot splitting, workload balance among machines and cells, cell size limits, and material flow between machines. The required capacity for parts production is modeled based on flow shop perspective and the aim of mixed integer model is to find optimal independent cells, the quantity of machine types in each cell, and production quantity of the parts during each period of the time horizon. Since this problem belongs to NP-hard class, a three-phase approach is developed to solve the model for practical purposes. Phase 1 finds a feasible solution, phase 2 finds the neighbor solutions, and phase 3 improves a feasible solution. To analyze the computational efficiency, eight test problems with different sizes are considered and the optimal and near-optimal solutions are compared. The efficiency of the algorithm in terms of the objective function values and computational times is shown by the obtained results.

This research was conducted to investigate the impact of equity in foreign direct investment (FDI) firms on accountants' loyalty in Thanglong industrial park, Hanoi, Vietnam based on Equity theory. Data were collected through a survey with 150 accountants from Thanglong industrial park. With this data, we have used Cronbach's Alpha, Exploratory Factor Analysis (EFA) and correlation analysis to determine the effects of the independent variables on the dependent variable, i.e. the loyalty of accountants. The results show that the equity in FDI firms had positive relationships with the loyalty of accountants in Thanglong industrial park. Based on this finding, this paper gives several recommendations for improvement the loyalty of accountants in Thanglong industrial park.

The present paper deals with the development of prediction model using response surface methodology and artificial neural network and optimizes the process parameter using 3D surface plot. The experiment has been conducted using coated carbide insert in machining AISI 1040 steel under dry environment. The coefficient of determination value for RSM model is found to be high (R2 = 0.99 close to unity). It indicates the goodness of fit for the model and high significance of the model. The percentage of error for RSM model is found to be only from -2.63 to 2.47. The maximum error between ANN model and experimental lies between -1.27 and 0.02 %, which is significantly less than the RSM model. Hence, both the proposed RSM and ANN prediction model sufficiently predict the surface roughness, accurately. However, ANN prediction model seems to be better compared with RSM model. From the 3D surface plots, the optimal parametric combination for the lowest surface roughness is d1-f1-v3 i.e. depth of cut of 0.1 mm, feed of 0.04 mm/rev and cutting speed of 260 m/min respectively.

Electrical Discharge Machining (EDM) is one of the most basic non-conventional machining processes for production of complex geometries and process of hard materials, which are difficult to machine by conventional process. It is capable of machining geometrically complex or hard material components, that are precise and difficult-to-machine such as heat-treated tool steels, composites, super alloys, ceramics, carbides, heat resistant steels etc. The present study is focusing on the die sinking electric discharge machining (EDM) of AISI H 13, W.-Nr. 1.2344 Grade: Ovar Supreme for finding out the effect of machining parameters such as discharge current (GI), pulse on time (POT), pulse off time (POF) and spark gap (SG) on performance response like Material removal rate (MRR), Surface Roughness (Ra) & Overcut (OC) using Square-shaped Cu tool with Lateral flushing. A well-designed experimental scheme is used to reduce the total number of experiments. Parts of the experiment are conducted with the L9 orthogonal array based on the Taguchi methodology and significant process parameters are identified using Analysis of Variance (ANOVA). It is found that MRR is affected by gap current & Ra is affected by pulse on time. Moreover, the signal-to-noise ratios associated with the observed values in the experiments are determined by which factor is most affected by the responses of MRR, Ra and OC. These experimental data are further investigated using Grey Relational Analysis to optimize multiple performances in which different levels combination of the factors are ranked based on grey relational grade. The analysis reveals that substantial improvement in machining performance takes place following this technique.

Vertex and p-center problems are two well-known types of the center problem. In this paper, a p-center problem with uncertain demand-weighted distance will be introduced in which the demands are considered as fuzzy random variables (FRVs) and the objective of the problem is to minimize the maximum distance between a node and its nearest facility. Then, by introducing new methods, the proposed problem is converted to deterministic integer programming (IP) problems where these methods will be obtained through the implementation of the possibility theory and fuzzy random chance-constrained programming (FRCCP). Finally, the proposed methods are applied for locating bicycle stations in the city of Tabriz in Iran as a real case study. The computational results of our study show that these methods can be implemented for the center problem with uncertain frameworks.

This paper focuses on the exploitation of the response surface methodology (RSM) to determine optimum cutting conditions leading to minimum surface roughness and cutting force components. The technique of RSM helps to create an efficient statistical model for studying the evolution of surface roughness and cutting forces according to cutting parameters: cutting speed, feed rate and depth of cut. For this purpose, turning tests of hardened steel alloy (AISI 4140) (56 HRC) were carried out using PVD – coated ceramic insert under different cutting conditions. The equations of surface roughness and cutting forces were achieved by using the experimental data and the technique of the analysis of variance (ANOVA). The obtained results are presented in terms of mean values and confidence levels. It is shown that feed rate and depth of cut are the most influential factors on surface roughness and cutting forces, respectively. In addition, it is underlined that the surface roughness is mainly related to the cutting speed, whereas depth of cut has the greatest effect on the evolution of cutting forces. The optimal machining parameters obtained in this study represent reductions about 6.88%, 3.65%, 19.05% in cutting force components (Fa, Fr, Ft), respectively. The latters are compared with the results of initial cutting parameters for machining AISI 4140 steel in the hard turning process.

Social commerce is a first-generation platform that uses a variety of social attributes to attract the attention of internet users. The impact of platform interactivity components; Content Usefulness, Engaging, Real-time communication, and E-wom on customers' purchase intentions was investigated in this study. A Quantitative research method was applied in this study using an online self-administrative questionnaire that was published using Google Docs to collect data, and 521 consumers participated in an online survey. The research used SPSS and AMOS to validate and test the hypotheses. All platform interactivity components were significantly affecting purchase intention. This study provides insights to marketing managers of how consumers perceive social commerce and what affecting them along with practical and theoretical implications.

This paper formulates robust optimization models for the problem of finding near-optimal locations for new intermodal terminals and their capacities for a railroad company, which operates an intermodal network in a competitive environment with uncertain demands. To solve the robust models, a Simulated Annealing (SA) algorithm is developed. Experimental results indicate that the SA solutions (i.e. objective function values) were comparable to those obtained using GAMS, but the SA algorithm could obtain solutions faster and could solve much larger problems. In addition, the results verify that solutions obtained from the robust models were more effective in dealing with uncertain demand scenarios.