A New Nonconvex quadratic programming Technique: Practical and Fast Solver Method

There exist many problems that nonconvex which are hard to solve. To overcome the nonconvexity of the problems, this paper presents a novel YALMIP-based nonconvex quadratic programming model to overcome the nonconvex problem. The proposed method is accurate, and no need to convexify the problem. Finally, some results are presented to show the effectiveness and merit of the model

A Novel Practical and Fast Economic Method Based on Nonconvex Quadratic Programming

In many industries such as power system, economic operations problems are usually nonconvex problems that are hard to be solved. This paper presents a novel YALMIP-based nonconvex quadratic programming model as a tool to find solution for which is accurate, and there is no need to convexify the problem. In the end, the effectiveness of the method is shown by applying it to nonconvex problems

Statistical Forecasting Modeling to Predict Inventory Demand in Motorcycle Industry: Case Study

International audienceA comprehensive method of finding seasonal patterns of demand and the accurate prediction of future demands are still critical elements for different industries especially in manufacturing companies as it contributes to effective planning and operation. In this paper, a statistical forecasting model has been proposed and implemented in a motorcycle accessories manufacturing company in the USA. Dataset for a 7-year timeframe of historical sales data has been mined, cleaned, and compiled using Python programming. Results have been compared to the conventional forecasting model used by the company. Based on this comparison, using the proposed statistical forecasting model can improve the Mean Absolute Deviation (MAD) by almost 61% and the mean squared error (MSE) by 82%. These improvements will drastically improve the chance of consistently maintaining the right levels of inventory in the right place and at the right time. It also provides the opportunity of ensuring the safety stock of its inventory is sized correctly to avoid inflated carrying costs and lost sales orders due to stock outs

A New Nonconvex quadratic programming Technique: Practical and Fast Solver Method

There exist many problems that nonconvex which are hard to solve. To overcome the nonconvexity of the problems, this paper presents a novel YALMIP-based nonconvex quadratic programming model to overcome the nonconvex problem. The proposed method is accurate, and no need to convexify the problem. Finally, some results are presented to show the effectiveness and merit of the model

A Novel Practical and Fast Economic Method Based on Nonconvex Quadratic Programming

In many industries such as power system, economic operations problems are usually nonconvex problems that are hard to be solved. This paper presents a novel YALMIP-based nonconvex quadratic programming model as a tool to find solution for which is accurate, and there is no need to convexify the problem. In the end, the effectiveness of the method is shown by applying it to nonconvex problems

Region Search Optimization Algorithm for Economic Energy Management of Grid-Connected Mode Microgrid

Economic energy management of grid-connected microgrid has been widely investigated. However, due to the binary variables of the generation unit’s status, the optimal result of the grid-connected microgrid is very hard. Thus, in this paper, the region search optimization algorithm (RSOA) is developed and adopted for the energy management of the grid-connected microgrid. The developed technique has higher convergence speed and accuracy, compared to the well-known heuristic techniques, such as genetic algorithm and particle swarm optimization. Results shows the effectiveness of the developed model

Solving the Grid-Connected Microgrid Operation by JAYA Algorithm

This paper aims to investigate the optimal operation of grid-connected microgrids (MG). In the grid-connected mode, the MG can connect to the main utility and also can exchange energy with the main grid. This potential can lead to higher reliability and less operation cost. In order to show the effectiveness of the proposed model, it is tested on a modified IEEE 33 bus test system

Solving the Grid-Connected Microgrid Operation by JAYA Algorithm

This paper aims to investigate the optimal operation of grid-connected microgrids (MG). In the grid-connected mode, the MG can connect to the main utility and also can exchange energy with the main grid. This potential can lead to higher reliability and less operation cost. In order to show the effectiveness of the proposed model, it is tested on a modified IEEE 33 bus test system

Solving the Grid-Connected Microgrid Operation by Teaching Learning Based Optimization Algorithm

In this paper, the grid-connected operation of microgrid is investigated where the microgrid can exchange power with the main grid. The proposed problem is modeled as the mixed-integer linear programming (MILP) and is solved by an evolutionary algorithm known as the teaching learning-based optimization (TLBO). Finally, the proposed model is tested on a modified IEEE 33 bus test system to show the performance of the method

Optimal Operation of Islanded Microgrid Operation Based on the JAYA Optimization Algorithm

Islanded microgrid (MG) is one of the most important challenges in the power system operation as the network can be safe and disconnected from the conjected area. Also, in the case that the market price is high, the islanded MG can have a lower operational cost by islanding from the main grid. However, optimal operation of the islanded MG is very challenging as the MG is a nonlinear problem. Hence, this paper proposed a new heuristic method known as the JAYA optimization algorithm to solve the problem. Finally, the proposed model is examined on a modified IEEE 30 bus test network to show the merit of the model