this paper considers the job scheduling problem in virtual manufacturing cells (vmcs) with the goal of minimizing two objectives namely, makespan and total travelling distance. to solve this problem two algorithms are proposed: traditional non-dominated sorting genetic algorithm (nsga-ii) and knowledge-based non-dominated sorting genetic algorithm (kbnsga-ii). the difference between these algor...

in this study, a two-objective mixed-integer linear programming model (milp) for multi-product re-entrant flow shop scheduling problem has been designed. as a result, two objectives are considered. one of them is maximization of the production rate and the other is the minimization of processing time. the system has m stations and can process several products in a moment. the re-entrant flow sho...

This paper considers the job scheduling problem in virtual manufacturing cells (VMCs) with the goal of minimizing two objectives namely, makespan and total travelling distance. To solve this problem two algorithms are proposed: traditional non-dominated sorting genetic algorithm (NSGA-II) and knowledge-based non-dominated sorting genetic algorithm (KBNSGA-II). The difference between these algor...

We present a new non-dominated sorting algorithm to generate the non-dominated fronts in multi-objective optimization with evolutionary algorithms, particularly the NSGA-II. The non-dominated sorting algorithm used by NSGA-II has a time complexity of O(MN(2)) in generating non-dominated fronts in one generation (iteration) for a population size N and M objective functions. Since generating non-...

This paper presents a bi-objective MIP model for the flexible flow shop scheduling  problem (FFSP) in which the total weighted tardiness and the energy consumption are minimized simultaneously. In addition to considering unrelated machines at each stage, the set-up times are supposed to be sequence- and machine-dependent, and it is assumed that jobs have different release tim...

In this paper, a Non-dominated Sorting Genetic Algorithm-II (NSGA-II) based approach is presented for distribution system reconfiguration. In contrast to the conventional GA based methods, the proposed approach does not require weighting factors for conversion of multi-objective function into an equivalent single objective function. In order to illustrate the performance of the proposed method,...

....... An important problem in the realm of evolutionary multi-objective optimization (MOO) is that of finding all non-dominated fronts (NDFs). We specifically address the computational efficiency of the non-dominated sorting algorithm for finding the non-dominated fronts for the non-dominated sorting genetic algorithm II (NSGA-II) algorithm. We introduce the Limiting Index Sort (LIS) algorith...

Based on the analysis on the basic principles and characteristics of the existing multiobjective genetic algorithm (MOGA), an improved multi-objective GA with elites maintain is put forward based on non-dominated sorting genetic algorithm (NSGA). NSGA-II algorithm theory and parallel hybrid evolutionary theory is described in detail. The design principle, process and detailed implementations of...

in this paper, a non-dominated sorting genetic algorithm-ii (nsga-ii) based approach is presented for distribution system reconfiguration. in contrast to the conventional ga based methods, the proposed approach does not require weighting factors for conversion of multi-objective function into an equivalent single objective function. in order to illustrate the performance of the proposed method,...

Among numerous multi-objective optimization algorithms, the Elitist non-dominated sorting genetic algorithm (NSGA-II) is one of the most popular methods due to its simplicity, effectiveness and minimum involvement of the user. This article develops a multi-objective variation of the Nelder-Mead simplex method and combines it with NSGA-II in order to improve the quality and spread of the solutio...