Solving a bi-criteria permutation flow-shop problem using shuffled frog-leaping algorithm

Abstract

Flow shop problems as a typical manufacturing challenge have gained wide attention in academic fields. In this paper, we consider a bi-criteria permutation flow shop scheduling problem, where the weighted mean completion time and the weighted mean tardiness are to be minimized simultaneously. Due to the complexity of the problem, it is very difficult to obtain optimum solution for this kind of problems by means of traditional approaches. Therefore, a new multi-objective shuffled frog-leaping algorithm (MOSFLA) is introduced for the first time to search locally Pareto-optimal frontier for the given problem. To prove the efficiency of the proposed algorithm, various test problems are solved and the reliability of the proposed algorithm, based on some comparison metrics, is compared with three distinguished multi-objective genetic algorithms, i.e. PS-NC GA, NSGA-II, and SPEA-II. The computational results show that the proposed MOSFLA performs better than the above genetic algorithms, especially for the large-sized problems.

The role of basic, modified and hybrid shuffled frog leaping algorithm on optimization problems: a review

“…To enhance convergence and quality of the shuffled frogs in basic SFLA, Rahimi-Vahed and Mirzaei (2007) introduced a new HSFLA to solve bi-criteria permutation flow shop scheduling problem. They applied a novel elite tabu search (ETS) algorithm for generating high-quality solutions.…”

Binarization Methods for Shuffled Frog Leaping Algorithms That Solve Set Covering Problems

“…The SCP has been solved using complete techniques and different metaheuristics [20,7,6]. SFLA has been applied to multi-mode resource-constrained project scheduling problem [21], bridge deck repairs [9], water resource distribution [11], unit commitment problem [8], traveling salesman problem (TSP) [17] and job-shop scheduling arrangement [19].…”

Modified Shuffled Frog Leaping Algorithm for Solving Economic Load Dispatch Problem

“…It combines the advantages of the genetic-based memetic algorithm (MA) and the social behavior-based Particle Swarm Optimization (PSO) algorithm and has found applications in areas such as optimizing bridge-deck repairs [12], materialized views selection [13], bi-criteria permutation flow shop scheduling problem [14], application to reservoir flood control operation [15] and a mixed-model assembly line sequencing problem [16].…”