The dynamics of the conflict in the South China Sea (SCS) have begun to enter a new chapter. Currently, the South China Sea (SCS) is a flashpoint in the Asia Pacific region. This study aims to provide an analysis of the concept of the Revolution in Military Affairs (RMA) strategy by the Indonesian Armed Forces (TNI) toward Competitive Advantage in the South China Sea region. This study employed an analytical approach with a qualitative sequence exploratory method supported by some quantitative data. PEST (Political, Economy, Socio-cultural, Technology) analysis and SWOT analysis methods were used to support the study. Furthermore, this study also utilized an Analytical Hierarchy Process (AHP) method approach to provide strategic value and sensitivity analysis as a strategy scenario analysis toward competitive advantage. Based on the results of the research analysis, a strategy under the development of Indonesian Armed Forces capabilities towards a competitive advantage in the South China Sea was obtained, namely the WO strategy which consists of 6 substrate aspects with eight subfactors, namely the combination of all components and strengths in handling security disturbances in the South China Sea (0.162), increased competence of human resources (0.159); development of integrated defense forces and capabilities (0.147), protection of information systems and state secrets (0.145), development of defense facilities and infrastructure (0.109), increasing the capacity and capability of modernizing intelligence technology (0.093), utilization and capacity building of the national defense industry (0.089), deployment of Indonesian Armed Forces troops in the South China Sea (0.075). This study is expected to contribute to the strategy for handling conflicts in the South China Sea and provide strategic steps in increasing capabilities on competitive advantage.

The objective of this research was to evaluate the effects of public debt sustainability on economic growth in the period 2000-2021 and establish a new optimal debt level that does not affect Peru's economic growth. The general method used to determine this effect was the hypothetical deductive method with a non-experimental and longitudinal trend design, because the data to be analyzed are variations that have occurred over time; the VAR (vector autoregressive) model was used as a specific method, because the evidence was insufficient to consider the simultaneity between the reactions of the variables to propose an SVAR model. Data were collected from economic portals such as the Ministry of Economy and Finance (MEF), as well as the Central Reserve Bank of Peru (BCRP). The estimated sample size was 88 observations representing all quarters from 2000 to 2021. As a result of the econometric regression, the impact of the level of public debt on economic growth is positive, since a one-unit increase in the percentage of public debt will increase the variation of GDP by almost 1.1%. Regarding the debt level forecast and according to the projection made, it was determined that the new debt level that does not affect the sustainability of public finances or the long-term economic growth of the Peruvian economy should be 38% of GDP.

Vehicle routing problem (VRP) is a classic problem studied in logistic. One of the most important variations within this problem is called Green Vehicle Routing Problem (GVRP), in which environmental aspects are considered when designing product delivery routes. This variant arises due to the high levels of pollution produced by transport vehicles, so it is a variation whose study represents a vital impact nowadays. This project will consider a GVRP and will be developed considering the characteristics of multi-depot (MDVRP) and multi-product (MPVRP) to minimize the costs of assignation of vehicles and CO2 emissions. To solve the problem, this project proposes a hybridization between the classic tabu search (TS) metaheuristic and the PAES algorithm (TS+PAES) to generate the Pareto frontier of both objectives. An integer mixed linear programming model is formulated and developed for each objective function separately to have an optimal point of comparison for the efficiency of the proposed algorithm. Also, the TS+PAES algorithm is compared to the nearest neighbor algorithm for large instances. Two computational experiments were carried out, one for small and the other one for large instances. The experiment for small instances showed that the GAP of each extreme of the frontier compared to the MILP model is on average 0.73%. For large instances, the metaheuristic improves in 0.1% the results presented by the MILP model showing that the metaheuristic provides closer near-optimal solutions in less computational time. Besides, the metaheuristic, in comparison with the nearest neighborhood heuristic, improves in 44.21% the results of emissions and in 3.88% the costs. All these results demonstrate the effectiveness of the metaheuristic.

E-commerce is one of the industries most affected by big data, from collection to analytics in the highly competitive market. Previous research on big data analytics in E-commerce focused only on particular applications, and there is still a gap in presenting a framework to evaluate big data applications from a challenges-values point of view. This study employs a three-phase methodology to evaluate big data applications in E-commerce with respect to big data challenges and values using a hybrid multi-criteria decision-making technique that combines BWM and fuzzy TOPSIS. The results showed process challenge and the strategic value obtained the highest weight for challenges and values criteria. Financial fraud detection is relatively the most challenging, and online review analytics is the most valuable application of big data in E-commerce among identified applications. Evaluating big data applications based on cost and benefit criteria is practical for E-commerce managers and experts to make decisions on implementation priorities to overcome the challenges and make the most of values. Moreover, the proposed approach is not only limited to big data analytics in E-commerce and can also be applied in other industries to evaluate emerging technology applications.