Multiple objective optimization involves the simultaneous optimization of several objective functions. Solving this type of problem involves two stages; the optimization stage and the post-Pareto analysis stage. The first stage focuses in obtaining a set of nondominated solutions while the second one involves the selection of one solution from the Pareto set. Most of the work found in the liter...

This paper presents a three-stage optimization algorithm for solving two-stage robust decision making problems under uncertainty with min-max regret objective. The structure of the first stage problem is a general mixed integer (binary) linear programming model with a specific model of uncertainty that can occur in any of the parameters, and the second stage problem is a linear programming mode...

background: hydrocarbons degradation is principally achieved by microorganisms in natural environments. the extent of hydrocarbons biodegradation is mainly conditioned by environmental factors and its success depends on the optimal condition for the crude oil degrading isolates. objectives: the aims of the current study was to isolate and identify crude oil degrading bacterium from surface sedi...

In this paper, an intelligent-gradient based algorithm is proposed to solve time optimal bang-bang control problem. The proposed algorithm is a combination of an intelligent algorithm called improved particle swarm optimization algorithm (IPSO) in the first stage of optimization process together with a gradient-based algorithm called successive quadratic programming method (SQP) in the second s...

To solve the problem that process capability optimization in multistage manufacturing processes has not been studied so much. Firstly, a multistage manufacturing process quality variation model is built to analyze the impaction of each stage quality on process capability. Then, a multistage manufacturing process capability analysis method with “Costutility ratio” is proposed to prioritize proce...

This paper describes algorithms that learn to improve search performance on largescale optimization tasks. The main algorithm, Stage, works by learning an evaluation function that predicts the outcome of a local search algorithm, such as hillclimbing or Walksat, from features of states visited during search. The learned evaluation function is then used to bias future search trajectories toward ...

Recently, Response Surface Methodology (RSM) has attracted a growing interest, along with other simulation optimization (SO) techniques, for non-parametric modeling and robust optimization of systems. In the optimization stage of this study, the authors use RSM to find optimum working conditions of a system. The authors also use discrete event simulation modeling, optimization stage integration...

Problem statement: The most important character within optimization problem is the uncertainty of the future returns. Approach: To handle such problems, we utilized probabilistic methods alongside with optimization techniques. We developed single stage and two stage stochastic programming with recourse. The models were developed for risk adverse investors and the objective of the stochastic pro...

Abstract: The most important character within the optimization problem is the uncertainty of the future returns. To handle such problems, we utilize probabilistic methods alongside with optimization techniques. We develop single stage and two stage stochastic programming with recourse with the objective is to minimize the maximum downside semi deviation. We use the so-called “Here-and-Now” appr...

The traveling salesman problem (TSP) is the problem of finding the shortest tour through all the nodes that a salesman has to visit. The TSP is probably the most famous and extensively studied problem in the field of combinatorial optimization. Because this problem is an NP-hard problem, practical large-scale instances cannot be solved by exact algorithms within acceptable computational times. ...