An application of extended elitist non-dominated sorting Genetic Algorithm in multi-objective linear programming problem of tea industry with interval objectives

Abstract

In this paper, we have modeled a decision making problem of a tea industry as a multi-objective optimization problem in interval environment. The goal of this problem is to maximize the overall profit as well as to minimize the total production cost subject to the given resource constraints depending on budget, storage space and allotted processing times in different machines. For this purpose, the problem has been formulated as a multi-objective integer linear programming problem with interval objectives. To solve the problem, we have proposed extended elitist non-dominated sorting genetic algorithm (ENSGA-II) for integer variables with interval fitness, crowded tournament selection, intermediate crossover, one neighborhood mutation and elitism. To develop this algorithm, we have proposed modified non-dominated sorting and crowding distance based on interval mathematics and interval order relations. Finally, to test the performance of the proposed algorithm, a numerical example has been solved.

VM reassignment in hybrid clouds for large decentralised companies: A multi-objective challenge

“…Then, GeNePi applies NSGA [8]: a genetic algorithm which in addition to coping with interval objectives distinguishes itself by (i) selecting and making a tournament between four parents before mixing the winners (using different operators: crossover and mutation) and (ii) mixing the offspring population (i.e., population resulting from an evolution) with the original one, thus only keeping the elite solutions for a faster convergence towards the optimal Pareto front. In our algorithm, we use One-point and Two-point 'cuts' as crossovers where random cut positions are selected in the parents' chromosomes and their respective parts exchanged.…”