International Journal of Modeling, Simulation, and Scientific Computing

In a declining market for goods, we optimize the net profit in business when inventory management allows change in the selling prices n times over time horizon. We are computing optimal number of changes in prices, respective optimal prices, and optimal profit in each of the cycle for a deteriorating product. This paper theoretically proves that for any business setup there exists an optimal number of price settings for obtaining maximum profit. Theoretical results are supported by numerical examples for different setups (data set) and it is found that for every setup the dynamic pricing policy outperforms the static pricing policy. In our model, the deterioration factor has been taken into consideration. The deteriorated units are determined by the recurrence method. Also we studied the effect of different parameters on optimal policy with simulation. For managerial purposes, we have provided some "suggested intervals" for choosing parameters depending upon initial demand, which help to predict the best prices and arrival of customers (demand).

The minimization of the profit function with respect to the decision variables is very important for the decision makers in the oil field industry. In this paper, a novel approach of the improved tabu search algorithm has been employed to solve a large scale problem in the crude oil refinery industry. This problem involves 44 variables, 36 constraints, and four decision variables which represent four types of crude oil types. The decision variables have been modeled in the form of fuzzy linear programming problem. The vagueness factor in the decision variables is captured by the nonlinear modified S-curve membership function. A recursive improved tabu search has been used to solve this fuzzy optimization problem. Tremendously improved results are obtained for the optimal profit function and optimal solution for four crude oil. The accuracy of constraints satisfaction and the quality of the solutions are achieved successfully.

The present paper demonstrates the applicability of finite element method of weighted residuals to study the effects of precipitation scavenging through raindrops on the steady-state dispersion of air pollutants in the atmosphere under realistic variable wind and diffusivity profiles. Here, the washout of pollutants by rain droplets is considered and the whole analysis for the pollutant concentration is carried out in two phases viz. gaseous phase and droplet phase. The model solutions are obtained by using local basis and asymmetric quadratic weighting functions which can provide a promising alternative to the standard Galerkin formulation for problems with advective and diffusive terms. The results of the model reveal that the precipitation scavenging by rain is quite effective in cleaning the polluted atmosphere. Anticipating the need of pollutant concentration in rain drops regarding acid precipitation (or acid rain), the concentration of the absorbed pollutant in the droplet phase are also analyzed. So, the present study provides an understanding on the concentration distribution of pollutant in gaseous and droplet phases, under the effects of variable wind and diffusivity profiles.

Congestion is one of the well-studied problems in computer networks, which occurs when the request for network resources exceeds the buffer capacity. Many active queue management techniques such as BLUE and RED have been proposed in the literature to control congestions in early stages. In this paper, we propose two discrete-time queueing network analytical models to drop the arrival packets in preliminary stages when the network becomes congested. The first model is based on Lambda Decreasing and it drops packets from a probability value to another higher value according to the buffer length. Whereas the second proposed model drops packets linearly based on the current queue length. We compare the performance of both our models with the original BLUE in order to decide which of these methods offers better quality of service. The comparison is done in terms of packet dropping probability, average queue length, throughput ratio, average queueing delay, and packet loss rate.

The focus of the research work presented in this article is the design of an industrial plant devoted to produce different types of hazelnuts-based products. The industrial plant design is initially based on a traditional approach (production process flow charts analysis and plant layout study) and it is then supported by a simulation model mainly used to investigate the system behavior in different operative scenarios. The industrial plant simulation model (called HAZIMUT, HAZelnuts Industrial plant design and management based on advanced simulation Models Utilization) recreates the entire hazelnuts production process (based on nine different production lines). The HAZIMUT simulation model is used to evaluate the effects — caused by multiple changes in the production lines capacities — on multiple performance measures based on machines levels utilization, work in process and production system productivity. The simulation results show how variations in production lines capacities generate an over-reaction of the system with major changes in some of the performance measures therefore stressing the importance to use the HAZIMUT simulator to tune the system correctly to improve the overall plant performances.

We propose a unified approach for systems integration and behavior regulation expressing the autonomy of Systems of Systems. For this, we precise the notion of autonomy as a transposition of the living organisms case and we develop the notion of artificial tendencies and behavioral intentions. We present a multi-agent model allowing the implementation with a new multi-scale control.

In this paper, we present a method for fluid simulation based on smoothed particle hydrodynamic (SPH) with fast collision detection on boundaries on GPU. The major goal of our algorithm is to get a fast SPH simulation and rendering on GPU. Additionally, our algorithm has the following three features: At first, to make the SPH method GPU-friendly, we introduce a spatial hash method for neighbor search. After sorting the particles based on their grid index, neighbor search can be done quickly on GPU. Second, we propose a fast particle-boundary collision detection method. By precomputing the distance field of scene boundaries, collision detection's computing cost arrived as O(n), which is much faster than the traditional way. Third, we propose a pipeline with fine-detail surface reconstruction, and progressive photon mapping working on GPU. We experiment our algorithm on different situations and particle numbers of scenes, and find out that our method gets good results. Our experimental data shows that we can simulate 100K particles, and up to 1000K particles scene at a rate of approximately 2 times per second.