In this work, the possibility of cross-border activities between two regions in the framework of the investment contract is viewed as optimal allocation problems. The problems of determining the optimal proportion of funds to be invested in liquidity and technology are analyzed in two different environments. In the first case, we consider a two-region and two-technology economy in which both regions possess the same productive technology or project, but a different stream of return. While in the second case, we examine an economy where two regions (i.e., Indonesia and Malaysia) hold different Islamic productive projects with identical returns. Allocation models are formulated in terms of investors’ expected utility maximization problem under budget constraints with respect to regional and sectoral shocks. It is revealed that optimal parameters for liquidity ratio, technological investment profile, and bank repayment are analytically characterized by the return of a more productive project and the proportion of impatient and patient investors in the region. Even though both cases employ different assumptions, they provide the same expressions of optimal parameters. The model suggests that cross-border Islamic investment activities between two regions might be realized, provided both regions hold productive projects with an identical stream of return. This paper also shows that by increasing the lower return of the project approaching the higher return, a room for inter-region investment can be created. An analytical framework of an investment contract in terms of optimal allocation model is provided.

This research aimed to study the approach of the community toward the decision to grow rice and economic crops, including appropriate resource allocation for use on a farm under a large plot agricultural system. The study areas were in Phan district, Chiang Rai province, Thailand, and the data were collected from a sampling of 400 field agriculturalists. The method used was to develop a mathematical model for growing crops with multi-objectives and in multi-periods, together with an agriculturist representative and experts in multiple-criteria decision-making (MCDM). This was to prioritize the importance of alternative crops and find the appropriate allocation of the resources to achieve the targeted goal. The results showed that agriculturists prioritized most toward the criteria for growing Japanese rice with a weight of 0.179 Kg., followed by transplanted rice, transplanted glutinous rice, garlic, sown paddy rice, and sown glutinous paddy rice, respectively. The study’s results also showed that the price fluctuation of the crop products resulted in more use of land and labor in order to increase the production to compensate for the low price, and this also resulted in the higher opportunity cost of growing transplanted rice. Therefore, growing transplanted rice during in season planting was considered the most effective way, while during the off season, either garlic or Japanese rice could be grown. A collective pattern for planning for using resources together in large plot agricultural areas, together with a clear marketing target would bring about effective use of the resources and reduce the risk in revenue from the fluctuation in prices and uncertainty of yields from drought. Moreover, technology development to solve the problem of the lack of labor would be deemed an important approach toward the enhancement of the competitiveness of agriculturists in the future as well.

The decision to purchase the best available electric power wheelchair (EPWC) for a person with a disability in a low-resource context is very stressful, whether it is based on financial circumstances or the availability of medical solutions. The study's objective is to assess the EPWC options available on the market, focused on a set of conflicting criteria. In this research, three multi-criteria decision-making (MCDM) approaches are used to make decisions. ENTROPY method for weightage calculation of various parameters, COPRAS and EDAS methods for evaluating and ranking alternatives are applied. Both COPRAS and EDAS are applied separately for ranking of selected wheelchair models, and to check the robustness of the applied method, sensitivity analysis on cost criterion is carried out. The result shows that for both methods, EPWC-1 is the top priority model to buy, whereas EPWC-7 is the worst model for COPRAS, and EPWC-10 is the worst model for EDAS among the ten alternatives.

COVID-19 has spread to more than a hundred countries worldwide since the first case reported in late 2019 in Wuhan, China. As one of the countries affected by the spread of COVID-19 cases, the local government of Malaysia has issued several policies to reduce the spread of this outbreak. One of the measures taken by the Malaysian government, namely the Movement Control Order, has been carried out since March 18, 2020. In order to provide precise information to the government so that it can take the appropriate measures, many researchers have attempted to predict and create the model for these cases to identify the number of cases each day and the peak of this pandemic. Therefore, hospitals and health workers can anticipate a surge in COVID-19 patients. In this research, confirmed, recovered, and death cases prediction was performed using the neural network as one of the machine learning methods with high accuracy. The neural network model used is the Multi-Layer Perceptron, Neural Network Auto-Regressive, and Extreme Learning Machine. The three models calculated the average percentage error (APE) values for 7 days and obtained APE values for most cases less than 10%; only 1 case in the last day of one method had an APE value of approximately 11%. Furthermore, based on the best model, then the forecast is made for the next 7 days. In conclusion, this study identified that the MLP model is the best model for 7-step ahead forecasting for confirmed, recovered, and death cases in Malaysia. However, according to the result of testing data, the ELM performs better than the MLP model.

Nowadays, companies are recognizing their primordial roles and responsibilities towards the protection of the environment and save the natural resources. They are focusing on some contemporary activities such as Reverse Logistics which is economically and environmentally viable. However, the integration of such an initiative needs flows restructuring and supply chain management in order to increase sustainability and maximize profits. Under this background, this paper addresses an inventory control model for a reverse logistics system that deals with two separated types of demand, for new products and remanufactured products, with different selling prices. The model consists of a single shared machine between production and remanufacturing operations, while the machine is subject to random failures and repairs. Three stock points respectively for returns, new products and remanufactured products are investigated. Meanwhile, in this paper, a modeling of the problem with Discrete-Event simulation using Arena® was conducted. Regarding the purpose of finding, a near-optimal inventory control policy that minimizes the total cost, an optimization of the model based on Tabu Search and Genetic Algorithms was established. Computational examples and sensitivity analysis were performed in order to compare the results and the robustness of each proposed algorithm. Then the results of the two methods were compared with those of OptQuest® optimization tool.

In this paper, we propose the binary version of the Social Group Optimization (BSGO) algorithm for solving the 0-1 knapsack problem. The standard Social Group Optimization (SGO) is used for continuous optimization problems. So a transformation function is used to convert the continuous values generated from SGO into binary ones. The experiments are carried out using both low-dimensional and high-dimensional knapsack problems. The results obtained by the BSGO algorithm are compared with other binary optimization algorithms. Experimental results reveal the superiority of the BSGO algorithm in achieving a high quality of solutions over different algorithms and prove that it is one of the best finding algorithms especially in high-dimensional cases.

The power-law memory effect is taken into consideration in a generalisation of the economic model of natural growth. The memory effect refers to a process's reliance on its current state and its history of previous changes. However, the study that focuses on natural growth in economics considering the memory effect with fractional order-linear differential equation model is still limited. The current investigation seeks to solve the natural growth with memory effect in the economics model and decide the best model using fractional differential equation (FDE), namely Adomian Decomposition and Variational Iteration Methods. Also, this study assumes the level of consumer loss memory during a certain time interval denoted by a parameter (α). This study showed the model of loss memory effect with 0 < α ≤ 1 given a slowdown in output growth compared to a model without memory effect. Besides that, this study also found that output Y(t) is growing faster with the Variational Iteration method compared to the Adomian decomposition method. Also, using graphical simulation, this study found the output Y(t) is closer to the exact solution with α=0.4 and α=0.9. In conclusion, this study successfully solved natural growth with memory effect in economics and decided the best model between FDE, namely Adomian decomposition and Variational iterative methods using numerical analysis.

The Indonesian Navy is the spearhead in maintaining maritime security in Indonesian waters. In carrying out its main tasks, the Indonesian Navy has components of an Integrated Fleet Weapon System in which there are elements of Ships and Naval Bases. To ensure the effectiveness of carrying out operations by ship elements, ship operations are supported by the Naval Base as the organizer of the support function. Naval Base's carrying capacity consists of 5 (five) support functions, including: (1) support for anchoring facilities; (2) support for supply facilities; (3) support for maintenance and repair facilities; (4) support facility maintenance personnel; and (5) support for base development facilities. Naval Base does not yet have its dock to support anchoring facilities for ship operations. In addition to cooperation in the use of the Naval Base anchorage facility, there is also cooperation in port security, both in terms of land and port water aspects. As the number of ship visits at Naval Base Harbor increases, the dock utility increases. The increase in dock utility resulted in a decrease in port services which also resulted in a decrease in the Naval Base Carrying Capacity. To improve port services, Pelindo III implements the port development program contained in the Naval Base Port Master Plan in Permen KP number 792 of 2017. In this study, an analysis of the impact of the Naval Base Port development policy on the carrying capacity of the Naval Base was carried out. The data analysis uses System Dynamics modeling with a simulation period of 30 years in 3 development scenarios, namely short-term scenarios, medium-term scenarios, and long-term scenarios. From the simulation results, it is found that the construction of the Naval Base port affects the Naval Base Carrying Capacity with an average increase of 1.8% in each policy scenario. The increase in Naval Base Carrying Capacity has an effect on increasing Ship Operations by an average of 1.8% and also increasing the Security of Naval Base Harbor by an average of 0.14%. The results of the analysis of this study can be used as consideration for policymaking by the Navy.

There is an acute need to evaluate the energy consumption of buildings in response to climate change. The “occupant” factor has been largely overlooked in building energy analysis. This research aims at investigating occupancy existence in the office environment using a hybrid artificial neural network with metaheuristic algorithms for improved energy management. It proposes and compares three classification models, namely particle swarm optimization (PSO), gravitational search algorithm (GSA), and hybrid PSO-GSA in combination with the feedforward neural network (FFNN). The inputs to these models are data related to temperature, humidity, light, and carbon dioxide emissions. Two data sets are used for testing the models while the office door is open and closed. The capabilities of the optimized models are evaluated using best, average, median, and standard deviation of the mean squared error. Most of the performance metrics indicate that the FFNN-PSO-GSA model exhibits better performance compared to the other models using the two datasets. The proposed model yields a classification accuracy ranging between 98.47-98.73% using one predictor (i.e., temperature). Besides, it yields an accuracy ranging between 85.45-94.03% using temperature and CO2 predictors. It can be concluded that the FFNN combined with PSO and GSA algorithms can be a useful tool for occupancy detection modeling.