The Lagrangian relaxation strategy (or dualization) is one of the most important methodologies of optimization for solving structured large-scale mathematical programming problems. The line search procedure is very often encountered in solving the dual problem by using some ascent algorithm, such as a bundle algorithm, or an interior point algorithm, etc.. The existing line search methods, for ...

COVID-19 is a novel coronavirus that was emerged in December 2019 within Wuhan, China. As the crisis of its severe, increasing dynamic outbreak all parts globe, forecast maps and analysis confirmed cases (CS) becomes vital excellent changeling task. In this study, new forecasting model presented to analyze CS for coming days based on reported data since 22 January 2020. The proposed model, name...

In this paper, an inventory model for deterioration items in a two-echelon supply chain including one retailer and one manufacturer is proposed by considering the stock and price dependent demand and capacity constraint for holding inventories. First, the model is presented as a leader-follower game in which the manufacturer announces wholesale prices. Second, the retailer decides for the order...

task assignment problem (tap) involves assigning a number of tasks to a number of processors in distributed computing systems and its objective is to minimize the sum of the total execution and communication costs, subject to all of the resource constraints. tap is a combinatorial optimization problem and np-complete. this paper proposes a hybrid meta-heuristic algorithm for solving tap in a h...

We propose a family of search directions based on primal-dual entropy in the contextof interior-point methods for linear optimization. We show that by using entropy based searchdirections in the predictor step of a predictor-corrector algorithm together with a homogeneousself-dual embedding, we can achieve the current best iteration complexity bound for linear opti-mization. The...

Getting a perfectly centered initial point for feasible path-following interior-point algorithms is hard practical task. Therefore, it worth to analyze other cases when the starting not necessarily centered. In this paper, we propose short-step weighted-path following algorithm (IPA) solving convex quadratic optimization (CQO). The latter based on modified search direction which obtained by tec...

In this work we present a new feasible direction algorithm for solving smooth nonlinear second-order cone programs. These problems consist of minimizing a nonlinear differentiable objective function subject to some nonlinear second-order cone constraints. Given a point interior to the feasible set defined by the nonlinear constraints, the proposed approach computes a feasible and descent direct...

response surface methodology is a common tool in optimizing processes. it mainly concerns situations when there is only one response of interest. however, many designed experiments often involve simultaneous optimization of several quality characteristics. this is called a multiresponse surface optimization problem. a common approach in dealing with these problems is to apply desirability funct...