Nader Barsoum

Hybrid genetic algorithms and line search method for industrial production planning with non-linear fitness function

Many engineering, science, information technology and management optimization problems can be considered as non-linear programming real-world problems where all or some of the parameters and variables involved are uncertain in nature. These can only be quantified using intelligent computational techniques such as evolutionary computation and fuzzy logic. The main objective of this research paper is to solve non-linear fuzzy optimization problem where the technological coefficient in the constraints involved are fuzzy numbers, which was represented by logistic membership functions using the hybrid evolutionary optimization approach. To explore the applicability of the present study, a numerical example is considered to determine the production planning for the decision variables and profit of the company.

HYBRID SIMULATED ANNEALING AND GENETIC ALGORITHMS FOR INDUSTRIAL PRODUCTION MANAGEMENT PROBLEMS

This paper describes the origin and significant contribution on the development of the Hybrid Simulated Annealing and Genetic Algorithms (HSAGA) approach for finding global optimization. HSAGA provide an insight approach to handle in solving complex optimization problems. The method is, the combination of meta‐heuristic approaches of Simulated Annealing and novel Genetic Algorithms for solving a non‐linear objective function with uncertain technical coefficients in an industrial production management problems. The proposed novel hybrid method is designed to search for global optimal for the non‐linear objective function and search for the best feasible solutions of the decision variables. Simulated experiments were carried out rigorously to reflect the advantages of the proposed method. A description of the well developed method and the advanced computational experiment with MATLAB technical tool is presented. An industrial production management optimization problem is solved using HSAGA technique. The re...

HYBRID GENETIC AGORITHMS AND LINE SEARCH METHOD FOR INDUSTRIAL PRODUCTION PLANNING WITH NON‐LINEAR FITNESS FUNCTION

Many engineering, science, information technology and management optimization problems can be considered as non linear programming real world problems where the all or some of the parameters and variables involved are uncertain in nature. These can only be quantified using intelligent computational techniques such as evolutionary computation and fuzzy logic. The main objective of this research paper is to solve non linear fuzzy optimization problem where the technological coefficient in the constraints involved are fuzzy numbers which was represented by logistic membership functions by using hybrid evolutionary optimization approach. To explore the applicability of the present study a numerical example is considered to determine the production planning for the decision variables and profit of the company.

Using ILOG OPL‐CPLEX and ILOG Optimization Decision Manager (ODM) to Develop Better Models

This session will provide an in‐depth overview on building state‐of‐the‐art decision support applications and models. You will learn how to harness the full power of the ILOG OPL‐CPLEX‐ODM Development System (ODMS) to develop optimization models and decision support applications that solve complex problems ranging from near real‐time scheduling to long‐term strategic planning. We will demonstrate how to use ILOG’s Open Programming Language (OPL) to quickly model problems solved by ILOG CPLEX, and how to use ILOG ODM to gain further insight about the model. By the end of the session, attendees will understand how to take advantage of the powerful combination of ILOG OPL (to describe an optimization model) and ILOG ODM (to understand the relationships between data, decision variables and constraints).