Multi-objective artificial bee colony algorithm for simultaneous sequencing and balancing of mixed model assembly line

Abstract

In recent years, mixed model assembly lines are gaining popularity to produce a variety of models on the single-model assembly lines. Mixed model assembly lines have two types of problems which include sequencing of different models on the line and balancing of assembly line. These two problems collectively affect the performance of assembly lines, and therefore, current research is aimed to balance the workload of different models on each station, to reduce the deviation of workload of a station from the average workload of all the stations and to minimize the total flow time of models on different stations simultaneously. A multi-objective artificial bee colony (multi-ABC) algorithm for simultaneous sequencing and balancing problem with Pareto concepts and local search mechanism is presented. Two kinds of mixed model assembly line problems are analysed. For the first and second problems, each model task time data and precedence relation data are taken from standard assembly line problems, from operation research library (ORL) and from a truck manufacturing company in China, respectively. Both problems are solved using the proposed multi-ABC algorithm on two different demand scenarios of models, and the results are compared against the results obtained from a famous algorithm in the literature, i.e. non-dominated sorting genetic algorithm (NSGA) II. Computational results of the selected problems indicate that the proposed multi-ABC algorithm outperforms NSGA II and gives better Pareto solutions for the selected problems on different demand scenarios of models.