in this study, an optimization model is proposed to design a global supply chain (gsc) for a medical device manufacturer under disruption in the presence of pre-existing competitors and price inelasticity of demand. therefore, static competition between the distributors’ facilities to more efficiently gain a further share in market of economic cooperation organization trade agreement (ecota) is...

Optimization of turning process is a non-linear optimization with constrains and it is difficult for the conventional optimization algorithms to solve this problem. The purpose of present study is to demonstrate the potential of Imperialist Competitive Algorithm (ICA) for optimization of multipass turning process. This algorithm is inspired by competition mechanism among imperialists and coloni...

This paper describes a study of comparison of application of global and one-dimensional local optimization methods to operating system scheduler tuning. The operating system scheduler we use is the Linux 2.6.23 Completely Fair Scheduler (CFS) running in a simulator (LinSched). We have ported the Hackbench scheduler benchmark to this simulator and use this as the workload. The global optimizatio...

We introduce a new, Matlab based version of the GLOBAL optimization method. This stochastic local global search algorithm does not make use of derivative information, thus it is well-suited for solving practical global optimization problems. We illustrate the effectiveness of the method on a problem by solving an optimization problem from the domain of pension system modeling.

Nowadays, there is much attention for planning of container terminals in the global trade centers. The high cost of quay cranes motivates both scholars and industrial practitioners especially in the last decade to develop novel optimization models to address this dilemma. This study proposes a coordinated optimization model to cover both Quay Crane Scheduling Problem (QCSP) and Quay Crane Assig...

Constrained optimization problems have a wide range of applications in science, economics, and engineering. In this paper, a neural network model is proposed to solve a class of nonsmooth constrained optimization problems with a nonsmooth convex objective function subject to nonlinear inequality and affine equality constraints. It is a one-layer non-penalty recurrent neural network based on the...