to use services transparently in a distributed environment, the utility grids develop a cyber-infrastructure. the parameters of the quality of service such as the allocation-cost and makespan have to be dealt with in order to schedule workflow application tasks in the utility grids. optimization of both target parameters above is a challenge in a distributed environment and may conflict one ano...

optimization through simulation is considered anefficient tool in dealing with optimization problems. it’s a tool that iscapable to cover real world problems to a much more complete way than the otheroptimization tools. the problem of this tool is the necessity of heavyprocessing computations. this problem is caused not only since the simulationprocess is time consuming, but the objective funct...

Deep learning is one of the subsets of machine learning that is widely used in Artificial Intelligence (AI) field such as natural language processing and machine vision. The learning algorithms require optimization in multiple aspects. Generally, model-based inferences need to solve an optimized problem. In deep learning, the most important problem that can be solved by optimization is neural n...

Deep learning is one of the subsets of machine learning that is widely used in Artificial Intelligence (AI) field such as natural language processing and machine vision. The learning algorithms require optimization in multiple aspects. Generally, model-based inferences need to solve an optimized problem. In deep learning, the most important problem that can be solved by optimization is neural n...

this study concerns the scope to improve water flooding in heterogeneous reservoirs. we used an existing, in-house developed, optimization program consisting of a reservoir simulator in combination with an adjoint-based optimal control algorithm. in particular we aimed to examine the scope for optimization in a two-dimensional horizontal reservoir containing a single high permeable streak, as a...

This article investigates a JIT single machine scheduling problem with a periodic preventive maintenance. Also to maintain the quality of the products, there is a limitation on the maximum number of allowable jobs in each period. The proposed bi-objective mixed integer model minimizes total earliness-tardiness and makespan simultaneously. Due to the computational complexity of the problem, mult...